Museum IT departments have traditionally served as support functions to manage security and systems. But the National Museum of Korea goes further, identifying and pursuing AI initiatives to preserve cultural heritage, broaden its use, and improve the visitor experience.

The National Museum of Korea uses technology to expand the possibilities to preserve and display physical artifacts, and its digital museum division (DMD) plans and oversees that transformation. A dedicated team responsible for digital strategy and content took shape in 2019, and in 2020, the museum informatics division adopted its current name.

“The director at the time placed great importance on investing in technology and felt we needed a department that could bring a strategic direction to work that had been carried out separately,” says Choi Seungae, former DMD head. “The digital museum division was created as a way to prepare for the next generation.”

A cultural transformation

In just over five years, the DMD has delivered a range of services, including AI guide robots, smart kiosks, and multilingual and sign language services. It’s also developed immersive experiences that use large-scale imagery and sound to recreate cultural heritage.

More recently, its focus has shifted toward data. The aim is to move beyond storage and preservation, making data previously confined to the museum available for wider use. Physical artifacts face restrictions on movement and display because of security concerns and risk of damage. Digital data can help overcome those limitations.

One example is an immersive content project featuring the Denny Taegeukgi, Korea’s oldest surviving national flag, undertaken last year to mark the 80th anniversary of Korea’s liberation.

The project displayed images of the 130-year-old flag once held by Owen Denny, a diplomatic adviser during the late Joseon period, on a large outdoor media screen in front of a department store in central Seoul.

At first glance, the project might appear to have involved little more than photographing an artifact and displaying it. But it required highly detailed data since enlarging existing photographs would’ve produced visible pixelation on such a large display.

Therefore, the division created a new, ultra-high-res dataset containing 10.8 billion pixels at a two-meter scale from the original flag, which measures approximately one meter. Precision scanning captured even the fine texture of both sides of the fabric.

Plan of action

Earlier this year, the museum was selected for the government-led K-Science initiative, through which it plans to pursue data projects beginning in 2027. The cross-ministerial R&D program combines Korea’s historical, cultural, and environmental assets with advanced science and technology to create economic, social, and cultural value.

Through the initiative, the museum will digitize over 2.5 million artifacts and develop technologies to expand the commercial potential of Korean cultural assets. The central goal is to connect the entire process, from archaeological exploration and preservation, to digitization and content development.

The resulting technologies are expected to help overcome technical limitations in detailed surveys of ancient tombs, for example. In preservation and collection management, AI could also automatically control temperature, humidity, and lighting in museum storage facilities.

Never a single right answer

Data presents challenges across both private and public sectors. For museums, however, they’re particularly complex because artifact data contains layers of interpretation, facts, and history.

Consider an AI system designed to answer questions about a particular artifact. It needs data to support its responses. But what should it say if records from Baekje and Silla, two ancient Korean kingdoms, offer conflicting accounts of the same event. Ideally, it would adequately represent different perspectives, but that’s not always possible since the same artifact can tell different stories depending on who used it, when, and for what purpose.

Choi says current LLMs don’t adequately account for these layers of context. When users ask for an accessible explanation, models can produce answers that overgeneralize or oversimplify. Reducing an event with multiple interpretations to a single sentence can obscure or distort its context.

But ensuring accuracy is another challenge. According to Choi, historical discrepancies introduced during initial training can prove difficult to correct, even when a model is subsequently asked to revise them.

The DMD is, therefore, concentrating on building data with clearly identifiable sources. The aim is to let users verify if an AI response is based on material from the National Museum of Korea to assess its reliability.

With these characteristics of artifact data in mind, the division is working with external experts to redesign its database structures around both the nature of the artifacts and the purposes for which people use the data.

Upgrade with AI

Where earlier databases focused on registering and managing objects, the new approach seeks to capture their different meanings and relationships while making relevant materials easier to find. Images, 3D data, videos, and related literature previously scattered across separate records are being connected. Metadata describing file sizes, formats, and how the data was created are also being expanded to support reuse so linking different names for the same artifact makes searching easier, for instance.

“Cultural heritage can’t be standardized like manufactured goods,” Choi says. “We need to organize and structure the data and materials we’ve accumulated in ways that reflect the character of our artifacts, while at the same time, identify connections between them. Once that foundation is in place, curators and external users like content creators, researchers, and educators will be able to find and use materials suited to their needs.”

Leaving room for discovery

After nearly two decades spent researching artifacts and developing exhibitions, Choi didn’t expect to find herself working in the DMD. Now director of the Jeju National Museum, she looks back on her digital work where there were many times her previous experience alone couldn’t guide her decisions. Exhibitions have clear opening and closing dates, and experience helps curators judge what further research and preparation will improve outcomes.

Digital projects offer fewer such reference points, however. It can be difficult to assess how far an initiative has progressed, or anticipate how emerging technologies will affect work already underway. Even so, her expertise provides a foundation to decide where change should lead, and help propose ideas to assess what new tech serves the museum’s purpose.

“Whether it’s curatorial or digital work, I hope the many things we do at museums can make a small contribution to help visitors think for themselves and arrive at their own insights,” she says.

For decades, CIOs have been challenging their organizations to shed one of the most persistent labels in our profession: order taker. Instead of waiting for the business to tell them what they want, technology leaders need to understand what the business is trying to accomplish, get invited into the first meeting, and earn the right to become a strategic partner. But in a fast-moving environment, is being a trusted advisor enough?

To reach the next level, top CIOs are increasingly transforming their IT organizations into what I call “innovative anticipators”: These IT orgs understand the business, customer, and possibilities so deeply that they can see around corners, anticipate what’s needed next, and create value before someone asks for it.

Janet Robertson, global CIO of RS Group, believes building that anticipation muscle isn’t simply about moving the IT organization to a higher level of maturity. Increasingly, it’s a business imperative.

For a recent episode of the Tech Whisperers podcast, Robertson and I explored the deeply human side of leadership: how trust goes both ways, how great leaders serve as a lighthouse when others are navigating through the fog, and how we prepare the next generation to lead in a world none of us has led in before. But hiding inside that conversation was another fascinating story to unpack.

RS Group is a global product and service solutions provider for industrial customers operating in 33 markets. Its journey has been remarkably similar to the one our IT profession has been making — from taking orders, to advising customers, to anticipating what they might need next. As Robertson evolves IT at RS into an innovative anticipator, the company as a whole is doing the same.

Two journeys, similar challenges. And as Robertson explains in the following conversation, strategic partnership may no longer be the destination for today’s CIO. It may be the starting point for what comes next.

Dan Roberts: You’ve said something that sounds simple but has enormous implications: There isn’t a technology strategy and a business strategy. There’s one business strategy. What changes in the way a technology organization operates when people genuinely believe that?

Janet Robertson: When the technology group is focused on an outcome, they’re looking at solving a business problem more than implementing a technical solution. That’s a nuance, but it’s really the most direct route to a solution, because they’re trying to solve a business problem and get a business outcome more than implementing a technology solution or a technology transformation. It’s a different mindset.

In practical terms, you end up with increased adoption because you’ve got buy-in from the business on the direct answer to the problem. You also end up with fewer bells and whistles — a solution that’s not gold-plated and that doesn’t have things you won’t need, which is more efficient, considering how fast things are moving today. Everything could be different.

In fact, we’re doing product development now as more of a continuous evolution using new, innovative tools that are available to us at each space. The days of planning a three- or four-year program are over. By the time you’re going live, the technology’s out of date, or the processes have changed, or the world’s changed around it. You’ve got to create solutions fast with the most direct available technology to support the business outcomes.

You can’t anticipate a business you only understand through dashboards. How do you get your technology leaders close enough to the work, the customers, and the people to see what they otherwise might miss?

I encourage my leaders to spend time in the everyday working life of the recipients of our solutions. Sit with some of our biggest customers and watch them place an order through our website. Sit with our middle- and back-office team and watch their process of paying an invoice, receiving an order, entering an order, processing an order. Go to the distribution center. Spend time with the people who are using your solution.

Our customer experience officer has been riding around with salespeople, experiencing what they are going through in a day-to-day environment. I’m going out to Milan, Italy, next week to open our new our automated warehouse there. I’m almost more excited about launching it and seeing what we’ve implemented in use than I was planning the program. I want to see the ship sail in clear waters.

When technology leaders spend the time doing this, they start to see the connection directly between what they’re putting out there and what actually has to take place. You get very different perspectives from different sides of the fence when you get hands-on. It’s less about KPIs and dashboards and the results out of the other end of it. It’s more about understanding who’s consuming what you’re producing and how you’re providing value to them. Because sometimes, their biggest pain point is something you put down as a priority 5, and it’s in the lowest level of the backlog, but would completely change their perspective, improve their experience, and drive value.

What have the best consumer experiences taught you about orchestrating a B2B customer experience that will continue to differentiate RS Group?

Our catalog is massive, and we’ve got so much to navigate. As a consumer, with the new developments you can go on to an Amazon experience and find anything you want using even cryptic keywords. Or you can go on to Google and find 20 different buying options. That is absolutely transformative for us, because a lot of our customers buy that way. They’re searching for something that’s maybe very specialized, so they expect the same kind of Amazon-type experience when they’re looking for a specialized manufacturing-line repair part.

For us in the technology space, it means we’ve got to make sure that our data and our foundations are prepared to enable those large language models for the AI and generative search for agentic purchasing. What’s a little bit different from the B2C space is that many of our customers are starting to implement agents to do a lot of their procurement. So we’ve got to be prepared on the receiving end of those agents to be able to place those digital orders. It’s not really an e-commerce, add-to-cart channel anymore. Now it’s a direct agentic experience in purchasing for our bigger customers that use agentic purchasing.

To lead the way on that is a huge differentiator, and we are aware of it, and we’re keeping an eye on it. We have to be able to move fast and pivot quickly. Who would have thought two years ago that we were going to be talking to agentic procurement?

For years, we’ve challenged IT organizations to do a better job demonstrating their value to the business and to become trusted advisors. How has your organization made that pivot, and what does the next evolution of that journey look like?

When I first joined RS, my department was seen as less of a technical advisor or an expert in new technologies and new ways of getting things done. We were seen more as order takers and a cost center, basically a necessary evil. The pivot we’ve gone through has happened by listening and engaging with the business to understand and help them articulate what problems they’re truly trying to solve. I say, “Please tell me the what, and let me and my experts provide you the how.”

It’s taken some time, honestly. That evolution from an order taker to a trusted advisor is a journey, because you’ve got to prove that you know what you’re talking about, that you’re up on the latest technology, you understand the latest tools, and you are thinking outside of the box — truly providing the vision and the art of the possible to a problem they don’t know how to articulate. The biggest driver for me is connecting my leaders and my team to the business in general. They’re out looking for tools to solve those business problems.

Now we’re starting to partner with our business partners to show them the art of the possible with some of the new trends that are coming, like agentic procurement and large language model search. SEO is gone; now it’s GEO, generative engine optimization. So instead of “Tell me your business problem,” it’s moving to “Let me tell you how to solve a problem you might not know you have yet. This is a way to anticipate what is probably coming down the pipeline, and it’s going to be a problem soon.”

Because we’re leading the way in talking to our business about where the future’s going in technology and customer search and customer analytics and data science, we’re being brought in to discuss these developing trends.

That level of anticipation requires intelligence, or what you’ve described as “intelligent automation.” What’s involved in building that kind of anticipation engine?

Just like you can’t expect a 300-year-old house to be a smart house overnight, you can’t turn a 100-year-old business into an efficient, highly data-connected enterprise overnight. We know that we’ve got some legacy plumbing that we need to repair, and we are well into that journey now. We know that the data we have is incredibly valuable, but we need to understand how to use it, how to interrogate it, and how to get the right answers out of it. Without a structured format, and without all the speed and the pipes and efficient use, we can’t take advantage of that.

Bad data and a bad foundation only accentuate bad decision-making from the data. If you put a lot of intelligence over the top of an unstructured or bad format and foundation, you get really bad data to make decisions from, and it just makes it that much worse. So sometimes you have to build a little bit of a foundation before you’re able to just run with it.

For years, the goal for technology organizations was to become the trusted advisor who gets invited to the first meeting, not just be handed requirements after the important decisions have been made. Robertson’s experience suggests that today’s CIOs need to challenge their organizations to go further, from “Tell me what you’re trying to accomplish” to “Let me show you what’s coming.” That requires getting close enough to your business colleagues and customers to see what they may not see yet. It’s the difference between being invited to the first meeting and being the reason the meeting gets called.

For more from Janet Robertson’s leadership playbook, tune in to the Tech Whisperers.

See also:

• IT reinvention requires mastering the art of translation
• What separates true enterprise leaders from strong tech execs
• The 7 new Cs of IT leadership for the AI era

The digital twin concept earned its credibility in engineering. Its origins trace to a 2002 University of Michigan presentation in which Michael Grieves described a “Mirrored Spaces Model”: a virtual system holding the information about a physical one, linked to it throughout its life.

For example, a jet engine manufacturer builds a precise virtual replica of a physical turbine, feeds it real-time sensor data, and runs simulations that predict failure before it occurs, optimize maintenance intervals, and test design modifications without touching the physical asset. This model is invaluable because the cost of being wrong about a turbine is high, the variables are measurable, and the physics are well understood. Organizations have applied the same principle to factory floors, supply chains and urban infrastructure, each time with the same underlying logic: a dynamic model of a complex system allows continuous optimization that periodic review cannot match.

However, the enterprise itself has received only part of that treatment. Even Gartner now evaluates organizational digital twin platforms as a category, describing them as dynamic models of how an organization operationalizes its business model, deploys resources and delivers customer value. Yet most of these twins model how work flows, not what it is worth, and that gap is consequential.  

A dynamic model of the enterprise

Still, most large organizations still navigate strategic decisions with a picture of themselves that is static, incomplete and economically unattributed. Org charts describe reporting relationships as they were drawn at the last reorganization rather than how people actually interact. Process maps describe intended or designed workflows, not actual ones. And planning documents describe resource allocations as they existed at the time of the last budget cycle. None of these artifacts answer the question that matters most for strategic decision-making: what is the work actually worth, at the level of granularity required to improve it?

A living model of the enterprise changes what is possible. Imagine a representation of how work actually flows across functions (not as designed or conceived, but as executed), where decisions accumulate and slow, where handoffs lose information, where risk concentrates until it surfaces as an incident, and where resources are applied to activities whose economic contribution has never been examined. Connected to real-time operational data, that model moves leaders beyond the static artifacts that currently substitute for organizational self-knowledge.

But a model of how work flows is useful only for operational diagnosis, not a complete economic understanding. Strategic redesign requires knowing what the work measurably produces at the level of individual activities, decision points and handoffs.

Enriching the digital twin with economic process modeling

As I have discussed in previous pieces, Economic Process Modeling (EPM) provides that additional layer. By decomposing processes into their constituent components and attributing value across five dimensions (revenue contribution, cost and friction, risk exposure, option value and information value), EPM transforms a process map from a descriptive artifact into an economic instrument.

The output is not a flowchart but a value ledger: which components are value drags that cost more than they contribute, which are misaligned (a person doing a system’s job, or the reverse), and where value is created but never collected, usually as data the process discards. That is the side of the business case that cost accounting alone cannot show. It is the value layer the enterprise digital twin needs to support strategic decisions rather than merely operational ones.

Enterprise, know thyself

The practical benefits of an enterprise digital twin featuring EPM are compound:

• It enables businesses to model the P&L consequences of a restructuring before headcount decisions are made.
• It surfaces the process components where AI investments could generate the highest return per dollar–rather than requiring that prioritization to be resolved through stakeholder negotiation and executive sponsorship, which is how it is currently resolved in most organizations.
• It makes visible the information assets being generated and destroyed by current process designs.
• And it shows where risk is concentrating in operational workflows before that concentration produces a regulatory finding or a customer failure.

These are not incremental improvements to existing planning processes; they represent a different quality of organizational self-knowledge.

What makes this achievable rather than speculative is that the required inputs exist in most large organizations already. Process mining tools extract actual workflow patterns from system logs,  ERP and CRM systems hold the operational and customer data needed to attribute revenue and cost at component level, and risk systems hold the exposure data.

The missing element is the economic model that sits above the infrastructure, translates operational patterns into value attributions, and makes the enterprise legible as an economic system rather than a collection of functions.

Economic process modeling in action

CentrePort, is the port and logistics operator in Wellington, New Zealand. Its CentreConnect freight service generated a high volume of small inter-port charges, each reviewed and reconciled by hand by a small commercial team working from spreadsheets. Rather than automating first, the company modeled the invoicing workflow down to individual container movements, linked each activity to time and cost data, and tested standardization and automation scenarios before committing to change.

Processing time per invoice fell from 25 minutes to 23 seconds. But the more telling outcomes were those a throughput metric would miss: fewer financial discrepancies and less overpayment risk, stronger data governance than spreadsheets allowed, and groundwork for scaling volumes without adding staff. Those are risk, information and option value, not only cost.

CentrePort has since applied the approach to other commercial processes and now treats it as a precondition for change.  “Before undertaking any projects, we now ensure that the current state is understood, along with the upstream and downstream impacts of change,” said Tanya Dednam, Head of Digital.

The evolution from physical to enterprise digital twins

As many businesses and the global economy continue to shift toward digital, AI-based and services-oriented offerings, economically modeling the enterprise can become more important than modeling physical things.

However, the analogy to physical digital twins carries an important limitation: Turbine physics are stable whereas organizational economics are contested, context-dependent and subject to strategic choice. An enterprise digital twin built on EPM does not produce a single authoritative answer about what a process component is worth. It produces a structured basis for the argument, with explicit assumptions that can be examined, challenged and revised as conditions change, which is precisely what distinguishes a useful analytical framework from the static planning artifacts it replaces.

The competitive advantage this capability confers is not in the model itself. Rather, it is in the organizational discipline of making economic attribution a continuous practice, of treating the enterprise as an asset to be actively managed rather than a structure to be periodically reorganized when the last reorganization stops working. And rather than an enterprise-wide focus, the practical aperture is a single process with high economic exposure–ideally one already under transformation or AI investment pressure, modeled well enough to inform a real decision or action.

Organizations that model, measure and analyze the EPM-enhanced digital twin of their processes will be able to adapt, optimize and transform with significantly greater agility than those navigating with onerous org charts and planning PowerPoints.

MIT’s finding that 95% of enterprise AI pilots fail to show measurable profit has been quoted at length, and the conclusions drawn from it are always the same: the models aren’t good enough yet, or enterprises don’t know how to use them. I don’t think either holds up. The problem starts earlier, with whose definition of success you’re measuring against, and I’ll come back to what to do about that.

I looked at this in my book Transform!. Bain surveyed over a thousand companies and found that only 5% of digital transformations achieved or exceeded expectations. 75% settled for dilution of value and mediocre performance. 20% failed outright. That was before ChatGPT existed. So whether it’s B2B software or a large language model does not really matter – what seems to go wrong every time, regardless of the technology, is that organizations are deploying tools whose benchmarks were never aligned with their actual enterprise requirements.

In the digital transformation world, misalignment sometimes came from the supplier side: vendor objectives didn’t match client needs, partly because the vendor’s definition of success was follow-on work, which rarely served the client.

Now the misalignment comes from the AI labs, and this time it is not so much an incentive problem that a better contract can fix but a limitation that is built into the tool. OpenAI and Anthropic are building towards artificial general intelligence, where “general” is the operative word. They offer enormous reasoning horsepower, but the benchmarks used to assess their performance don’t accurately represent the inside of a real business. Their models are crystallized, frozen at the moment they’re trained. The cost to retrain an enormous model is, well, equally enormous. The model never learns your business as it works. It knows the public record of the world, but that does not include your enterprise: the deep domain knowledge that runs it, much of which never gets documented. It starts every task knowing only what you hand it. And most things a business actually cares about are very specific: a particular use case, a specific workflow, the number the CFO has to report at month-end. It’s why a model can top every benchmark out there and still deliver limited measurable value. What’s missing is the specificity, and no amount of extra horsepower makes much difference in this respect.

Every approach to fixing this – RAG, fine-tuning – has notable limitations.  What’s actually needed is a model that controls the context fed to the LLM and learns the organization as it operates. Until that exists, enterprises have to work with what they have.

The starting point is simple: if the labs’ definition of success is different from yours, how do you make sure your pilots don’t join the 95%? Here are three things I would do.

Write your own definition of success

Start with why. What do your board, your team and your leaders want from AI, and what role do you expect it to play? The answer influences what “good” looks like for your stakeholders. Furthermore, if you pick a project where the outcome cannot be measured, you cannot show value.

Then pick the right pilot. Not the hardest or the easiest, but the one that looks like something that you would eventually want to scale. If the workflow you want to automate has messy data, unwritten rules or needs three different sign-offs, put all those constraints into the pilot itself. That means confronting complexity early, because it is usually the hard stuff that derails programs, not the easy stuff. Doing the easy stuff first is probably why so many of these pilots are struggling: they prove something that didn’t matter in the first place.

Give the model the context it doesn’t have, and know the limit of that

The state of the art for closing the specificity gap is what has become known as a harness. You re-inject the relevant context into every prompt and put guardrails around each workflow. The discipline is called context engineering. It is a real step up from buying licenses and hoping for the best: getting the prompt right at the outset improves accuracy and cuts the tokens you use on your way to actual results.

But a harness is static; it doesn’t learn the enterprise. As conditions change, the harness or the context has to be amended by hand, which usually means a human in the loop monitoring inputs and outputs, even for relatively straightforward tasks. That undermines much of the value agentic AI is supposed to deliver.

So, a harness is a good place to start, but it only goes so far. GenAI is probabilistic: give a model the same prompt twice and it will not give you the same answer twice. Almost everything in your organization, by contrast, was built for humans, who at least try to behave deterministically. An incorrect output from a model is not a bug; it is a signal that the context needs modifying, ideally dynamically and automatically, until the machine gets it right. Therefore, you need a loop: context goes in, output comes out, you evaluate it against a framework you have set, and the context is refined before it goes back into the machine. That is what turns a static process into something that continually improves. Experimentation has to happen inside that loop, and stakeholders have to see what it produces early and often, so you know where the model falls short before there is real money committed.

Put the people who hold the knowledge inside the pilot

An agent needs context in the way a trained, experienced SME has it and a new graduate does not. The problem is that most knowledge in most organizations is tacit. Enterprise information isn’t just data in the sense of bits and bytes you can put in a spreadsheet and feed to the machine. A lot of it is in people’s heads, and sometimes the deepest expertise is in the heads of a handful of people.

I saw a large financial markets data business try to shortcut this: they needed new data models built out of what a few long-serving experts knew, so they interviewed them, recorded the interviews and fed the transcripts into Copilot. It didn’t work very well. Knowledge is hard to capture and validate that way.

What does work is actually older than AI: build a small team that combines deep domain knowledge with technical capability and place the technologists next to the people who do the job, even part-time. The gaps in your enterprise information show when the expert sees the model’s output and says, “that’s wrong, because” not when you interview them. McKinsey’s Kate Smaje made the point recently that every successful AI implementation sits on top of a successful people transformation. In 25 years of working with organizations as diverse as Deutsche Bank, Shell, BP and Anglo American, I can stand by that.

This isn’t an argument against using rented intelligence from the leading AI labs. The horsepower they offer is real and you should use it as appropriate. But those labs are attempting to create artificial general intelligence, while business managers have a business to run. Current models fall short of that need, which is part of why it is so hard to get out of “pilot mode.” The specificity you need to guide that horsepower is probably somewhere in your organization already. The challenge is to give the model the context it needs to do the job even better, and in doing so, free users from drudgery, reduce key-person dependence, and accelerate business growth wherever possible.

IT leaders seeking promising AI use cases may be overlooking a potential goldmine: deploying the technology to assist with major IT modernization and digital transformation initiatives.

Leading CIOs at a handful of large enterprises have leveraged generative AI and related tools to rewrite code, redesign workflows, and migrate databases during major digital transformation efforts, a practice that isn’t as widespread as its potential suggests.

“The potential is actually, I don’t want to say ‘limitless,’ but it is,” says Vijay Thangella, head of AI and machine learning at contact center technology provider Avaya. “There’s a very high potential.”

Avaya, using AI tools built from frontier models, claims it can help customers cut contact-center modernization timelines drastically. Revamping legacy contact centers typically requires IT teams to spend months deciphering and manually rebuilding years of workflows, routing logic, integrations, and business rules. AI-assisted modernization can cut the time to a few days, Thangella says.

Managed services provider Ensono also uses AI tools to help customers with tasks such as code discovery and cleaning up legacy code. While most organizations still haven’t used AI to assist with digital transformation, the technology can significantly cut the costs and time associated with large IT modernization projects, says Brian Klingbeil, chief strategy officer at the company.

An emerging sweet spot for digital transformation

Gartner AI analyst Arun Chandrasekaran sees significant potential to accelerate digital transformation initiatives through use of generative AI and related tools, in particular for gathering requirements, mapping legacy code, and performing code quality assurance.

“There’s a lot of interest out there today, but customers are not fully there yet,” he says. “The models are getting better at a lot of these tasks, but they are still often hindered by hallucination rates.”

In many cases, CIOs are still working to improve their organizations’ AI governance and data infrastructure, two foundational steps they must take before their teams can use AI to assist with digital transformation, Chandrasekaran adds.

Interest in the concept is high among CIOs of midsize companies, who often have limited budgets to commit to large-scale, multi-year digital transformation projects and a higher comfort level with risk than IT leaders at multibillion-dollar multinational enterprises, he says.

“It seems like you need to accept or tolerate a certain level of risk to trust your AI to do some things that you were doing yourself,” Chandrasekaran adds.

Leading the charge

Rama Donepudi, SVP and global CIO at Mead Johnson Nutrition, is among the vanguard of IT leaders using AI to assist with digital transformation.

Back in 2024, Donepudi turned to a combination of gen AI and AI-enabled automation to assist with the baby formula maker’s transition from legacy SAP ERP to S/4HANA.

Donepudi’s IT team used gen AI coding copilots to remediate their custom programs for compatibility with the newer ERP package, and they used UiPath AI-enabled bots to automate data validation between the legacy environment and S/4HANA.

In addition, Donepudi’s team used AI-enabled tools such as Enable Now and WalkMe to support the development of training and other learning content development. They also deployed GPT tools to create program communications to employees throughout the transformation.

Gen AI was one tool in the toolbox, Donepudi notes. “We deliberately applied different AI capabilities across development, validation, training, and communications rather than treating AI as a single use case,” he says.

Using AI for content creation resulted in a significant cost savings, he says, and enabled the company to target content toward specific employee roles and make immediate changes as the project progressed.

“AI can generate all of this content if I give it the right prompts, and everything becomes easier,” he says. “My change management overall become easier.”

The benefits of AI were significant, Donepudi adds. Without using AI tools, the company projected the modernization project to take 17 to 18 months, but by using AI, it completed the project in 11 months. The cost savings from the compressed timeline added up to between $5 million and $6 million, he says.

“We went down on Friday evening,” Donepudi told his CIO peers at this year’s CIO 100 Awards and Conference in August of the company’s final transition. “Monday morning the business came back and they were like, Rama, are we on S/4 now? Yes, we are on S/4. They didn’t even realize that we were able to do all of that. That’s the beauty of using AI extensively in our training and testing, documenting. We showed them what AI can do. That’s our story.”

While Donepudi believes there is great potential for AI in digital transformation projects, he also sees hesitation among his peers. As Gartner’s Chandrasekaran observed, many larger organizations don’t have the same risk/reward tolerance that Mead Johnson Nutrition was comfortable with, he says.

In addition, big consulting firms still have fans among the CIO set, Donepudi adds.

“The reason we engage all these big consulting forms primarily is for that program management, change management, comms management, all of that handholding,” he says. “Where these Deloittes, IBMs, and Accentures come into the picture is that they know how to do these large transformations, and you’re touching your entire company — finance, supply, procurement, and sales — and they help navigate the change.”

But for CIOs willing to take a risk, the rewards can be great, Donepudi says.

“It’s a new approach and it’s risky, and I am taking full ownership,” he says. “I’m fully accountable for this one versus, ‘I gave this deployment to IBM for $50 million per year, and it’s on IBM to help us if something goes wrong. IBM is on the hook.’”

The project also gave Donepudi the opportunity to inspire the rest of his organization to trust in the power of AI, he says.

“If I don’t walk the walk of using AI department, how can I go tell my finance, supply, or marketing guys to use AI because it gives you productivity, it gives you an efficiency?” he says. “I took the risk at the end, and it paid off for me.”

Today’s most successful IT leaders must be master translators, interpreting technical complexities with clarity and precision for the business units that rely on their services for into decisions and action. Deluxe Chief Technology and Digital Officer Yogaraj “Yogs” Jayaprakasam is among the best at putting this into practice, a strategic imperative he describes as “humanizing” technology.

Rather than beginning with cloud, architecture, or AI, Jayaprakasam frames Deluxe’s IT strategy as one focused on technology “that powers profitable growth.” This translation mastery can also be seen in the five-part series he and CEO Barry McCarthy co-developed to establish a common language around Deluxe’s IT transformation, a sea change that improved not only the way Deluxe does business but the way its customers do as well. And you can see it in how he translates strategy to measurable business, customer, and talent outcomes. Jayaprakasam’s widely read newsletter, “AI Signals: Navigating Change,” is perhaps the clearest expression of his knack for finding the signal and translating it into pragmatic, immediately useful leadership insight.

After a recent episode of the Tech Whisperers podcast, Jayaprakasam and I spent some time discussing why being a master translator is critical for today’s technology leaders and how he separates signal from noise to serve Deluxe’s next reinvention. What follows is that conversation, edited for length and clarity.

Dan Roberts: You’re an engineer by training. When did you realize knowing technology wasn’t enough and that one of your most important jobs was helping others understand what technology makes possible?

Yogs Jayaprakasam: It comes from a pattern I’ve seen throughout my career. As a technologist, you can provide an elegant technology architecture, but the customer wants to know, “If I invest half a million dollars in that, what will I get in return?” I’ve had some of my most high-performing senior technology leaders tell me, “Here are all the great things our team has done,” but they still struggle to figure out, “What does this mean to the company?”

For example, as AI adoption increases, so does exposure to third-party operational risk beyond traditional cybersecurity threats. Recognizing this shift early, my team deployed capabilities such as synthetic monitoring, blast-radius analysis, and proactive service desk operations. The outcome was an 80%+ reduction in system incidents over the last nine months, demonstrating the value of anticipating emerging risks before they impact the business. But when I asked the team what that meant to the business, they couldn’t articulate it.

Technology earns credibility when its impact is expressed in the language of the business — financial performance, risk reduction, customer satisfaction, and employee experience. In this case, I helped the team recast the message as what it truly was: a direct contribution to EBITDA protection and enterprise risk reduction.

I rewrote it as, “It directly protects the EBITDA of the company and reduces our risk posture meaningfully.” Because every time there is an impact, it takes additional time — and costs — for us to fix the problem. That chips away at EBITDA while impacting our customer and employee experience. That’s what the business wants to know.

Deluxe has one of the most inspiring business success stories. Can you talk about where the company started out and where you are today?

Our founder, W.R. Hotchkiss, used the most cutting-edge printing technology of 1915 to introduce uniformity to American bank checks, and he invented what we recognize today as the modern checkbook. Deluxe was a dominant force in paper payments and used that trusted history and massive business network to move with the times. Today, we are a digital payments and data company offering merchant services solutions that provide credit card processing, payment settlement enablement, the payment gateway, and data-driven marketing solutions for B2B businesses, financial institutions, and various industries.

When this business transformation journey started, we were around 30% payments and data. As of this past quarter, we were at 52% payments and data, and we are potentially trending towards 60% payments and data with the recent acquisition of Celero Commerce. So we are a payments and data company with the legacy checks division still being profitable, supporting our cash flow and business growth.

You’ve described Deluxe’s journey as one of reinvention, not simply transformation. Why is that distinction important?

Typically, people use modern technology to do the same thing they were doing before. It could be a manufacturing company that still does the same manufacturing, but now you are applying a digital order management system to accelerate fulfillment. At legacy Deluxe, the product we were building was checks, so we identified ourselves as a manufacturing company. Adopting digital technology for that process simply enabled it to be more efficient. That’s transformation.

But the job customers trusted us to do for more than 100 years has largely moved to credit cards, digital payments, embedded payments — all things that no longer require manufacturing. Now, technology is how we build those services and deliver. The mission is the same — to make sure the payments transactions are safely and quickly completed — but we had to reinvent what we do, how we do it, and the whole mindset of delivering a software-driven product.

Every function inside the company had to change. Finance had to change the way we do revenue recognition. In manufacturing, you have to think about inventory management to optimize your balance sheet. It’s totally different on the payments side. The talent is different. The sales talent is different — somebody who used to sell a tangible product versus somebody who is selling a service level and functionalities. The tech stack is different. For the technology function, this means you have to build applications and platforms that are always on to facilitate those digital payments and data solutions. Every part is evolving in every sense to make the whole bigger than its parts. That’s why it is a reinvention story, not just a transformation story.

How do you translate your technology strategy into something everyone, from the boardroom to an engineer at the keyboard, can understand and act on?

I have a one-page strategy slide that begins with “Technology’s mission inside Deluxe is to power profitable growth.” This was co-created with my CEO and the rest of the executive leadership team. We connected that to five strategic pillars:

First is Deliver Measurable Business Value. That means aligning technology investment to growth. This an executive leadership team and board pillar — it is of special interest to the leaders responsible for investment decisions — but my team will experience it through the projects we invest in.

The second pillar is Build Modern Products and Platforms. That is where the investment turns into initiatives. Can the core platforms we are building be reused in many business units? We identified data, AI, and APIs as a core part of our platform strategy to drive reuse and leverage. For example, if I built an API that can extract invoice solutions, every business that needs an invoice solution can consume it.

Third is Scale AI Responsibly. This is about using AI with our data to drive more automation efficiency for the internal functions. We used AI to eliminate a 50-year-old mainframe and move to the cloud. We are using AI to continue to drive resiliency on that platform but also to improve our legacy unit, because, according to the Federal Reserve, 60% to 80% of businesses still use the check product, and many of those businesses are banks, even our largest banks.

Number four is Strengthen Cyber Resilience and Trust, where we continue to innovate in not only cybersecurity but also systems availability, proactive resiliency, all those things to protect our revenue, EBITDA, and our existing business.

The fifth pillar is Grow Next-Generation Talent and Culture, which is investing in our people across the company, giving them the right tools and AI skills in an accountable, collaborative culture.

Stepping back, the first pillar talks about strategic investment through the lens of the ELT and board. But the next three pillars talk about technology’s job: driving profitable growth and improving the margin. Products and the platform contribute to driving the growth. AI usage contributes to expanding the margin. The trust and governance comes from next-gen cyber resiliency. Then we’re investing in and training our people.

You’ve told me the CEO and executive leadership team are your “first team.” How has being on that team and serving on boards, changed the way you think and lead?

Becoming part of the executive leadership team is your best opportunity to truly become a business leader, not just a technology leader. Your first duty is to the team that you are a part of, which is the ELT. But serving on boards gives another dimension for a technology leader. How you think and act in the executive leadership team is different from being on the governance team of a board. How you observe and guide operating teams is different.

It makes you realize that you are not going to play the same role on every team. For my function, I am the head of technology. For my ELT, I’m one of the teammates who makes a decision come to life. As part of a board, my job is to help assess as a group if the operating team is representing the right outcomes for the shareholders that you represent as well.

These are, in my opinion, the best opportunities to evolve to be a fully rounded leader. Business is the ultimate team sport. Individual excellence is valuable, but collective excellence is what creates lasting impact. I believe the hallmark of a successful technology leader is the ability to seamlessly shift between leading and serving, always doing what best advances the success of the team and the enterprise.

There’s no shortage of AI content. What did you believe was missing when you created your newsletter, “AI Signals: Navigating Change,” and how are you using it to help audiences separate signal from noise?

It began when ChatGPT was released in November 2022. I observed many of my peers trying to figure out how to bring ChatGPT into their organizations and how to drive value with it. Then, many AI companies started coming out. Many models started coming out. It was extremely hard for any company to decide what to do with the technology.

That triggered me to research and review the last 70 years of technology evolution. I wanted to understand, when a moment like this happens, whether it is through the internet, web development, mobile, or big data and machine learning, what occurs afterward that becomes durable, and what lessons can we learn from those moments?

What quickly became clear is, all of those changed how you solve a problem inside your business, not what you need to change. Eighty percent of what you do inside every business is the same. We are a 112-year-old company; 80% of what we do is the same. Go through every department — we all have to do the same things, but we have to do it faster, better, and cheaper.

So instead of chasing what is changing, I tried to go back to what is not changing in this chaotic moment. Then I thought, how can I draw attention to those things that are not changing? That’s how “AI Signals” was born. I realized that I need to write about it so that it is clear in my head. The newsletter provides a forum for input and collaboration with industry peers so we know how to make the right decisions for our organizations.

What signals are you paying closest attention to right now, and how are they shaping what you’re preparing Deluxe for next? How do you know when it’s time to change your mind?

I track what’s happening because of AI through three lenses: One, because Deluxe is all about payments and data, I keenly watch what’s happening in the payments industry, paying close attention to agentic commerce and how much it will pick up steam, and how much trust humans are placing in agents to make decisions on their behalf to make purchases, and things like that.

The second is a continuous focus on cybersecurity. As AI continues to mature, that space will continue to expose what we took for granted in the past, where we’ve said some vulnerabilities are medium and low, you can get exceptions that linger, and also how you handle the end of life for a technology. All those things are dramatically changing, because several low vulnerabilities chained together by an AI tool could become a path for malevolent AI to penetrate. And that doesn’t even include the threat posed by well-intended frontier models like those made by OpenAI, which accidentally penetrated a government database last week. The focus is on vigilance about patterns of AI behavior and how to effectively defend our companies.

The third lens is much more personal to me but also relevant to my job, which is how we view AI’s impact on talent. It is already redefining our jobs, and the biggest work in front of us is legal, compliance, HR, and technology partnering together to really figure out how to retrain our talent to use AI to stay relevant for the jobs we need to deliver. Whether you apply your lens as a leader or you have broader community concerns, we all have to contribute to the talent adjustments.

I don’t just rely on my opinions and my own research. I talk to real people at every level, and if my research does not fit well, is not practical, or is a couple of steps ahead of where the reality is, I rein it in and go from there. I have about a dozen volunteers for “AI Signals” who meet with me every Sunday, and we go through the ideas before I post. And there are articles that they’ve brought their own real examples to. Those people come from different industries and different backgrounds, so I’m pressure testing in those areas as well.

Perhaps the biggest lesson in Jayaprakasam’s approach to being a master translator is that it starts with being a master learner. He is equally intentional about staying current to develop an informed point of view as he is in challenging his own thinking, engaging peers with different perspectives, and changing course when the signals suggest he should. With virtually every company confronting reinvention in the AI era, the best leaders will need to strike that same balance, while still having the courage to move.

For more insights from Yogaraj Jayaprakasam’s leadership playbook, tune in to the Tech Whisperers.

See also:

• What separates true enterprise leaders from strong tech execs
• The 7 new Cs of IT leadership for the AI era
• The rise of the AI operating executive

Companies are moving fast to put AI into chatbots, virtual assistants, and other customer service tools. But for CIOs, that comes with a risk: if the technology gives customers the wrong information, makes biased recommendations, or mishandles data, it can immediately damage the trust companies have worked hard to build.

Unlike conventional software, gen AI doesn’t always give the same answer to the same question. An answer can sound confident and convincing while still wrong. And when customers rely on AI for information about their finances, insurance, or health, a mistake can have much greater consequences.

Sumit Johar, CIO at cloud-based software company BlackLine, understands that inaccurate AI responses hurt the customer experience, force companies to spend money and resources to fix problems, damage the brand, and create regulatory risks. AI systems can also expose confidential information, making it important for companies to understand the data sources behind their responses.

“An AI failure creates a massive trust issue around the core solution being provided,” Johar says. “In the financial sector, bad AI advice or outputs can compromise balance sheet accuracy and lead to severe legal and personal liabilities.”

For Citizen Health CTO Daniel Wang, for instance, the stakes are particularly high because the company’s users include rare disease patients and caregivers.

Citizen Health works to ensure that important information provided by its AI can be traced back to the original source, such as patient records, clinical information, or medical studies. The company also reminds patients to talk with their healthcare providers before making medical decisions.

Wang says one of his biggest concerns isn’t just that AI can make a mistake, but that a wrong answer can sound right.

“An AI system can present an answer that sounds caring, informative, and authoritative, even when the underlying information is inaccurate,” he says.

That risk has affected how Citizen Health releases AI products, and has delayed AI launches when the company needed more time to understand how the technology was coming up with its answers, or to make sure those answers were accurate. So instead of rolling out new AI tools to everyone at once, smaller groups of patients might be tested first, and their feedback used to make improvements before a wider launch.

Citizen Health also tests its AI tools before launch using questions with known answers, and uses both automated testing and human review, watches for unusual AI responses, limits what the AI can answer, and gives patients ways to provide feedback.

Moving at the speed of trust

Other tech leaders are taking a similarly cautious approach. Jonathan Tushman, CTO and CAIO at Hi Marley, an insurance technology company, says it’s had tech that was ready to ship for years but chose to spend more time on testing, observability, legal review, and training before releasing it.

“If it weren’t for that trust-and-governance bar, we’d have gone live a year earlier and had faster adoption,” Tushman says. “We chose not to.”

Hi Marley calls that approach working at the speed of trust. For Tushman, releasing customer-facing AI also requires the ability to see what the system is doing once it’s running. That includes monitoring individual conversations as well as looking for patterns across tens of thousands of interactions. Companies also need kill switches that can shut down a system, and the ability to roll back changes if something goes wrong.

But technology alone isn’t enough. Customer service, IT, DevOps, and CloudOps teams all need procedures to respond to AI problems, Tushman adds.

Udaya Bhaskar, senior application security analyst at global farming company Corteva Agriscience, says one mistake companies make is moving too quickly after seeing an impressive AI demonstration without fully testing how the tech will perform with actual customers.

He adds that companies can also run into problems when they rely too heavily on vendor claims, or launch AI tools without giving customers an easy way to reach a person when something goes wrong.

The hidden cost of AI mistakes

The pressure to deploy AI is often tied to the promise of greater efficiency. A chatbot that handles more customer questions, for example, can reduce employee workloads. But those time and cost savings can shrink quickly if employees have to spend their time checking and correcting the AI, something referred to as the hallucination tax.

Companies may budget for developing the technology without fully accounting for ongoing costs of running and monitoring it.

Jess Lampe, global lead technologist at Launch Consulting says those hidden costs can include employees spending time checking AI responses and redoing work when the technology gets something wrong, as well as regulatory and legal risks. Another cost can be harder to measure: loss of customer trust.

Chris Slovak, global field CTO at managed enterprise AI delivery platform Unframe, sees the resulting damage to a company’s reputation as one of the largest hidden costs.

“Anything customer-facing is a representation of your brand,” Slovak says. “Yes, poor implementations with bad experiences have a material cost from the perspective of tokens. However, the impact on your brand is far more costly. Often, companies add AI to deflect customer support. Consider you have a customer who’s already frustrated, and meeting AI makes it worse. The loss in trust can be massive.”

But Kevan Yalowitz, global software and platforms lead at Accenture, says mistakes are going to happen whether work is performed by people or technology.

“The true hidden cost is destroying user trust when mistakes happen, regardless of the source,” Yalowitz says. “The most successful organizations quickly admit when these mistakes happen, provide a transparent explanation, and explain how it was an opportunity to learn and ensure the mistake doesn’t happen again.”

Human oversight remains critical

For CIOs, preventing those problems means putting controls around AI before it reaches customers.

Johar says human oversight is particularly important for critical decisions. In highly regulated industries such as finance, AI shouldn’t independently handle decisions where accuracy is essential. Companies also need reliable data feeding their AI systems, and the ability to audit what happens when something goes wrong.

Lampe says the level of oversight should depend on what an AI system is allowed to do. An AI tool that summarizes information, for example, doesn’t pose the same risk as one that can take action involving a customer’s money or account.

Companies should also continue testing after launch. AI behavior can change over time, meaning a system that performed well when it was first released may not continue performing at the same level later on.

Toyan Espeut, chief customer officer at cloud-based work management and collaboration platform Smartsheet, says companies also need strong rules around customer data.

“It can’t be mixed across tenants or used to train third-party models, and AI can never operate outside a customer’s existing permission structure,” she says. “If a person can’t access something, the AI acting on their behalf shouldn’t be able to either. Every AI action needs to be fully auditable, and customers should always have transparency into when AI is involved, and the ability to review or validate AI-generated outputs.”

Who’s responsible when AI gets it wrong?

Even with extensive controls, companies need to prepare for their AI to make mistakes, but the AI itself can’t be solely to blame.

At Citizen Health, Wang says a problem involving customer trust quickly becomes a company-wide issue. The company has support and on-call procedures, but serious concerns are also brought to leadership. Citizen Health also maintains direct channels for users to report problems so they can be investigated quickly.

Tushman at Hi Marley makes a distinction between the person who initially handles an AI problem and the executive ultimately responsible for it. A customer success or help desk team might receive the first complaint, but he says accountability for customer-facing AI rests with him as CTO. “It isn’t something you outsource to the system or hand to the vendor,” he says.

Corteva Agriscience’s Bhaskar similarly says the company deploying the AI remains responsible for what it does since AI is a tool rather than a decision-maker, and companies can’t shift responsibility to the technology when something goes wrong.

And Lampe adds that every customer-facing AI system should have a named person responsible for its behavior and performance, along with a clear process for escalating problems, and allow customers to challenge incorrect outcomes and reach a human.

Speed: only one measure of success

The push to adopt AI is unlikely to slow down. But tech leaders say companies need to be careful about the standards those investments work by. Simply counting how many customers use an AI tool may not say much about whether it’s making their experience better.

Espeut says companies should look at whether customers are solving problems faster, making better decisions, and getting better business results.

“Companies should also expect fewer support tickets because that tells you customers are actually succeeding with the tool, not just using it,” she says. “Expanded engagement across the organization is also an indicator instead of staying siloed in one buying center. They should pay attention to the quality of customer feedback and feature requests as well. When customers actively help shape your product, it’s a strong signal they trust the technology and see its long-term value.”

Every CIO in a regulated industry has a system the business depends on, that no one fully understands and that must be replaced. In banking, it is often a core platform that predates the internet. In my own career, that system took two distinct forms: first, a VAX minicomputer running a parking operator’s entire business across nearly 200 New York City locations; later, the on-premises core of a global financial technology company supporting payments, banking and card issuing. Both had to move to the cloud without interrupting the customers that depended on them. The technology differed each time. The reasons the migrations succeeded did not.

Why regulated migrations fail differently

Migration failures come in two kinds. The loud kind takes a website down, engineers fix it and customers move on. The quiet kind — a pricing discrepancy, a missed filing, a mismatched reconciliation or a lost audit trail — may not surface for weeks. Its cost is measured in regulatory findings and lost client contracts rather than in hours of downtime.

Regulated migrations face both. The loud kind can be ruinous, as TSB learned in 2018 when a big-bang platform migration locked customers out for weeks and, after regulators found insufficient contingency planning and untested cutover configurations, drew a £48.65 million fine. The loud kind is at least visible. The quiet kind breaks the business without anyone noticing.

This changes what “without breaking the business” means. It does not mean zero outages. It means that at every point during the migration, the organization can prove that the new system produces the same outcomes as the old one for the cases that matter, or a difference the business has approved, and has a tested way to recover if it does not. That standard is stricter than most migration plans are written to, and it determines whether the program survives its first serious incident. Meeting it comes down to five operational disciplines.

Map the dependencies you were not told about

The formal dependency map in the architecture repository is always incomplete. The dependencies that break a migration are the informal ones, which grew up around the legacy system without ever being documented. A team in another department builds a nightly job that reads a file the legacy system happens to produce at 2 a.m., without the system’s owners knowing. An analyst in finance writes a spreadsheet macro that queries a database table directly, bypassing every official interface. A report goes to a regulator every quarter, generated automatically for so long that no one remembers how to produce it by hand.

Finding these dependencies takes careful observation. Configure the legacy environment to record every connection, every file it produces or consumes, and every scheduled job. Collect this data over a full business cycle, including month-end and quarter-end processing, before relying on the dependency map. This step is often dismissed as unnecessary, but it provides the evidence needed to plan the migration sequence. A workload should move only when its dependencies have moved, remain reliably accessible, or are confirmed to be no longer required.

The map must also account for people. Behind every critical legacy workload is a group of engineers or business analysts who know why an undocumented parameter exists or why a batch job runs only on the third Thursday of the month. Identify them at the outset, protect their time and capture what they know in reproducible test suites before they retire or move on.

A thorough map also reveals what to prune. Over decades, legacy estates accumulate dormant batch jobs, deprecated extracts and orphaned interfaces. Retiring these before cutover shrinks the migration scope and removes testing cycles spent on workflows that no longer serve anyone. Never decommission an idle process based solely on the monitoring window; verify with the responsible business owner and check historical run schedules for low-frequency jobs first.

Sequence cutovers around the business calendar, not the project plan

A project plan wants cutovers on a steady cadence. A regulated business has dates on which nothing may change, such as settlement days, filing deadlines, rate resets and month-end close. Build the migration calendar from the business calendar outward, placing cutover windows where you can detect and recover from failure before the next immovable date.

The discipline that makes this work is proving it at every stage before scaling to the next. Start with one workload, such as monthly billing or a single settlement feed, in the incremental style the Azure Architecture Center calls the strangler fig pattern. Run it on the new system in shadow mode alongside the legacy system for a full business cycle. Keep the legacy system as the system of record during this period. Capture the new system’s intended actions, such as payments and client messages, without sending them. Next, compare both systems’ outputs for each transaction and resolve any differences. Test external integrations, peak-load performance and recovery procedures separately, since shadow running never exercises the actions it suppresses.

Only then make the new system authoritative for that workload. Retain a tested recovery path through an agreed stabilization period, with a method for handling transactions recorded after cutover, and move on to the next workload. Where the architecture does not allow workloads to move independently, apply the same discipline to a coordinated group of them, with rehearsed cutovers and explicit acceptance criteria in place of the workload-by-workload sequence. Where even shadow running is not feasible, establish readiness through representative transaction replay, end-to-end testing and full cutover rehearsals, against the same acceptance criteria and with the same tested recovery path.

This is slower than a big-bang migration by a factor that sometimes makes executives uncomfortable. It is also the approach that produces evidence, rather than hope, before each increase in production exposure.

Keep the old system running until the new one has earned trust

The strongest temptation in any migration is to switch off the legacy system early, because running two systems costs money and because the old one is what the program exists to eliminate. Resist it. In a regulated environment, the legacy system is the reference implementation. It is the baseline against which every output of the new system is evaluated. Explain and resolve every material difference between the two systems, never mistaking agreement for correctness; then check the results against the business rules and the authoritative records.

When we moved a parking operator’s business from a VAX minicomputer to a cloud platform, the VAX ran alongside the cloud platform for months, and we reconciled every transaction between them until the new system earned the business’s trust. The parallel run let us catch and resolve issues before they reached customers. The cost of the parallel run was visible and budgeted. Had we skipped it, our customers would have discovered the errors, such as mischarged accounts and failed payments, before we did.

Rehearsal matters at every scale. When Commonwealth Bank of Australia moved its core banking platform to the cloud in 2025, it conducted five full dress rehearsals of the cutover, having originally planned for three. During the actual cutover, it kept customers online throughout the three hours the core was down.

Reconciling parallel outputs exposes discrepancies, and not every discrepancy is a defect in the new platform. Shadow running frequently uncovers latent bugs in the legacy system, such as rounding oddities, edge-case truncations or mishandled time zone changes, that went unnoticed because no independent system had ever checked the arithmetic. Classify each discrepancy as a new-system defect, an undocumented legacy defect or a deliberate change in method. New-system defects must be fixed. Legacy defects need an impact assessment, remediation and, where required, disclosure. Deliberate changes require approval from the business and, where applicable, the regulator. Working through that classification transparently earns the consensus needed to retire the legacy host.

Reconciling transactions does not prove that the migrated data is complete. Before retiring the legacy system, validate balances, record counts and relationships between records, and confirm that historical records and audit evidence remain retrievable, whether in the new system or in a validated archive.

Build the controls into the platform, not around it

Regulated migrations often treat security, resiliency and auditability as a compliance workstream that runs beside the technical one. That is how gaps appear at cutover. Build the required controls into the target platform before the first workload arrives: automated data residency enforcement, encryption with customer-managed keys where the data classification calls for them, continuous monitoring, immutable audit trails and granular identity and access management. Every migrated system then inherits these controls from the start, and each workload’s use of them is verified. Compliance is not a post-deployment audit; it is a primary design constraint that dictates the perimeter of the technical solution.

This matters doubly when the platform is intended to host AI. Models and agents will act on the data the migrated systems hold. Audit trails must be immutable and retrievable for years, and decisions that affect customers will need an explainability that many AI systems do not provide by default. If data lineage, access controls and the audit trail are not in place at migration, retrofitting them afterward is costly and often incomplete. Evidence that was never captured cannot be reconstructed, and the AI program will inherit a foundation that cannot withstand regulatory scrutiny.

Govern with standards the business can read

Enterprise architecture frameworks, such as TOGAF, provide the architecture team with a process; industry reference models, such as BIAN, give the business a vocabulary. The Banking Industry Architecture Network (BIAN) publishes a standard model that partitions a bank’s work into named business capabilities, such as payment execution or customer agreement, each with a standardized interface. When migrated services are mapped to that taxonomy rather than to legacy program names, a product owner or risk officer can read the migration plan, verify that a capability functions and sign off without needing to know what the old modules were called. The same artifacts, tied to test results and control evidence, can be presented to a regulator without translation. That shared language is what turns a migration from an IT project the business tolerates into a modernization the business owns.

What it takes

The five disciplines set out above hold whatever the technology: build a dependency map from observed system behavior; plan the cutover around the business calendar; prove correct transaction processing, through parallel running where feasible and through replay testing and rehearsed cutovers where it is not; establish platform controls before workloads arrive; and define governance in terms the business and its regulators understand.

None of these practices is new. What is rare is the resolve to keep to them when delivery schedules tighten. The organizations that modernize successfully are not those with the most sophisticated target architecture. They treat careful planning, respect for legacy knowledge and business continuity as engineering requirements rather than aspirations, refusing to sacrifice verification for speed.

Google is overhauling its cloud modernization portfolio by bringing its existing migration and modernization tools along with new agentic migration tools and a new Modernization Hub under a new Google Cloud Modernize umbrella to accelerate infrastructure and application transformation at enterprises.

The new agentic tools include an EKS-to-GKE Migration Agent, designed to help enterprises move Kubernetes workloads from Amazon EKS to Google Kubernetes Engine (GKE).

The agent can analyze existing EKS environments, translate Kubernetes manifests, map storage and networking requirements, and help prepare workloads for deployment on GKE, with human approval points built into the process, the company wrote in a blog post.

Alongside that agent, Google is also adding new capabilities to its Migration Center’s Quick Estimator Agent. These new capabilities will allow enterprise users to generate total cost of ownership estimates from infrastructure data, including VMware inventory exports, and test different migration scenarios and cost assumptions in natural language.

The Migration Center itself is being rolled into Cloud Modernize and will continue to offer tools around migration planning and assessment.

Beyond the agentic capabilities and the Migration Center, the hyperscaler is consolidating its application modernization tools, such as the App Modernization CLI, Java and .NET modernization capabilities, and mainframe tools including the Mainframe Assessment Tool, Dual Run and Mainframe Connector, under a new Modernization Hub.

The new Hub, which will be accessible via the Google Cloud Console, will also be a part of the umbrella offering, the company wrote.

Unification of tools could simplify operations

For CIOs, the consolidation of existing modernization and migration tools under a broader umbrella offering could simplify operations and decision-making as it turns what was previously a collection of separate tools into “one end-to-end process”, said Pareekh Jain, principal analyst at Pareekh Consulting.

“An enterprise might have VMware workloads, Java applications, mainframes, Oracle databases and Kubernetes applications running across different clouds. Previously, these could require separate tools, assessments and migration teams. With the broader offering, CIOs require fewer handoffs, faster decisions and better visibility across a large modernization program,” Jain said.

The consolidation of the Modernization Hub also provides similar benefits.

“Instead of fragmented tools, the new Hub provides a consolidated assessment workflow. It gives CIOs one place to assess, plan and manage application modernization,” said Viet-Anh Nguyen, machine learning lead at Scopic Software.

“Earlier, capabilities were spread across different Google tools. Teams can now understand applications and dependencies, get modernization recommendations and decide what to migrate, rehost or refactor from a more unified interface,” echoed Jain.

Similarly, the EKS-to-GKE Migration Agent could reduce the manual work for developers and platform teams.

“Since the Agent can automatically discover and translate AWS-specific infrastructure and Kubernetes configurations, validate the outputs, and create pull requests for human review, it reduces repetitive engineering,” Nguyen said.

That, in turn, could make switching clouds faster, cheaper, and less risky, Jain pointed out.

However, the Agent doesn’t eliminate testing, architectural decisions, data migration, or production cutover, which means teams still have to own these steps, Jain said.

The article originally appeared on NetworkWorld.

Sumit Johar has been modernizing BlackLine’s IT environment since becoming CIO of the software company in early 2024.

As a longtime IT leader, Johar has plenty of experience overhauling tech stacks, but he says the work has taken on new meaning — and urgency — in the AI era.

“The definition of modernization has literally switched in the last two to three years,” he says. “Suddenly everything that you built in the last few years looks like legacy, because the SaaS model, which we have been using for the last 10 to 15 years, there is an opportunity now to totally change that and modernize with AI and for AI.”

Johar is doing just that, surveying the company’s SaaS portfolio, particularly zeroing in on point solutions, asking whether each is worth the cost, whether it delivers true business value, or whether IT can use AI to build a service better suited to his company’s needs.

At the same time, AI is changing how workers interact with technology, he says, enabling people to query applications via natural language and chat, which has his IT team considering which applications may need to be modernized or abandoned altogether as a result.

Take, for example, dashboards. “We built dashboards because there was no other or better way to report on your data,” he says. “Now I can simply ask a question and get an answer, which is what I was interested in anyways. So if I have my ERP and CRM talk to me in a human language, I’m happy with that. That’s modernization.”

As a result, he adds, “some of us might be getting rid of [our dashboards], and some might be slowly reducing the dashboarding tools we have.”

As organizations remake and automate workflows with AI, software that enables those workflows could meet a similar fate, Johar says.

Even chatbot-building products that IT implemented a few years ago are prime targets for rationalization due to AI advances, he adds. “I still need chatbots, but I don’t think I need specialized software to build a chatbot anymore. Any generic AI platform that I have will give me the ability to basically build them,” he explains.

High-priority tasks

For Johar, the longstanding CIO tasks of modernization and rationalization remain make-or-break activities for IT.

Johar and other IT leaders see modernization and rationalization as vital to controlling IT spend — as high of a priority for CIOs as it’s ever been, freeing up dollars for innovation (the No. 1 objective for nearly every IT leader and their CEO’s top priority).

IT leaders also see modernization helping them create a future-ready IT environment, one that’s not encumbered by legacy technologies and complexities that don’t play nice with emerging technologies.

“CIOs don’t have much of a choice than to continue modernization; they’re in a perpetual state of modernization,” says Amar Aswatha, senior vice president for global business engineering at CGI, a consulting and services firm. “They’re trying to simplify yesterday’s tech state while creating resilience and the right environment for AI and to be ready for whatever will emerge.”

Other factors also drive the need for ongoing modernization, Aswatha notes. The rapid pace of innovation is reducing the shelf life of many technologies more quickly, necessitating more rapid overhaul cycles than in the past. Meanwhile, the past decade’s mass migration to the cloud has meant that CIOs “traded server sprawl for subscription sprawl,” he says. The rush to adopt AI is having a similar effect, leading to AI and agent sprawl — and the need for new and more widespread rationalization and modernization strategies.

Still, CIOs must be more surgical than they’ve been in the past, using a scalpel not an ax to shed legacy technology and simplify IT environments, Aswatha advises.

That means accounting for both current and emerging business needs when determining what systems should be modernized — not simply relying on age or other such non-business-related factors. And IT leaders can’t see rationalization as an exercise in reducing application count to a set number; unnecessary complexity should instead be the target.

Such approaches require CIOs and their teams to understand current and future business needs, as well as how those needs, and the organization’s overarching strategy, shift over time, Aswatha says.

“CIOs have to watch for business signals on when and what to modernize and rationalize. It’s not about spring cleaning, it’s about what apps are slowing the business down,” he advises.

‘Best for the organization’

Rachelle Putnam, vice president and CIO of General Dynamics Land Systems, which manufactures military vehicles, is taking such an approach.

Putnam, who has led IT at the company since 2014 and managed a move off a mainframe about a decade ago, says modernization today means asking whether existing technology “allows us to have the flexibility and the functionality we need.”

Like many CIOs not on IT’s bleeding edge, for Putnam savvy modernization is about strategy, not technology. “Some might say we’re not on a modern tool set, but that doesn’t mean we don’t have the functionality we need,” she says.

For example, Putnam embraces a cloud-smart strategy, modernizing on the cloud where it makes sense and on-prem when that’s the better or required option based on government regulations.

She applies a similar lens to rationalization, asking whether any given application uniquely fulfills a business need or whether other apps in the portfolio can do the job. Such questioning helped her standardize the company on two CAD tools, down from 10, and standardize on one reporting tool instead of using three.

Putnam is also increasingly asking whether she even needs a vendor solution.

“With tools like Claude Code, when we look at [vendor-offered] apps, we ask if we even need them or whether we can create our own and not pay for maintenance,” she says. “Sometimes we want SaaS, where we want those updates, but there are others that we don’t need updates and won’t need to change the application much, so those are the ones we’re going to look at creating ourselves.”

Like other CIOs, Putnam believes modernization produces benefits for IT and the company as a whole, ensuring the organization not only has the technology necessary to perform but also a standardized data environment so that “everyone is looking at the same data, and it means the same thing,” she says.

In addition to cutting IT maintenance and management costs, modernization also reduces risks, by eliminating unsupported legacy tech and difficult-to-manage complexity.

And it makes IT talent more effective and efficient, by eliminating the need for outdated skills, which are typically hard to find and thus come at a premium. With rationalization, IT teams no longer must learn multiple vendor solutions or assign different workers to different solutions.

Modernization challenges

Of course, Putnam and other IT leaders acknowledge there are challenges to modernization and rationalization, too.

“Cost is certainly one. Sometimes it costs more to get off one than continuing to run multiples,” Putnam says.

Even in such cases, however, hard-to-quantify benefits make the move worth it, she adds. For example, rationalizing to one common system used throughout the company’s multiple geographic locations means workers anywhere can easily be assigned to help when one region faces a surge in work.

Data migration is another common challenge. But data issues can usually be overcome with “creativity, mostly, and having someone dig down to really get it done,” Putnam says. Sometimes it may mean shelving the data and only retrieving it if necessary.

Then there’s the challenge of limiting future rationalization by controlling what gets added to the IT environment today. To that end, deploying and using a common tool is the default standard now at General Dynamics Land Systems.

“That’s a rule that everyone abides by,” Putnam says, acknowledging some pushback. “Sometimes people will argue for that extra 15% that one tool might have that the existing tool doesn’t have. But we stress that standardization is best for the organization as a whole.”

A staple of CIO work

Krishna Prasad, CIO of UST, a digital transformation and IT services provider, similarly sees modernization through a business — not a technology — lens today.

Whereas modernization was once triggered by advancements that made existing tech outdated, obsolete, or no longer supported by the vendor, now modernization is triggered when business advancements render a deployed technology deficient, he says.

This new scenario has CIOs, including himself, modernizing faster than in past eras, Prasad says. And it’s why modernization remains a core component of the CIO role.

“Modernization means meeting today’s business needs, and that may mean we need to deploy a new version of [a technology] to get a better experience, speed, agility, or in a way that we can provide transparency,” he adds. “So, at UST we have always thought about modernization as a continuous process.”

For example, Prasad is currently modernizing the company’s CRM platform, even though the existing one would not be considered legacy tech using a traditional definition. Yet the deployed CRM wasn’t providing the capabilities UST needed for its sales department to deliver on the company’s vision.

“We weren’t having a good user experience, and sellers weren’t updating the information as they should, so we didn’t have real-time data and then the operations team had to do the data entry, so you had cost there, too, and we had reduced agility as a result,” Prasad explains.

Similar factors are at play when it comes to rationalization, which he believes should also be an ongoing concern for CIOs.

Despite the benefits these efforts bring, Prasad acknowledges the heavy lifts involved, ones that many organizations aren’t yet prepared to undertake. For example, UST still has a financial system running on premises, and because the complexity of “disentangling the financial systems from the guts of the business is challenging, it’s a change that the business isn’t willing to make right now,” he admits.

Such situations ensure modernization projects will remain a core IT task in the years ahead.

See also:

• 6 strategies for accelerating IT modernization
• 8 IT modernization traps CIOs must avoid
• Massive modernization: Tips for overhauling IT at scale

I’ve seen organizations reach a point in a modernization program where the technology itself is no longer the biggest risk. A decision was made for a full rip-and-replace. The new environment is ready. The migration plan has been tested. The business case has been approved. But the team responsible for delivering it is also responsible for keeping the existing estate running. A change freeze is approaching; business units are nervous about the cutover and what started as a technology upgrade has become an exercise in managing operational risk.

That is the uncomfortable reality behind many large-scale modernization programs.

We often make assumptions that legacy infrastructure is the problem and replacement is the solution, but in practice, rip-and-replace can create as much disruption as it removes.

So rather than asking — what do we need to modernize, CIOs should be asking a more practical question — what needs to change right now? Do we really need to change everything?

The reality is, there is only so much an organization can take on at once. AI is demanding investment and attention, workplace technology is changing, security expectations continue to rise and the existing estate still must keep running reliably in the background. All those priorities are competing for the same budgets, infrastructure and crucially, the same people.

Modernization shouldn’t mean replacing technology simply because it is getting older. It should mean focusing time and investment where change will make the biggest difference.

Modernization needs triage, not a starting gun

With legacy technology, age is usually the trigger for change. A platform reaches a certain point in its lifecycle, an OEM support deadline approaches or a newer alternative becomes available, and replacement can start to feel like the obvious next step. But the age of an asset doesn’t necessarily tell us whether it is still doing its job, or whether replacing it is the right decision.

A better approach is to assess infrastructure against four things: performance, risk, business value and future requirements.

Is it still delivering the performance the business needs? Can it be operated securely and reliably? Does it support a business-critical workload? And will it be capable of supporting what the organization expects to do next?

A piece of older infrastructure might be stable, well understood and supporting a workload whose requirements have barely changed. If it can continue to be monitored, maintained and supported effectively, replacing it may deliver little additional business value.

But another system of the same age might be approaching capacity, difficult to secure or be preventing the business from adopting a new application or operating model. That is a very different modernization case.

This is why CIOs should start to move away from thinking about estates in terms of “legacy” and “modern”. The more useful distinction is between infrastructure that is still fit for purpose and infrastructure that is becoming a constraint.

The real constraint may be your people

Modernization is often discussed as a capital allocation problem, but the harder constraint is often technical capacity within the organization. The same infrastructure teams tasked with migrating workloads, introducing new platforms and supporting transformation are usually responsible for the day-to-day estate. They cannot simply stop patching systems, responding to incidents or maintaining availability while a two-year modernization program takes place.

If a team spends six months replacing infrastructure that could safely have remained in service for another two years, what did that team not work on during those six months?

That question is becoming much more important since AI has arrived as another major infrastructure demand. Organizations are trying to understand where AI fits into their technology strategies while dealing with practical questions about compute capacity, GPU-intensive workloads, data availability, networking, power and cooling. The physical demands behind that AI expansion are significant, with the International Energy Agency (IEA) expecting AI to be the biggest driver behind its prediction that global data center electricity consumption will more than double to around 945 TWh by 2030.

And none of this replaces what came before it. AI infrastructure is being added on top of estates that still support ERP platforms, databases, storage, customer applications and everyday business operations. CIOs have to create room for what comes next, without destabilising what the organization already depends upon.

With so many competing demands, replacing infrastructure unnecessarily becomes harder to justify. Extending the lifecycle of infrastructure in one part of the estate can release budget, skills and operational capacity for AI or another strategic priority elsewhere.

But the same principle also works in reverse. CIOs should not preserve technology simply because replacing it is difficult. If an existing environment can’t accommodate the organization’s future requirements, the case for modernization becomes much stronger. The decision is about where change is worth the disruption.

Modernization doesn’t stop at the data center

Modernization also needs to recognize that the way people use technology has changed. Employees are no longer accessing systems from one place, on a device owned and managed by the business. They might be working from home, a customer site, an airport or a shared workspace, using a corporate laptop, a smartphone or even their own device. The infrastructure itself might still be working perfectly well, but the way people are accessing it can introduce new risks that weren’t there when it was first put in place. 

BYOD is a good example. Giving employees greater flexibility can make sense from a productivity and user-experience perspective, but it can also leave organizations with less visibility and control over the devices accessing corporate systems. Verizon’s 2025 Data Breach Investigations Report found that, among compromised systems containing corporate logins, 46% were unmanaged devices that also contained both personal and business credentials.

That does not mean BYOD is the wrong decision. It means workplace modernization needs the same kind of triage as infrastructure modernization. What information does the employee need to access? From which device? How much confidence do we have in the identity of that user and device? What happens to corporate data once it reaches an endpoint the organization does not own?

Modernization cannot simply be a hardware refresh program. Sometimes the server doesn’t need replacing, but the access model does. Sometimes the application remains perfectly capable of supporting the business, but the security controls around how people connect to it need to evolve. And sometimes a modernization program should focus on identity, endpoint management, monitoring or access rather than migrating the workload itself.

CIOs can use a relatively simple decision test when thinking about these choices.

First, what problem are we trying to solve? If we can’t articulate that without referring to the age of the technology, the case for replacement probably needs more work.

Second, what happens if we do nothing for another 12, 24 or 36 months? That exposes the difference between genuine risk and an arbitrary lifecycle milestone.

Third, can we reduce the risk without replacing the asset? Better monitoring, maintenance, security controls, capacity upgrades or changes to access may sometimes achieve the desired outcome with far less disruption.

Fourth, does retaining this technology constrain something the business genuinely needs to do next? AI, new digital services, workplace transformation and changing security requirements can all alter the answer.

Finally, where would our people create the most value? Every modernization project consumes engineering time, operational attention and organizational energy. Those resources should be treated just as carefully as the capital budget.

None of this is an argument against modernization. It is an argument for being much more deliberate about it. The CIO’s job is not to build the newest possible estate. It is to create an environment capable of supporting the organization securely, reliably and efficiently while remaining adaptable enough for whatever comes next.

Sometimes that requires replacing technology. Sometimes it means evolving it. And sometimes good modernization starts with having the confidence to leave something alone.

Enterprise AI transformation has moved beyond experimentation. Organizations are no longer asking whether artificial intelligence can improve productivity, decision-making, customer engagement and operating efficiency; they are asking how to adopt it responsibly, repeatedly and at scale. A Client Zero strategy offers a practical answer to this challenge. In this approach, an enterprise becomes the first serious user of its own AI capabilities, platforms, governance models and operating practices before extending them to customers, partners or external markets. It is a disciplined form of internal-first transformation, where the organization uses itself as a proving ground for value creation, risk management, adoption patterns and enterprise readiness.

The strength of Client Zero lies in its realism. AI initiatives often look convincing in demonstrations, controlled pilots or innovation labs, but enterprise environments are rarely neat. They contain fragmented processes, uneven data quality, legacy systems, security constraints, cultural resistance, fragmented accountability and regulatory obligations. By applying AI internally first, the enterprise learns where its assumptions fail, where its architecture needs reinforcement and where employees need support. These lessons become reusable transformation assets, including reference architectures, governance templates, reusable agents, adoption playbooks, training pathways, measurement frameworks and change management models.

Understanding the Client Zero strategy

Client Zero is more than a limited technology pilot. A normal pilot may prove that a tool works in a narrow setting, but a Client Zero model tests whether an enterprise transformation approach can withstand real operational pressure. It brings together people, process, technology, governance, security, compliance and economics in one practical learning environment. Because the organization is both the sponsor and the first user, the accountability is sharper. When an internal AI solution improves productivity, quality, risk control or experience, the enterprise gains evidence that can be used credibly in later client-facing programs. This aligns with broader industry guidance that enterprise AI value depends on governance, measurable outcomes and practical adoption rather than isolated experimentation.

This strategy matters even more for AI because AI systems are probabilistic, context-sensitive and dependent on the quality of enterprise knowledge. They cannot be assessed only by checking whether they execute a fixed instruction correctly. Leaders must examine accuracy, explainability, fairness, privacy, security, grounding, cost behavior, human oversight and the consequences of AI-supported decisions. A Client Zero environment gives the organization a realistic but still controlled setting to observe these factors before expanding AI into customer-facing or mission-critical work. The need for such discipline is consistent with responsible AI guidance from Microsoft, IBM and NIST, which emphasizes accountability, risk assessment, transparency, monitoring and human oversight across the AI lifecycle.

Approaches in a Client Zero strategy

A successful Client Zero strategy should begin with use cases that are tied to measurable business value. Enterprises should be careful not to start with fashionable AI ideas simply because they attract attention. The better approach is to identify work patterns where AI can reduce manual effort, improve decision quality, shorten cycle time, enhance employee experience, strengthen compliance or open new revenue opportunities. These opportunities should be prioritized through a balanced lens that considers business impact, technical feasibility, data readiness, risk exposure, reuse potential and scalability. The objective is not to run dozens of disconnected experiments, but to create a focused set of internal success stories that can be scaled with confidence.

Another essential approach is workflow-led transformation. AI should be placed inside the way work is actually performed, not bolted on as a disconnected assistant outside the process. For instance, an enterprise may rethink employee support, sales enablement, software engineering, finance operations, procurement, IT service management or knowledge discovery by redesigning the workflow around intelligent assistance, automation, human review and outcome measurement. This prevents AI from becoming a novelty tool and instead positions it as a practical mechanism for improving how work gets done. McKinsey and Harvard Business Review both stress that AI value increases when organizations redesign workflows and operating models rather than merely distributing new tools.

A third approach is platform-based enablement. Enterprise AI should rest on reusable foundations such as secure data access, identity-aware authorization, model governance, prompt and agent lifecycle management, observability, cost tracking, responsible AI controls and integration patterns. Without these foundations, every team tends to create its own local version of AI adoption, which leads to duplicated effort, uneven controls and growing operational risk. A Client Zero organization should therefore treat internal adoption as a way to harden its AI platform and define reusable patterns for later business-unit and client programs. Microsoft’s Cloud Adoption Framework, IBM’s governance guidance and NIST’s AI Risk Management Framework all reinforce the importance of repeatable controls, risk management and lifecycle oversight.

A fourth approach is people-centered adoption. AI transformation often disappoints when employees experience it as a tool rollout rather than a genuine redesign of work. Client Zero should therefore include role-based learning, communities of practice, prompt literacy, ethical usage guidelines, practical examples and trusted feedback channels. Employees need to know not only how to use AI, but also when to trust it, when to challenge it, when to escalate and how to combine machine-generated output with professional judgment. This human-centered discipline reflects the broader industry view that adoption, trust, learning and change management are central to sustainable AI transformation.

A fifth approach is outcomes-based governance. Client Zero should not be evaluated only by the number of AI tools deployed, users onboarded or prompts submitted. The stronger measure is whether the program improves business outcomes, such as productivity, cost performance, revenue influence, customer satisfaction, decision speed, software quality, incident reduction, compliance cycle time and employee experience. This outcome discipline helps leaders separate meaningful transformation from surface-level adoption. It also ensures that AI investment conversations remain grounded in evidence, value realization and accountable ownership, themes repeatedly emphasized by McKinsey, Deloitte and CIO.com.

Risks and mitigation plans

A Client Zero strategy reduces uncertainty, but it does not remove risk. In practice, it brings risks to the surface earlier because AI is placed within real internal operations. That visibility is valuable only when the enterprise is ready to respond with strong governance and disciplined execution. Business risks may appear through unclear ownership, weak value tracking, employee resistance, poor communication, excessive dependence on automation or unrealistic executive expectations. Technical risks may arise from data leakage, hallucinated outputs, weak integration design, limited monitoring, cost overruns, poor explainability or insufficient controls over agents and autonomous workflows. NIST, Microsoft, IBM and the World Economic Forum all highlight the need to manage these risks through accountability, transparency, monitoring and proportionate controls.

The mitigation plan should combine prevention, early detection and controlled response. Prevention includes secure architecture, policy guardrails, approved data zones, role-based access, responsible AI reviews and careful use case selection. Detection requires continuous monitoring of model behavior, adoption quality, user feedback, cost patterns, bias signals, exceptions and policy violations. Controlled response involves fallback procedures, human review, incident management, model rollback, retraining cycles and clear escalation routes. The intention is not to slow AI adoption but to make it safe, auditable and reliable enough to scale. This approach is consistent with NIST’s govern-map-measure-manage structure and Microsoft’s guidance on integrating AI governance with broader risk management processes.

Risk Category	Possible Risk	Potential Impact	Mitigation Plan
Business Risk	Unclear value realization	AI initiatives may consume budget without demonstrating measurable improvement.	Define baseline metrics, expected outcomes, benefit owners and review checkpoints before implementation.
Business Risk	Employee resistance and low adoption	Users may avoid AI tools or use them inconsistently, reducing transformation impact.	Provide role-based training, transparent communication, feedback channels and visible leadership sponsorship.
Business Risk	Overautomation of judgment-heavy work	Critical decisions may be made without sufficient human review, increasing operational and reputational exposure.	Classify decisions by risk level and mandate human approval for sensitive, regulated or high-impact actions.
Business Risk	Misalignment between business units	Different teams may develop competing or duplicated AI solutions.	Create an enterprise AI council, common prioritization framework, reusable patterns and shared funding principles.
Business Risk	Unrealistic executive expectations	Leadership may expect immediate enterprise-wide returns from early-stage adoption.	Set a staged roadmap, communicate learning milestones and separate pilot value from scaled transformation value.
Technical Risk	Data privacy and leakage	Sensitive enterprise or customer data may be exposed through prompts, logs, connectors or model interactions.	Use approved data zones, encryption, identity-based access, prompt filtering, data loss prevention and audit logging.
Technical Risk	Model hallucination and inaccurate output	AI recommendations may be misleading, incomplete or factually incorrect.	Apply retrieval grounding, confidence scoring, source traceability, validation workflows and human review for critical outputs.
Technical Risk	Weak integration architecture	AI solutions may fail when connected to legacy, fragmented or unstable enterprise systems.	Adopt API-led integration, reference architecture patterns, resilience testing and staged rollout across systems.
Technical Risk	AI cost escalation	Token usage, infrastructure consumption and licensing costs may grow faster than expected.	Implement usage quotas, model selection policies, cost dashboards, caching, prompt optimization and FinOps controls.
Technical Risk	Insufficient monitoring and observability	Performance drift, security anomalies and adoption issues may remain undetected.	Track model quality, latency, cost, user feedback, exceptions, data drift and policy violations through centralized monitoring.

Roadmap for adopting a Client Zero strategy

The roadmap should begin with strategic alignment. Leaders must clarify why the organization is adopting Client Zero, which enterprise outcomes matter most and where AI can create credible value. This stage includes identifying priority domains, assessing data and platform readiness, defining responsible AI principles and establishing executive sponsorship. A clear transformation charter should describe the ambition, scope, governance model, funding approach, risk tolerance and success metrics. Gartner, McKinsey and Deloitte all point to the same underlying lesson: AI programs scale better when leadership moves beyond experimentation and connects adoption to business priorities, governance and value realization.

The second stage is discovery and portfolio design. Business and technology teams should map internal processes, identify friction points, evaluate data availability and select use cases that balance impact with feasibility. The best candidates are often repetitive, knowledge-intensive, measurable and bounded enough for safe experimentation. At this stage, the enterprise should classify use cases by risk level so that low-risk productivity scenarios can move quickly while sensitive decision-support scenarios receive stronger oversight. This portfolio discipline reflects guidance from TechTarget, NIST and Microsoft on prioritization, risk classification and oversight.

The third stage is foundation building. This includes establishing secure access to enterprise data, defining model and platform standards, integrating identity and access controls, creating prompt and agent management practices and putting observability in place. The enterprise should also define reusable design patterns for retrieval-augmented generation, intelligent agents, human review, auditability and cost control. These foundations reduce duplication and prevent early success from becoming later complexity. IBM and Microsoft both emphasize that scalable AI requires a disciplined base of governance, data protection, lifecycle management and operational monitoring.

The fourth stage is controlled internal implementation. Selected use cases should be implemented with clear release boundaries, pilot groups, feedback loops and operating metrics. Teams should track not only whether AI produces helpful outputs, but also whether employees change their work habits, whether managers trust the results, whether risks are being controlled and whether business value is visible. Lessons from this stage should be documented as reusable playbooks instead of remaining project-specific knowledge. Deloitte and CIO.com both note that moving from pilots to scaled adoption requires practical learning, adoption support, governance and patience.

The fifth stage is industrialization and scaling. Once the enterprise has validated a pattern internally, it can expand the model across functions, geographies, processes and business units. Scaling should be supported by stronger governance, reusable assets, training programs, support models and a value realization office. This is also where Client Zero learning can become market-facing capability through advisory frameworks, accelerators and reference stories. McKinsey, World Economic Forum and Gartner research all suggest that the organizations gaining the most from AI are those that convert lessons into repeatable operating models rather than treating AI as a series of disconnected pilots.

The final stage is continuous improvement. AI transformation does not end when tools are deployed. Models evolve, regulations change, business priorities shift and user expectations mature. A Client Zero organization should therefore review performance regularly, retire weak use cases, refine governance, optimize AI consumption costs, strengthen security controls and refresh workforce skills. NIST’s AI Risk Management Framework and Microsoft’s responsible AI guidance both support this lifecycle view by emphasizing ongoing monitoring, risk review and continuous management.

Role of critical stakeholders in the strategy roadmap

Executive leadership plays the most visible role in making Client Zero credible. The chief executive officer, business unit heads and transformation sponsors must define the ambition, allocate funding, remove barriers and communicate why AI transformation matters. Their role goes beyond approving budgets. They must model adoption, reinforce responsible use and hold teams accountable for measurable outcomes rather than isolated activity. CIO.com, Gartner and Harvard Business Review all underline that leadership commitment and change management are essential for turning AI ambition into durable enterprise transformation.

Business process owners are responsible for grounding AI in real operational needs. They understand where work is slow, where decisions are delayed, where experience is poor and where manual effort can be reduced. Their involvement ensures that AI is not designed in abstraction. They help define use case requirements, validate outputs, redesign workflows and confirm whether the solution truly improves business performance.

The chief information officer and technology leadership provide the engineering backbone of the strategy. They ensure that AI solutions are secure, scalable, integrated, observable and reliable. Their teams define platform architecture, integration standards, identity controls, model operations, resilience practices and technical debt management. Without this discipline, Client Zero may produce attractive prototypes that fail under enterprise demand. Microsoft, IBM and NIST guidance all reinforce the importance of secure architecture, operational monitoring and accountable lifecycle management.

Data leaders and analytics teams are equally important. They assess data quality, lineage, access rights, metadata, governance and readiness for AI consumption. They also help determine whether a use case should rely on structured data, unstructured content, knowledge graphs, retrieval techniques or a combination of sources. Their role is to make sure AI outputs are grounded in trustworthy enterprise information rather than fragmented or outdated content.

Risk, legal, compliance, privacy and security stakeholders define the guardrails that make AI adoption responsible. They review data handling, regulatory exposure, ethical implications, intellectual property concerns, audit requirements, contractual obligations and cyber risks. Their early involvement helps avoid the common problem of discovering compliance gaps after a solution has already gained momentum. In a mature Client Zero model, these teams are not blockers; they are design partners. This view is consistent with guidance from IBM, Microsoft, NIST and the World Economic Forum, which all treat governance as a practical enabler of trustworthy AI adoption.

Human resources and learning teams support workforce readiness. They help employees understand how AI changes roles, skills, career paths and performance expectations. They can design targeted learning journeys, identify new capability needs and support managers in leading teams through change. Their contribution is critical because Client Zero transformation is not only a technology shift; it is also a behavioral and cultural shift.

Finance and value realization teams track the economic discipline of the program. They help quantify benefits, validate savings, monitor AI consumption costs and compare investment against outcomes. Their involvement ensures that productivity claims are defensible and that scaling decisions are based on evidence rather than enthusiasm. In AI programs, this role is especially important because value may appear in different forms, including saved hours, faster cycle time, improved quality, risk reduction and better customer experience. McKinsey, Deloitte and TechTarget all highlight cost management, ROI discipline and value realization as core elements of enterprise AI scaling.

Employees and frontline users are the final test of whether Client Zero is working. They experience the solution in daily work, expose usability issues, identify missing context and reveal where AI helps or distracts. Their feedback should be treated as strategic input. A Client Zero strategy becomes stronger when employees are not passive recipients of AI, but active contributors to its refinement.

Conclusion

A Client Zero strategy gives enterprises a grounded, credible and disciplined path for AI transformation. It replaces abstract ambition with lived experience. By applying AI internally first, the organization learns how to create value, where risks emerge, which controls are necessary and how employees adapt to new ways of working. The approach also strengthens market credibility because the enterprise can speak from evidence rather than assumption.

For enterprise AI to scale responsibly, Client Zero must be treated as a strategic operating model, not a branding exercise. It requires executive sponsorship, strong governance, platform discipline, workforce enablement and rigorous value measurement. When these elements come together, the organization not only transforms itself but also creates a practical blueprint that others can trust, adapt and scale.

This article was made possible by our partnership with the IASA Chief Architect Forum. The CAF’s purpose is to test, challenge and support the art and science of Business Technology Architecture and its evolution over time as well as grow the influence and leadership of chief architects both inside and outside the profession. The CAF is a leadership community of the IASA, the leading non-profit professional association for business technology architects.

It seems intuitive: Give AI to the people who are crushing it in your organization and watch them fly even higher. So why does it so often fail to work? Why do so many leaders I talk to complain that their AI transformations simply aren’t delivering the results they had hoped for?

One of the most dramatic and successful AI transformations I’ve seen happened at a bank where the underperforming investment team approached management for help. These folks were struggling and truly needed the help of something like AI. Sure, there was risk involved, but they were willing to take the chance because they had intense motivation to improve.

And when they got that help from AI, they helped create a blueprint for what the technology could do in every other part of the bank. The key was recognizing that AI changes how people work, not simply what tools they use. While a conventional technology rollout can often be taught as a new process or capability, AI requires people to rethink how work itself gets done.

That’s how this bank approached it. They found the people with enough need and motivation to change, then gave them the support to make that change produce a measurable business result. Not just individual productivity gains, but the kind of improvements in revenue, costs and P&L that got the attention of the entire organization.

They started with the scufflers, and it made all the difference. 

Why your best performers may be the wrong place to start

One of the biggest paradoxes of the AI age that I’ve observed is that transformation is particularly difficult in successful organizations. Why? Because the people who are best at the current system are often the least motivated to reinvent it. There’s a certain risk-assessment logic at work:

• Employees have agreed-upon goals.
• Changing how they work introduces uncertainty and friction.
• If they believe they can hit their goals without changing, the rational choice is often to avoid the risk.

The result is often adoption theater. People put on a show of participating in the transformation, but for the most part they simply continue to work much as they did before. And AI initiatives stall or fizzle out.

That’s why organizations that have had greater success have started by shifting their efforts to the teams that are under pressure and the business lines that have been struggling, losing ground or discovering that established approaches aren’t working. For these teams, AI isn’t simply a corporate initiative. It can represent a potential way out of a real business problem.

But not every struggling department is going to be fully on board with transformation. Which means need alone isn’t enough. What really helps AI take hold is to identify the rare early adopters and risk-takers on those teams, and get them behind the effort. Then you can really start to see the intersection of AI imperatives take shape:

• A genuine business problem.
• Someone willing to challenge established ways of working.
• A willingness to experiment and take a few risks.
• A situation where success can create measurable business impact.

Identifying those risk-takers is the difference between finding adopters and finding adopters who matter. The ones who can model success not just through happiness or engagement, but through top-line or bottom-line impact.

But finding the right people is just the first step. If the next move is to give them a training course and send them back to work, you’re wasting their potential impact.

Don’t train the scufflers. Work alongside them until they win

Those early adopters are a great resource. But they need concentrated, hands-on support that helps them solve a real business problem to realize their AI potential.

With AI, you’re asking people to work in a fundamentally different way. A conventional training model – with new tools, a two-day training and a handful of coaches or consultants – is not going to get the job done.

AI demands a program with a little more skin in the game. That means support staff working directly alongside the team to apply practices to the company’s actual constraints, solve real problems together and adapt the theory to the specific situation.

For example, when the bank was introducing AI to its investment team, leadership committed to 100 AI experts working alongside 100 client employees, rather than a few consultants attempting to support thousands of employees. This wasn’t coach work. This was actually doing the work together.

The result was a meaningful business win. The transformation changed how the team worked, from producing faster research and underwriting to initiating more client interactions. And the team reversed three consecutive years of market-share losses within 12 months.

That’s the kind of bottom-line result that can be so powerful for an organization. It wasn’t an AI-adoption metric, it wasn’t based on employee satisfaction, and it wasn’t just an impressive number of people trained. It was a tangible business outcome: market share increased.

In other words, it was the kind of measurable win that does more than just prove that transformation is possible. Inside a large company, it can change the behavior of everyone watching.

Turn one team’s win into organizational pull

The investment team’s experience was a perfect example of how to turn AI success into FOMO throughout the organization by sparking curiosity, reducing perceived risk and spreading the capability.

Make the win visible

The retail banking team had previously seen itself as “killing it” and therefore had little reason to change. But seeing the investment team succeed helped change their collective attitude from “Why should we change?” to “How did they do that?” This is the transformation flywheel in action:

• Find a willing risk taker.
• Provide concentrated support.
• Produce a measurable win.
• Make the win visible.
• Let other teams get curious.
• Give those teams experienced practitioners to help them adapt the approach.

It’s an approach to AI transformation that favors pull over push, while simultaneously eliminating the familiar objection that “something like this won’t work here.”

Replicate the people, not just the playbook

But it’s not enough to simply hand the next team a blueprint for AI success and hope they figure it out. Every business unit, after all, has different problems, constraints and organizational dynamics.

Instead, the bank took some of the employees from the investment team and moved them onto other teams. Their lived experience helped them demonstrate the difference between the theory of what a transformation model says should happen and the practice ofhow to adapt that theory to the messy reality.

In doing so, the investment team members became something of a mobile transformation capability, carrying their experience from one successful team to the next. And a relatively small group of early adopters helped influence the entire organization.

Make the CEO the loudest cheerleader

Executive sponsorship in an AI transformation isn’t primarily about speeches, steering committees or generic declarations of support. The CEO’s most valuable role is to provide tangible support and visible recognition.

At the bank, the CEO publicly praised the investment team in a town hall with a straightforward message: after three years of losses, the team was now gaining market share. That recognition turned curiosity into FOMO.

Did the CEO get into the nuts and bolts of the technology? Nope. He simply celebrated what happened, expecting that that would be enough to make the other smart people in his organization want to understand how it happened.

Don’t transform everyone at once

Every organization wants to see big results from AI. That leads to programs that plan the rollout of the technology everywhere at once or in waves, which usually doesn’t work because practical knowledge is spread too thin and the change is challenged by a crowd of detractors.

The more successful AI transformations tend to begin with a minority willing to take the risk. That means identifying people who have a real problem to solve, are willing to change how they work and can produce measurable business impact.

They’re the scufflers. And when they have concentrated hands-on support, a meaningful business result to work toward and proper recognition of their job well done, they can be an organization’s AI vanguard. And then they can move onto the next team, where their example of proof, curiosity and reduced risk will ignite the pull for broader adoption.

They may not be your rock stars now. But if they kick-start your AI transformation efforts, they soon will be.

For many years, Musinsa, South Korea’s premier online fashion platform and marketplace for Korean designer brands, streetwear, and beauty products, has described itself as a company dedicated to AI. They bring in AI-native talent and give employees room to experiment, yet meticulously manage token usage and work performance.

And instead of handing over AI completed by engineers to the field, they have the field restructure their own work using AI. So if external solutions are deemed lacking, they analyze the cause and create their own solutions.

Gil Gi-yong, the company’s director of core AI and CX engineering, began actively utilizing AI shortly after joining Musinsa last year when he saw AI evangelists, from the CTO to new employees, directly create results. He says that Musinsa changes faster than organizations he’s experienced before, so he’s diligent about anything new that comes along to augment its developing internal AI platform.

Director of operations Kim Dae-ho is on the same page, too. Kim, who joined in September 2022, currently leads the operations strategy team that’s responsible for planning and executing tasks to improve customer and partner experiences, and improve operational efficiency across Musinsa’s commerce ecosystem. Recently, most of his work has been closely related to AI, so he’s collaborating with the development team to lead AX projects.

Musinsa is investing so much in AI is because trends change rapidly and fashion consumers can be impulsive. So platforms have to be fast, anticipatory, and intuitive to meet demand. In addition, they should support areas such as sales management for partners, and AI is the means to meet these needs and boost business competitiveness. That’s how Musinsa has started solving unique fashion industry problems.

A prime example is how outfit photos are shot. To showcase products in the past, models were hired, they dress themselves, and it’s all shot in a studio. Now, AI is used as a coordinator to generate images and the goal, according to Gil, is to elevate visual AI tech to a top standard by H1 2027.

Plus, AI is used in the product registration process of partner companies, so when a product is registered, AI automatically infers categories, colors, materials, and more. In the past, these tasks were manually entered. Holistic AI has also been integrated into customer service centers. The AI chat agent operated by 29CM, a South Korean fashion and lifestyle platform operated by Musinsa, currently handles about 25% of all customer inquiries.

From business operations to ROI

Companies typically approach AX in two ways. One is for the engineering team to get the requirements of the field, develop the system, and then say try it, and pass it on. If it’s hard to solve internally, the other way is to leave it to an external vendor. But Gil sees that AX often fails in these two approaches.

“The reason is that engineers do AX, but they don’t fully understand the processes of the field,” he says. “That’s why we focus on creating an environment where field experts can directly improve the actual work processes.”

In fact, Musinsa’s AX-related ideas are directly proposed by each business team. For example, the planning and engineering departments review how to improve on repetitive tasks with LLMs by setting quantitative goals such as reducing tasks from 270 man hours per week to three.

The core of the approach Musinsa choses is next. Engineers don’t write scripts for them. Instead, the core AI team suggests guides until target figures are achieved through repeated testing. Gil says this process ultimately helps the business operator measure how to write prompts and what improvements they use to improve them.

This approach has also changed the process by which AI spreads. Instead of copying systems created by developers, colleagues directly use AI to improve actual work. As small success stories accumulate and are shared, overall AI utilization capabilities also increase.

Gil adds that this approach must align with the direction of leadership. Simply sending tasks down by saying try using AI and costs will be covered isn’t enough to drive adoption. At Musinsa, many leaders, including the C-suite, first experiment with AI and freely share results. This culture encourages voluntary experimentation and accelerates the spread of AI. 29CM’s AI chat agent was reputedly developed using this method.

In fact, this tech was developed in collaboration with outsourced companies, and produced a certain level of output, but the strategy was revised to in-house development. Those results were actually better, according to Gil, and they succeeded in launching quickly while reducing costs to about a third of the outsourcing method. The development period was also about a quarter of similar projects at other companies.

However, this speed isn’t simply due to AI. ROI verification is thorough and Musinsa first fixes timelines and budgets, then prioritizes requirements to implement within that framework.

Even before starting, cost issues are carefully considered. For example, they calculate a five-year TCO and first check when investment costs can be recovered as savings. When using external services, they don’t sign long-term contracts based on simple discount benefits. Kim says that starting a project based solely on rough cost estimates may result in significant investment but doesn’t achieve the expected efficiency.

For AI chat agents, they quantified the estimates generated by the technical team, and simulated how much customer satisfaction would change and inquiry rates decrease. Investment costs were also calculated by year and month.

After implementation, monthly tracking is conducted to see how costs differ from expectations, how the AI processes inquiries, and how close performance is to the original target. If differences from expectations occur, improvements are made, including which variables to add, if the scope of inquiries handled by AI should expand, and if agent workflows should change.

Generous support, but objective evaluation

For this approach to continue, a supportive organizational culture is necessary. First, Musinsa provides a substantial amount of tokens so its members can fully utilize AI. But token usage is actively managed, so each leader regularly checks it using separate monitoring tools. If too many are used for tasks that could be handled with fewer, it’s looked into. Conversely, tasks that could be completed much faster with AI are still subject to inspection even if they’re handled using traditional methods.

Kim says that ultimately, what matters isn’t whether AI was used, but what results were achieved and how many resources were invested in the process. If AI can improve the quality of deliverables and reduce time in the work, it should be actively utilized. But the costs invested and the results achieved should be considered together.

Naturally there are times when using AI when multiple rounds of testing are gone through before desired results are reached. This means more tokens and time are required, and costs increase. But Musinsa focuses more on what trial and error was experienced, what was learned in the process, and what can be done differently next time. Failure is also seen as data that increases the chances of success next time.

AI leader standards for talent

What kind of talent do AI-led companies need? Not so much proficiency in AI itself but the ability to properly define problems. Kim defines planning as a journey of problem-solving, meaning a series of processes to identify them, develop solutions, and execute. It’s become even more important for humans to define what to ask of AI. If the problem definition is ambiguous, no matter how plausible the AI is, it’s difficult to apply it to actual work.

Of course, acquiring these skills isn’t easy. Kim says that training to constantly ask why is helpful. He doesn’t just accept initial causes as they are, but searches until a real solution emerges.

“At first, it’s confusing,” he says, “But through intense discussions and debates, capabilities develop. If the opponent isn’t a human but an LLM, you can repeat the same process much faster.”

Gil’s standards to evaluate development talent are similar. He believes people who can think differently from traditional methods are now needed, and relying solely on experience and technical expertise is no longer a sufficient nor competitive advantage.

In fact, this is reflected in Musinsa’s AI native developer recruitment. The focus isn’t on simple algorithm proficiency, but on how well the applicant defines real-world problems, grasps the essence of the problem, and uses certain indicators to determine whether to solve the problem, even when freely using AI.

The principles of leadership aren’t much different. Kim defines his style as autonomy-based leadership, while Gil prefers to clarify responsibilities and authority. The common goal, however, is to have an organization that doesn’t rely on any single individual.

In terms of next steps in AI transformation. Gil believes that competitiveness depends on who’ll first leverage new technologies, and how quickly that experience will transform into organizational capabilities. But AI led by tech organizations has its limits. Models that worked in commerce don’t work offline as is, and a single model can’t transplant across all areas of the company. So each organization must handle AI based on its own domain of expertise.

This is where Musinsa is headed. It’s not a company that relies on a few AI experts, but a place where anyone can wield AI in their work. “I want to establish a company where every employee solves their own problems with AI, and accumulates that experience as an organizational asset to show we use it well,” says Gil.

IT modernization remains a key pillar of CIOs’ strategic agendas, and even CEOs are coming around to the value that addressing legacy systems has in fueling transformation and innovation.

Yet a solid majority of application and infrastructure modernization projects are failing to meet expectations, with many IT leaders having to cut IT corners to divert money for AI, in addition to all the traditional problems that plague them.

Sixty-one percent of organizations have had to pause, delay, scale back, or fully abandon IT modernization initiatives in the past 24 months, according to a recent survey from managed services provider Ensono.

Major IT projects fail for a variety of reasons, including poor project management and a lack of clear metrics, but tight budgets and cost overruns create tangible impediments. More than seven in 10 respondents to Ensono’s survey say their modernization initiatives have exceeded the original budgets, with 27% exceeding budgets by 51% or more.

The failure rate is often due to a combination of factors, but IT budget diversion to AI projects is a major challenge for IT leaders, says Brian Klingbeil, chief strategy officer at Ensono.

“People are redeploying spend toward AI projects,” he says. “There’s a lot of pressure to keep up with everyone else.”

Reallocated budgets

Many organizations are stuck in long-term IT modernization traps, such as scope creep, while facing declining budgets for traditional IT upkeep due to AI.

“In what I see, scope creep contributes the most, and it is a discovery problem more than a governance one, because the organization learns what the legacy estate actually does at the moment it starts to move it,” says Michele Grant, executive global director at Cognizant, of IT modernization delays and failures.

“But AI budgets are a real competitor for the same money, and over the past two years I have watched modernization funding reallocated toward AI initiatives that promise a faster visible return,” she says.A recent report from digital adoption platform provider WalkMe found that, while digital transformation budgets grew from an average of $39.4 million to $54.2 million in a single year, most of that spending went to AI-related projects, says KJ Kusch, global field CTO there.

“I wouldn’t call it scope creep,” she adds. “It was a deliberate move.”

Kusch also sees adoption problems contributing to poor IT modernization outcomes when newer IT systems are too complicated for employees to use. “Users experience too much friction, so they go back to the ways they worked before,” she says.

The irony, Ensono’s Klingbeil says, is that AI can help accelerate IT modernization projects by performing tasks such as code discovery and cleaning up legacy code. Most organizations haven’t tried it yet, but using AI tools can significantly cut the costs and timelines of IT modernization, he adds.

The irony, Ensono’s Klingbeil says, is that AI can help accelerate IT modernization projects by performing tasks such as code discovery and cleaning up legacy code. Most organizations haven’t tried it yet, but using AI tools can significantly cut the costs and timelines for IT modernization, he adds.

“Two years ago, if you had a legacy platform you weren’t happy with, for whatever reason, choice A was to invest $150 million and five years of your life and maybe get lucky and pull it off,” he says. “Choice B was suck it up and live with it, and there was no choice C.”

The price of misalignment

Another factor that can sink an IT modernization initiative relates to organizational outcome and cost misalignment, Cognizant’s Grant says.

“Modernization is approved as a technology program and then asked to carry a business outcome that requires process and organizational change, and the cost of that change was never inside the original number,” she says.

It doesn’t surprise Grant that 61% of organizations in the survey had to pause, scale back, or scrap IT modernization programs. A pause is sometimes the right choice, she says.

“When the business case was written against an operating model that has since changed, continuing to spend against that case is the more expensive choice,” she explains. “What I would look at in that 61% is not the pausing. It is that the organizations I see pause without naming the condition that would let them resume, and a program that stops with no restart condition turns into spend that nobody owns.”

A good way to control IT modernization costs is to fund the program in phases tied to named outcomes and give one executive the authority to stop the work, Grant recommends.

“A program that can only be slowed by consensus is never slowed; it is extended,” she adds.

AI needs modernization

Even as AI initiatives take spending away from modernization efforts, the two transformation priorities are closely linked.

Stu Bradley, senior VP of risk, fraud, and compliance solutions at analytics and data management vendor SAS, emphasizes that IT modernization leads to better AI outcomes.

“Seeing AI and modernization as competing budget lines is a bit of a trap,” he says. “In reality, AI and modernization are increasingly two sides of the same coin. Many organizations are discovering that the AI capabilities they want depend on investments in data, integration, infrastructure, and governance that they’ve deferred for years.”

Recent SAS research found that only 17.5% of organizations have data infrastructure fully optimized to support agentic AI readiness, he notes.

“Rather than argue over competing budgets, leaders need to understand which modernization investments are prerequisites for the AI outcomes they’re aiming to achieve and fund them as part of the same business case,” he adds.

It also doesn’t surprise Bradley that most IT modernization pause or fail. “When you consider the pace of technology and how quickly priorities are shifting around AI, I’d frankly be more surprised if organizations weren’t regularly reevaluating their transformation projects,” he says.

But pauses or delays in project aren’t always failures, he notes. The priority during a pause should be identifying and addressing the issues that stalled the project, he recommends.

“CIOs should be willing to reevaluate initiatives rather than let momentum blindly drive continued investment,” he says. “Where organizations get into trouble is simply bumping the project into the next budget cycle hoping that somehow conditions will be different.”

See also:

• 6 strategies for accelerating IT modernization
• 8 IT modernization traps CIOs must avoid
• Massive modernization: Tips for overhauling IT at scale

As CIO of DaVita, Madhu Narasimhan drives IT priorities that align with the business mission.

For the healthcare company, that means providing kidney dialysis and other services for patients with chronic kidney failure and renal disease.

“We’re being super intentional on how we bring in technology. We want to make sure we’re solving the problems of the organization and focusing on the needs of the patients,” she explains.

That has Narasimhan focusing on several critical projects: bringing more AI capabilities to the company’s custom-built electronic medical records (EMR) system to enable caregivers and staff to work more effectively and efficiently; establishing a strong governance program for those AI capabilities; improving the interoperability of the EMR and other systems to ensure the continuity and orchestration of patient care; and ensuring adequate cybersecurity protections are in place.

To achieve those objectives, Narasimhan’s team implemented Glean, an enterprise work AI and search platform, to make searching, retrieving, and summarizing the vast trove of medical, insurance, and administrative data easier and faster for employees.

IT team members are also working with clinicians to deploy and scale ambient listening capabilities. They’re also launching AI models to identify patients at risk of missing treatments schedules so caregivers can proactively intercede. And they’re using AI coding capabilities to speed their development work.

Narasimhan’s list of top projects — and the reasons behind them — will sound familiar to most CIOs.

Research consistently shows that AI adoption remains the No. 1 priority for IT leaders. Studies also find data initiatives, business process improvements, organizational transformation, and cybersecurity improvements atop CIOs’ agendas — and their budget investments.

CIO.com’s 2026 State of the CIO Study found that accelerating AI-driven innovation is the top business initiatives driving IT investments this year, cited by 39% of respondents. Increasing operational efficiency (32%), increasing cybersecurity protections (32%), and improving profitability (31%) rounded out the top four areas where IT investments are going.

All this work aligns with what CIOs say is their No. 1 objective today: to transform IT into a business value–driven organization.

“Boards and CEOs are looking for return on their technology investments,” says David Ulicne, who as executive director of executive education at Carnegie Mellon University’s Heinz College of Information Systems and Public Policy oversees the CIO Certificate program.

Here is a look at several IT projects top of mind for IT leaders today.

1. Driving AI innovation that matters

That demand for ROI is playing out when it comes to AI initiatives, says Thomas Phelps, CIO at Esri and Innovate@UCLA chief strategist. He lists as a top task implementing AI in a governed and responsible way so that users adopt it and it deliver returns.

To ensure that, Phelps pursues AI initiatives that analyses show will drive top-line revenue growth, optimize costs, improve margins, deliver exceptional customer or employee experience, enable world-class operations, and/or enhance security.

He points to the deployment of AI in Esri’s contract lifecycle management process as a case in point. Esri now uses AI to review contracts to determine which are standard and which “need that heavy human touch.”

Phelps also cites as an example the use of AI in the sales process, where intelligence is being deployed “not just for looking at optimizing our sales approach and process but also for our ongoing care and feeding of an account and for customer experience.” He says AI helps stitch together the various components of customer sales and support, “so we have a holistic view on how the organization is supporting a customer.”

2. Boosting AI adoption across the business

As has long been the case, technologies won’t deliver returns if people won’t use them. That’s true with AI, too, which faces a unique change management challenge.

“If employees aren’t incentivized to use AI, if they’re afraid of AI, then they won’t adopt it,” Phelps says.

That’s why numerous CIOs say boosting AI adoption is among their top projects today.

Phelps works with business leaders to identify what manual tasks they’d like to shed using AI, which helps get both adoption and create champions of AI use.

Similarly, Steve Januario, CIO of BILL, maker of a financial operations platform, is also prioritizing AI adoption and responsible AI use as part of the company’s roadmap toward becoming an AI-native entity.

“Almost everything we’re doing right now is hyperfocused on AI, getting the most from it, and making sure we keep it under control,” he says. “So we’re teaching people the right way to use it and encouraging people to have an AI-first mentality. We’re telling them that if there’s [a task] you’ve done twice, you should think about automating it the third time.”

Januario admits that AI adoption was slow at first. But he says consistently promoting AI, offering targeted training, and creating champion groups have spurred more workers to use AI and workers to use it more often. (The company has dashboards to show employee use of deployed AI tools.)

Januario now focuses on ensuring workers “use the right AI tools for the right job” and become more skillful users of AI overall. “[Earlier this year] we measured just if they were using the [AI] products, now it’s more about how they’re using the AI products,” he adds.

3. Laying the foundation for the agentic enterprise

Like many other CIOs, Brian Conneen, CIO of Finance of America, a reverse mortgage provider, is deploying agents that can perform the tedious work that humans have been doing. He says this improves employee and customer experience as well as the company’s operational efficiency and profitability.

To accelerate his company’s use of AI, Conneen created an AI strategy and built an IT platform for actual AI deployments. He says this two-pronged approach ensures AI initiatives align to business strategy, that the company has a strong data foundation to fuel AI use, and that the company doesn’t end up with numerous separate AI services. He notes the approach creates “cohesiveness” as it advances its AI agenda.

However, Conneen says building a company that’s ready for a future where agents work alongside humans takes more than a strategy and a platform to become a reality. Humans must learn to lean into using agents, too, he says. To ensure that happens, Conneen works on changing human mindsets so that workers are comfortable delegating their tasks to agents and trust that AI can successfully perform the tasks.

“I’m working to get them to embrace this and shed the [belief] that ‘Only I can do this work’ by showing that agents can make a valuable difference to the business, and showing how when people lean into this, it allows them to do so much more and to have more time for the work that really needs their attention,” he explains.

4. Re-engineering business workflows for future states

CIOs also remain committed to process improvements, seeing it as essential to gain value from their AI initiatives as well as critical to boosting workplace efficiency and productivity.

Research firm Gartner found that 63% of CIOs list improving processes as a goal when operationalizing AI, making it the second-most cited objective after improving business outcomes (cited by 74%).

Phelps lists process improvements as one of his top projects at Esri. “You don’t solve business problems with just technology,” he says.

He considers the current state and the to-be state and then works with his business counterparts to determine what is needed to get to the desired future state. He also tasks the process-improvement consultants in his IT department’s enterprise architecture team to work through the analysis and identify whether new technology is required and/or whether changes in workflows are necessary.

Most times they can improve the process without implementing new technology but instead by adjusting workflows and how the existing technology is used to “deliver the [desired] net increase in business capabilities,” Phelps says.

5. Modernizing the tech stack

Modernization likewise remains a top IT initiative for CIOs, as a means for improving efficiency, productivity, and user experience as well as enabling AI innovation and cybersecurity improvements.

CIO Deanna Towne has been undertaking such work at nonprofit Contexture, due in part to mergers and acquisitions that brought three health information exchanges (HIEs) into one. “What were three different platforms are now a single net new one: the Contexture platform,” Town explains.

The work started three years ago with a migration off a legacy HIE to a new one that could carry the organization into the future. It also involved implementing data standards and ensuring all data adheres to those standards, so that patient information is accurate and readily usable by clinicians.

Towne is now improving the platform. “We continue to grow and expand it, bringing in more and new data types and building out new solutions, new report types, and new data products to ensure that we’re evolving to meet new needs in the market,” she says.

“Continuous service improvement is embedded into how we operate, so we’re always looking for opportunities to improve. It’s built into how we function. That’s very intentional. That takes a lot of focus, because it’s so easy to be done and move to the next thing and never take a look back. But that then quickly piles up and becomes tech debt,” Towne says.

That continuous improvement approach applies to Contexture’s cybersecurity posture, too, Towne adds. “We always have a focus on cybersecurity and making sure we have all the controls we need in place and are doing all we can.”

6. Transforming the business

Bobby Cain, SVP and CIO for Schneider Electric North America, lists business transformation as one of his top IT projects, stressing that everything IT does should be “about driving business outcomes, not managing technology.”

He also lists initiatives that ready the company for the future, a category of work dubbed J2R, for Journey to Readiness.

“The top IT projects are going to be in either of those two buckets, readiness or actual transformation,” Cain adds.

J2R projects include data transformation initiatives aimed at improving data quality, cleanliness, accuracy, and availability. Cain is also rethinking IT roles by creating new career paths for individual contributors, something he believes helps Schneider attract, retain, and develop the talent it needs now and in the years to come.

Recent projects that fall into the second bucket include Schneider’s Titan Program, which is centered on a global SAP S/4HANA implementation accompanied by a transformation of the company’s pricing and contract management processes.

7. Remaking IT for the future

Devan Dewey, a Boston-area CIO, believes IT itself must be remade, an assertion he detailed in his post “A Mismatch of Scale.” Speaking with CIO.com, Dewey explains that IT once had dominion over big enterprise systems but is now in charge of a multitude of “small agents and apps.”

“And everything we learned over the past 40 years, the processes and the mindset, are antithetical to this world of small things,” he says. “If you look at the portfolio of a typical company four or five years ago, it was a portfolio of large, expensive slow-moving things. Now it’s small, cheap fast-moving things. That will take a complete reinvention of how IT works; it requires a reimagining of the pipeline.”

Dewey is remaking his IT department for the modern business environment of “small agents and apps.” He created a team that in a 45-day sprint stood up new infrastructure designed specifically to deliver small new things and tested it out by successfully getting two items — prototypes built with Claude — through the newly created pipeline.

“Call it a proof of capabilities,” Dewey says. His goal now? To continue building this new infrastructure by adding needed components, such as connections to outside data sources, and becoming faster at moving agents and apps through the pipeline.

“It’s really about building an architecture suitable for hundreds of small things, which should set us up for future success.”

---

---

At its global convention in Las Vegas in June, McDonald’s unveiled its strategy for the coming years. Among the announcements was one about the company’s AI platform ArchIQ and its order-taking voice assistant Archy, in limited testing, at a handful of drive-thrus in the US.

In the summer of 2024, efforts of a similar system were shut down and the company subsequently told franchisees it was terminating its global order automation agreement, and no further phase was communicated.

This wasn’t just a trial run. It was deployed for three years since its launch in 2021, with an automated voice handling the drive-thru window at about a hundred US locations. What happened during that time is known primarily through videos of customers experiencing a parody of errors. Sources familiar with the tech later explained that the system had several difficulties, including interpreting different accents and dialects.

Behind closed doors, however, the problem was documented long before it reached social media. Analysts following the project noted franchisees’ discontent not seeing progress, and updates were infrequent.

So the numbers didn’t add up. The system had to be cost-effective compared to having a person at the counter, and it failed to offset operating costs. When it was shut down, the company declined to comment.

Two years later, though, the technology has advanced but underlying challenges of dialects and ambient noise at the order windows remain. No one repeats a move, though, that costs three years, a public retreat, and a collection of critical videos, so the question isn’t so much what’s new about Archy, but why McDonald’s decided to revisit this AI initiative.

Always in the picture

The day the system shut down, the company stated that the project gave them confidence that a self-service voice ordering solution would still be part of their future, and the case for a comeback became urgent. In August this year, after reporting lower than expected results in the US, CEO Chris Kempczinski asked analysts to sympathize with restaurant managers overwhelmed by several product launches in just a few weeks, each with its own training. Teams were spread thin, service times lengthened, and customer satisfaction plummeted. And in a model where drive-thru service generates around 70% of business in the US, the pressure builds up on the front line.

But returning to where something has failed, there has to be a clear understanding of what went wrong, and that’s no easy task since the most plausible explanation isn’t necessarily the correct one. During that time, industry analysts spoke about AI accuracy as the main challenge, and the debate centered on how much it should be increased. But the real problem was something else.

A definitive clue came from the QSR Drive-Thru Report, which audits the self-service systems of major American chains. In its 2025 edition, the study visited locations of three brands that already used automated voice systems, and found that when theAI ​​took an order, it was 81% accurate. When it got stuck and transferred the conversation to an employee, the accuracy rose to 95%.

So the difference wasn’t how well the system listened, but what it did when it didn’t understand the customer. That was the scenario on which the pilot program was decided, and the difference from the first case in 2024 where it simply misunderstood and kept going. In fact, for three years there were employees near those loudspeakers who could’ve resolved issues right away. But they didn’t intervene because nobody, or nothing, called them.

None of this is unique to a fast-food chain. Any organization that puts an automated system in front of its customers reaches the same point when a machine is left alone with a person with no one watching. That moment doesn’t appear in any performance metric, and it’s the only one that matters when something goes wrong. The good news is that almost everything that’s going to happen there can be known beforehand.

When AI fails

When an automated system makes a mistake and continues operating, the error can go undetected, so the customer is left with a problem without anyone taking responsibility. And that gap won’t be filled by putting someone in charge of everything since no one can supervise every order at a service window, or every conversation on a customer service channel. What’s needed is for the system to pause during an interaction and transfer it to another user. And that needs rigorous testing.

For example, it can easily redirect the customer when the product is out of stock or the order isn’t on the menu. But it might not stop when it misunderstands the customer.

The other part lies with the employee who receives the handover and how they resolve the issue. What needs to be considered is how many incidents are closed in real time without the customer having to restart the process. This requires that the employee taking over can see what’s happened to that point and has the autonomy to resolve it.

Directing responsibility, not counting mistakes

The errors seen before aren’t random. In self-service kiosks with automated voice commands, most incorrect orders come from customizations, or requests that deviated from the ordinary. So there’s a small group of customers who account for the majority of errors. That’s the catch: the system is correct nearly all the time, while these customers often receive something different from what they ordered. It’s precisely these cases, the ones the average leaves out, that sink a pilot program.

Therefore, the question that reveals the problem isn’t how accurate the system is, which any technical report can answer. It’s who the customers are who bear the brunt of the errors, how much they’re worth, and what happens if they leave.

Before deployment, someone has to put in writing what specific errors a specific client will tolerate, and ask if the organization accepts it. That’s not a percentage, it’s a decision, and it can’t be signed off by just the tech team because it’s not a technical statement. This is where the bigger business comes in.

This case shouldn’t push an organization to retreat into internal practices to avoid what happened to McDonald’s. If AI is only introduced where the error is invisible, the value will also be invisible. That’s why McDonald’s has returned despite the reputational cost it’s already paid.

Reaching the customer doesn’t depend on the system never making mistakes. Teams depend on what’s done when the system does make mistakes, and knows when to stop.

AI is accelerating the pace of change, shortening the shelf life of expertise, reshaping operating models, and compressing the time IT leaders have to learn, decide, and act. As a result, IT leaders are increasingly forced to make consequential decisions faster, often with less information, greater ambiguity, and higher stakes.

Formore than four decades, I’ve worked with hundreds of CIOs, building trusted relationships with many of the world’s most accomplished technology executives. Through my Tech Whisperers podcast, CIO Whisperers interviews, the CIO100 Leadership Masterclass, and other conversations, these leaders have candidly shared what they’re wrestling with, where they’re struggling, and what they’re doing differently as the world around them changes.

For years, I’ve shared my “7 Cs of Great Leaders,” which are rooted in the brilliance of these practitioners. Customer Centricity, Courage, Change, Cultivate, Communication, Collaboration, and Culture — every one of these facets of leadership matters to this day. But the conditions under which we’re being asked to lead are changing. And through my conversations of late, new signals have been growing louder, and a new set of 7 Cs is emerging to help guide leaders through the challenges they face today.

AI transformation requires developing new mindsets and muscles, in ourselves, our teams, and ultimately our organizations. Those muscles — Coherence, Communication, Currency, Confidence, Conviction, Courage, and Change — are being demonstrated by leading technology executives as they accelerate business impact in environment marked by Compression.

BNY CIO Leigh-Ann Russell has made Coherence a defining leadership discipline for this era. Johnson & Johnson CIO Jim Swanson’s emphasis on continuous learning and remaining future ready captures the essence of Currency. Liberty Mutual CIO Monica Caldas is demonstrating what it means to build Confidence in people so they’re equipped to adopt new ways of working. Cox Automotive Chief Product and Technology Officer Marianne Johnson’s willingness to move where there is no established playbook embodies Courage. And GSK CTDO Shobie Ramakrishnan’s emphasis on assertive, high-velocity decision-making captures the reality of Compression surrounding all of them.

These capabilities build on, reinforce, and compound one another. It starts with making sense of a world that increasingly doesn’t and then giving people something coherent to move toward.

Coherence: Making the pieces make sense

Confused minds don’t act. And in today’s environment, overwhelmed minds don’t transform. Before we accelerate, everyone must know where we’re going and how the pieces connect. Otherwise, speed creates chaos faster.

The easiest way to understand coherence is to consider its opposite. Between AI initiatives, transformation programs, business priorities, technology investments, talent strategies, and new operating models, very few organizations today suffer from a shortage of activity. Instead, they suffer from incoherence: too many disconnected activities. Everyone can move quickly, work hard, do good work, but still be moving in different directions.

Coherence answers a deceptively simple question: How does everything fit together? It turns disconnected activity into aligned movement, creating the foundation for everything that follows.

Great leaders create coherence by connecting strategy to execution, ambition to capability, and today’s decisions to tomorrow’s destination. They help people understand what they’re doing and why, as well as how it connects to the collective mission and how their work helps get us there.

In a pressure-cooker environment where workloads are growing, roles are changing, and people are wondering what AI means for their futures, that connection between work and impact may be the most important thing a leader can provide. People can handle demanding work when they know why it matters and can see they are making a difference. Nothing drains energy faster than working incredibly hard without knowing whether any of it matters.

Coherence energizes people by creating focus, prioritization, and meaning. Great leaders connect the dots so their people can see the impact they’re having, whether that’s showing how their work changed an outcome, helped a customer or colleague, improved a decision, or created value for the business.

Communication: Creating shared understanding and movement

The best leaders consistently differentiate themselves through their ability to communicate — not simply to convey information, but to take people on a journey. They help people see the next hill, understand why it’s worth climbing, and believe they can get there together, even when the path is uncertain and the risks are real.

Master communicators start with their audience, recognizing that different stakeholders have different motivations, concerns, and hot buttons. They ask: What matters to these people? What do they need to understand, believe, or feel before they’re ready to move?

Many leaders, however, start instead with what they want to say. They organize the facts, build the deck, and deliver the message. Great communicators reverse that lens, starting not with “What do I want to tell them?” but “What do they need to hear from me?”

Master communicators also don’t stop at the “what.” The what may be a new AI capability, transformation program, operating model, or organizational change. It’s also rarely what causes people to move. Great communicators translate the what into the “so what”: Why does this matter? What opportunity does it create? How will it make the business, team, or my work better? Because different audiences care about different outcomes, the so what must be translated through their lens, not ours.

Then the best communicators go one step further, providing the “what’s next.” They help people see where we’re going, what will be expected of them, and what they can expect from their leaders. In an environment filled with uncertainty, people need leaders who can help them anticipate — not predict — the future and give them enough confidence to take the next step.

RS Group Global CIO Janet Robertson offers this simple question for any communicator: “What’s it like to be on the receiving end of you?”

Rather than how polished your presentation is, the real test is what the person on the receiving end heard, understood, and felt, and whether they’ve been given reason to be excited about the path forward.

Currency: Keep earning your relevance

Experience without continuous learning can become an anchor. J&J’s Jim Swanson has talked about the extraordinarily short half-life of an IT professional’s skillset and the need to remain continually future ready. That’s what is meant by currency.

The CIOs I see thriving invest more time in learning. They read, study, take classes, experiment, put hands on keyboards, spend time understanding how AI-native companies operate. They observe what’s changing outside their industries, spend time with people who know things they don’t, and get close enough to emerging technologies to distinguish reality from hype. The higher they rise, the more intentional they become about remaining a “leader as learner.” They know that relevance must be continually earned.

But currency isn’t just learning more about technology. Great CIOs get closer to the business and the work itself. They spend time in the field, sit with customers and frontline employees, observe workflows, ask questions, and see firsthand where friction, work-arounds, opportunities, and unmet needs exist. That proximity creates context you can’t get from a PowerPoint or conference room.

One of the reasons Afshean Talasaz, my Practitioners for Practitioners colleague, was so effective as a data scientist and as a CIO was that he went into the field. He put on the steel-toed boots and hard hat, rode in trucks, talked with the people doing the work, and watched them do it. He experienced the realities technology was supposed to improve, and that gave him something enormously valuable: context. He wasn’t learning about the business; he was learning the business.

Context matters even more in the AI era. To redesign workflows, automate processes, and fundamentally change how work gets done, we need to understand the work and the people doing the work before we try to reinvent it. You can’t transform what you understand only from a distance.

Currency is a function of curiosity plus proximity. Keep experimenting, questioning, observing, and learning while staying close to the technology, business, customer, and people doing the work.

Confidence: Building the muscles to move despite uncertainty

Currency produces earned confidence. Not swagger, but what comes from experience, experimentation, and enough repetitions to trust yourself when the answer isn’t obvious.

Resistance to change is certainly an issue, but one of the greatest obstacles to transformation is fear of incompetence. Accomplished people are accustomed to being good at what they do. AI suddenly puts us into beginner mode. Asking someone to change how they work without helping them develop confidence in their ability to succeed is a recipe for hesitation, avoidance, and stalled adoption.

The best CxOs I know building confidence, not just in themselves but in others as well. They give their teams the tools, context, permission, and opportunities to develop new muscles, and then room to use them as they move into unfamiliar territory.

Confidence can’t be mandated or taught in a classroom. It has to be developed through real-world “reps” — experimenting, learning, making mistakes, and trying again. Every successful rep creates more competence, and with it more confidence.

This is something Liberty Mutual’s Caldas reinforces in how she thinks about leadership and transformation. Great leaders don’t eliminate uncertainty for their people, she says; they equip them to operate successfully within it. They create environments where people can experiment safely, ask questions without embarrassment, challenge old assumptions, and stretch beyond their comfort zones. In the process, they give their people the confidence that, together, they can figure this out.

Conviction: Shortening the distance from signal to decision

Confidence gives us the conviction to make critical judgment calls, because seeing signals isn’t enough. In a quickly moving environment, leaders must determine which signals matter, which problems are worth solving, which opportunities deserve action. This is a vital source of competitive advantage, one Deluxe CDTO Yogs Jayaprakasam calls “speed to conviction.”

As AI makes technical capability more accessible, knowing how to build something becomes less differentiating. The premium shifts to knowing what is worth building and how quickly you can reach a well-founded decision to act. Jayaprakasam notes that startups don’t necessarily have better technology or smarter people; they’re often able to reach conviction and act while larger organizations are still studying the question. Every unnecessary layer between a signal and a decision adds latency. That’s a leadership challenge, not a technology problem.

This isn’t just “move fast and break things” while sacrificing judgment. Great leaders know which risks must be understood, whose perspective needs to be in the room, and when they have enough information to move. They resist impulsiveness on one side and analysis paralysis on the other.

In a fast-paced, uncertain world, waiting for 100% confidence can become a costly decision. The advantage going forward belongs not to the leader who knows everything but the one who knows when they know enough to move.

Leadership today demands making consequential decisions with incomplete information, stopping work that no longer matters, confronting legacy assumptions and deeply embedded ways of operating, and asking people, including ourselves, to leave comfortable territory. With conviction, leaders become willing to venture into territory where there may not yet be a playbook. And that’s when courage comes into play.

Courage: Choosing to take the shot

Courage isn’t the absence of fear; it’s what we choose to do in fear’s presence. That’s particularly relevant right now, because the fears are real — fear of getting it wrong, making a career-limiting decision, not knowing as much as we think we should. Fear can be paralyzing, and in an era of unprecedented uncertainty, there are plenty of legitimate reasons to hesitate.

Cardinal Health CIO Michelle Greene reminds us, “The land of comfort has no growth.” For leaders and the organizations we’re asking to transform, growth only happens once we venture beyond what we already know and feels safe.

But we can’t ask our organizations to experiment, reinvent work, and embrace AI while creating an environment where people are afraid to mess up. If we want more shots made, we have to create permission for more shots to be taken. That means creating intelligent guardrails, making thoughtful bets, learning quickly from the misses, and getting back in the game.

Cox Auto’s Johnson talks about leading when there is no map or established playbook. Great leaders can’t promise people certainty but they can provide a North Star, the right fundamentals, and the courage to take the next step. Sometimes that means taking the shot themselves. Increasingly, it means creating an environment where thousands of other people believe they have permission to take theirs.

Change: Turning movement into outcomes

This is where the preceding Cs lead us, because AI transformation ultimately comes down to the organization’s ability to change how work gets done. Leaders must be able to take people on that journey, while every individual has to strengthen their capacity to adapt, learn new skills, redefine what they contribute, and continually reinvent themselves.

The importance of IQ (intelligence quotient) and EQ (emotional quotient) remain essential. But legendary CIO Rob Carter makes the case that what will increasingly differentiate leaders and organizations going forward is AQ (adaptive quotient), the ability to adapt as the world around us changes.

Skills, jobs, operating models, and entire industries are being reinvented at extraordinary speed. As a result, adaptability is a prerequisite for relevance as a leader. AI’s value is unlocked when people adopt it, work changes, and decisions improve. Our ability to adapt determines how quickly and successfully we can make that happen.

Change creates new information, capabilities, and complexity, which means we have to find coherence all over again.

There’s one more C that belongs in this conversation, and it describes the environment the other seven operate in: compression. Technology cycles, strategy cycles, transformation cycles, and decision cycles are all compressing. The 7 Cs are how great leaders respond to this environment.

The compounding leadership flywheel

I see these new 7 Cs as a flywheel: Coherence creates clarity. Clarity enables communication. Communication creates the shared context for currency. Currency builds confidence. Confidence fuels courage. Courage enables change. Change creates new learning that requires a new level of coherence, and the flywheel turns again.

With every turn, those capabilities strengthen. The more we learn, the more confidently we can move. The more confidently we move, the more willing we become to act courageously. The more courageously we act, the more change we create. Every cycle produces learning that allows us to move through the next one faster and stronger. That’s compounding leadership.

My original 7 Cs described enduring attributes of great leaders, and I still believe deeply in every one of them. The new 7 Cs are an evolution of these fundamentals. They describe the mindsets and muscles great leaders need to continually strengthen when disruption isn’t an event but the reality we’re leading in.

We know the world around us will continue to change. The question is: Are our leadership muscles compounding fast enough to change with it?

In companies where digital transformation (DX) isn’t progressing as expected, the common barrier is not technology but the organization.

You’ve introduced AI tools. You’ve organized your data. You’ve developed DX talent. Why aren’t you seeing results? Could the reason be that the organizational culture hasn’t been established where people feel safe to take on challenges and safe to fail?

The essence of DX is transformation — in other words, embracing change. The key players in adapting to that change are people, and DX will not move forward unless people take on the challenge.

Especially in today’s VUCA era, no one knows the right answer for how to manage a business or drive DX. There is no secret manual that guarantees success. It is commonplace for the goal to become obscured, change, or for the path to become interrupted while you’re in the midst of the process. That is

precisely why an agile approach is the best way to reach the true goal quickly:

Try it first → Fail more and faster than others → Learn from failures and pivot quickly → Turn that into growth

But what is the reality within your organization? Is it:

• The person who speaks up gets the short end of the stick
• Failure leads to a lower performance evaluation
• New plans and proposals get rejected because there’s no precedent or no other companies have done it?
• Elaborate plans are drawn up, but since no one wants to fail or take responsibility, the plans are never executed

If such a negative, stagnant atmosphere prevails, it is quietly and surely robbing the organization of its spirit of challenge.

As such, the greatest enemy of DX is not failure. It is stopping the pursuit of challenges.

What exactly is organizational culture in the first place?

What exactly is organizational culture in the first place? Since culture is something you can’t see, don’t you get the impression that it’s elusive and difficult to improve?

I, too, have seen and heard various definitions of the term organizational culture over the years, but I’d like to share the one that makes the most sense to me. It comes from Ryosuke Ishii, author of the book How to Create Psychological Safety, who states: Organizational culture is the “memory of trigger words → actions → feedback” within that organization over time.

In this context, triggering words refer to the words spoken by others before you take a certain action, while feedback words refer to the words spoken by others after you’ve taken that action.

Let me give you a concrete example to illustrate this. Imagine a scenario where you’re explaining a new project to your supervisor at work.

After explaining the details of the project to your supervisor, consider these “triggering words”:

• Person A was told by their supervisor, “Why don’t you give it a try?” and “I will take responsibility for it.”
• Person B was told by their boss, “I don’t think it’ll work out,” and “It’s impossible anyway.”

They both carried out the project (i.e., took action), but unfortunately, both Person A and Person B ended up failing.

After reporting the failure to their boss, consider this feedback:

• Person A’s boss said, “Nice try!” and “What can you take from this for next time?”
• Person B was told by their supervisor, “Why did you fail like that?! (angry)” and “It was impossible from the start.”

After this series of exchanges, what do you think Person A and Person B will do going forward?

• [Person A’s future actions] “I will learn from this failure and apply what I have learned next time,” and continued to take on new challenges.
• [Person B’s future actions] Thinking, “If I am just going to get yelled at for failing, I am never doing that again,” Person B stopped taking on new challenges.

What kind of organizational culture do you envision?

• An organization with many people like Person A?
• An organization with many people like Person B?

The answer is obvious.

So, what should you do if you want to build an organization full of people like A who are willing to take on challenges?

Let’s revisit the definition of the term organizational culture. There is one word in particular I would like you to focus on: the word memory in the definition that states, “Organizational culture is the memory of trigger words → actions → feedback that has occurred within that organization in the past.”

The true nature of organizational culture lies in the memories that exist in each person’s mind — the recollections of various things that were said to me before and after taking action within the organization in the past. These are memories, not records. This means that if you want to foster a positive organizational culture, you need to continuously overwrite each person’s memories within the organization with positive trigger words and feedback. Doing so will increase positive, proactive efforts, transform the organizational culture for the better, and open the door to a brighter future.

Without psychological safety, people won’t take on challenges

The first and foremost step in transforming an organization into one with a positive culture is ensuring psychological safety.

According to the Success Cycle proposed by Daniel H. Kim, an expert involved in organizational learning research and practice at MIT, if you first prioritize improving the quality of Relationships, then raise the quality of Thinking, and finally enhance the quality of Behavior, good Results will naturally follow. Conversely, it is said that if an organization first demands Results, it creates a failure cycle that leads to failure.

When working to improve relationships, ensuring psychological safety is crucial.

Psychological safety consists of four elements: Speak, Support, Challenge and Innovate.

• Speak: Being able to express one’s opinions with confidence and admit when one doesn’t understand.
• Help: Being able to ask others for help and support one another when in trouble.
• Challenge: Being able to take on challenges with confidence, failing without fear and learning and growing from failures.
• Innovation: A state where new ideas continuously emerge and are welcomed.

Kansai Electric Power also places a high priority on strategically fostering an organizational culture where employees can take on challenges and make mistakes with confidence, and is promoting company-wide organizational culture reform under the slogan “Notice, Speak Up, Take Action.” It is only when such a foundation of positive relationships exists that people can view DX as something that directly concerns them, take on challenges with a positive attitude and grow.

A learning organization transforms failure into an investment in the future

There is one point to keep in mind when aiming to ensure psychological safety: Simply having a high level of psychological safety is not enough. If we stop there, the workplace will become what is often called a complacent workplace. What is essential is a learning organization that strives to balance high psychological safety with high standards and goals for work.

The defining characteristics of the AI era are that change is happening far too quickly and that change never ends.

In the Pre-Generative AI era, before the advent of generative AI,

• Once you acquired a skill, it remained useful for a long time
• Once a system was implemented, it could be used stably for several years to over a decade

However, in the Post-Generative AI era,

• AI technology evolves on a monthly basis
• The skills required of people are changing by the minute, and the way we learn is changing dramatically
• And people’s roles, work styles and lifestyles themselves are undergoing major changes

In other words, what will be important in the coming Post-Generative AI era is not learning something once, but the ability to continue learning continuously. That is precisely why what we need going forward is not an organization that teaches and learns, but a learning organization.

• Organizations must accumulate and share knowledge, including tacit knowledge
• Sharing not only success stories but also lessons learned from failures
• People and AI learn from and inspire each other
• Building networks both inside and outside the company to support one another

This kind of organizational culture enables both individual and organizational growth to coexist, which in turn drives the company’s overall growth.

Companies that are advancing in DX do not simply experience a lot of failures. They turn failures into assets.

Why did we fail? What did we learn from it? What should we do next? There is a culture of reflecting on and sharing these questions.

When this happens, a single person’s failure becomes wisdom for the entire organization. It becomes an asset for the company as a whole, rather than a liability. It becomes an investment in the future, rather than a cost.

Strategically building an organizational culture that embraces challenges

John P. Kotter, a leading authority on leadership, proposes the Eight Accelerators of Change in his book “The Execution-Oriented Organization”:

1. Heightening a sense of urgency while seizing major opportunities
2. Form a core group
3. Articulate a vision and define the initiative
4. Increase the number of volunteers
5. Removing obstacles
6. Achieve early results and celebrate
7. Maintain Momentum
8. Embed change into the organization’s culture

Kansai Electric Power Company also follows this framework to plan and implement its various initiatives.

Of the eight transformation accelerators, it is most effective to implement 1–3 using a top-down approach.

In particular, #1 is crucial as the starting point for transformation. This sense of urgency is not meant to instill fear or anxiety, but rather a healthy awareness that staying as we are will only lead to regression and without change, there is no bright future. At the same time, it is important to recognize the significant opportunities, hope and bright future that lie ahead after the transformation.

For 4–6, it is effective to proceed with a bottom-up approach while incorporating top-down elements. It is essential to advance each initiative with a strong focus on making DX a personal priority—ensuring that DX is not seen as something done reluctantly or out of obligation because a superior told you to, but rather as the overlapping area of the Venn diagram representing Will (want to do), Can (able to do) and Must (required by customers or society).

Regarding points 7–8, based on my experience observing various companies, I’ve noticed that the organizations achieving the best results with DX seem to be having the most fun.

There is constructive discussion, mutual support, a spirit of continuous challenge, excitement and joyful smiles. My ultimate goal in reforming our organizational culture is not simply to make the organization a more comfortable place to work. It is to create an organizational culture where people enjoy taking on challenges. I believe that once we reach this level, we can say we have succeeded in #8, making change a part of our DNA. Therefore, the Kansai Electric Power Group aims to create an organizational culture where people enjoy taking on challenges and will accelerate various initiatives toward that end.

In conclusion

The essence of DX is not technology. It is people.

And the key to changing people, changing the company and changing the future lies in the organizational culture that serves as the foundation.

That is precisely why I believe the final challenge a CIO must address is not AI or data, but organizational culture:

• Feeling secure enough to take on challenges. Feeling secure enough to fail.
• Learning from failure, taking on new challenges and continuing to grow.

When that cycle begins to turn, DX ceases to be a fleeting project and becomes a capability deeply ingrained in the organizational culture, evolving into a sustainable competitive advantage for the company.

That is why I believe it is essential for CIOs to strategically cultivate organizational culture rather than leaving it to chance.

For years, ERP customization was the smart play. When SAP or Oracle systems didn’t bend easily to the way a factory ran shifts or a distributor priced products, CIOs built around it, modifying, extending, or hard-coding until the system matched the business rather than the other way around.

And, for some time, customization worked. It also quietly became one of the most expensive decisions many companies never explicitly signed off on.

There’s a correction under way in enterprise IT, however, and AI is what’s forcing it out into the open.

Angela Maragkopoulou, chief information and digital officer at Sunlight Group Energy Storage Systems, has been direct with her leadership team about what standardizing on an ERP system core buys them. “Don’t sell standardization to your board as technical cleanup,” she says of the case for a “clean core” ERP strategy. “Frame it for what it is: the price of admission to AI.”

That price isn’t one every CIO is willing to pay yet. Even as AI investment accelerates elsewhere in the business, the share of organizations that prefer a customized approach has barely moved: from 52% in late 2024 to 51% by August 2025, according to IDC’s most recent research. “It appears to be a strategic minority move today, concentrated in large enterprises with the technical debt pain, and not yet a broad-based CIO spending pattern,” says Mickey North Rizza, group vice president of enterprise software research at IDC.

James Baker, a partner in Bain & Co.’s Enterprise Technology practice, is seeing nearly the same thing among his clients: Companies, he says, are not yet “funding wholesale de-customization purely for AI.”

Until then, a competitive gap is likely to widen between those CIOs addressing a decades-old problem and IT leaders still deciding whether it’s worth touching at all. At stake is the ability to make the most of AI going forward, as grafting AI onto an existing mess quietly inherits every flaw in it, too.

Why the customization happened in the first place

No one got here by mistake,” says Maribel Lopez, founder of Lopez Research.

“You have to think of why they customized in the first place,” she says. “ERP companies have been trying to get customers to move to the cloud for years. One of the reasons they wouldn’t is they are loath to give up the customization. Customization equals specialized business process in ERP.” It was a rational choice at the time.

What’s changed is who, or what, is trying to use that customized system. Older reporting and automation tolerated customization because a human was still reconciling the exceptions. “AI agents, especially agentic AI, need a single, trustworthy version of the process to act on,” North Rizza says. “Customization fragments this version; … custom code, bespoke workflows, and legacy architecture become a ‘black box’ risk rather than just maintenance overhead.”

Lopez likens customized ERP to a bespoke bicycle. A bike built for a person five feet tall would be hard for someone six feet tall to ride. “You could get some things to work, but it wouldn’t work the same way,” she says. A vendor’s embedded AI is built for the standard bike.

Customization doesn’t make the system unrideable. But it means every mile will cost you more.

Where this shows up first is fairly consistent across the companies IDC tracks: finance close and reconciliation, procurement contract management, and supply-chain demand planning. That is, the high-volume, rules-based processes where agentic AI has the clearest early payoff, and the least tolerance for a fragmented version of the truth.

A CIO who reconciled this early

Sunlight’s S/4HANA implementation was, in Maragkopoulou’s telling, a standardization decision first and an AI decision in hindsight. The triggers were concrete: Its legacy SAP system was heading out of support in 2027, and running parallel systems across factories in Greece, Germany, and the US was expensive and fragile; moreover, leadership was eager for real-time, role-based reporting.

“AI wasn’t on the agenda when we scoped it,” Maragkopoulou says. “But standardization turned out to be the best AI decision we made, long before AI was the headline.”

Sunlight kept customization only where it was a genuine differentiator — a variant configurator that automates complex B2B order entry — and cut the rest, including what she calls a “convenience layer” of old screens and reports that had quietly accumulated without anyone questioning them.

The payoff came later. “The simplified data model and embedded analytics mean the data is consistent and trustworthy, which is most of the battle,” says Maragkopoulou. “A clean foundation makes AI possible, not automatic.”

Maragkopoulou’s advice to other CIOs is to standardize first, or don’t bother. “If you bolt AI onto a heavily customized landscape, you don’t get intelligence,” she says. “You get your complexity, automated and scaled.”

A realization, or a trap?

Not every CIO reads this the way Maragkopoulou does.

Scott Hicar, a fractional CIO who has lived through several SAP platform transitions (R/3 to ECC, and now ECC to S/4HANA), calls these shifts a vendor revenue strategy, not a strategic CIO choice. CIOs get pulled into them whether they intend to or not.

“ERP companies strategically, every five to 10 years, need to force a re-licensing or re-upgrade sort of event to generate enough top-end revenue to meet their plans,” he says. AI, in his view, is this cycle’s justification: new capability available natively only on the latest platform, paired with the pressure of an end-of-life deadline.

“It always made me mad as a CIO that my maintenance dollars were funding the R&D of a next platform I ultimately wasn’t licensed for,” he says.

Hicar isn’t just wary of being swept into an expensive re-licensing event on a vendor’s timeline. He’s also skeptical of CIOs stacking custom AI atop a legacy system rather than paying to standardize. That custom AI, he says, risks going obsolete, or simply being duplicated for free, once a company finally upgrades to the standardized platform.

“The shelf life of AI investments is really short, given the speed of innovation,” Hicar says. “It’s hard to make these one-off bets pay off before obsolescence.”

Even CIOs who accept the case for standardization may not agree on how to get there. Some go underneath, cleaning up the core system itself; others layer AI agents on top of legacy systems instead, leaving the customization in place. SAP HANA migrations alone average roughly 15 months, according to an IDC survey fielded in May 2025 — and that’s before a company has touched a line of custom code. Either path is expensive.

“The question,” Lopez says, “is where will people spend the money.”

The risk of waiting

The cost of sitting on the fence compounds. IDC’s CIO Sentiment Survey, fielded in April 2025, ties technical debt to 49% to 60% higher maintenance costs at large enterprises. North Rizza is frank that it “slows AI deployment and scaling.”

A November 2025 IBM study found that unaddressed tech debt can consume 18% to 29% of AI implementation costs and stretch a 30-month project to 36 months. Even before AI enters the picture, 80% of ERP transformations miss their goals, according to Bain’s AI-Enabled ERP X-Ray. Layering AI on top of an already unstable foundation, Baker says, “doesn’t solve the problem; it just makes it harder to unwind.”

For now, many companies are doing neither. They aren’t standardizing, and they aren’t sitting still. They’re piling AI onto whatever already exists, inheriting the same fragility that has always come with customization, just with higher stakes attached. Maragkopoulou is convinced that’s a mistake CIOs will come to regret. Hicar agrees the workaround is no safer; AI built to avoid the upgrade risks going obsolete before it pays for itself, he argues.

The bill is coming regardless. What’s left is a simpler question: whether CIOs choose when they pay it or let a vendor’s deadline choose for them.