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.”

2026 arrived as the year AI ROI would need to get real. After years of experiments and pilots that largely failed to scale, CEOs’ No. 1 priority for CIOs was to achieve demonstrable benefits from AI investments.

Under that pressure, CIOs started to feel the heat, with 71% of IT leaders in February saying 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. For many, experience may have informed that anxiety, as three-quarters of CIOs surveyed then also said they had remorse over at least one major AI vendor or platform selection made in the past 18 months..

Six months later, and passed that midyear mark, CIOs who have set their course for AI ROI are finding that destination remains elusive. Still, there hasn’t been a spate of CIO firings, and AI spending continues to grow. About 71% of organizations plan to increase AI spending this year, but only 27% expect near-term ROI, according to recent research by IT solutions provider TEKsystems.

That metric aligns with findings from CIO.com’s State of the CIO survey from earlier this year, when 40% of IT leaders said some AI initiatives (between 30% and 70%) were meeting ROI goals. While progress remains the same, the drumbeat to prove value goes on.

“CIOs are feeling pressure to demonstrate that AI is delivering measurable business value, not just experimentation,” says Jed Dougherty, SVP of AI and platform at Dataiku. That’s because, while CIOs’ worst fears haven’t been realized, organizations are putting greater scrutiny on AI investments, he adds.

“The CIOs who are succeeding aren’t deploying AI everywhere,” he says. “They’re building the governance, data, and operational foundation that lets the business scale AI responsibly and demonstrate real outcomes.”

A pronounced focus on AI spending and costs

Bob Hutchins, CEO at AI advisory firm Human Voice Media, believes CIOs’ early year anxiety wasn’t likely based on any formal deadlines.

And if any CIOs have been fired since February because they missed AI targets, those changes are likely hidden from public scrutiny either in reorganization efforts or other leadership changes, he says.

“Midyear came and went and there was no apparent bloodletting,” he adds. “I haven’t seen credible evidence of mass firings of CIOs due solely to missing return on investment targets for artificial intelligence.”

But organizations do seem more focused on spending their AI budgets wisely, Hutchins notes. Nearly half of all organizations surveyed recently by KPMG have delayed, stopped, or scaled back AI projects due to budgetary constraints, he says.

“Companies are stopping poorly performing projects, scaling back pilot programs, decreasing the number of vendors they use, creating cheaper models of products and services, and giving more control over AI approval to the financial department,” he adds.

Ryan Ries, chief AI and data scientist at AI and cloud consulting firm Mission Cloud, also sees IT leaders still under pressure to improve AI results.

While firing a CIO midyear looks bad on an earnings call, IT leaders now face budget triage efforts related to AI, he says.

“Money still flows to projects with a hard number attached,” he adds. “Pilots without one get quietly starved but not killed outright. The AI landscape is constantly changing, and companies are trying to figure out all the new tools like coworking and coding solutions.”

Moreover, facing increased uncertainty over AI pricing, CIOs are re-examining AI adoption metrics and becoming more aware of the hidden costs of AI.

Cost control is also receiving greater emphasis as some early agentic forays have shown how an AI agent can cost more than an employee without limits in place.

CIOs still on the hot seat

CIOs are also finding that it is taking their organizations more time to figure out how to use AI tools to their full advantage, and that they are constantly reacting to errors, Ries notes.

As a result, IT leaders appear to be putting in more effort to find AI value than they were earlier this year, he says. “Fewer are succeeding than leadership wants to admit,” he adds.

While most organizations were experimenting with the so-called “art of the possible,” IT leaders seeing success are focused on attaching a metric to every AI project before launch, not after, he says.

Many IT leaders still aren’t taking that approach, however. “The CIOs still stuck are the ones running pilots that never graduate to production, usually because nobody can explain what the model is doing under the hood, and teams are trying to answer the wrong questions with AI,” he says.

It’s possible that CIOs have gotten a reprieve due to the ongoing complexity of the AI ROI mandate, Ries adds.

“Boards don’t fire on a spreadsheet’s calendar, but the underlying pressure was real, and it hasn’t eased,” he says. “Instead of a hard cutoff, CIOs now face constant reporting. Monthly board briefings on AI performance are becoming standard, not optional.”

CIOs should remain on their toes and focus on driving AI value, Ries says. “The fear of a July guillotine was overstated,” he adds. “The fear of ongoing, permanent scrutiny was not, if anything it has increased, due to how quickly cost overruns can happen.”

Budget pressure is real

Like other observers, Mridul Nagpal, CTO and co-founder of AI software development company Krazimo, sees a growing focus on AI budgets and untargeted spending.

“The pressure is real, but it’s reshaping spend more than cutting it,” he says. “What’s actually at risk is the undifferentiated AI budget — the ‘we’re doing AI’ line item with no outcome attached.”

Many CIOs are now starting to show AI value, but by narrowing their approaches, not expanding them, he adds.

“CIOs who funded broad experimentation are the ones sweating; CIOs who tied spend to a specific, measured workflow are defending, and often growing, their budgets,” he says. “The fallout is landing on unaccountable AI spend, not AI spend per se.”

The CIOs showing returns have quietly killed sprawling AI pilot portfolios and doubled down on a handful of use cases that reached production, Nagpal adds.

Like Ries, Nagpal believes that earlier CIO fears were a bit overblown and, at the same time, they’ve gotten more time to prove AI value.

“Boards softened the ‘or else’ because the whole market discovered the pilot-to-production gap is real and hard, so the deadline quietly moved,” he says. “But the underlying expectation didn’t disappear — it matured from ‘show me AI’ to ‘show me AI that pays for itself.’”

The AI revolution has created new opportunities for CIOs, with expanded responsibilities and more authority, but some observers see the opposite happening at some organizations.

While many CIOs have become the main executive leading AI strategy and initiatives, some organizations have set the responsibility for AI deployment and adoption with another executive.

That puts CIOs in a real danger of being sidelined during the internal AI debate, according to some IT leaders and observers. Many organizations, for example, have appointed chief AI officers, and other industry experts suggest AI initiatives should be the purview of the CEO.

AI adoption is too important and pervasive to be confined to a single department, argues Nishith Rastogi, founder and chief executive and technology officer of AI-driven logistics solutions provider Locus. As a result, the CEO needs to be the main champion of the technology, he adds,

Rastogi recently combined the CEO and CTO at Locus to focus on AI. For now, he wants to be directly involved in all new AI initiatives at Locus, which doesn’t currently have a CIO.

“The job of a CEO is to be worrying about the leading indicators and the lagging indicators, and we are a technology company,” he says. “We must be at the absolute forefront of that, and the profound shift that is happening today is that AI is the new IT.”

At some point in Locus’ growth journey, however, the company will need a CIO, and the CIO will be heavily involved in AI-related operations, Rastogi predicts.

“I may get the ball rolling, I will put the hamster wheel in motion, but I would definitely need a CIO and CTO to take it to closure and truly realize all the impacts,” he says. “If everybody starts using LLMs and AI and everybody becomes more efficient, the need for someone to manage, to deploy, to create infrastructure actually expands.”

The rise of the CAIO

When it’s not the CEO taking the reins, many enterprises are passing over the CIO to create a new title to lead deployment efforts. A report from IBM’s Institute for Business Value found that 76% of surveyed organizations now have a CAIO, up from just 26% in 2025.

Appointing a CAIO can undercut the CIO’s mandate in some cases, says Debbie Madden, founder and chairwoman of AI consulting and software engineering firm Stride.

“Here’s how this might play out,” she says. “The board applies pressure to adopt AI, the CIO responds with infrastructure or cost savings answers, and now there’s a chief AI officer who owns the most strategic budget in the company.”

But getting into a turf war about AI and IT budgets is the wrong approach for CIOs, Madden says. “The moment you’re defending the IT budget, you’ve already positioned yourself as a cost center, and cost centers don’t get handed the AI mandate,” she adds.

Instead, the CIOs gaining scope inside their organizations tie every AI initiative to revenue growth, margin protection, risk reduction, or speed, she says. Proactive CIOs also take on difficult AI governance issues.

“They claim the governance questions before anyone else does: who owns the output, who reviews it before it touches a customer, and what’s the rollback path when the system is wrong,” she explains. “Whoever answers those questions owns AI. That’s the turf worth taking.”

Madden sees the CIO role splitting as AI becomes more pervasive within enterprises. “There’s the infrastructure job, keeping systems running, and there’s the value job, deciding where AI changes how work gets done,” she says. “AI is pulling those apart, and the CIOs consolidating power are the ones taking the value job.”

Broadening the definition

Dustin Engel, founder and principal consultant at AI consulting firm Elegant Disruption, also sees CIOs potentially losing responsibility as the organization deploys AI, but it’s often because the CIO role is defined too narrowly.

“CIOs are not being sidelined because AI is too technical,” he says. “They are being sidelined when AI becomes too strategic for the way the CIO role has been defined inside the company.”

When some organizations appoint a CAIO, it signals that leadership doesn’t believe that the CIO’s existing technology function can turn AI into a practical operating agenda, he suggests.

“If the CIO is viewed as the person who keeps systems running, AI will move around them,” Engel adds. “If the CIO is viewed as the person who helps the business redesign how work gets done, AI expands their influence.”

Engel agrees that CIOs should avoid turf battles. At many enterprises, AI will show up across the organization, in marketing, sales, finance, legal, HR, operations, and the product team, and the CIO can’t be everywhere.

“If the CIO tries to control all of that from the center, the business will either slow down or go around IT,” he says. “The CIO does not need to own every AI project. The CIO needs to make sure the company does not end up with disconnected experiments, weak governance, and tools that cannot scale.”

There’s also a danger in removing AI responsibilities from the top IT executive at an organization, some experts say. Good CIOs can help AI integrate into other IT systems, says Ken Ringdahl, CTO of expense intelligence vendor Emburse.

“If AI sits outside the CIO’s remit, the CIO may lose scope, but the company also loses coherence,” he says. “AI is not a feature that can simply be bolted onto the business. It has to be integrated into applications, data, security controls, and everyday workflows, and without that integration, organizations end up with fragmented experiments, inconsistent results, and a much heavier burden of training and enablement.”

A CAIO can bring focus and expertise to AI deployments, but the role often doesn’t have the authority to change systems, workflows, and operating models across the enterprise, he adds. The CIO, however, typically does have that remit.

“A chief AI officer can accelerate the AI agenda but cannot substitute for the authority required to transform the organization,” he says.

Working together

Like Locus’ Rastogi, Ringdahl believes the CEO has an important role to play during AI deployment and integration. But the CIO has a role as well.

“The CEO owns the AI mandate; the CIO owns the machinery that makes it real,” he says. “The CEO must establish why AI matters, where it should create value, and how leaders will be held accountable for adoption and results. The CIO then turns that mandate into an integrated, secure and scalable capability across the company’s systems and workflows.”

Rastogi encourages CIOs to stay in the game by turning themselves into AI experts.

“The encouraging part here is that the entire art is just a couple of years old, so in under a month, you can pretty much catch up to the very top,” he says. “The moment you do that, you become literally the most indispensable and the most needed resource in the organization because every leader wants to partner with you to supercharge them.”