There are two prevailing schools of thought regarding the AI-agent workforce. One says organizations should prepare for agentic AI, in which the human-in-the-middle role is largely transitional and serves to buy time to improve agents’ accuracy and build trust in their decision-making. Others say AI agents will largely augment humans, but expect workflows to change drastically from task-based processes to more asynchronous, choreographed operations.

Businesses will likely have a mix of agentic and human-augmented AI agents, with many more in pilot stages. As part of this transformation, CIOs need to consider how to evolve the IT organization and upskill IT employees for this future. According to Deloitte’s 2026 Global Technology Leadership Survey, 75% of IT leaders agree their operating models and processes must change within the next 12 to 18 months to drive greater value.

“Upskilling IT for an AI-agent workforce requires more than training; it requires behavior change because as AI takes on more routine development activities, technology professionals increasingly focus on validating, governing, and directing AI-generated outputs,” says Doug Vargo, VP of consulting services and head of the national AI and alliances team at CGI. “The cognitive habits that define experienced engineers are deeply ingrained, so they need to develop new ways of working focused on reviewing outputs, framing intent, and curating the context that keeps those outputs accurate, secure, and aligned with business objectives.”

How CIOs upskill their organizations will follow several career tracks. Here are the most essential to consider.

Developing business acumen and AI literacy for IT leaders

AI is requiring more IT professionals to shift left into transformational leadership and change-agent roles. These leaders will advise business managers on when to use AI versus other technologies to automate tasks, and when to consider top-down re-engineering workflows based on AI capabilities.

“Leaders need to help their teams understand how work flows across the business, where AI fits into that process, and where humans need to stay accountable,” says Jamie Lyon, chief product and strategy officer at Lucid Software. “As AI agents take on more of the execution, critical thinking becomes even more important because people still need to provide the context, define the process, and make the decisions AI can’t.”

One of the top barriers in delivering value from AI is employee adoption. CIOs need more change agents to drive enthusiasm and help department leaders reimagine emerging job responsibilities. Upskilling IT leaders for change-agent roles often requires embedding them in business units so they can learn their processes and build relationships.

Upskilling focus: AI literacy, critical thinking, business relationship management, and change management are four primary skills. To connect problems to solutions, developing skills in architecture, design thinking, and analytics is also needed. 

Extending AI and data governance for everyone

According to Adobe’s 2026 AI and Digital Trends, 78% of technology leaders say data integration and quality is a top AI challenge, and 52% say limited data unification is holding back AI initiatives. CIOs facing data governance, integration, and management challenges risk seeing their businesses fall behind their competitors who are aggressively pursuing AI-driven opportunities.

“Upskilling for an AI agent workforce starts with understanding that the biggest challenge is the data and operational layer underneath the model itself. IT teams need to know how to connect fragmented data, engineer the context and memory that make AI agents more reliable, and support transactional, analytical, and vector workloads on a unified platform without breaking the budget,” says Adam Luciano, VP of product management at MariaDB. “They also need to understand governance, security, and observability so autonomous systems can safely execute real business processes and expand to higher-value use cases instead of simply generating recommendations.”

Data governance used to be a compliance team’s responsibility, but AI now requires many more in IT to be versed with policies, practices, and related technologies.

“As AI agents begin executing work across enterprise environments, IT teams need to build governance skills, not just AI literacy,” says Doug Gilbert, CIO and chief digital officer at Sutherland. “They should know how to assign accountability, monitor data access, enforce human-style approval workflows, and maintain complete audit trails so AI operates under the same controls as any employee and not as an exception to them.”

Upskilling focus: One upskilling focus should be on data governance, DataOps, data engineering, and data management. A second focus should address data risk management issues, such as data security and AI governance.

Expanding knowledge management to develop AI’s context layer

CIOs looking to scale from dozens of AI agents to thousands of AI-orchestrated workflows will need to develop an AI brain for their organizations, including knowledge graphs, a semantic layer, and a context layer.

“One critical place for CIOs and CISOs to focus upskilling is building the information layer that has to replace the human management layer everyone’s trying to collapse,” says Lior Gavish, co-founder and CTO at Monte Carlo. “A real part of what managers do is information work, including passing context, surfacing priorities, and keeping decisions aligned with the bigger picture. Flatten the org without replacing that function, and you get people, or agents, making locally optimized decisions on incomplete information.”

Organizations will need cross-disciplinary teams to develop and improve their context layers. Data skills to develop include extending unstructured data governance, evolving data fabrics, and building data products.

“The challenge is no longer just teaching employees how to use new tools, but ensuring teams know how to structure, manage, and govern the knowledge that powers them,” says Adam Field, chief AI officer at Tungsten Automation. “This will require new skills around contextual AI training, knowledge management, and information stewardship. Organizations that can effectively connect AI systems to trusted institutional knowledge, while maintaining appropriate security and access controls, will be better positioned to accelerate product development, improve collaboration, and increase access to critical information across their company.”

Upskilling focus: To develop the context layer needed by AI agents, CIOs should promote collaboration and communication skills alongside key data management, integration, and governance skills. In addition, agile data teams will need strong business acumen to partner with department leaders and subject matter experts.

Establishing an AI quality center of excellence

DevOps teams accelerating their deployment cycles while underinvesting in continuous testing were left with one of two bad options. Some tried to get business users to perform extensive user acceptance testing. Others deployed applications with minimal testing, hoping their observability and monitoring would catch errors before users escalated issues.

Underinvesting in testing and automating evaluations of AI agents can lead to significant issues, including increased costs, compliance violations, and operational impacts.

Sanjay Gidwani, CEO and founder at Kosmos, says the skill that will matter more than building AI agents is in confirming their accuracy. “Agents increasingly act on correlations drawn across disconnected systems, and a correlation that a human never confirmed is a decision waiting to go sideways at high speed. Upskill your teams to serve as the confirmation layer for what agents do before anything is acted on,” Gidwani says.

CIOs should think about AI agent quality from three perspectives:

• When are AI agents in experimental and pilot stages delivering high enough quality to be released into production?
• Once in production, how are quality metrics used to build trust in which decisions AI agents can automate, versus those that require people’s involvement?
• How are AI agents’ quality benchmarked in production to detect when their models are drifting and the agents’ performance degrading?

Upskilling focus: CIOs should upskill teams in data quality, test automation, and analytics. Organizations scaling the number of AI agents in production should consider developing an AI quality center of excellence.

Revisiting the skills needed by product and program managers

Before developing that center of excellence, consider how AI is changing the nature of team collaboration. Three examples:

• CIOs are developing product-based IT organizations to deliver business value from AI investments.
• Teams are revisiting their agile practices in the AI era to ensure that developer productivity results in faster feature delivery and less technical debt.
• DevOps has new challenges with AI agent observability and agent deployment risks.

These three spinning process wheels inside IT, with evolving AI capabilities, are one reason why many CIOs are rethinking the IT organization for the AI era. According to Atlassian’s The State of Teams 2026, AI-augmented teams need more coordination, not less: 77% say they expect more horizontal teams with fewer layers, and 73% have blended roles with hybrid responsibilities.

Mal Vivek, CEO and founder at Zeb, says the most valuable capability CIOs can build for an agent workforce is judgment. “Teach teams to decompose work into clear objectives, constraints, and feedback. These skills won’t come from a one-off course or certification; it takes redesigning roles so that human judgment compounds,” Vivek says.

Upskilling focus: CIOs will need more business-facing roles to lead discussions on where to invest in AI. Skills to develop include Six Sigma process skills, product management disciplines, and agile planning practices.

Upskilling junior developers beyond coding skills

If 41% of all global code is AI-generated, do CIOs still need engineers?

According to Karat’s AI Workforce Transformation Report, 73% say strong engineers are now worth at least three times their total compensation. That’s likely because the top engineers were never just coders; they were stewards of the software development lifecycle, drivers of sound architectures, and advocates for addressing technical debt.

“Agent verification should be a top priority for CIOs and CISOs, training professionals to look beyond raw AI outputs and to get ahead of the review burden that can come with increased AI use,” says Samar Abbas, CEO at Temporal. “As agents move to writing more code, tech talent needs to embrace becoming primary evaluators, interrogating an agent’s design decisions, defending the generated architecture under questioning, and confidently proving its correctness.”

Upskilling focus: CIOs should consider apprenticeship programs to accelerate junior developers into senior-level roles and entry-level architecture responsibilities. To start, junior developers will need training in systems thinking and in resolving issues flagged by code review tools. Beyond these basics, guide developers to build technical domain expertise in two to three focus areas such as testing, data, identity management, application performance, API development, integration, and security.

Maturing AgenticOps in IT operations

While many organizations are still in pilot stages with AI agents, others are deploying thousands into production and using AI orchestration platforms to build complex workflows.

“As apps evolve from traditional software into autonomous AI agents, IT’s role shifts from maintaining systems to managing a digital workforce,” says Nikhil Mungel, head of AI R&D at Cribl. “IT teams will need to learn how to onboard and supervise AI agents, ensure they comply with company policies, and monitor for unusual or harmful behavior. The organizations that succeed will be those that invest in teaching IT teams to govern and manage AI systems in production.”

Upskilling focus: AgenticOps skills to focus on include identity management, root cause analysis, and monitoring AI agents. CIOs deploying hundreds of AI agents should plan to extend site reliability engineering to include tracking AI agent reliability and diagnosing their performance issues.

Developing a world-class IT department is not just about delivering business value. Top CIOs recognize that they need to plan their IT organizations to support future needs and update their skills and learning development programs. AI capabilities are evolving quickly, and CIOs need to guide employees on the new skills needed to enable the AI agent workforce.

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.

I’ve always thought it was interesting that we’re willing to fight and die to live in a democracy, but everyone is happy to work in a company which is structured like a dictatorship. This thought feels even more pertinent given the rise of AI. As AI continues to transform the world of business, we’re starting to notice a clear gap between those implementing a ‘throw it at the wall and see if it sticks’ approach, and those examining the fundamental changes that need to be made to a business.

While we don’t need to get into the pros and cons of oligarchy, over the past year of leading consultation and training sessions for over 80 organizations, I’ve realized that, if you introduce AI by working from the middle out, you can actually move a lot faster.

I’ve seen organizations try to bolt AI onto their existing workflows, and while there may be initial productivity gains, this generally doesn’t work out in the long term. We often see scattered pilots which don’t go the distance, duplication of tools or inefficient processes. The organizations setting themselves up for success are redesigning how teams experiment with and implement solutions.

We can use the transition from steam to electricity as an example. Paul A. David notes that there was a 40-year lag between the electric dynamo’s introduction and its productivity impact. When factories first adopted electricity, many simply replaced their steam engines with electric motors, while leaving the rest of the factory unchanged. Productivity gains were modest. David argues that the bottleneck was organizational structure. It was only when engineers redesigned factories around small electric motors throughout the factory that we began to see the benefits. General purpose technologies, like electricity — or, in this case, AI — require co-invention and firm restructuring before we can see the benefits.

It’s time to restructure.

AI is developing fast and it’s difficult for companies to keep pace

While most companies are built with a top-down model, this is not an effective way to identify and roll out technology, particularly when it’s moving as quickly as AI is.

We’re already seeing the impact that the speed of AI development is having. Companies are struggling with things like AI sprawl and shadow AI. AI sprawl is when employees are using tools everywhere, without shared norms or strategy. Gartner estimates that by 2028, an average global Fortune 500 enterprise will have over 150,000 agents in use, up from less than 15 in 2025. This creates significant agent sprawl, IT complexity and management challenges. Take a retail company for example, if sales uses one AI chatbot, support uses another and marketing uses a third we could start to see inconsistent customer experiences, where customer-facing AI chatbots give conflicting answers about pricing or return policies.

Shadow AI is the unauthorized use of AI tools by employees without IT or security approval. Common examples include employees pasting code into ChatGPT, uploading customer data to public web apps or using unvetted browser extensions to speed up daily work. Today, over one-third (38%) of employees say they share sensitive work information with AI tools without their employers’ permission. This introduces severe risks like intellectual property leaks, data privacy violations and non-compliance.

Building the assembly line of the AI era

The companies solving these challenges are finding ways to convene subject matter and AI experts from every part of the company to create a center of excellence, steering committee or a power user group. Once assembled, this group should be empowered to experiment, vet and validate new technology for the company. This is what I’m calling the new ‘assembly line’ of the AI era.

This assembly line is a dedicated team with the power to implement new solutions. They can vet any tools being used and compare them to systems already in place. They can then make decisions on whether the identified tools should be rolled out across the company, and the training and processes that need to be in place to make this rollout a success.

Some of the companies I’m working with are already putting this into practice. One water pumping company in Minnesota wanted to figure out how AI could be used to educate, train and inform employees, as well as preventing AI sprawl or shadow AI usage. Together we have mapped out who should be part of their center of excellence, who owns what and how to set up approvals. With the model we’re creating, we are establishing AI as a force for empowerment, education, training, tooling and most importantly change management.

On the flipside, I’m also working with a healthcare company that has an existing center of excellence trying to oversee ALL AI projects at the business unit level. This recreates a hierarchical problem that slows adoption, since it doesn’t empower individual units to move on their own. It makes sense, given HIPAA compliance means healthcare companies have to be cautious, but the focus of a Centre of excellence should be enabling teams through approved AI tools, not taking complete ownership of every project themselves.

By giving these teams authority to make AI specific decisions, you prevent the bottleneck which usually happens at the executive level either due to busy schedules or a less in-depth technical understanding. The center of excellence can redirect sprawl, combat shadow AI usage and escalate things when necessary.

Turning individual experiments into company decisions

A center of excellence gives AI adoption a working rhythm instead of leaving it to Slack threads, scattered pilots or executive guesswork. Each team should have someone close enough to the work to spot where AI is useful and where it’s a distraction. A finance lead might see value in automating invoice checks. A legal lead might reject a tool because it mishandles client data. A customer service manager might test whether an AI assistant actually improves response quality or just produces faster, worse answers.

That group can then turn individual experiments into company decisions. They can test tools, compare them against existing systems, check the security risks and decide what needs training before anything is rolled out. They can also stop bad habits early, like teams uploading sensitive documents into public tools because nobody gave them a safer option.

If companies want AI to work, they need to completely overhaul their processes. The companies that move fastest will be the ones that give people in the middle the authority to test, challenge, approve and teach. That’s where the real work happens: close enough to daily operations to know what’s useful, and connected enough to turn that knowledge into practice across the business.

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.”

What changes when AI becomes part of how the business runs

The more I speak with CIOs and technology leaders, the more I realize most of us are working through variations of the same AI challenge.

How quickly should we move? Which opportunities are worth pursuing? What risks are acceptable? And how do we move from an impressive demonstration to something the business can reliably use?

Enterprise AI began with possibility and experimentation. Now the conversation is changing.

The harder question is not whether AI can perform a task. It is what changes once the business begins depending on it. At that point, the conversation expands beyond technical capability. Value, capacity, security, ownership, change management and operational resilience all become part of the equation.

The demo is not the operating environment

A strong AI demonstration can be compelling. The data is clean, the use case defined and the operator knows the technology. The result can look effortless.

Real environments rarely behave that way.

I have seen intelligent automation use cases appear straightforward until actual business data and processes were introduced. Documents varied, requirements evolved and manual workflows contained accumulated exceptions. What looked like one process turned out to be several versions held together by human judgment.

The technology may be capable, but it does not resolve unclear requirements, inconsistent inputs or a process that was never standardized.

I prefer to test with real enterprise data as early as practical. Vendor demonstrations naturally emphasize the happy path. Your own data exposes the conditions the solution will actually have to survive.

Watching an expert operate a platform is different from asking employees to use it every day. Users have to understand the capability, trust the result and know what to do when the output is wrong or incomplete.

Change management cannot be treated as the last step. It affects the timeline, effort and whether the expected value shows up.

McKinsey’s State of AI research continues to show broad adoption while enterprise-wide scaling remains much less common. That gap is understandable. The distance between an interesting use case and a production capability is where data, process design, testing, security, integration and adoption all become real.

Value has to compete with capacity

Once a use case survives the technical question, the discussion has to become more pragmatic.

What is the value?

Within an enterprise, that should translate into something leadership can evaluate: cost reduction, increased throughput, greater efficiency, less manual work, more time redirected toward higher-value activities, better customer outcomes, revenue opportunity or the ability to absorb growth without adding proportional headcount.

Not every AI initiative needs an immediate hard-dollar return. But leadership should know the intended outcome and how it will determine whether further investment is justified.

AI does not create unlimited organizational capacity. Technology teams still have roadmaps and operational priorities to deliver. Business subject matter experts still have day jobs. Someone has to define requirements, provide data, validate the process, test the outcome and help employees adopt a different way of working. And when the organization chooses to build rather than buy, additional work may be required to prepare data, evaluate model performance and, where appropriate, fine-tune models for the specific use case.

That is why being able to build a use case does not automatically make it the right priority. The value, effort, timing and business readiness still have to justify the investment.

Sometimes the smaller opportunity is better because it produces value sooner and builds reusable experience.

The same discipline should apply to whether the organization builds internally or brings in external expertise.

AI is evolving too quickly for most internal teams to master every emerging capability while operating the rest of the enterprise. A proven external partner can sometimes add expertise, speed or capacity.

The test is whether that partner accelerates internal capability or creates an unsustainable dependency.

Board expectations are also increasing, and rightfully so.

AI now touches competitive positioning, investment priorities, workforce decisions and enterprise risk. Boards should ask where value is emerging and whether the organization is moving with enough urgency.

BCG research on CEO and board perspectives has highlighted a useful tension: in some organizations, boards are pushing for greater urgency around AI, while management teams may take a more measured view of what can realistically be delivered and sustained.

The better question is not simply how fast the organization is moving. It is how fast it can move while still producing something it can support, protect and sustain.

That is where risk stops being only an IT discussion.

Before an AI capability moves deeper into the environment, CIOs need to understand what it touches. What data can it access? Does information leave the enterprise? What permissions does it require? Could it introduce a new attack path? What happens when the capability begins taking actions across systems instead of simply producing an answer?

The control model should reflect the consequence.

An AI tool used for everyday productivity does not require the same oversight as one that can modify records, interact with customers or access sensitive enterprise data. The NIST AI Risk Management Framework provides a useful structure for thinking about risk in context rather than applying the same controls everywhere.

For CIOs, that is the balance: we are still responsible for protecting the enterprise, but protection cannot become an excuse to make every new capability unnecessarily difficult to adopt.

Guardrails should be strong enough to protect the business and flexible enough to evolve with the technology.

Sometimes that requires more common sense than textbook governance.

The stakes change when AI moves beyond the office

For many organizations, AI adoption starts with office productivity: summarization, knowledge search, coding assistance, meeting support and other relatively contained uses.

Eventually, the question changes.

When can AI move deeper into operations?

That can include intelligent document processing, computer vision, IoT and sensor-driven capabilities, drones or other technologies that begin influencing operational decisions and physical processes.

Some companies will move there gradually. Others may move sooner when the capability sits inside an established vendor-managed solution with defined controls, support and accountability.

If an AI tool used for everyday productivity produces a poor response, an employee can usually identify and correct it. If an AI-enabled capability begins influencing an operational process, reliability, cybersecurity, fallback procedures and ownership become much more important.

That progression from experimentation to deeper enterprise dependence is not new. We have seen it in other technology cycles.

Cloud, SaaS and mobile all moved through periods of enthusiasm, rapid adoption and eventual normalization.

AI will likely follow parts of the same pattern, but the cycle is moving faster.

It did not enter primarily through the traditional IT corridor. Employees, business teams, vendors and executives gained access almost simultaneously. The technology continues advancing while organizations are still deciding how it should be used and controlled.

Much like smartphones and the internet became embedded into daily life, AI is already becoming part of the applications people use every day. Capabilities are being built into enterprise platforms, whether users think of them as AI or not. The next shift is deeper dependence as AI becomes part of workflows, decisions and operating processes.

The difference is that AI can operate at a higher altitude. It can influence decisions, interact with enterprise data and increasingly take actions across systems, which raises the consequence when something goes wrong.

That should change the questions boards and CEOs ask. The conversation should move beyond “What are we doing with AI?” to questions that expose whether the enterprise is actually ready to depend on it:

• How do we move faster without putting the business at unnecessary risk?
• What are we asking AI to compensate for that we should be fixing ourselves?
• Where are process ambiguity, system fragmentation or operating habits creating unnecessary friction?

AI can automate around a weak process for a while, but eventually the exceptions catch up with it. It can work around inconsistent information only so long before confidence in the output becomes the problem.

CIOs will need to hold firm on responsibilities that do not change while staying flexible in how those responsibilities are carried out.

We also have to be realistic about what our organizations can absorb. Trying to boil the ocean can create more activity than value. There is nothing wrong with narrowing the focus, proving an outcome and using specialized expertise when internal capacity or experience is not there yet.

The first phase of AI rewarded experimentation and curiosity.

The next will reward judgment.

The organizations that navigate it well will not necessarily be the ones with the most pilots, the largest budgets or the boldest promises. They will be the ones that know where to move quickly, where to hold the line, what needs to be fixed internally and when an idea has earned the right to scale.

That is when AI stops being another technology experiment and starts becoming part of how the enterprise actually runs.

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.”

I often hear the same types of complaints from leaders struggling with underperforming AI projects. Things started out great but then tailed off. They’re not seeing the ROI they expected. Maybe AI wasn’t a good fit for their organization.

These leaders are not alone. But whatever their specific underlying issue, I usually respond with the same question: How are you handling change management?

And the answer, in most cases, is not very well. It’s not that they’re not including any change management in their AI efforts. It’s just not getting the attention it should.

They’re using outdated methods and techniques. They’re treating change management as a commodity instead of a strategic advantage. They’re checking a box.

But that approach won’t work for AI.

AI is transformative by definition. It’s technology that makes change part of what organizations do. And when change is constant, change management needs to be constant as well. You can’t just run through the same standardized list of stakeholder interviews, communication plans and training regimens that you used for the last software rollout and hope for the best.

But what you can do, ironically enough, is use AI itself to reinvent change management for the age of AI by making your process more continuous, employee-centered and measurable. And in doing so, you’ll be transforming change management from a one-time project into an ongoing organizational capability.

Why traditional change management is breaking down

Here’s the paradox. When asked, every one of those leaders who told me about their AI issues would describe change management as essential. Yet when budgets get tight, it’s likely the first thing to be pared back. So how “essential” is it, really? If we thought it had real value, we wouldn’t be so quick to give up on it.

The truth is, change management hasn’t changed much over the past two decades. As a result, many of us are probably still relying on longstanding programs or disciplines that haven’t kept up with the pace of change. For example, we may be listening to the wrong people. Most programs still collect feedback through a handful of our colleagues and time-tested methods, including:

• Small interview groups
• Executive stakeholders
• Representative committees
• Broad but shallow surveys

The problem with this approach, particularly when it comes to AI, is that the people actually doing the work often have little voice. In other words, frontline employees are expected to change their behavior without helping to shape the solution.

Think about trying to use AI to redesign your sales function. If all of your change management effort is focused solely on the input of your sales operations team instead of your actual sellers, you’re likely to wind up with technology that looks good on paper but doesn’t fit real workflows.

And that’s why you see the familiar sparkle-and-fade patterns when it comes to AI adoption:

• Initial compliance, as everybody is willing to give the new tech a try.
• Post-launch celebration, as the organization sees early adoption as a sign of long-term success ahead.
• Steadily declining usage, as users begin to realize that AI doesn’t fit well with their work.
• A frantic after-the-fact scramble, as the organization attempts to diagnose problems and salvage its investment.

You can just hear the air coming out of the balloon as you read down that list. Listening to the wrong people – or not listening to enough of the right ones – is a recipe for discovering adoption barriers after the rollout instead of while the change is happening.

In most cases, that’s way too late.

How AI can reinvent change management

I can picture those leaders with their stalled AI projects shaking their heads at me. Even if we wanted to upgrade our change management process and listen to more employees, they might  say, how are we supposed to do that?

It’s not a bad question. After all, they probably weren’t just being stubborn by not interviewing their thousands of employees for previous rollouts. It was simply a prohibitively expensive proposition.

But AI makes continuous listening possible. AI voice agents, for example, can conduct simultaneous conversations at enterprise scale.

The result? We can now work with organizations to compile feedback from hundreds of employees in under a week, a task that previously would have required months of interviews and surveys. AI gathered the information, and human change leaders interpreted it and made decisions.

AI, in other words, makes it possible to move beyond the limitations of traditional surveys and instead build an organizational nervous system. It allows for things like:

1. Continuous employee listening through voice agents that enable open-ended conversations to yield rich sentiment and clear identification of pain points.
2. Organizational network intelligence that uses data analytics to identify informal influencers, communication patterns, key adoption champions and disconnected teams that might need additional support.
3. Behavioral monitoring and real-time feedback to identify where users get stuck, detect adoption friction early and recognize patterns before they become widespread problems.

For example, if sales reps consistently abandon an AI assistant before generating customer proposals, leaders can detect the pattern within days instead of discovering it months later through adoption reports.

These are the kinds of upgrades that can help organizations move from reactive change management to proactive adjustment. And with AI automating everything from data collection to synthesis to ongoing monitoring, experienced change leaders can use their expertise to focus on broader, more impactful activities like executive coaching and strategic communication.

In other words, less time and effort managing large administrative teams and more time influencing outcomes.

From change management project to continuous capability

I’m sure all of those leaders looking to reverse their slumping AI projects are probably thinking that while this approach to change management sounds great, what can they do about it right now? Well, Rome wasn’t built in a day, but those builders didn’t have AI. Here are three practical principles leaders can start using immediately:

1. Give every employee a voice

The old playbook of relying solely on representative groups doesn’t cut it anymore, particularly if you’re not getting feedback from those who will be directly affected by the change. At the end of the day, those are the groups that will actually be responsible for operating in a new system, so if you want it to stick, you need to hear their pain points.

Fortunately, getting input from everybody is possible now, so it’s time to create scalable mechanisms that continuously capture those frontline experiences.

2. Anchor every decision to measurable business outcomes

Start with your desired KPIs – whether sales effectiveness, marketing performance, customer satisfaction, productivity or employee experience – and then work backward. If a proposed change doesn’t support your chosen outcomes, reconsider it.

3. Embed change management into operations

For far too long, many organizations have made the mistake of treating change management as a parallel workstream or something tacked onto a project plan. Now it’s time to start integrating everything – listening, enablement, measurement and communications – to help make change management part of how the organization operates.

Of course, in order to use AI to make real progress in any of these areas, you have to have buy-in. And employees may initially distrust something like AI-driven listening. That’s why it’s important to focus on…

• Transparency about data use.
• Anonymity where appropriate.
• Personalized, contextual communications.

Most importantly, employees are more likely to come around on AI if they see that the organization is actually responding to what they’re sharing. Don’t tell them how AI makes things better, show them.

AI gives organizations the change management they’ve always wanted

For years, too many organizations neglected change management. Why? Because they could get away with it for many projects. But not AI.

AI demands a change management process that is continuous, measurable, adaptive, employee-centered and embedded in daily work. Ambitious, sure, but also impractical with yesterday’s technology.

But AI changes that equation.

It may seem ironic, but AI is what can enable change management to become what it was always intended to be. That is, an ongoing capability that continuously listens, learns, adapts and helps organizations evolve alongside their workforce.

And just in time for the massive AI changes that have arrived.

When Sastry Durvasula joined TIAA in early 2022, he saw an organization fighting against outdated legacy technologies and in need of a major IT refresh.

Since then, the financial services organization has completed two phases of a comprehensive transformation initiative called Technology Ecosystem Transformation, or TETRIS, leading to a huge reduction in tech debt and a major expansion of functionality for customers.

The ongoing project, anchored in cloud and AI technologies, started in 2023 with phase one that modernized the core technology stack with 10 new enterprise platforms. Phase two, launched in late 2024, went further by enabling 87 use cases across all major lines of the business.

The project, for example, allowed TIAA to launch its MyChoice Multi-Year Guaranteed Annuity product, and helped create the TIAA Gateway portal, an API-based suite that integrates with partners in retirement and wealth planning using industry standards.

TIAA Gateway took home a CIO 100 Award in 2025, and phase two received a CIO 100 Award in 2026.

Durvasula, TIAA’s chief operating officer, pitched the multimillion-dollar TETRIS project to the board as a three-pronged strategy, with empowering business growth, fueling innovation, and transforming the IT core as its key goals.

Not only did TETRIS need to modernize the company’s IT systems, decommission legacy processes, and automate other processes, but Durvasula pitched it as the way to expand the reach of TIAA’s products and move the company into the future.

“As you expect in a company of our size, we have problems of yesterday, today, and tomorrow being solved at the same time,” he says.

Focus on business use cases

As TETRIS moved into phase two, project leaders shifted their goals from pure technology modernization to business outcome-driven prioritization. So once phase one delivered needed IT platforms like a data cloud and design studio, TIAA pivoted toward enabling business use cases.

This business-first approach ensured continuing executive support and clear ROI at every key milestone, TIAA says.

In 2022, just before the project launched, more than 80% of TIAA’s IT workloads resided in fragmented, end-of-life platforms, which created operational risk, compromised security and resilience, and constrained its ability to innovate. Through TETRIS phase two, however, the organization has cut that tech debt nearly in half.

And consolidating 17 design systems also led to digital products looking and behaving differently, depending on the team that designed them, and accelerated product launches by 35%, enabled multi-lingual capabilities, and increased accessibility to more than 185,000 customers who don’t speak English.

In addition, TETRIS allowed TIAA to combine multiple middleware systems and data lakes, Durvasula says, and the organization moved mainframe applications and data center infrastructure to the cloud.

A giant leap forward

TETRIS has been a huge project, with the company saying it empowered TIAA to have one of the largest leapfrog moments in company history in its submission for the 2026 CIO 100 Award.

Despite the reported failure rates of large transformation projects — some estimates suggest up to 95% fail to meet their goals — TETRIS was essential to keep TIAA competitive and move it forward in the market, Durvasula says.

A big part of the project has been workflow modernization, he says, because TIAA were using some technologies and workflows that were decades old.

“There’s your classical platform and application rationalization, and then there’s your end-of-support, end-of-life stuff that should’ve been remediated long ago,” he says. “Some of the processes we have, because we’re such a large, old company, were designed when the internet just came along.”

Stick to the metrics

Two keys to pulling off such a large project are establishing metrics for success and transparency with leadership, Durvasula says. Project leaders set milestones to indicate when things went well, and they planned for bumps in the road so the TIAA board knew when setbacks happened.

“Not everything is as pretty as it sounds in an awards application, but the success measures we established with our board were based on both phases,” he says. “For the first one, we said we’d deliver enterprise-grade platforms and accomplish migration objectives, but not tied to any specific business objectives.”

Phase two metrics focused more on business objectives, and the project team kept the TIAA board updated as TETRIS moved forward. Setting realistic goals was important, he says, with the team determined not to overpromise results.

“Large programs have a range of objectives, and if you publish the outcomes you’re looking for, people start looking for them, especially stakeholders, the C-suite, and board,” he says. “You have to be honest about which metrics or KPIs you can deliver in the first and second year, and when you’ll start seeing real business scale and impact, which definitely won’t be that soon in a large program like this.”

Goals also need to be flexible, Durvasula says, so transparency with leadership sometimes means telling them the project needs to reset. “If something doesn’t go well, what’s the level of fungibility you have?” he says. “We pick this tool, but what if it doesn’t work? You need to have a plan B.”

So TIAA’s IT team is heavily focused on flexible systems, and what was contemporary three years ago is probably legacy now, especially thanks to AI.

The power of change management

Another big lesson from a project of this size is the need to focus on change management. Retiring old IT systems requires the organization to bring employees along on the journey and convince them the changes are for the better.

TIAA established a multi-disciplinary team to implement a change management program focusing on breaking down silos and setting common adoption goals across the organization and its lines of business. Stakeholder forms and a huge focus on continuous collaboration helped employees understand the need for the changes.

“It’s a big organizational change,” Durvasula says. “If you’re working on a legacy system, and you think at some point it’s going to be modernized, then you become a legacy talent, and won’t have a job.” But the right change management program can convince these employees they can upskill and bring value to the new systems.

“You can bring your functional knowledge of the business and learn new technical skills,” he says. “It’s a massive culture- and people-change initiative as much as tech initiative.”

TIAA’s change management efforts were also made easier because TETRIS happened at the same time as the recent AI boom and involved AI elements. So it wasn’t hard to convince employees they needed to improve their AI skills.

“Because of AI, everybody woke up to this new reality,” he says. “We rode that wave when transformation drove from a cultural and organizational change management point.”

Enterprises of all sizes in all industries are rapidly deploying generative and agentic AI to automate processes. But the efforts aren’t always panning out.

Some reasons are new and unique to this technology. But others are related to issues we should’ve been prepared for because we saw them during the age of RPA. And in the rush to adopt new tech, some of these lessons are being forgotten.

“This new era of agents puts the same challenges again in front of us, and we need to think about the things we faced back when that revolution happened years ago,” says Agustin Huerta, SVP of digital innovation and VP of technology at Globant, a digital transformation company.

Those challenges often include selecting the right processes for automation, setting up systems to manage those processes, making sure automated processes get the right inputs, and managing the wider impacts of automation, including cultural.

1. Automating the right processes

All the lessons of RPA are carrying over, says Stephanie Bova, digital transformation officer at Novo Nordisk, including the biggest one that just because you can automate something, does it mean you should.

“We think hard before we start creating something,” she says. “Who’s going to maintain it, and where is it documented?”

And of course, is the process itself a good process. “Nothing gets built on a process that hasn’t been optimized anymore,” she adds. “We haven’t done a technology deployment on an unoptimized process for two years.”

And the company is now a lot more selective about how much automation it rolls out, but that wasn’t always the case with RPA. “At one point, everyone who wanted a piece of automation could get something built for them,” she says. “That’s not the case on how we’re approaching agents.”

There has to be real business benefit to the project, she says. “If you can show me the business outcome, we’ll consider it,” she continues. “But we don’t want or need hundreds or thousands of agents deployed. We want them all standardized and monitored, controlled, and auditable.”

Something similar happened a decade ago with RPA, says Huerta, when easy-to-use automation tools became available to people.

“When they were deployed without proper governance, systems got exposed,” he says. “They started stressing the overall infrastructure of the company, and some robots weren’t created in a way for a return on investment. The process ran faster, but consumed more in the cloud, so you ended up putting all the money you saved in the process into your cloud infrastructure, and the total ROI was zero.”

2. It’s not “set and forget”

Legal services company Purpose Legal uses the same basic approach for gen AI-based automation as it did with the previous generation of automation, based on ML, human oversight, and careful validation of the automated processes.

Take for example legal discovery, where documents are produced and shared with the opposing party in a legal case.

“Inadvertent production of sensitive data is a nightmare,” says Jeff Johnson, Purpose Legal’s chief innovation officer. “We always have to evaluate the data. Especially in the legal services context, we need people in the guardrails to make sure the process is on track.”

Without that oversight, problems can escalate quickly.

“If you make a bad decision you may get chastised by the court, lose the case, or lose the client entirely,” he says. “That happened in the past if you trusted automation too much.”

The AI tools today may be more sophisticated, he says, but they’re not perfect. “Even in the world of gen AI, it’s still something we need to watch out for,” he adds.

If anything, the oversight is even more important because of the scale at which AI can work, and how authoritative it can seem.

“Attorneys are more inclined to trust automation now because it interacts with them much more like a person would,” Johnson says. “It’s actually giving attorneys summaries of documents that look like another attorney wrote it, but that doesn’t mean it’s right.”

3. Reaping what’s sown

The need for good inputs goes back to the beginning of the computing era, if not earlier. “If we aren’t proving good inputs and putting good guardrails in place about where the AI gets its input, we get bad decisions,” says Johnson.

After all, data quality is a concern for any company rolling out automation, whether RPA or gen AI.

“Agentic AI won’t solve the entire data quality issue,” says Sabrina Joos, director of program and lifecycle management for new systems for the Americas at Siemens. But there are some differences, she says, in how it plays out.

In the RPA world, data quality was mostly about structured data and stable inputs. So, for example, if the data was formatted in a way the RPA didn’t expect, it might not execute.

“With agentic AI, the data quality issue becomes much more complex,” she says. “It’s no longer just about whether the data is correct, but if it’s complete and meaningful in context.”

AI systems can accept unstructured inputs or ambiguous data and make sense of it, but it doesn’t always interpret that data correctly.

“We don’t care too much about the format or typos since that’s not as much of an issue anymore,” Joos says. “But if there are assumptions that aren’t right, the process or workflow will still be executed. And this is where you have a risk that it will scale.”

For example, an AI can mix up two projects because they sound similar, she says. “Or, working on manufacturing solutions, it might not recognize the physical constraints of a system and will try to optimize and do something that a machine can’t do.”

Or two people might have a different understanding of an issue, and there might not even be an objective truth.

“You need to know where the interpretations are going to be made because there’s not enough information,” she says. “If we can identify this, we can trigger clarification questions. If I get a description from a customer, I might have a different view of it than you.” Solving the problem could involve additional conversations with the sales team, or double-checking with the original sources.

These data quality issues need to be considered early, says Jon Knisley,

director of AI value management at ABBYY.

“It’s really easy to run a pilot when it’s not in production,” he says. “But when you try to move it there, you get data issues. Where is the data coming from, and what’s the risk component?”

That’s also when the governance problems arise, as well as other challenges. These are all fundamentals that companies needed to learn in the previous era of automation and RPA, he says, since we’ve seen this technology cycle before.

4. Respecting change management

The biggest thing being forgotten about is change management, says Knisley.

“An AI project isn’t going to fail because of the model,” he says. “It’ll fail based on people and process. And there’s not that balance yet between the technology, people, and the process. Especially in North America, we want to solve every problem with technology. And that’s just not how the world operates.”

Back in 2019, according to a Forrester survey conducted on behalf of UiPath, 82% of respondents said change management was a challenge for RPA deployments. The same is true today. In a recent Kyndryl survey of over 1,100 business leaders, the speed of AI has outpaced workforce, governance, and operating models for 79% of organizations, and only 9% of organizations have implemented change management, redesigned roles around AI, and built workforce readiness.

“The biggest challenge we had in any digital transformation — and still have — is change management,” says Rahul Chhabra, director of applied AI at Herbert Smith Freehills Kramer, a leading global law firm.

And it’s gotten harder. With AI in particular, the technology is evolving so fast that change management is a quickly moving target.

“With RPA, it was sort of simple,” Chhabra says. “We had frameworks we could use to train people. We still have those learnings, but we have to enhance those processes.”

Something that works today might no longer work tomorrow, either. “You have to constantly iterate,” he adds. “What has worked can fail fast and only work in modules. So don’t try to solve the entire problem in one go.”

And employees don’t just have to keep learning new skills and adapting their work processes. Knowledge workers in particular also have to face the constant fear that AI will make them irrelevant. It doesn’t help when AI leaders amplify these fears. For example, Dario Amodei, CEO of Anthropic, predicted that AI will be capable of doing most or all jobs, not just entry level, in less than five years.

“Today, if lawyers do 10 tasks, maybe four of them will become obsolete,” says Chhabra. But that doesn’t mean four out of every 10 lawyers will be laid off. Even if most of the work is automated, Chhabra adds, there’ll be more for lawyers to do, not less.

“Today, a litigation matter might be worth $1 million,” he says. “But if it’s just $150,000, then a lot more matters are brought forward. So there’s going to be an increase in litigation and, therefore, more work for lawyers.”

RPA isn’t dead

So is RPA over? RPA wasn’t smart, says Traci Gusher, data and analytics leader at EY Americas. “It was useful, but it wasn’t intelligent. You couldn’t rewrite the process with RPA because it wasn’t technologically advanced enough.”

So, about 15 years ago, during the big RPA wave, organizations looked for ways to use RPA inside their processes, but the benefits were extremely limited.

“It was never so demonstrative that it would catch investors’ eyes,” she says. “It never got to that level of impact.”

Today, many companies are making the same mistakes with AI, she says. Instead of adding AI to existing processes, they need to rebuild them from scratch. “If you’re chunking it, you’re not going to get the results you want because it’s too small and incremental. That’s why I think over a period of time, RPA died a slow death.”

But AI can actually bring RPA back to relevance, she adds.

“There’s still very much a place for RPA in the AI wave,” she says. “You can use RPA for tasks and transaction-level activities, and integrate with agents. That might be the most cost-effective way.”

Unlike agentic AI, RPA is deterministic and completely predictable, it can run on-prem without leaking any sensitive data, and it incurs no token costs.

“We’ve seen consultants say we need to do this with agentic AI,” says ABBYY’s Knisley. “And they don’t have any reliability or governance. What they’re ultimately trying to do they could’ve done with regex for a tenth of the price, and 10 times the efficiency. You’ve got to figure out when you need to use agentic, script, or regex.”

So instead of throwing out RPA and going all-in on agentic AI, many companies are taking a more nuanced approach, using traditional RPA for processes that don’t require intelligence. Meanwhile, they use AI to help set up, test, manage, and upgrade the RPA, getting the best of both worlds.

“I think RPA is a very powerful technology and it has a place in the world today,” says Chhabra. “Especially on things that need to be deterministic, or you’re automating high risk or compliance workloads. You can mask the PII, but it’s still a risk to the company, so I’d rather use a script or some form of RPA automation.”

And a lot of governance will be rules-based, he adds, or based on RPA.

“There’s a lot of marketing speak that RPA is dead,” he says. “I don’t think that. Even the AI vendors are using RPA in the back, but now they’re calling it workflow automation.”