With 2027 quickly looming, IT leaders should soon be finalizing their objectives for the new year, one certain to be rife with many unknowns if 2026 is any indication.

This year began with the call for a pivot from AI pilots to AI ROI, but questions around AI governance and accountability quickly took center stage as agentic AI made further inroads in the enterprise and high-profile incidents showed the risks of AI gone awry and the value of security-first approaches to AI agents.

Now, CIOs must anticipate how AI will evolve in 2027, how they’ll get their enterprises ready for all the risks and opportunities of that evolution, whether their teams will have the skills to seize on technology advances, and more.

To get a glimpse of how CIOs are approaching next year’s IT agenda, we asked IT leaders to share the key questions they are asking to shape their strategies for 2027. Here’s what they shared.

How do we ensure our AI investments provide tangible value as the cost calculus changes?

The big question as CIOs plot their IT agendas for 2027 still centers on providing verifiable business value, as 98% of CIOs report that board pressure to demonstrate measurable AI ROI has increased since 2024, according to a 2026 report based on a Dataiku/Harris Poll survey of 600 enterprise CIOs worldwide.

Doug Gilbert, CIO and chief digital officer at Sutherland, a digital transformation services company, is among those IT execs pondering this question as he executes his fiscal 2027 budget, which started in July.

“When I’m out speaking to people and they say, ‘It would be great if AI could do that,’ I say, ‘Yes, but would it provide value? Are we building value or are we just burning tokens?’” Gilbert says. He notes that he and his CIO peers are being increasingly judged on the outcomes they deliver for their organizations, meaning they have more incentive than ever to ensure the technology initiatives they undertake deliver ROI.

But the calculus on that is getting trickier than it was at the beginning of 2026 — in part because AI total cost of ownership (TCO) is challenging to calculate, but also because an AI pricing reset is on the horizon. So, to that end, CIOs such as Gilbert are now thinking about what level of ROI is high enough to justify the investment.

That’s a key question to ask now and into 2027, Gilbert says, particularly as the cost of tokens continues to rise. An AI use case that provides marginal ROI could stop producing any value in the near future if tokens increase even slightly, he notes.

As a result, Gilbert is working to factor in those dynamics when he and his team work with the business to identify which AI use cases to pursue — and he expects to do so even more moving forward. “I plan to pay a tremendous amount of time thinking about whether [a proposed project] is the right one to pursue,” he says, to ensure “when you put math on paper there is value with the project.”

How do we ensure observability and auditability of our AI systems?

According to a 2026 survey from OneStream, 47% of senior finance and IT executives say they have made a material business decision based on inaccurate, incomplete, or outdated financial data in the past 12 months.

Citing that statistic, Gilbert says he’s also asking critical questions about the observability and auditability of all of Sutherland’s AI uses cases. He wants to be sure he, other executives, and users are confident the company’s AI systems always have high-quality data — and thus accurate outputs. And he wants IT to achieve that observability and auditability so that the company can accurately track, measure, and improve its AI deployments over time.

“We will not deploy any AI that we can’t observe,” he says, a policy that he put in place about a year ago and will continue in 2027.

A wide range of tools under the moniker “AgentOps” are emerging to help IT leaders monitor AI activity, tools, and costs, starting with observability.

How do we move to more autonomous agentic processes?

“The challenge most technology executives are grappling with at the moment is how you move AI from human-assisted agentic to much more autonomous agentic processes with minimal human intervention or supervision,” says Jody Fullman, CIO of Reconomy and recently recognized as one of the UK’s top 100 CIOs in the CIO 100 UK Awards 2026.

Moreover, how do you achieve that securely and in a way that’s economically viable? he asks.

Fullman is implementing strategies to address this challenge. He is strengthening IT’s agentic controls by aligning Reconomy’s governance program to an industry-recognized framework. He is also implementing tools to monitor and manage tokens. And he’s moving some AI workloads to lower-costs models.

All that, he says, will set up the organization to develop and deliver more autonomous agentic capabilities ahead, noting that he and his team have been transitioning successful proof-of-concept agentic initiatives into MVPs to put the company on the path to more autonomous agentic processes.

How should OpenAI’s Hugging Face incident shape my security strategy?

Security has been a central factor in IT agendas for years. But Jeff Stovall, CIO for Dallas, Texas, says the 2026 Hugging Face incident, in which autonomous AI agents built by OpenAI escaped their sandbox testing environment and hacked into the infrastructure of AI open platform company Hugging Face, has him adjusting his plans for 2027.

“Hugging Face demonstrated that there was a lot more peril in the world of AI as it relates to cybersecurity than what we were ramping up for,” he says. “There’s now this notion that AI may have more downside for us than upside for us in the near future,” he adds, explaining that AI-fueled threats to the organization are more significant than the positives its use brings.

“In the upcoming six months to a year, what I see is that we have to take a hard turn in thinking about how AI poses a significant malevolent threat to our organization when wielded by actors seeking to do harm,” Stovall says. He points specifically to hackers’ ability to use AI to create swarms of bots that can autonomously collaborate and exploit all vulnerabilities nearly instantaneously and all at once. “That’s a dramatic acceleration and amplification to the threats we face.”

In response, Stovall says he’s shifting more resources to maturing his security program in 2027 than he initially budgeted — drawing funds away from internal AI deployments to “shore up the walls even further” by adding more AI automation and agents to have “much more machine speed in security response.”

How do we use AI to transform?

Marco Bill, SVP and CIO at Red Hat, believes using AI for productivity gains is now table stakes. So as he develops his plans for IT for 2027, he’s looking to push that notion even further, asking how the company can use AI to transform processes as a whole.

“This question helps me to keep reminding ourselves that we’re not just here to do IT. It’s more of a guiding principle,” he says.

And that in turn is underscoring the importance of transforming the IT organization into a more collaborative and business-centric one.

As an example of what’s brewing at Red Hat, Bill points to the work his IT team is doing with the company’s sales team to devise ways to use AI to enable account managers to have better conversations with customers to close deals faster. IT is also working with the marketing department to make go-to-market process more powerful, and his teams are adding agentic AI to their own IT operations to create fully autonomous workflows that cost less to run and manage.

All told, Bill and the IT team at Red Hat are building further on the work many CIOs began in 2026 by helping to reimagine business processes to reap AI benefits and bringing AI transformation home to IT workflows.

How do we empower users to transform their work with AI?

“Citizen AI” is a rising trend in the enterprise, and this is something Reconomy’s Fullman will continue to address in 2027, explaining that his company has found that much of its “success with AI was not by being a tech project but by empowering our users and business colleagues to transform their work and processes with AI.”

Fuller believes that his company will see more AI-enabled transformation, and see that transformation faster, by continuing to put more AI in the hands of users throughout the organization instead of expecting IT workers to become experts in all the processes that could be transformed by AI.

As such, Fuller says the upcoming year’s IT plans will continue to put AI tools in the hands of the users, and do so with governance, security, and transparency in place. He’ll also leverage the skills of workers in process transformation roles, which were recently created and installed in Reconomy’s business units but still work closely with IT architecture and data teams.

“They’re plugged into tech and the business to make sure we’re prioritizing the right things and doing things the right way as we drive transformation,” he explains.

How do we prevent our AI governance council from becoming a bottleneck?

Company leaders at intelligent automation platform provider Celigo want every team to build with AI, something CIO Amy Farrow knows makes security and controls critical. But Farrow also wants to make sure she enables speed and doesn’t unnecessarily slow down innovation, noting that barriers could lead to shadow AI.

“If [teams] all have to wait too long, they’ll find a way around the process,” she says.

To ensure that governance isn’t a bottleneck, Farrow explains that her “governance council doesn’t approve projects. It sets the rules: risk tiers, design criteria, a checklist every agent has to pass.”

She adds: “The teams approve their own work against those rules, and we audit. My team owns the integrations, access controls, and observability. My job is making it faster to build the right way.”

What controls do we need to put in place for AI agents?

As AI agents increasingly enter enterprise workflows, IT leaders will need to the level of permissions agents should be granted relative to the people on whose behalf those agents will be operating.

Farrow, who previously served as global CIO at Infoblox and Lyft, believes agents should resolutely have fewer permissions than the people whose tasks they will be performing.

“Someone in finance can issue a credit or update our ERP because we trust their judgment. An agent doesn’t get that by default,” she explains. “We start every agent with the least autonomy required to achieve the outcome. It can interpret a request or recommend an action. Changes to records go through checks the model can’t override, and bigger changes need a person to sign off.”

This approach also has a fiscal benefit, she says, noting, “Less autonomy means fewer model calls, which keeps costs in check.”

As AI increasingly goes agentic, CIOs will increasingly need to plan for ways to address this issue in the year ahead.

What happens when vendor agents start acting inside our systems?

“Many of our SaaS vendors are shipping agents, and some can make changes to our data by default,” Farrow says of another key security- and governance-related question many CIOs are facing. “For 2027, I want vendor agents held to the same standard as any other integration.”

Farrow has other questions related to the rapid rise in vendor agents, asking, “What can they touch? Can we see what they did?”

IT leaders need to ensure they can answer those types of questions, noting that California’s and Colorado’s new rules on automated decisions start in January. She wants to be able to give strong answers to those questions, stressing that “‘the vendor’s agent did it’ is not a defensible answer.”

How do we meet immediate business needs while building a foundation for growth?

Mieko Shibata, CIO at R&T Deposit Solutions, sees this as “the overarching question” for him as CIO.

“That question guides our investment decisions and how we allocate our team’s time,” she explains. “Our strategic direction is consistent, but each year’s agenda must balance immediate business priorities with progress toward our longer-term technology foundation. We consider both the value each investment delivers and how it contributes to the environment we are building.”

Shibata is taking a multiprong approach in her response to that overarching question.

First, she’s ensuring technology modernization turns into measurable business value and that modernization initiatives are prioritized based on that value.

She is also considering how IT responds to growth opportunities while keeping its longer-term transformation on track. “Our response is to consider the immediate commercial value alongside the longer-term technology implications. Can we reuse the capability? Will it scale? What will it take to support? Does it help simplify our environment?” she says.

And she’s thinking about how the company prepares for AI’s evolution while maintaining clear accountability for its use. “For 2027, my focus is on building a repeatable approach to evaluating and adopting AI as it evolves, with clear business benefits, appropriate controls, and accountability for the results,” she says.

Is our AI vision ambitious enough?

For Lauren Wilkinson, CIO for financial advisor services at Vanguard, AI ambition is top of mind as she develops her IT strategy for 2027.

AI strategy should accelerate the business strategy and “push leaders to reconsider what outcomes with AI are possible, rather than simply finding ways to make today’s processes faster or more efficient,” she says.

“It helps to start with an aspirational business vision, including what a few big outcomes could look like. Then you can work backward to determine where AI and technology can fundamentally change what is possible,” she explains.

In her work with financial advisors, she’s found that they’re primarily using AI to support administrative tasks such as drafting emails, conducting research, and taking meeting notes. But she sees an opportunity to build on those use cases in the upcoming year to deliver on that objective for a more ambitious AI program.

Like many CIOs, Wilkinson wants to take AI to the next level at her organization and ensuring that ambition is high enough is paramount to achieving that goal.

Do we have the talent to win in an AI-native world?

Wilkinson believes how CIOs address the AI talent question will be critical for organizational success.

“As AI continues to evolve, the skills that differentiate great technologists will also evolve, placing an even greater premium on AI expertise and deep business acumen,” she says. “CIOs should be building teams that understand the business, can identify the highest-value problems, and know how to orchestrate AI-native workflows to solve them at dramatically greater speed. The differentiator won’t simply be who has access to the best AI, but who has the people who know where and how to apply it.”

Most CIOs know this, so it is not surprising that this year’s State of the CIO survey found that AI tops the lists for both most sought-after skills and the hardest to find. As a result, CIOs will have to get creative to build up their teams’ AI know-how in the year ahead.

How should IT be reshaped to maximize value creation?

CIOs, including Shelia Anderson, EVP and chief information and digital officer at Unum Group, are also asking how they should shape IT as an organization to ensure they can deliver value to the business.

“That has been a big focus for us this year and will continue to move down that path in 2027,” she says.

The question has shaped Anderson’s work at Unum Group since she started in her role in May 2025. She has organized her IT department around end-to-end value streams and says that approach has helped her IT staff work more closely with the business and more readily identify where improvements can be made. She also has her IT organization working to better understand business processes, and has been working on building more capabilities to measure and manage AI costs as Unum seeks to transform using AI. And she’s looking at what investments and improvements are needed in IT’s foundation.

All these moves will enable the IT department to get better at value creation, Anderson says.

She plans to augment this work in the coming year by providing AI training and education for IT workers and employees throughout the company “so they know how they can drive value and find opportunities in their everyday use of AI.”

Ensuring she has the right mix of talent — meaning she has the right skills in the right seats to deliver value — will also shape the actions she takes as CIO in 2027, Anderson says.

“In 2025 and 2026 there was a lot of testing and learning, but 2027 will be a year of maturing. You’ll see more formal processes and settling in around how you’re going to leverage the skills in your organization to drive value,” she adds.

In Foundry’s 2026 State of the CIO survey, 83% of CIOs say their role is becoming more of a hybrid to lead both business and tech initiatives, rather than simply manage IT. As the two become more inseparable, the CIOs who see collaboration as a competitive advantage are putting daylight between them and those who see it as a cursory soft skill. And C-suites are responding as they rely more on IT to drive growth and accelerate efficiencies. So the CIOs who deliver the most value will bring collaboration skills to drive change and technical expertise.

Silos between IT and the business are falling

As the World Economic Forum notes, not every tech initiative requires deep collaboration. But as businesses move beyond routine IT investments into cross-functional, innovation-focused transformations, collaboration becomes essential.

For example, how can IT decisions help an organization open new markets faster, or emerging technologies reduce risk in areas like data privacy. It’s no surprise, then, that Deloitte found that 65% of CIOs reported directly to CEOs in 2025, up from 41% in 2015.

Because tech is now part of almost every business decision, it takes everyone’s expertise and insights — from the CIO to the CFO — to see the full picture of how an initiative will affect the company. Shared knowledge leads to better-informed decisions that give tech its biggest impact.

When silos persist, CIOs struggle to get buy-in for their initiatives. Protiviti’s Global Transformation survey found that while 61% of CIOs report being confident in their tech transformation outcomes, only 34% of CEOs and boards feel the same — and the gap is even wider for AI implementations.

That gap is, at its core, a collaboration problem. Without effective collaboration and communication, others can’t see the value of transformation efforts. CIOs who excel in these areas can more easily demonstrate value, win buy-in and keep their organizations at the forefront of technology.

Delivering speed and innovation

Collaboration goes well beyond getting buy-in. While it may be thought to slow IT down, it actually brings in the insights that make change more effective and faster.

As I’ve highlighted in the past, when organizations move away from rigid data ownership, cross-functional teams can share what drives further innovation.

The same holds across the C-suite. Perspectives from outside IT show CIOs how a tech implementation works for the company as a whole, and where new opportunities lie. That lets CIOs test, refine, and reshape solutions faster.

Most importantly, when teams share a common purpose and goals, everyone stays aligned on the best overall outcome for the business. Iteration happens much quicker because everyone is focused on the big picture rather than individual agendas. With increasingly rapid tech cycles, that speed is a competitive advantage in its own right.

Building your collaborative abilities

For CIOs used to the old way of doing things, shifting to a collaborative mindset can be a challenge. But like any capability, it can be developed and improved by applying some fundamental principles.

Earn trust through follow-through. Trust isn’t built at a kickoff meeting or with a mission statement. It’s built when people see you do what you said you would, again and again. For CIOs, that means delivering on commitments or flagging early when you can’t. Clear and consistent communication is one of the most reliable ways to build trust across an organization.

Make transparency the default, especially when news is bad. Transparency means more than status reports. It means being candid about costs, risks, trade-offs, and setbacks before they surprise anyone. When leaders sidestep difficult discussions, trust erodes and goals drift. And in technology programs, small unspoken issues have a way of becoming expensive ones.

Seek cultural fit in how you work, not who you are. Cultural fit isn’t about sameness or shared backgrounds. It’s about compatibility in management philosophy, values, decision-making, and communication. A CIO who values speed and experimentation, and a CFO who prioritizes predictability and control will clash unless both recognize the difference and agree on how to bridge it. The same is true when it comes to finding the best technology providers.

Focus on the what, not the how. Few things undermine collaboration faster than micromanagement. When leaders dictate how a colleague or partner should work, they signal distrust, stifle creativity and innovation, and tie up their own time policing activities instead of driving results. The better approach is to agree on clearly defined, measurable outcomes, and then step back. Letting go of the how isn’t giving up control but building enough trust that everyone gets there together.

This requires adjusting your workflows and skills, but the payoff is valuable insight from your collaborators and wins that lift the entire organization.

The new life of the CIO

The days when the CIO could simply be the tech expert behind the scenes are long gone. With AI and other technologies ingrained in every aspect of the business, the CIO must lead well beyond IT.

By making collaboration a priority, you can gain critical insights across teams to guide your tech strategy, and deliver the greatest value to your organization.

According to a quartet of Accenture intellectual heavyweights, what CIOs need now to account for and control AI costs is tokenomics, sweet tokenomics.

The idea of linking AI spend to business outcomes may be a noble one, but “Tokenomics” only pretends to do so, unless you think tallying costs and measuring business outcomes are one and the same thing.

But before we get to Tokenomics we need to set the stage.

Start with Function Point Analysis (FPA), a venerable and mostly forgotten discipline, which should say something about its efficacy.

The folly of cost metrics you can’t understand

Back in the day, IT leaders worried that boards of directors worried that business executives worried about the cost of information technology and how to manage it.

IBM introduced Function Point Analysis in 1979 to solve this issue by breaking a software application into chunks — function points. Analysts could then sum these function points to compare the application’s overall complexity — and therefore its cost and risks — with that of any other piece of software.

When presented with the number of function points a proposed software project would require, your average, hopelessly naïve business leader asked IT to point to a few of the project’s function points, expecting mocked-up screenshots, sample reports, and so on.

Sadly, IT had to decline because, the design analysts would explain, a function point is (as Google’s AI Overview explains) “an abstract, synthetic unit of measurement rather than a physical object or a single piece of code.”

The Function Point Analysis value proposition, then, was that business leaders could manage IT costs and risks better by relying on cost metrics they couldn’t understand.

Function points for the AI era

Fast-forward to today, and the equivalent function-point cost metrics for AI are tokens — the chunks of text that a large language model reads, processes, and generates. Which, in case you missed the briefing, supposedly makes tokenomics a way to manage the costs and risks of AI.

As the aforementioned Accenture gurus “explain” (scare quotes intended): “Enterprise AI is creating a new cost of doing business: tokens. As consumption scales, token economics is becoming a material risk and opportunity, drawing increasing attention across the C-suite and the board.”

Which raises the question: Where are our tokenomics pundits finding C-suite and board members who give even an infinitesimal damn about tokens? They’re probably the ones who still pay attention to function points, too.

A short lesson in Brickonomics

But before we delve deeper into Tokenomics, capital T, we need to understand building maintenance and the critical role Brickonomics plays in it.

It’s like this: Brick walls are made of bricks. So if a brick wall needs attention, a building management project estimator should need to understand the costs and risks associated with managing a standard brick in the crumbling wall they’re supposed to fix. Multiply the standard management cost of a standard brick (a brickon?) by the number of bricks in the wall, and voilà! Building management knows …

Well, it knows less than it would have if had it instead invested in a conventional tuckpointing analysis. Among the missing pieces: In tuckpointing analysis, what usually needs and gets attention is the mortar between the bricks, not the bricks themselves.

Putting it all together — because you will have to

Now we’re ready for Tokenomics, and its conjecture that dividing an LLM into tokens and assigning a cost to each of those tokens will prove more useful than dividing software into function points or a wall into individually managed bricks.

The next questionable aspect of Tokenomics is that it depends on the proposition that all AI implementations are built on large language models or similar architectures.

To be fair, Tokenomics doesn’t have to be limited to analyzing LLMs. It can be adapted to any collection of thingies that’s composed of chunks that can be parameterized.

But that really isn’t good enough, is it? Because given the current pace of AI innovation, assuming that anything about it is stable, especially that its future core architecture will be tokenizable, is a shaky assumption at best.

It’s a mortar versus bricks challenge.

Then there are those pesky function points. Just because AI is involved in an implementation, that doesn’t mean software isn’t also involved. Which would seem to suggest that Tokenomics will have to integrate Function Point Analysis into its methods and practices as well.

Imagine you’re the one who has to explain this mess to those boards and business leaders who are, supposedly, clamoring for it. Because, as CIO, you will be.

Chargebacks and culture

We haven’t even gotten to the other big risk of Tokenomics: that it focuses decision-makers on AI’s costs, with only token attention — no pun intended — paid to its benefits.

Deep down inside, Tokenomics’ value depends on its use for chargebacks and showbacks. The logic for imposing these is superficially appealing — it makes business owners take responsibility for the AI spend driven by their consumption.

Those CIOs who dove into the chargeback rabbit hole discovered just how futile chargebacks can be. That’s because of a time-honored practice updated to the digital age: arguing about the bill.

Because that’s all a chargeback is: a bill. It’s a statement of how much money will change hands so that someone who has provided a good or service is reimbursed for the value they’ve created.

By the numbers: (1) a chargeback is a bill; (2) the point of Tokenomics is to support chargebacks; and (3) no business leader will be able to understand the bill they’re supposed to pay given Tokenomics impenetrable vocabulary and construction.

But what about showbacks, then? Letting business leaders understand the costs they’re driving would seem harmless and potentially useful.

And in fact showbacks can be harmless and useful, so long as they’re applied in the right business culture. That’s a culture in which managers consider themselves to be leaders of the whole company, basing their AI utilization decisions just as they base their other decisions — on the extent to which they benefit the whole company.

With a culture like this, IT can limit its application of tokenomics, little t, to its technical architecture practices. This way, the CIO can inform business managers of AI’s costs just as the CIO informs business managers of the other costs of running IT.

Where, the business manager might ask, did these cost numbers come from?

“You have two choices,” the CIO might reply. “You can trust me, or I can explain it to you.”

Now there’s a threat worth paying attention to.

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

CIOs entered the year with ambitions to bring more AI into their companies and start using AI to transform how work gets done. At the same time they also wanted to leverage tech to cut costs and boost productivity throughout the business. And they hoped to use their post to help drive the strategic vision for their organizations.

Now into the fourth quarter, CIOs say they’re still pursuing those objectives and plenty more. They say they still have much work to do, and they’re aiming to close the year with wins in myriad areas.

Here, IT leaders share advice on what their peers should focus on based on what they are racing to finish before year’s end.

1. Be good to your word

One of the top to-do items for many CIOs as they near year’s end is delivering on their promises.

As Robert Field, global CIO at tech advisory firm Adler Advisors, says, people may not remember all the work IT did in any given year, “but they’ll always remember the things you didn’t do that you said you would.”

Field also cites fiscal reasons for his drive to finish up all IT work planned for 2026, noting that the company, which runs on a calendar year, has already allocated the money for the work in its annual budget and has pegged some of it to anticipated tax credits.

Moreover, employees’ performance reviews are done on the calendar year, he says, which means planned projects need to be completed so leaders can accurately assess employees’ work and contributions.

Jason Brucker, managing director for CIO solutions at consulting firm Protiviti, says every year around this time he hears from many CIOs who are looking to finish what they started. But it can be a hard-fought win, he says. End-of-year tasks are piling up on workers, teams are starting to take time to plan for the upcoming year, and the C-suite is stressing the need for IT to deliver measurable value to the business.

Still, despite the distractions, closing the books on promises made can help prove the value of IT’s ability to not only deliver solutions but make good on allotted investments — an important win for securing funding for the next slate of projects.

2. Prove this year’s solutions deliver when it matters most

The last months of the year are always the busiest for Kenco Group, which as a large third-party logistics provider experiences a spike in demand for its services. Pal Narayanan, its chief digital and information officer, wants to use that peak volume to make clear that his IT department has no problem keeping operations up and running smoothly even at full tilt.

Although demonstrating IT’s ability to keep pace with the business is always critical, Narayanan says he wants a win this upcoming busy season because IT has added more automation and robotics within company processes “and we want to make sure those work flawlessly.”

But his IT team needs to be prepared to work quickly if there are any issues that need to be addressed, as Narayanan notes that this is the first peak season the company will go through with the new technology in place. “Every minute matters during peak season, so I’m making sure everyone is in place, and everything is built for scale, and if any additional capability needs to be turned on, our vendors are ready,” he adds.

3. Fortify business alignment for the year ahead

For Adler Advisors’ Field, building relationships with colleagues is an ongoing task. But year-end provides a ripe opportunity to check in with his peers, tie IT work to hoped-for business outcomes, and discuss goals for the upcoming year, he says.

“I use this time to talk to people and to meet up and align IT with their strategic plans for next year, so my team is aligned to achieve that with them,” he says.

Field is disciplined in his outreach, as he keeps track of meetings and his colleagues’ preferred communication modes. He has had workers on his own IT team also work on building relationships with their business counterparts to ensure those team members understood the business and how IT could provide “exponential value” to it.

And while Field meets with leaders in the business all year, he uses the fourth-quarter meetings to reflect on IT’s performance and solicit feedback on how it could do better and be more impactful. “It’s an opportunity to hear where they need me or my department to lean in on more, so we can deliver what they need,” he adds. “Inevitably there’s something I wasn’t thinking about.”

4. Advance the AI agenda

Many CIOs are looking for AI wins to close out the year, says Brucker of his client work for Protiviti. Today that means not just deploying AI capabilities but “rolling out AI to gain value.”

“They’re looking to go from the experimentation phase to the business value realization phase,” he says, noting that how they plan to do that varies from one organization to the next.

Kenco Group’s Narayanan hopes to accelerate his company’s AI strategy and plans to use the rest of this year to position his IT department to deliver on that. He’s currently identifying what is working and what is not so that IT can focus on what’s bringing success and shed what isn’t. To deliver on his goal before year-end, Narayanan is also establishing partnerships with new vendors — both nimble startups and mature, established providers — to bring a combination of agility, innovation, and experience. He’s implementing AI training and education to build competency and excitement, and he’s putting in financial metrics that can be used to measure the ROI on new AI initiatives. He believes doing this work now will allow for faster selection and adoption of AI in 2027 as well as faster achievement of benefits.

Meanwhile, Zach LaValley, CTO of RapidSOS, an intelligent safety platform and public safety AI company, is creating what he calls an “internal intelligence plane that everybody — humans and AI — can tap into.”

LaValley, who oversees internal IT at the company, envisions this replacing the data silos that limit AI use in organizations, with the plane having the needed security protections, access controls, and integration points that would allow AI applications to quickly and easily connect with it. LaValley says by year’s end he wants to connect all the data sources to this new plane. For additional 2026 wins, he wants to port over all the AI apps built by employees and start making those discoverable so that employees won’t have to reinvent apps that already exist.

Nina Tatsiy, CIO of Quadient, a global automation platform, wants to spend the next two months convincing everyone in the company “to embrace agentic AI.” She’ll consider it a win if she can accomplish that.

Along with that, Tatsiy wants to technically ready the organization for agentic AI transformation. “My biggest focus is how to set up an operating model for agents, governance for agents, and building a strategy around what that means for the rest of the technology stack,” she says.

5. Rein in AI costs

Like many IT execs, Sebastian Arriada, CIO of Globant, a global technology and IT services company, is seeing the cost of using AI spike as the number of “tokens we consume at the company grow almost exponentially.”

So, with an eye on reducing those bills, Arriada’s infrastructure team implemented Nvidia boxes so that Globant can deploy open-source models on them. Arriada explains that the move will let Globant balance the use of tokens between commercial and (less costly) open-source AI models.

There’s more work to be done before the boxes are operational, Arriada says. His goal is to have them up and running as well as expand the use of the open-source models by 5X this year. He expects to hit that goal by the end of November, noting that he wants to deliver another 5X in increased capacity in 2027.

Not all CIOs are ready to make the leap to hosting their own open weight models on expensive Nvidia hardware, but eyes on AI costs is common theme. Finishing out the year with a battle-tested plan for reining in AI spend is a worthy win regardless of how it’s executed.

6. Devise a 2027 budget for maximum impact and future-proof potential

Research firm Gartner projects that IT budgets will grow by an average of 3.7% in 2027 but cautions that, accounting for inflation, that increase will be marginal for most CIOs.

Actual budget figures for next year vary (and are not publicly disclosed), but CIOs of every stripe say one of the last wins they want for 2026 is to lock in an IT budget for 2027 that can maximize impact while keeping the price tag down.

It’s a task that “comes with a lot of pressure,” Adler Advisors’ Field admits. He expects to draft multiple iterations of his 2027 budget this fall, working with his IT team and consulting with business counterparts to come up with figures capable of supporting all of IT’s necessary run, grow, and transform costs — without breaking the bank. He’ll consider it a win when the budget is not just done but accepted by the board.

Quadient’s Tatsiy has a similar objective as she looks ahead for IT, saying she’s working “to transform all the functions I’m managing, not just for cost savings but to future-proof them.”

She says she’s looking at what she can rationalize and where she can deploy agentic AI to “change my function to be less focused on run and more focused on investing in the future.” Like Field, she says devising a 2027 budget that moves her further along that path would be a laudable way to close out the current year.

Many organizations have licensed language models, tested AI agents, and asked DevOps teams to develop code-generation standards. But production deployments are still a work in progress for CIOs, with only 50% of organizations moving more than half of their AI projects from pilot to production, according to Outsystems’ State of AI Development 2026 report.

Increasing production deployments is a key step for many CIOs, followed by fostering more widespread employee adoption. But usage doesn’t always translate to business outcomes, leaving many IT organizations struggling to deliver business value on their AI initiatives.

Much of that has to do with how they’re measuring AI value.

“Too many organizations are treating AI adoption as the metric when the real question is whether it’s improving business outcomes,” says Rob Scudiere, CTO at Verint. “The metrics that matter are the ones executives already track, and if those numbers aren’t improving, it’s hard to argue that AI is delivering meaningful value.”

Measuring how AI impacts the business strategy and its relevant KPIs is a vital first step, especially when it comes to identifying AI’s business-specific opportunities and risks. After all, AI’s capabilities, practices, and security considerations are changing so quickly that it’s essential for C-level leaders to discuss AI’s strategic business value frequently — and to do so with the right data to back those discussions up.  

That, in turn, requires a more collaborative leadership approach.

“Organizations should align with business stakeholders upfront on the ‘so what,’” says Keyur Ajmera, CIO at Boomi. “CIOs should partner with CMOs, CHROs, CFOs, and business practitioners to identify use cases, establish appropriate governance and standardization, prepare employees for new ways of working, and create financial guardrails as usage-based AI costs become more prevalent.”

I heard from more than 30 leaders on their picks for the business value metrics that matter most when tracking your AI strategy’s impact. Below are my top five worth adopting.

Growth driven by organizational expertise

For AI to be transformational and not just reshaping business, executives must show that building greater expertise through AI drives business growth.

“Executives primarily talk about AI ROI in terms of cost savings and efficiency, and that doesn’t adequately measure long-term AI success, because it doesn’t capture whether the organization is actually getting smarter,” says Sarah Edwards, chief product and strategy officer at Kantata.

“I’d point them to what I think of as an organization’s expertise compounding rate, which is its ability to capture, synthesize, scale, and continuously build on institutional and tribal knowledge,” she adds. “Firms also need to look beyond productivity and efficiency and consider how AI drives revenue growth, including new pipelines, better client retention, new pricing models, and the ability to take on more projects without adding headcount.”

Ways to measure: Track how expertise developed via AI capabilities drives revenue. Examples include:

• DevOps teams, newly trained on vibe coding tools, help increase revenue by adding AI capabilities such as shopping concierge agents and genAI search capabilities to customer-facing websites.
• Benchmarking return on ad spend (ROAS) between two campaigns, one developed by a team using AI tools, the other without using AI.
• Contact centers using AI to support service-to-sales or sales-through-service competencies can measure increases in revenue per agent hour or offer conversion rates. In one famous case, Ikea built a new €1.3 billion business line, thanks to a contact center chatbot freeing up associates’ time.

The key pattern is that when AI is introduced into a sales, marketing, or support function, it can be correlated with revenue increases.

Improved customer retention

Improving customer retention is a key metric for products or services, especially those with subscription business models. Customer loyalty and the ability to influence repeat purchases are similar KPIs when the brand influences pricing and customer-perceived value.

How CIOs implement data and AI governance can positively or negatively affect customer retention. According to the State of Digital Trust 2026 from Usercentrics, consumers will now pay 7% more for the brands that have earned their trust around AI. Meanwhile, 24% of consumers canceled a subscription or stopped buying from a brand because they were concerned about how their data was used in AI.

Donna Dror, CEO at Usercentrics, says, “Most CIOs can measure what AI saves them, but the harder number, and the one that shows up in revenue, is what mishandling data in AI costs in lost customers.”

Ways to measure: Measure consent withdrawals, Data Subject Access Requests (DSARs), or AI feature opt-out rates. Businesses with subscription products can track renewal rates in the 90 days following an AI feature launch and add a data-and-AI-use reason code to the cancellation flows.

Employee capabilities beyond productivity

I’ve warned CIOs that focusing on AI’s productivity improvements is a trap, because at some point the CFO will look for cost savings through headcount reductions. Other metrics beyond productivity may matter more, especially for organizations investing in agentic AI workflows.

The first is time-to-decision: AI agents are either capable of taking action autonomously, or they are implemented to include human collaboration in decision-making. CIOs and the business owners of AI agents should determine how to measure the business impact of faster, smarter decision-making.

“Most AI scorecards measure activity, including agents deployed, tasks automated, and prompts processed,” says Vikram Bhandari, chief technology and innovation officer at Riveron. “The metric I push clients toward is time to decision, such as how much faster finance or ops teams can act once AI is in the workflow, and what that speed translates to in revenue or risk avoided. Headcount metrics tell you AI has replaced work, while decision-speed metrics tell you AI changed the business.”

Enterprises can scale beyond individual productivity improvements by re-engineering business processes using AI orchestration platforms. But CIOs have to get AI democratization right by influencing experts to build the organization’s knowledge base and the AI context layer utilized by AI agents.

“Data democratization’s real number is the percentage of institutional knowledge anyone can self-serve, without tracking down one specific person,” says Chris Cope, VP of engineering at CADDi. “Manufacturers map every flow on the factory floor and fixate on single-source supplier risk, yet miss the same risk when it’s a person holding the knowledge.”

Ways to measure: In risk management functions, measure time-to-decision as the difference between mean time to remediate (MTTR) and mean time to detect (MTTD). Measure knowledge management impacts by tracking increased utilization of internal LLMs, while capturing the reduced time spent on answering requests for information (RFIs) by subject matter experts.

Impacts to customer and employee experience

CIOs should hunt for examples of value delivered to customers and for employees that go beyond doing today’s job better. This means exploring areas where AI provides a business capability that is not feasible, or is economically unscalable, if people were performing the function without AI capabilities.

Venkat Ramakrishnan, president and COO at NeuBird, shares this example in IT and security incident management: “The focus has shifted from how fast you resolved an incident to how many users never felt it in the first place. When AI prevents an incident 30 minutes before failure, or resolves it in under 5 minutes instead of 175, it eliminates the experience impact rather than only reducing it,” Ramakrishnan says.

Other examples require exploring game-changing operational transformations in other departments, or finding areas where AI impacts the connected experiences of customers and employees (CX and EX).

“CIOs should measure genAI by whether it changes the economics of the business such as decision velocity, capacity created per employee, exceptions resolved without escalation, revenue leakage recovered, customer and employee effort avoided, and ultimately the rate at which AI benefits reach the P&L,” says Arun Raghavapudi, chief customer officer at R Systems (RSI). “The strongest AI strategy is therefore one where CX, EX, engineering, and business KPIs form a single value chain rather than separate technology scorecards.”

Ways to measure: CIOs should identify AI-native capabilities, those business and operational competencies that are net-new AI use cases. Once identified, business value metrics determine the value delivered, and the AI-native capabilities become marketable innovations to communicate across the company.

Measure AI’s operational impacts

Many CIOs used lift-and-shift cloud migration strategies or overly empowered DevOps teams to procure cloud infrastructure, only to find skyrocketing costs. CIOs applied FinOps best practices and shifted-left financial considerations to reduce costs and instill fiscal discipline.

Even as token costs decline, AI cost debt is very real as consumption of AI models and use of AI agents increases. Tokenomics tracks and manages both costs and the business value delivered from spending on AI tokens. On the cost side, CIOs should factor in the human-effort costs, including the AI hallucination tax when employees have to recognize when AI models and agents are wrong.

“Token spend is becoming a real line item, and if it grows faster than the outcomes it produces, that’s a unit economics problem, not a scaling milestone to be proud of,” says Yasmin Rajabi, COO of CloudBolt. “The metrics that matter include how often AI-generated work is accepted without rework, how many decisions or actions run without a human in the loop, and value per token you get for every token you spend.”

Measuring the value of operational impacts needs to consider both tactical impacts based on individual contributions versus team, department, and business-level outcomes.

“Indicators that AI is creating measurable value rather than simply generating activity include whether critical workflows are completed quicker, decisions are more consistent, and AI-generated recommendations are traceable back to authoritative information,” says Tony Grout, chief product and technology officer at M-Files. “The most successful AI programs will be judged by whether they deliver better business outcomes like faster project completion, higher-quality customer service, improved compliance, and more consistent decision-making.”

Ways to measure: My research on AI agent orchestration platforms identifies several platforms that include tokenomics capabilities, including solutions from Boomi, Cisco, Databricks, Kamiwaze, LangChain, Microsoft, Nutanix, PagerDuty, Salesforce, Snowflake, Tray.ai, and Workato.

Setting realistic expectations

CIOs must lead a progression from AI experiments to production deployments, enterprise adoption, and delivered value. But the organization’s velocity to adopt and deliver value requires a strong change management program that adjusts to business demand, especially if the AI bubble bursts.

“The reality is that enterprise AI follows a productivity J-curve, where companies invest in new skills, operating models, governance, and process redesign long before the full economic benefits become visible,” says Kris Lovejoy, global head of strategy at Kyndryl. “As a result, usage often rises months before measurable business outcomes appear, and this is one reason many executives feel uncertain about the return they’re getting from AI.”

While AI agent deployment and employee adoption are not the end game, CIOs must excel at these before overpromising business value. Two places to start include upskilling IT for agentic AI while avoiding mistakes when deploying AI agents.

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

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.”

Even when CIOs don’t have visibility into AI agent actions or don’t choose the vendor, chances are they’ll still take the blame when anything goes sideways.

IT leaders already see this happening inside their organizations, with 52% saying CIOs are blamed for agent mistakes, according to a recent survey commissioned by business communications provider 8×8. Just 6% of respondents say legal or compliance leaders shoulder responsibility when an agent goes rogue.

CIOs are now on the hook for whatever mad dash their organization has undertaken to deploy AI tools in recent years, says Morgan Watts, vice president of IT and business systems at 8×8.

“Going back a year and a half, there was the rush to adopt, and there is now some element of the penalty for that,” he says. “The rush came without the governance frameworks, as well as some of the visibility controls that should be there to help ensure the actual outcome.”

In some cases, AI tools were approved by the board, other executives, or other business units or were adopted as shadow IT by employees, Watts notes. In other cases, vendors shipped agents into platforms running mission-critical applications, resulting in a governance vacuum, according to the 8×8 report.

“The CIO is now the last line of defense for AI, and in a lot of organizations, that’s a job they didn’t apply for,” Samuel Wilson, CEO at 8×8, said in a statement. “The CIO didn’t choose the vendor, and they don’t always have the audit trail. But when an AI agent gets it wrong, their name is the one in the incident report.”

That model — blaming the CIO — isn’t sustainable, and it doesn’t lead to an AI governance model that holds up under scrutiny, Wilson adds.

With CIOs left holding the bag, they need to become AI governance evangelists, Watts says. “That’s not something you can pass off,” he adds.

Watts hasn’t yet seen evidence of IT leaders losing their jobs because of AI mistakes. IT analyst firm IDC has predicted, however, that up to 200 large companies will face either lawsuits, regulatory fines, or CIO firings over the next four years because of inadequate controls and governance of AI agents.

Meanwhile, 71% of IT leaders said in February that they believed they had until midyear to prove AI value or face budget or job fallout, according to a survey published by AI platform provider Dataiku. The impact on budgets or IT leader jobs doesn’t appear to have happened yet, however.

Still, the frenzy for AI, agents in particular, and the pressure of accountability continues to mount for IT leaders.

CIOs want to fix governance

CIOs are working to fix existing governance gaps but they shouldn’t go it alone.

“There’s a pragmatic approach to how this can be an organizational responsibility,” Watts says, recommending that, while CIOs should lead the charge, organizations must spread the responsibility for better controls and for AI training and adoption.

Boris Kolev, global head of technology at youth-focused nonprofit JA Worldwide, agrees that AI governance should be a top priority for IT leaders, especially given that a malfunctioning agent can quickly become a huge problem when it changes a database record, sends a message, or triggers a transaction.

But Kolev believes CIOs should not be held responsible for agent mistakes if they don’t control the technology.

“It is a serious governance problem whenever responsibility exceeds authority,” he explains. “If a business team chooses an agent or an AI platform, a vendor controls its behavior, and the CIO is expected to answer for every failure, the organization has created an accountability gap before the agent takes its first action.”

CIOs have a responsibility to challenge existing accountability gaps, he adds. “If we cannot inspect an agent’s activity or stop it, we should make that limitation explicit before deployment and require an executive decision about the risk,” he says.

Gil Elbaz, cofounder and chief AI officer at Onyx Security, agrees that CIOs have a responsibility to fix governance problems.

“CIOs shouldn’t be blamed for every agent mistake, but they are accountable for putting effective governance and automated controls in place, alongside CISOs from a security perspective,” he says. “Accountability rests with the people and organizations responsible for governing agents — not with the agents themselves.”

Moving at machine speed

Agents create a governance challenge because they operate at a speed that human oversight cannot keep up with, Elbaz adds. IT leaders need to deploy automated governance tools that allow them to know what agents are operating, what they can access, and whether their actions align with user intent and organizational requirements, he says.

“Unlike human employees, AI agents cannot take responsibility for their mistakes or face meaningful consequences — even when their actions cause serious organizational harm,” he adds. “This creates a new accountability gap that human oversight alone cannot address at machine speed and scale.”

Even as a seasoned IT leader, Richard Mackey’s jaw drops in amazement over what AI and AI agents can help him do — especially when it comes to the tasks he dislikes the most. “It’s like my long-lost friend,” says Mackey, CTO of healthcare company CCS.

For example, while Mackey has become adept at creating PowerPoint presentations, he appreciates how fast an agent can make one for him. Recently, Mackey delivered a presentation to CCS’s CEO and others that an agent put together, which he admits was pretty good.

“This would have taken me four hours … and the basic part of it was done in 15 minutes, which is crazy.” Having an agent do this shifts the work for humans, he says. “I like this approach because it creates the starting point.” Mackey adds that he used a watermark to attribute the presentation to AI and modified about 20% of it, but “those are the kinds of things that I find all the time are helping me go faster.”

It’s that kind of ringing endorsement that is helping fuel the rise of AI agents, prompting IT leaders to fundamentally rethink how IT operates as they move beyond chatbots and copilots to autonomous systems that make decisions, execute workflows, and interact with multiple enterprise systems. That reset includes how they should organize IT teams, leverage data, re-engineer their technical stacks, and interact with business partners to make the most of agentic AI.

The big picture

Three-quarters of leaders at enterprise organizations are adopting agentic AI, but only a small minority have implemented meaningful production applications, according to Forrester’s State of Agentic AI 2026 report. Even companies at the leading edge have not yet realized the expected value promised by agentic, the report states.

“In 2026, we see agentic technology rapidly changing software markets, even as enterprises slog through the morass of challenges between promise and payoff,” the report notes. “The core question is, will the engine of agentic pull the enterprise payload?”

While that remains to be seen, agentic AI requires organizations to move beyond individual models toward governing the whole system in which models operate, observes Rashmi Shetty, vice president of enterprise AI at Capital One. “That means governing the data and context moving across agents, their identities and permissions, the tools they can access, the actions they are permitted to take, and the points where a human needs to step in.”

Amid the exuberance surrounding agentic in the enterprise, continuous evaluation and observability remain important, Shetty stresses. “Teams need to understand how agents are behaving in production, which tools they are invoking, whether agentic systems are achieving their intended goals, how the system performs end to end, and where latency or failure occurs.”

The organizations best positioned to scale agentic AI “will build these capabilities into enterprise platforms from the outset, creating a governed path from rapid experimentation to safe, reliable production deployment,” Shetty says.

Reimagining how work is done

Understanding the difference between AI and agents is critical, says Meerah Rajavel, CIO of Palo Alto Networks, noting that AIs involved in automation are sometimes misconstrued as agents.

As opposed to generic AI automation, agents perform specific work and must be capable of reasoning, continuously learning, and updating its memory, Rajavel says. Agentic workflows take that concept a step further, clustering agents to work together to achieve a task.

“An agent is an atomic task level, and an agentic workflow is achieving a business outcome in a complex world,” she explains.

“Getting to a true agent does take a complete reimagination of how you do the work,” she adds.

At Palo Alto, all employees have access to Panda AI, an internal agent built with AI workflows more than a year ago to handle IT, travel, expenses, and HR issues. Panda AI has automated 82% of tickets and reduced IT operational costs by almost 70%, Rajavel says.

Palo Alto responds to more than 900 RFPs annually, and IT has built another AI agent that automates the first draft of complex RFPs, which is then validated and refined by a team of consultants.

“It used to take six to eight weeks to get a good RFP done,” Rajavel says. “Today, it’s a matter of hours.” The company is in the early stages of building more agents, but it’s a process, she says.

“Before you build an agent or agentic workflow, you really have to go and spend a lot of time looking at the data” and determine what data is and is not available, Rajavel says. “You need to really do a deep rip out of your current state, and then you have to completely forget [how work is done] today and go to a clean sheet and say, ‘What is our first principle thinking in an agentic world? Should we then bridge it?’ That’s where the trick comes.”

Always test your infrastructure

Mike Tria, CTO of online payroll provider Gusto, says his company is moving toward agentic capabilities that will be autonomous and proactive. To get there requires investing in an automated testing infrastructure, he says.

When building agentic capabilities, the goal is to ensure that the systems are doing what they’re designed to do, Tria says. It’s no longer just about continuous integration — the second layer is adding the ability for the agent to conduct evaluations.

“Having a really good test infrastructure will tell you when you have outdated data,” Tria explains. “It’s expensive for companies to do vast exploration. Agents can be pretty smart about knowing when something is outdated. Lean on that first.”

Companies are investing in knowledge layers and ontology systems, and IT should build something that sits above the data that helps describe the business and how its systems work, he says. “Give your agent access to that. You should fix the data, but you want to make sure you fix the right stuff.”

Changing the nature of work

CCS has just over 1,000 employees and serves hundreds of thousands of clients. Mackey says that when IT began looking at how agentic could help its business, there were many potential areas, including corporate, sales and marketing, and the contact center. Like many organizations, CCS opted to begin with the latter because officials saw a good opportunity to increase call resolution over time.

So the IT team introduced an agent called CeeCee at the start of 2026, and now over 30% of calls are being resolved autonomously. What officials didn’t anticipate was that as agents handled more calls, the ones that reached humans became more complex. It wasn’t that the agents were failing, but that the human conversations required more judgment and problem-solving, changing the nature of the work.

 “What we like to say is we’re not in the business of trying to replace humans with machines,” Mackey notes. “We’re in the business of augmenting our human capabilities with these tools.”

The goal is to help patients and teams automate where it makes sense, he says. At the same time, Mackey adds, they are not trying to replace clinical judgment.

“We’re not trying to have machines make decisions around or judgments or evaluations of any kind of a clinical nature, but we are trying to automate tasks that may be more mundane and more mechanical,” he says. Employees are now addressing “more of the exceptions” that come into the contact center, and those exceptions take longer sometimes to resolve, he says.

When CeeCee was first rolled out, IT didn’t anticipate that there was, and will likely always be, a significant number of people who want to talk to a human no matter what, Mackey notes.

However, “the next wave of investment” will be to build features that enable the agents to offer up what they can help with when a caller asks to speak to a human. The agent will then ask whether they want to continue interacting with it — with the understanding that someone can still talk to a human if they want, Mackey says.

Apply guardrails

Another key concern when moving forward with agentic AI is the need to apply guardrails so that IT has accountability for how agents operate in production. That’s critical for CCS as a healthcare entity. Even though agents are only performing tasks that speed up administrative work or share information, Mackey knows that will change.

“We see that as a technology team, a year or two from now we’ll be using more agentic AI in how we develop software and how we help answer service desk calls — even how we run projects with our business partners,” he says. “But we’ve made certain decisions that we should have a human in the loop [and] that we should not be automating clinical decisions.”

Palo Alto Networks is implementing security and privacy guardrails, but Rajavel says there are “business boundaries type of guardrails” as well.

“We definitely hold the business to say what are the guardrails that they need,” she says, which involves enlisting stakeholders to decide what to allow an agent to do and not do.

“For example, there are certain approval agents that we have, and the entire approval framework of what is allowed and what’s not allowed is a pure business decision,” she explains. “And then, when it really comes down to making sure the agent cannot operate out of its boundary, … how do you stop the agent? That’s actually what I focus on.”

Gusto’s Tria says compliance regulations are useful guidelines in the agentic world. To avoid having an agent go rogue, IT puts it in a sandbox “where quite literally, they can’t break out,” he says, adding, “that doesn’t mean you don’t have to audit them. We still have our security team doing regular audits.”

“In a lot of ways, you want to treat these agents like you treat an employee — trust and verify,” he says. That means giving employees the tools to do their jobs and periodically conduct performance reviews on the agents in the same way humans are reviewed.

There are also trust issues enterprises need to consider. A new report from SAS and IDC finds that nearly 89% of respondents say AI agents already play a role in decision-making, yet trust in agentic AI is 10 points lower than trust in generative AI.

The changing face of IT

Mackey believes the structure of IT will likely change in the next year two. Gone are the days when IT would run a job at night, and if part of that job failed, an email would be sent informing employees to hold off on accessing certain information until IT gave the green light.

“If you’ve got an agentic system that’s accessing data, it doesn’t know to read the email unless you tell it,” he says. “So, a lot of our systems now need to be reengineered to elevate the importance of the data integrity, and we’ll have to have new systems.”

Moreover, in the future IT will also need to devise “new ways of managing these agents, because the speed at which they work requires that the other systems be integrated so that they are aware of that,” Mackey says. “That is a realization we’ve had in the last six months.”

Developer skills will also change of time as their workflows become more agentic. “It’s less important that our developers are writing actual lines of code and more important that they understand the context and the inputs and the outputs that are being required in the change,” he says.

Gusto CIO and CISO Mike Wittig says the company has already created AI agent manager roles where people are responsible for the performance, roadmap, and metrics of AI agents.

“This is a hybrid of product and engineering roles where the agent manager needs to understand their stakeholders and meet regularly with them to review health metrics and roadmap items for the agent, as well as contribute to the backlog by building new agent functionality,” he says. “This also ensures human accountability for agentic activity, which is a core tenet of our strategy as we continue to scale with AI.”

Palo Alto’s Rajavel also sees roles, such as in software development, evolving over time. While engineers still need to make modifications to AI-generated code, she says, “[AI] does a significant lift. So I have to reimagine the role of some of our software engineers.” Now they spend more time on design thinking and building security into the code, as well as scalability and performance.

Also, pursuing agentic AI is changing how IT and the business collaborate, she adds.

“Our [product managers] are not anymore just sitting with the business talking about the requirements. They are actually sitting with the business building out a prototype version before even getting out of the conversation,” Rajavel says. “That means you need the product manager to be more tech savvy and more comfortable with using AI tools to go from a business problem to a conceptual solution.”

Similarly, she adds, software engineers should be more comfortable “cutting across rather than [making] a narrow slither to more depth and breadth.”

Deciding where agentic is a logical fit

When deciding where agentic makes sense, Rajavel looks at whether a workload requires reasoning, some level of understanding, and continuous learning, she says.

Her roadmap for the next year is to determine which AI technologies have the highest ROI for disruption. Often, it takes 12 to 18 months to gain the full benefits, she notes. That was the case with Panda AI — it took about 12 months to go from 12% automation to 16% automation, and today, it stands at 83%.

Further, IT is constantly revising what its business priorities are, which makes targeting the right workflows for an agentic overhaul all the more important.

“Make sure your use cases are the right fit for it,” she advises, adding that here data volume and integrity is important. Often, people hold onto a lot of “tribal knowledge,” she says, and you have to figure out how to give the agents that knowledge, “otherwise your agents are not going to be effective.”

She also advises not underestimating the importance of “keeping the agents continuously on track.” That requires “having clarity of ownership and making sure … you have a person who’s accountable to make sure this is actually behaving as intended and having the right guardrails and right evaluations,” she adds.

While all the IT leaders say that AI and agentic are helping their organizations move faster and be more efficient, Gusto’s Tria says that creates a perspective that security is being sacrificed.

“Moving faster does not mean you are dropping rigor. Moving faster does not mean that you are less reliable or less secure,” he says. “We have a lot of confidence internally that the very same agentic capabilities that make us go faster also increase the reliability of our systems. … It’s actually a win-win.”

Vibe coding is rapidly changing who can build software and how quickly an idea can become a functioning application. Using natural-language prompts and AI-assisted development tools, people can translate concepts into prototypes without mastering every element of traditional software engineering.

The experience reminds me of Topgolf.

Topgolf creates a carefully curated, technology-enabled environment where almost everyone can feel capable. The tee is automated. Performance is tracked. Wind, water, sand, and other hazards are largely virtual. You can experiment, compete, and receive immediate feedback — all from the comfort of a lounge-like setting.

Vibe coding can create a similar sense of confidence. You describe what you want, AI helps build it, and a working application begins to emerge. The demonstration succeeds, colleagues are impressed, and momentum builds.

Then comes the inevitable question: “How quickly can we put this into production?”

That is when the game moves from Topgolf to Torrey Pines.

On a championship course, the controlled environment disappears. You must account for shifting winds off the Pacific, hidden bunkers, changing pin placements, difficult terrain, spectators, and countless other variables. Success requires more than the ability to strike the ball. It requires course knowledge, preparation, situational awareness, discipline, and the ability to adjust when conditions change.

Production technology environments are no different.

An application that performs well in a controlled setting must now contend with scale, cybersecurity, data quality, privacy, integration, resilience, accessibility, regulatory requirements, technical debt, and unpredictable user behavior. The number of variables expands exponentially — and every variable can affect performance, trust, cost, and organizational reputation.

Across my career as a CIO and CTO, I have seen promising technology initiatives encounter difficulty not because the underlying idea lacked value, but because the organization underestimated what it would take to operate that capability reliably at scale. AI accelerates development, but it does not eliminate operational complexity.

That distinction matters because vibe coding is not simply another developer productivity tool. It changes who can participate in creation. A business leader who previously described a need and waited for a development queue can now sit beside an AI assistant and begin shaping the solution directly. That is a meaningful and welcome shift. It creates faster learning cycles, brings domain expertise closer to the product, and gives the business a more active role in technology delivery.

But democratizing development also democratizes responsibility. The person who can create a compelling application in an afternoon may not yet have the experience to recognize an insecure dependency, an ungoverned data source, a fragile integration, or a design that becomes prohibitively expensive at scale. A successful demonstration answers whether something can work. Production requires answering whether it should operate, how it will operate, who will own it, and what happens when it fails.

This is where experienced technology professionals remain essential.

A skilled enterprise architect is like having Tiger Woods help you read the course: someone with a mental model of the topology, dependencies, and conditions most likely to affect the outcome. The architect can see the dogleg beyond the tee box — the downstream consequences that are invisible during a prototype.

The course ranger resembles the change manager, helping teams navigate competing priorities, shared resources, release schedules, and operational conflicts. Cybersecurity professionals manage the equivalent of those pesky paparazzi trying to get inside the ropes, while also preparing for threats far more consequential than a distracting camera. Data teams act as the performance analysts, measuring behavior, outcomes, crowd reaction, and whether the solution is actually producing value. Platform and operations teams ensure the course remains playable when demand surges or something unexpected happens.

These professionals are not standing in the way of innovation. They are helping the organization play the real course successfully.

Before promoting a vibe-coded application into production, leaders should review a simple production-readiness scorecard:

This review does not need to become a six-month obstacle course. Governance should be proportionate to the application’s risk, reach, and potential impact. A low-risk internal productivity tool should not face the same controls as an application making consequential decisions about customers, employees, or vulnerable populations.

The answer is not to force every vibe-coded experiment through the heaviest enterprise process. That would squander much of the speed and creativity AI makes possible. Instead, organizations need risk-based pathways: a safe practice range for experimentation, a clearly marked route for internal tools, and a more rigorous qualification process for applications that touch sensitive data, critical operations, external users, or consequential decisions.

Technology leaders can make those pathways easier to navigate by providing approved AI tools, reusable components, secure development environments, automated testing, standard integration patterns, and transparent criteria for production readiness. In other words, build guardrails and paved roads rather than relying exclusively on gates. When the safest path is also the easiest path, governance becomes an accelerator.

The goal is disciplined acceleration.

The business also has to accept a different role. Vibe coding should not mean throwing an AI-generated application over the wall to technology once the demonstration is complete. If business teams help create these capabilities, they must remain hands-on collaborators and accountable owners. They understand the domain, intended outcomes, acceptable errors, and human consequences better than anyone. Technology brings the architecture, engineering, security, data, and operational disciplines required to make that capability sustainable. Production readiness is a shared responsibility.

This partnership represents an entirely new organizational muscle. Data and AI literacy provide the foundation, but literacy alone is not transformation. Leaders must learn how to evaluate AI-generated work, challenge confident outputs, understand where automation requires human judgment, and make informed tradeoffs among speed, risk, cost, and value. Technology teams, in turn, must learn to engage earlier and more collaboratively so they are not perceived as impediments arriving at the end of the process.

I am a cautious optimist about AI. Vibe coding can democratize development, accelerate experimentation, and bring business and technology teams closer together. It can also help reposition business professionals from passive recipients of technology to active participants in creating it.

But confidence developed in a controlled environment should not be confused with production readiness. The easier development becomes, the more important it is to strengthen organizational literacy about architecture, data, cybersecurity, scale, and responsible ownership.

The central leadership question is therefore not whether organizations should permit vibe coding. That debate will soon be overtaken by adoption. The better question is how organizations will preserve its creative energy while establishing the discipline necessary to earn trust at scale. Leaders who answer that question well will move faster because their teams will know where experimentation is encouraged, when additional expertise is needed, and what evidence is required before a capability reaches customers, employees, or mission-critical operations.

Perhaps we will eventually reach a world in which AI can immediately transform a vibe-coded idea into a secure, scalable, resilient production capability — making 18 holes at Torrey Pines feel as approachable as an evening at Topgolf.

We are not there yet.

Until we are, organizations must pair AI-enabled speed with sound system hygiene, architectural discipline, testing, governance, and risk management. The objective is not to slow innovation. It is to ensure that what we accelerate can survive — and succeeding on — the real course.

Together, we will get there responsibly.

IT leaders have been making generative AI tools available across the enterprise for just three years, and a significant majority of their business users has already had enough.

According to a report from Adaptavist, 65% of 2,500 knowledge workers surveyed say they “regularly feel nostalgic about how work operated before the widespread adoption of AI.”

This “pre-AI nostalgia” appears to be due in part to business users feeling overwhelmed by the responsibility of learning how to use AI on top of their day-to-day job tasks. Moreover, 46% of workers say their concerns about AI have gone unaddressed by management.

“Transparency is critical to truly drive AI engagement; organizations must establish clear guardrails and maintain an open dialogue around AI use and employee choice where workers feel they are being listened to,” Jobin Kuruvilla, field CTO at Adaptavist, tells CIO.

Generational gaps in AI acceptance

Despite an assumption that younger workers are more intuitively adept with AI tools, Gen Z workers (42%) are more likely to prefer the pre-AI world compared to their Gen X colleagues (26%). This may support the growing concern that AI is quickly is hitting entry-level workers the hardest, while creating new career opportunities for more skilled workers who have been in the industry longer.

When asked about fears surrounding job obsolescence due to AI, 54% of all workers surveyed said they are “concerned AI could reduce the need for their role within the next five years.” Broken out by organizational level, junior employees (23%) and C-level executives (29%) expressed the most concern about AI job loss, compared to 13% for mid-level employees and 12% for senior employees.

Additionally, 47% of C-level executives and 36% of directors are looking to move industries, change careers, or step away entirely due to concerns of AI eliminating their positions. Still, plenty of workers are ready to face the new challenges of an AI-driven workplace, with 74% saying they are actively learning new skills to stay relevant, and 85% of C-level leaders saying the same.

Lack of transparency drives AI fatigue

One in three workers (36%) are already experiencing “AI fatigue,” leading to less frequent use of AI tools and active resistance to AI for day-to-day tasks. More than a third of workers (36%) also appears to be confused about AI use expectations in their role.

When implemented quickly without proper training and transparency, AI initiatives can lead to hidden productivity costs. Of those surveyed, 42% say they “spend more time verifying AI output than they save using it,” while 52% say they regularly spend time correcting AI-generated work from colleagues. Additionally, 49% say low-quality AI outputs slow down projects, 55% say AI-generated content reduces overall team efficiency, and 46% say it makes their work feel “more repetitive and less meaningful.”

Half of all workers also feel their performance is now “directly or indirectly compared to AI-generated output.” Providing clarity about how AI impacts or doesn’t impact an employee’s career is important to staving off AI fatigue.

For those chalking this all up to change resistance, know this: 67% of workers surveyed say they want their organization to increase the use of AI, and 69% say they believe AI is being used ethically within the organization. What they lack is a roadmap, guidance, and training to understand how to best implement AI at work, and to ensure it’s being used effectively.

“Ultimately, by automating the mundane tasks that make work feel repetitive —organizations can refocus their specialists on high-value creativity, transforming AI from a source of fatigue into a powerful engine for meaningful human achievement,” says Anand Unadkat, a senior solutions architect at Atlassian.

IT leaders and their executive colleagues need to focus more on the change management artistry necessary to help get them there.

While many organizations are still experimenting with AI and debating governance models, a small but growing group of market leaders is already operationalizing AI at scale. Marianne Johnson, executive vice president and chief product and technology officer at Cox Automotive, is one executive creating business impact today.

With responsibilities spanning product, technology, data, AI, engineering, and cybersecurity, Johnson is spearheading an integrated operating model that enables Cox Automotive to move, learn, and deliver customer value faster than the competition.

Johnson joined me on a recent Tech Whisperers podcast episode to discuss how she’s rewriting her leadership playbook to orchestrate one of the largest business transformations in the industry. Reinventing how her company thinks, operates, and creates value in the AI era, Johnson offers a blueprint for a new kind of leader, an “AI operating executive.”

Johnson and I spent time after the podcast exploring this leadership model and what it takes to transform the way the business creates value. What follows is that conversation, edited for length and clarity.

Dan Roberts: Why is a leadership model that encompasses all of product and technology becoming so important today?

Marianne Johnson: When those roles are combined, your ability to get out of your own way is unprecedented. I talk to peers who have a different product leader, a different CIO, maybe a chief data officer in security; only a few have the flywheel spinning at full speed because they’re aligned and on the same page. By having it all in one org, we have one common vision we shape and execute together, continually creating an environment where we feel comfortable to challenge things.

When you think about pre-agentic and agile software delivery, product couldn’t say engineering wasn’t delivering, and engineering couldn’t say product wasn’t giving them the right “what” if you were on one team. You had one vision, one outcome, and you could go as fast as you possibly could.

Then agentic comes in, and everybody can be a builder. Now the lines are blurred. An agent becomes a role on the team. The fact that we’ve had a unified team for eight years now gave us a jump-off point four years ago, and an accelerated jump-off point two years ago. When the big disruption in how software gets created happened, we were already aligned as a team. That allowed us to break down those next-level barriers.

We’re still redefining what that looks like: How do you rethink team size and shape? What are the roles on the team? Who requires what skill set? These questions led us to stop looking at traditional roles. We’re looking at what key activities need to happen, asking who does those activities, including an agent as part of the “who.”

It’s also about flexibility. Your ability to take any talent and say, “You don’t have to just work on that tech stack because that’s your domain expertise, or this product line because that’s your domain expertise.” It’s the ability to create context fast by having your data together and then forming new teams to take on this crazy idea and rapidly move it. Then maybe you go back to your home base, work on another one. Needs are rapidly changing, so you have to have that lens to make competitive advantage happen.

What do CEOs need to do to build a more future-ready organization capable of sustaining that competitive advantage?

The CEO or senior leadership team needs to redefine what leadership you need in place so you don’t limit your opportunity. A year or two from now, everybody in the company can be a builder. But you have to have a control plane to do that safely, reliably, and without creating tech debt, especially in a token economy. Because you could have unintended expenses without the return on investment.

What you put together now to accelerate that opportunity — and do it while managing risk — has to be super intentional. I don’t think a lot of leaders have that map yet, or even the first five steps of that map right now. What an organization’s structure looks like and how work gets done in the future is going to fundamentally shift.

Companies that move in that direction intentionally and lift up to see what’s the next shift will have sustained advantage in the future. There will be very clear delineations for those who don’t do that, and it will be significantly disruptive to the viability of their business model.

I don’t know that I would call that leadership role the chief product and technology officer anymore. The right leader role redefines the current disciplines to get to different outcomes in the future. And depending on what your business is and what roles you have today, you need to determine what that is.

What’s your advice to a CEO who wants to develop this kind of leader, or for someone who wants to grow into this role? What are the essential leadership muscles tomorrow’s AI operating executives need develop today?

They need to look for a leader who has multidisciplinary skills and has executed at scale. That matters, because your business needs to scale fast. These capabilities are changing so fast, you have to have somebody that’s dealt with high change.

What’s challenging is that there is no resume that says this person has successfully operationalized AI at the scale necessary today and has a track record to prove it. You have to seek indications of managing high change, AI fluency, and then the attributes of a leader who can help you navigate through that.

I’ve had a lot of consultants come in and say, we can help you, and I’m like, well, let’s talk about that, because there is no playbook. We’re writing the playbook. If you want to come along beside me and give me extra arms and legs and brains to contribute, you can do that. I’m not going to pay you for that, but you can learn and go on that journey with me.

There will be maps down the road, but you can’t wait for that map to be so clear that you’re doing exactly what somebody else will do. Some business models might be okay with that, but depending on your posture and your current business model and the health of your business, you might not be able to wait. So you need to think about the attributes of your leadership team, their technical fluency, their AI fluency. Even if you’re not a pure tech company, you better have more of your leadership team with that aptitude than not.

Every leader needs to ask what they’re doing to equip themselves. Yes, I had all these experiences with software, data, security, IT, transactional systems, multiple industries, healthcare, credit risk, fraud, payments, now automotive. But it really goes back to curiosity, the aptitude to learn and apply. I spent hours and hours of my own personal time in the evenings learning, listening, asking questions, and putting my hands on keyboard. If I was going to lead this transformation, I had to have a point of view that was grounded in signals and some reality.

If you’re a CFO, a chief marketing officer, the tools are available for you to practice and learn. But you have to make the commitment. If you do that, then you’re preparing your organization to follow you. As a CEO, if you have all your leaders doing that, your opportunities are going to be unlimited. It doesn’t require the background. It requires an aptitude to lean in towards technology.

You mentioned maps. The journey’s not always straight and clear. Can you think of any moments where you realized, we have to redraw the map?

I’ll give you two that are applicable to everybody right now. When Mythos came out, that was a whoa moment. And it’s not just Mythos. It’s any model that has the power and intelligence to find vulnerabilities that have never been found before and, the scariest part, chain them together. The next scariest part is that you could have a bad actor take advantage of those.

So, now how you architect and approach security has to change. Many enterprises scanned monthly; that cadence is now obsolete. Who you partner with has to change or be evaluated to make sure they’re on top of these pivots and changes.

Another example is the fact that models are doing exactly what they have the ability to do, but humans aren’t putting the necessary guardrails around that. We’ve recently seen reports of models in controlled testing attempting to act outside their intended boundaries, behaving in ways their designers didn’t intend. To use a house analogy, if you want a child to stay safely in the house, do you leave the doors unlocked? Are the windows open? Do you put a toddler gate at the top of the stairs?

When we’re seeing signals of model behavior, we better have human on the loop, not just human in the loop. On the loop is, when you see behaviors and signals, you better have enough guardrails and frames so that the model is doing only what you’re allowing it to do. Many models are so goal-oriented, they’re moving mountains to get to that goal. If you say, this is a mountain I don’t want you to climb over, and then you’re not giving them the equipment to climb it, it’s not going to climb it. But if you give them the equipment, and you don’t tell them not to go climb the mountain, it’s going to climb that mountain.

The pace at which these types of realizations and signals are moving requires you to be able to call plays, call actions, and try to think ahead, knowing that you’re not going to think of everything ahead. But you need to be nimble, and the more foundational components you put in place, the easier it’s going to be to react, take action, and put yourself into a posture that’s safe and reliable.

As an executive who owns product, engineering, data, AI, cybersecurity, and technology, what are some of the biggest breakthroughs you’ve seen?

I think the big unlock is alignment and vision. All these functions have interdependencies across each other in a more historical way of working, and that allowed us, for example, to go to the cloud in a transformation journey at an unprecedented pace. It allowed us to unlock our data across the whole company, because it wasn’t somebody trying to talk somebody into adopting the data standards and contribute to our data intelligence engine.

All those things combined allowed us to take advantage of this massive change with generative AI four years ago and agentic two years ago. We didn’t know that when we made those decisions, but it allowed outcomes to be achieved more easily without having an organizational alignment challenge.

It’s exciting to think how the work is changing, how the roles are blurring, what’s possible now as my entire organization moves from an AI-enhanced model to an AI-transformed model. Team sizes are changing, roles are changing. Even if you’re not in that agentic development lifecycle, we’re asking, what are the jobs to be done? If you put an agentic lens on it, how does that work change? We’re starting from an agentic mindset first, and we will reimagine that entire function. Our goal is to be able to give choices back to the business: Where is our margin expansion, where are there reinvestment opportunities, how can we go faster?

Companywide transformation is the hardest part. We are actively engaged in that, focused on the biggest use cases across every function. How do you help your partners transform your call center, your customer engagement platform, your sales effectiveness, your marketing effectiveness, all of those things? We’ve been focused on the everyday AI that helps every employee be better at their job, but the biggest use case is bifunctional areas that have the opportunity to be transformed.

Tell us about your AI Credo, which includes ideas like “Code is no longer the bottleneck,” and how you came up with the product creator role.

We all said we never have enough engineers. Well, now you have this unlimited supply with agents being able to create code. That changes the opportunity but also shifts the bottleneck to ideation, discovery, whether you’re working on what matters most.

Now that you can code faster, how many more ideas do you have? How do you have enough people with the critical thinking skills that can do the right discovery and voice of customer and see around the corner and look at the signals for what the white space opportunities are? Any resource we have that may have been more heads-down coding in the past and has the aptitude to be a critical thinker and do the upfront business part, we want to make sure we equip them to do that and that we are going to be self-funding with the actions we’re taking.

It’s about being able to create more creators. We had a lot of debate around the product creator title, and we realized, it’s not just about building; it’s about creating a higher order opportunity.

Regardless of whether you’re building with agents, the bottom line is you better have a good methodology to think about what matters and why, what you’re building, and what problem and opportunity you’re solving. That front end has never been more important, because that’s going to be your gate in the future.

What should we be telling our people as they move into this next chapter? Why should they be optimistic amid so much uncertainty?

If you’re a software engineer and you know the majority of code is going to be created by agents, you have to find your joy in different places and different ways. As a leader, you have to help people through that change curve, encourage them to choose to be part of it. I’ve asked my team to lean in and make a choice to invest in yourself.

My commitment to them is to equip them as fully as I can with the most advanced tools and cutting-edge approaches so they are equipped no matter what changes down the road. I know the shape of my org will change, I know the work is going to change, but I always say, go with me on this journey, because whatever that change is, you will be more ready and more equipped than anybody else. Make that decision and investment choice for yourself, and I’ll be right alongside you, because I care about you as an individual, and we’re working on the same purpose.

Marianne Johnson is proving that leaders courageous enough to create their own AI operating executive playbooks today are setting the stage for organizational advantage in the years to come. For more from Marianne Johnson on how she’s rewriting the leadership playbook for the AI era, tune in to the Tech Whisperers.

See also:

• How AI takes flight at GE Aerospace
• AI success requires a full-stack CIO
• What next-generation IT leadership looks like

CIOs are increasingly expected to drive IT adoption in their organizations, with change management becoming a huge — and more challenging — imperative in the age of AI.

Evangelism of the latest technologies has long been part of the job, but many CIOs now say resistance to AI adoption and the fast-paced evolution of IT tools have raised the stakes.

Change fatigue has become a major challenge as Andrea Ballinger, CIO of Rensselaer Polytechnic Institute, tries to update the IT systems and provide a tech-driven ultra-personalized student experience at the university, she says.

“It’s not even inside of our institutions or our private companies, but the world is throwing so much at us,” she adds. “What you heard today, you’re being told something else tomorrow.”

For CIOs, change management means recognizing that some employees are on a slower journey and, at the same time, encouraging staff to embrace progress, Ballinger says. Good leaders will recognize that some employees will resist, but it’s their responsibility to help employees navigate the changes, she adds.

“Change management is understanding where people are at,” she says. “It’s having that sense of urgency, but a sense of urgency does not mean running without a parachute or without a plan. It means you act today.”

Change management was a big topic of conversation at the CIO 100 Awards and Conference in Frisco, Texas, in mid-August. Several speakers mentioned the challenge, with Ravi Malick, global CIO at cloud-based content sharing service Box, saying change management now represents about 80% of the job, far outpacing pure IT issues.

The change management aspects of a major digital transformation are often what makes or breaks the effort, he says.

AI in particular has forced CIOs to pay more attention to change management because it fundamentally changes the way employees work, he adds. Some past technologies, like the internet and mobile computing, largely started in the consumer space, then leaked over into the enterprise, giving employees time to get comfortable, he notes.

“AI is something that’s reshaping both the consumer space and the enterprise at the same time,” Malick says. “Both the enterprise and individual people are trying to figure out how to get the most value out of it.”

Some revolution, some evolution

As a company, Box is moving forward quickly on some AI initiatives while taking a wait-and-see approach on others, in part to manage the changes required, notes Malick, who sees adoption of AI and other new technologies as a major challenge.

“There are parts of this that are revolutionary, and there are parts that need to be evolutionary,” he explains. “The best way to get somebody pointed in a different direction is to make them realize they haven’t done an 180-degree turn. Get them to realize, ‘I turned on my own, and I actually like the direction that I’m pointed in.’”

To encourage adoption, Box has pitched AI to employees as an enabler and amplifier, not as a technology that will replace their jobs, Malick says.

“We’re asking, What are the things that we can do now that we weren’t able to do before?” he says. “How can we apply your years of the experience and intellectual power toward other areas that we just couldn’t get to before?”

Box isn’t closely tracking how employees are using the time saved through AI tools, he adds. If employees are using the extra time to improve their quality of life, that’s ok, he says.

“Maybe they’re not working on the weekends at the end of the month closing the books,” he says. “Maybe they actually have weekends now and can spend more time with their families.”

Change across the organization

Other CIOs say the change management piece of the job has increased significantly in the past two to three years.

In recent years, CIOs have been pulled into change management roles within other parts of the business as teams identify AI opportunities, says Orla Daly, CIO at skills management company Skillsoft.

“As AI blurs the lines between technology, operations, and people strategy, the CIO role is becoming closer to that of a COO,” she adds. “Workforce strategy is folding in alongside technology strategy, so leading change now sits at the center of the role rather than being one piece of it.”

The rapidly changing technology landscape has also thrust change management to the forefront of the CIO role, she says. “The pace at which decisions need to be made has increased so dramatically that you can’t lead at a distance and expect strategy to translate cleanly into action,” Daly says.

Daly also notes that slow adopters aren’t always active resisters. Skillsoft’s 2026 Workforce Readiness Report found that while 86% of employees use AI tools at work only 24% feel fully equipped to use them effectively, and just 16% receive training before a new tool is introduced.

“That gap suggests an over rotation on tooling without understanding how it changes how work is executed,” she says. “In most cases, it’s uncertainty and a lack of confidence to take the first step, not a lack of interest.”

Daly and other CIOs suggest that mandating the use of a new tool is rarely the right approach.

“Requiring it can create activity, but activity isn’t the same as adoption,” she explains. “If you hand people tools without clear use cases, guardrails, and training, a mandate just accelerates inconsistent use, and you mistake activity for progress.”

NTT DATA focuses on employee AI fluency instead of mandated activity, and the CIO has a huge role to play, says Barry Shurkey, CIO at the company. The CIO role increasingly sits at the intersection of technology, business strategy, and people, he says.

“AI success is not just about moving quickly; it is about helping people understand the change, embrace it, and move forward with confidence,” he adds.

NTT DATA’s own research suggests that AI front-runners use AI to amplify the impact of experienced, highly skilled employees rather than to replace them, Shurkey says.

“As AI accelerates transformation, CIOs are doing more than implementing technology,” he adds. “They are redefining how people work, make decisions, create value, and just as importantly, managing the intensified resistance that’s driven by fear of job loss or control.”

Lately most execs have been focused on making sure their AI projects pay off.

With good reason: The rate of failure for AI initiatives has been notoriously high.

But AI projects aren’t the only ones that need attention. In fact, CIOs and their executive colleagues should be putting that kind of focus into all IT projects, given that success on more conventional initiatives — from new software deployments to ERP implementations — is far from perfect.

Statistics vary. Some often-quoted reports about IT project failure rates of 70% date back several years, making them unreliable reflections of the landscape today. But project consultants say a good percentage of IT projects still fail, with estimates ranging from about a third to as much as high as that 70% mark.

In the Project Management Institute’s 2026 Pulse of the Profession report, researchers report that 31% of complex projects fail to achieve the full scope of their originally intended benefits.

CIOs, project leaders, researchers, and IT consultants generally define failure for an IT project as not delivering expected benefits within the expected timeframe. Failure can also mean a project doesn’t produce returns, runs so late as to be obsolete when completed, or doesn’t engage users who then shun it in response.

Why do IT projects continue to fail? Here are 12 common culprits.

1. Lack of project management expertise

Expensive and highly visible projects get the benefit of being led by professional project managers, but small and midsize projects often don’t, says Eric Bloom, executive director of the IT Management and Leadership Institute.

So those small and midsize projects are assigned to someone like a business analyst without any true training, he says. Those workers typically don’t have the expertise or experience necessary to succeed in the project manager role, nor are they given enough time to learn what it takes to manage a project or to complete the extra project management tasks.

CIOs would see higher success rates if more projects have trained project managers, Bloom says. They’re better able to corral and schedule resources, coordinate staff schedules, and get everyone moving in the same direction — and do so across multiple projects. They’re also more capable of implementing the governance needed to keep projects on target to deliver what’s expected and not let scope creep run up costs and schedules without adding additional value.

2. No alignment with business objectives

Some projects still fail because IT teams and business teams aren’t on the same page about the organization wants to achieve. The result is misalignment between the project objectives and business goals, says Shane McDaniel, CIO for the City of Seguin, Texas, and a Project Management Professional.

That’s both avoidable and fixable with communication. CIOs, their project leaders, and even team members need to cultivate strong relationships and engage in ongoing conversations where “they have the ability to raise their hand and say, ‘We have to get our heads together,’” McDaniel says.

“It boils down to communication, awareness, being proactive, and holding people accountable,” he adds. “There is a whole ecosystem around it to make that investment worthwhile.”

3. Ambiguity around measures of success

It’s impossible to succeed if success is undefined, yet executives continue to launch projects without articulating clear, concrete metrics to meet, says George Reed, CIO at auntEDNA.ai and a Project Management Professional.

Project owners must think about their future state, Reed says. “They need to ask, ‘If we were already done, what does winning look like?’”

Then project teams can determine milestones, leading indicators, and metrics to evaluate their progress and their final product. “[Project teams] need to know what needs to be true and what are the tangible benefits they need to deliver. No project should be approved if you don’t have targets for measurable results,” Reed adds.

4. Not enough scrutiny of AI outputs

Project managers and IT teams are using AI to help with scoping, scheduling, and myriad other tasks. The technology helps them move forward fast, but maybe not more accurately or as precisely as if they had done the work themselves.

That can be a problem for project success, says Te Wu, CEO and chief project officer at PMO Advisory.

“If you use AI, you get something quickly and it may look good, but the problem with AI is it can make stuff up,” Wu says.

AI tools may not introduce big errors; it might just have minor mistakes or misalignments, he explains. But if AI creates lots of those that are riddled throughout the project, then they add up and can tank the whole initiative.

“So, you have to have a sharper eye to spot issues; the reviewer has to be super diligent in reviewing it,” Wu warns.

5. Failing to work at the pace of AI

Wu has spotted another problem when project leaders bring AI into the process: It works way faster than the humans on the team.

That’s a benefit in many ways, Wu says. But as AI speeds through tasks, humans still need to run with the outputs. And if there aren’t enough people assigned at that point, work can pile up and projects fall behind schedule or need more staff than anticipated to keep up.

Wu advises project managers to adjust processes to accommodate the speed that AI introduces to avoid bottlenecks.

“This is just the reality, that we humans are too slow to review all the AI,” Wu says. “You can certainly use AI to accelerate IT project delivery, but project managers can’t then treat IT projects in the traditional ways.”

6. Mismatch between assigned resources and planned projects

There’s a long history of projects failing due to a lack of needed resources, as projects suffer delays or quality issues if the right experts aren’t available at the right time to tackle the needed work.

That under-resourcing continues to plague IT projects, says Noah Fletcher, a partner in the operations excellence practice at consultancy West Monroe.

Moreover, AI may be making the problem worse. Yes, project teams can use AI to speed through certain tasks, such as coding, and the project leaders can use AI to reduce the number of people required to handle those tasks. But business and IT execs often overestimate the time and resource savings that AI brings to a project and as a result ask for faster project delivery while assigning fewer resources. In other words, Fletcher says, people are being asked to do more with less — and often too much more with too much less.

Many organizations can indeed reduce the time and people they’re assigning to projects, Fletcher says, but they must train project teams on how to optimize their use of AI tools. Even with fully trained teams capable of optimizing their use of AI in project delivery, organizational leaders must have realistic expectations about AI’s contribution to a project’s timeline and resource needs.

7. Poor prioritization practices

Unrealistic AI expectations isn’t the only reason project teams end up with more than they can do, Fletcher says. Poor prioritization also plays a role in many organizations.

“They’re not making hard choices on what are the really critical things to drive through,” he says. “They sometimes have to make hard choices about what to push forward, but that whole prioritization governance function is something I frequently see as very ineffective.”

The result is that too many people are working on too many things, with diluted efforts leading to poor business outcomes for multiple projects.

Business and IT execs must work together to prioritize projects based on each project’s anticipated business value and then shepherd projects to completion based on that priority list — “which means cutting out a lot of the lower priorities,” Fletcher says.

8. No business ownership

Even when IT is perfectly aligned with business objectives, a project can still tank when no business leader has accountability, says Eric Stettler, a partner in the digital practice at Kearney, a global strategy and management consulting firm.

A business owner with clear accountability is needed to ensure that business resources are available when required, and that process changes and worker adoption happen, Stettler says. Having CIOs instead of a business owner try to make those happen “would be a tail-wagging-the-dog scenario,” he adds.

“CIOs can make sure the right process ownership is in place, and that leaders are aligned to a common set of objectives, but ultimately the business has to decide whether it’s going to operate differently,” Stettler adds.

9. Lack of business sponsor engagement

Business leader ownership is not enough; the owner also must commit adequate time for involvement and oversight.

Otherwise, they can miss signs that the project is going off track, or they can fail to cultivate enough trust that project leaders feel comfortable escalating issues early enough.

Moreover, if sponsors aren’t actively involved, if they’re just looking at dashboards, and only attending briefings, then all the decision-making is left on the project team who may not have all the information needed to make the best choices, says Lenka Pincot, chief of staff to the CEO at PMI.

“What is really needed is active sponsorship and help,” Pincot says. “You need someone to stand behind the idea, ensure funding for the project in the beginning and then when it’s running, to help navigate the business alignment with other stakeholders.”

There can be more than one sponsor, she adds. And if it’s a business project with an IT component — as practically all are these days, then sponsors should be the CIO and someone from the business.

10. Not involving all stakeholders

IT project manager Krista Phillips recounts one case in which a large multinational corporation implemented a new technology across its companies but caught one division completely unaware of the ongoing implementation work.

Turns out that specific division had been left out of all the planning and project processes.

Phillips acknowledges that project teams don’t usually overlook entire divisions, but they sometimes fail to identify and include all the stakeholders they should in the project process. Consequently, they miss key requirements to include, regulations to consider, and opportunities to capitalize on.

11. Slow or no decision-making mechanisms

Another issue that can put a project at risk: slow or no decision-making mechanisms.

Rick Catalano, partner with AMIGO, which provides project management consulting, training, and software, says many organizations lack a strong decision-making muscle and as a result projects grind to a halt or go off-track.

“Too often there is no one empowered to make decisions, and too often project managers are left waiting for answers and then get asked why things are late,” says Catalano, author of the book The AI Project Manager.

Catalano explains that the executives in charge and the project’s governing board need to have the authority to make decisions and the capacity to make them at a pace that aligns with the project’s timeline. But execs and project sponsors also need to empower project leaders who in turn need to empower those beneath them to make certain decisions, too.

This isn’t a project problem, Catalano says; it’s a cultural one. The C-suite must recognize that delayed or failed IT projects imperil the business and that it is worth their effort to remove roadblocks to success. From there, they need to implement a decision-making matrix, empowering the right people at the right level to make the right decisions, emphasizing the importance of making calls in a timely manner. And project leaders must know how to provide guidance so team members can quickly make informed decisions.

“Build the decision-making into the governance model, so everyone knows exactly who owns what and who is empowered to do what,” Catalano adds.

12. Shortchanging change management

Projects need more than skilled project managers; they also need leaders skilled in change. If projects don’t have skilled change managers and a plan to drive adoption of new technology, they’ll likely fall short of expectations, Fletcher says.

Given how critical technology — and particularly AI — is for business transformation today, “the impact of not having a plan is high right now,” he says. “So leaders need to demand and prioritize change.”

That makes change management particularly important now, he says, as most workers are dealing with so much change they need guidance to absorb it all.

Skilled change managers know how to align incentives to get people to accept new ways of working, and they’re deft at identifying and counteracting obstacles that could hinder adoption of new technologies, says Nick Kramer, a principal for applied solutions at consulting firm SSA & Co. They’re often able to get reluctant workers to get over their hesitations by helping them understand the why behind change.

“Change management is often viewed as just a communications plan, and there’s lip service done to it, but change management is really difficult,” Kramer adds, noting that he has seen more projects fail because of poor change management than poor technology implementations. “To succeed, projects need a CIO or someone else to be an agent of change, they need someone who knows how to drive change.”

Mohan Sankararaman calls the old approach to technology-driven transformation — the kind of change that lands every few years and reshapes the organization in one push — a trap. As executive vice president and CIO of First Horizon, a regional bank headquartered in Memphis, he’s focused on driving digital transformation the way a bank funds risk: incrementally with room to pull back.

Every CIO is under similar pressure to rethink change management for the AI era. Wanda Wallace, managing partner at Leadership Forum, has advised CIOs on change management for years, and she thinks the job itself hasn’t changed much.

“The hardest and most critical aspect of making change happen and stick is convincing people to adopt a new approach,” she says. “AI doesn’t change that need or that process. It is a human-to-human dynamic.”

Talk to the practitioners and researchers closest to the work, and a version of her view emerges again and again. What has changed is how many things are competing for an organization’s limited capacity to absorb them — AI chief among them. Here are five hard truths IT leaders face about change management today.

1. There’s no finish line

Ashish Parmar, CIO of Standard Industries, a global industrial conglomerate with more than 20,000 employees across roughly 50 countries, has watched the nature of transformation shift beneath him. In the past, he says, change was treated like a project with a start date and an end date — whether the trigger was a new ERP system, a reorg, or a cost-cutting mandate. That model doesn’t hold anymore.

“Today, change is continuous,” Parmar says. “Our strategy is focused on building resilience and adaptability rather than getting to a single destination.”

AI is the clearest example of how the old model breaks down, says Fran Maxwell, who leads Protiviti’s people and change practice, though he’s quick to note it isn’t the only one. Unlike an ERP rollout, which lands as a discrete event, an AI transformation keeps moving.

“The technology evolves continuously, use cases emerge rapidly, and the impact on roles is often uncertain,” Maxwell says. The common misstep is treating any major shift, AI-driven or not, like a one-time project with a training curriculum and a communications plan, he says. The fix is building a permanent capability for adaptation rather than staffing up for a single push.

None of that continuous adaptation is possible if the underlying systems can’t support it, notes Manosiz Bhattacharyya, CTO of Nutanix.

“Technology is not the barrier to transformation; application modernization is,” he says. Years of accumulated dependencies, legacy integrations, and fragmented data are what actually slow an organization down. And layering new tools on top doesn’t make that debt disappear.

“Applying AI blindly does not remove technical debt,” Bhattacharyya says. “It amplifies it.”

2. Bandwidth isn’t just a network problem

A 2026 survey of roughly 3,000 HR leaders by talent firm LHH found that no single cause dominates why companies reshape their organizations: AI and automation, skills mismatches, M&A activity, and strategic shifts were each cited as drivers in the previous year by about a fifth of respondents. In other words, most organizations are contending with several forms of change at once, not just AI.

All that change at once runs into a hard limit: An organization can absorb only so much at a time.

“Every organization’s capacity for change is finite, so leaders cannot endlessly stack new initiatives on top of existing workloads,” Parmar of Standard Industries says.

Rather than treat that ceiling as a constraint, he argues CIOs should use it to force discipline. IT leaders should determine their non-negotiables and point the team’s energy there instead of spreading it thin across AI pilots, reorganizations, and everything else competing for attention.

Sankararaman arrived at nearly the same conclusion at First Horizon. Banking used to reward slow, occasional overhauls, the kind that could take years to prove out, he says. But that approach has become untenable.

“It’s tempting to treat transformation as one big initiative, but with technology evolving this fast, that’s a trap,” he says. Instead, Sankararaman releases funding in stages, each tied to a measurable result before the next is approved. Then, the organization can adapt and build confidence as it goes rather than betting everything on a single multi-year plan. “We reward progress, not perfection,” he says.

Kevin Martin, chief research officer at the Institute for Corporate Productivity (i4cp), has data that supports the value of incremental improvements. When leaders want to move faster, the reflex is to restructure: delayer, widen control, redraw reporting lines. i4cp’s research found no statistical relationship between those structural moves and organizational agility or market performance. What separates agile organizations are routines: scenario planning, faster resource reallocation, clear decision rights, continuous workforce planning, targeted reskilling, and disciplined execution.

“You don’t reorganize your way to agility,” Martin says. “You build it into how the organization operates.”

3. Shadow IT doesn’t belong in the shadows

Employees finding their own tools to get work done isn’t new. Shadow IT has taken on various forms over the years, from personal file-sharing accounts to unsanctioned SaaS subscriptions.

Today, it’s shadow AI, and Sankararaman argues most CIOs still treat it as a security or compliance issue rather than what it really is: information about what the organization needs and isn’t getting.

“Shadow AI is already happening in every organization. If you’re not addressing it through your change management strategy, you’re addressing it too late,” Sankararaman says.

Sankararaman’s approach starts with curiosity rather than restriction: understanding what employees are trying to accomplish with the tools they’ve found on their own, which makes it easier to agree on how the business should govern those tools.

“That’s a change management conversation, not just a policy conversation,” he says.

4. Trust has to be designed in, not repaired later

Every new system that changes how decisions get made must earn trust before it achieves adoption. Agentic AI raises the stakes because it doesn’t just inform decisions; it takes actions on its own within workflows.

That’s a fundamentally different dynamic from anything change leaders have managed before, Sankararaman says. The resistance it produces is often quieter, showing up in questions about how a system reached a conclusion, who’s accountable when it’s wrong, and whether it’s replacing what someone does. “Those questions deserve real answers, not reassurance,” he says.

At First Horizon, IT is building trust into the foundation with permissioned access, centralized guardrails, human oversight, and outputs that are consistent, reviewable, and explainable. “If people can’t understand how the technology reached a conclusion, you haven’t earned their trust,” Sankararaman says. “And without trust, adoption doesn’t hold.”

Employees today worry less about learning a new tool than about what it means for their role, their skills, and how their performance will be judged once a machine does part of the job.

“They worry about career relevance, accountability, job security, and how performance will be evaluated,” Protiviti’s Maxwell says. Closing that gap, in his view, takes more than a rollout plan. It demands transparency about what’s changing, what isn’t, and how people add value once the tool is in place.

5. Tired isn’t the same as unwilling

Ask IT leaders about change fatigue, and they frame it as a capacity problem rather than resistance. Late 2025 saw a wave of five-day return-to-office mandates that landed on top of continued layoffs. Tech companies alone cut more than 66,000 jobs between May and November, according to Newsweek and TechCrunch, exactly the kind of concurrent disruption that erodes an organization’s capacity for change.

Among what i4cp calls “coasting incumbents” — companies that still perform well despite low organizational agility — 51% of employees report finding change fatiguing, and just 8% say change management is an organizational strength. At “agile pacesetters” — the highest-agility, highest-performing organizations in i4cp’s research — only 12% report high fatigue.

“AI is an accelerant,” Martin says. “But organizational friction is the fuel.”

Protiviti’s Maxwell argues that change fatigue is often less about resistance and more about capacity. “Employees are far more likely to embrace change when leaders are clear about what matters most, what success looks like, and just as importantly, what is not a priority right now,” Maxwell says. His advice to CIOs: Empathize and prioritize before accelerating.

One structural fix most CIOs underuse is shared ownership. “Don’t go at it alone,” advises First Horizon’s Sankararaman. “Partner with others across the business, your CHRO, CFO, COO, and make them co-champions of the change, not just stakeholders who get updates.” A message that arrives from multiple leaders, he’s found, carries more weight and lasts longer than one delivered by IT alone.

The absence of fatigue, in Wallace’s view, is its own warning sign. “If your organization isn’t change-fatigued,” she says, “then I am worried about what you have been doing.”