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

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.”

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.”

There is a tidy story going around about the end of enterprise software. Call it the SaaSpocalypse. AI and vibe coding have made it cheap enough to rip out your software-as-a-service contracts and build your own replacements.

The rush to rebuild carries its own risk, one that surfaces only after the SaaS is gone. Teams can almost always build the replacement. What they build, too often, is a copy of what they already had.

Few people are better placed to see that risk than Mike Anderson. As chief digital and information officer at Netskope, the cloud security company that went public on the Nasdaq in September 2025, Anderson runs both IT and the company’s strategy office, a seat that spans his own operations and the broader go-to-market. He is a 2026 inductee into the CIO Hall of Fame, sits on a long list of advisory boards and venture capital innovator networks, and is one of the industry’s most connected executives, fielding peer questions about AI nearly every week. Before Netskope, he was CIO for North America at Schneider Electric. He lives in Dallas.

It starts with how fast the ground has moved. “We’ve gone from AI is my assistant, to I’m delegating a task to an agent, to I’m actually delegating full bodies of work to agents,” Anderson says. “We’re in that last one now.” The trouble is that our instincts have not caught up.

The trap of rebuilding what you already have

Anderson is not interested in slowing anyone down. “I’m a big believer in innovation at the edge of your company. Innovate closest to the people, closest to the problem,” he says. “As CIOs, we don’t want to be the people who say no. We want to let them experiment and learn.”

The danger he points to is quieter than recklessness. It is the pull to aim powerful new tools at rebuilding the past. “The risk is we’re not thinking differently. We’re thinking based on the bias of how things work today,” he says. “Today’s systems are built around people: dashboards that serve us insights, forms we fill in, workflows that pass work between teams. If we go vibe code something, it’s probably going to look a lot like that,” Anderson says. “And it’s not designed for agents, who don’t need a form and don’t need the dashboard. They just need access to the data, the API to call or the other agent to talk to.”

As Anderson sees it, the opportunity is bigger than software. “It’s about reinventing processes with agents in the middle of the process,” he says.

Start with the outcome, not the keyboard

Doing that well means fighting the urge to start building. “Before you put any fingers on keyboards, get a small cross-functional team together, look at reimagining the process and start from the outcome. Then work back,” Anderson says.

It also means changing the question. The reflex has been to ask whether a task can be done with AI. Anderson wants a sharper test. “It’s this work I could delegate to an agent in a deterministic way, where there’s predictability in the outcome,” he says. “That’s a different pivot from where we were three or six months ago.”

The teams that pull this off do not need to be big. Anderson keeps them deliberately small: a subject matter expert who lives the problem, a product owner who frames the context and an engineer who turns it into something an agent can act on. “The old rule was a two-pizza team,” he says. “Now maybe the two pizzas are for three people who are just really hungry, because they’re working tirelessly.”

The unglamorous foundations

Before any of that, Anderson puts discipline around what gets built at all. Every candidate is weighed against three levers: whether it accelerates growth, whether it takes out cost and creates leverage, and what the risk is if it goes wrong.

Then come what he calls the primitives: consistent user management, observability in the tools and standards encoded where agents will read them. “I have markdown files that determine the technologies I want used, down to the database,” he says. “I don’t want agents deciding today that they’re going to introduce a database that’s never been in my environment, because at some point this has to move to a production state.” That last part is what he thinks teams underestimate. When you replace a vendor, you inherit the job the vendor used to do. “Someone has to keep it running. We didn’t have that responsibility in SaaS. Now we do,” he says.

The foundations reach past code, too: style guides, shared libraries, reusable AI assistants and skills that help non-engineers describe what they want, and documentation he insists must be “a first-class citizen.” Netskope IT team built one called Professor Vibe Code that turns a recorded description of an outcome into instructions an AI can build from. “It’s not ‘I need a field on a screen,'” he says. “It’s describing what you want as context with a clear definition of success.”

Why this is really a people problem

For all the talk of architecture, Anderson keeps steering back to people. “We’ve always said building the technology is easy. Getting people to use it is hard,” he says. “That’s even more true here.” He has lived through the internet, SaaS and cloud, and ranks none of them with this. “I can’t point to a technology that’s as disruptive, or that introduces as much change, as AI.”

Which is why he now treats his chief human resources officer as just as critical a partner as his CISO. The two of them talk constantly about the human risk of AI and how to bring people along. “Everyone is worried. Is AI going to replace me? What’s my future in an AI-first world,” he says. “So much of this comes down to giving people clarity about where they fit.”

Borrowing from Maslow, he notes that psychological safety rests on more basic needs, the paycheck and the roof, and that people who feel threatened do not stay neutral. “Without psychological safety, on one extreme you get AI sabotage. On the softer end you get passive resistance, where people just resist using the AI,” he says. The aim is to keep humans at the center of processes that increasingly run without them.

Where to start

For CIOs facing the same moment, Anderson’s advice comes down to three moves. Start with a phone call. “If you’re not talking to your CHRO, get them on speed dial,” he says. “We’ve always kept the CISO on speed dial because security matters so much. You have to add the CHRO now.”

Then be honest about the impact, even when the picture is incomplete. “You may not have all the answers today. Telling people that is important,” he says. And lay the foundations so teams can build at the edge without recreating the old pattern of work handed off later with no context.

The ground keeps shifting, and Anderson does not pretend otherwise. The leaders who come out ahead, in his telling, will be the ones who redesign the work, lay the foundations and never lose sight of the people doing it. For him, the discomfort is the job now. “The world is moving at a pace I’ve never seen before,” he says. “Every day, I have to get comfortable being uncomfortable.”

Organizations are investing heavily in technology platforms, data, AI capabilities and governance frameworks to drive transformation at scale. Yet when it comes to quantifying value — ROI, operational cost savings, improved customer experience — many leaders struggle to demonstrate meaningful impact.

Research points to a growing challenge: Technology adoption is getting easier; proving its value remains difficult. An IBM CEO survey reports that only 25% of AI initiatives have delivered expected ROI over the last few years, and only 16% have scaled enterprise-wide. Part of the challenge may be that leaders are not aligned on what success looks like. A Gartner survey found an expectations gap between CFOs, who primarily use AI to improve productivity and efficiency, and boards, which expect AI to drive revenue growth, intelligence and competitive advantage. The result is a disconnect between AI activity and perceived business value.

Recent Protiviti research suggests that this misalignment extends across the executive suite. Executive confidence in achieving transformational objectives, operational cost savings and AI ROI — even perceptions of the organization’s technological maturity — varies significantly depending on which leader you ask.

Among those responses, the technology leader — CIO or CTO — emerges as one of the most confident, with priorities centered on AI, data and compliance, and a strong view of their organizations’ technological maturity. But being the most confident is not always an advantage; when other executives do not share the same conviction or see the same results, transformational activity can lose momentum or fall short of its full value.

When CIO confidence outpaces CEO conviction

Technology leaders’ confidence that AI is driving revenue growth and cost savings is more than double that of CEOs and board members, according to the Protiviti survey. Net confidence was 61% among CIOs and CTOs, compared with just 30% among chief executives and directors.

There is a reasonable explanation for the more than 30-point gap. Technology teams tend to focus on metrics they can directly measure and influence — user adoption, model performance, productivity gains, process automation, cycle-time reduction, system utilization, platform stability. CEOs and directors, by contrast, evaluate AI through the lens of broader enterprise outcomes, including revenue growth, profitability, margin expansion, customer experience, competitive differentiation, risk reduction and shareholder value. When they cannot see how AI affects these outcomes directly, confidence declines. Both groups are looking for evidence of success, but through fundamentally different lenses.

The implications are real. If executives cannot agree on whether AI is creating value, it becomes difficult to sustain investment, prioritize initiatives and scale adoption.

In my conversations with technology and business leaders, AI adoption is no longer the primary challenge. Demonstrating value is. It’s one thing to automate X number of tasks to improve employee productivity (activity metrics); it’s another thing entirely to demonstrate how AI is helping retain customers, reduce product defects, accelerate sales conversion rates or improve profitability (business impact metrics). I believe closing the gap between AI adoption and business value realization begins with technology leaders asking a basic question: Why are we doing this and how will we measure it?

Advancing to maturity: the alignment accelerator

The most technologically advanced organizations do more than solve technology challenges; they align leadership around change. Leadership alignment goes beyond AI value to positively affect nearly every aspect of transformation and move the enterprise higher on the technological maturity scale. Mature organizations with closely aligned leaders consistently express higher confidence (more than 70% on average) in areas such as trust in enterprise data, ability to integrate new technologies quickly and securely, achievement of transformation objectives, ability to manage risks and ability of the workforce to adapt to the changes. By contrast, the confidence of their peers in less mature organizations is below 20%, with significant variation in agreement from one leader to the next.

This suggests a different way to think about technological maturity. The MIT CISR’s Enterprise AI Maturity Model already defines maturity as more than technology adoption alone; rather, it’s technology with governance, workforce readiness, decision-making practices and organizational capabilities. The Protiviti survey points to another element that appears to be just as critical: leadership alignment. Organizations make progress more quickly when executives agree on priorities, share a common definition of value and align on the outcome’s transformation is intended to deliver. Executive alignment is not a byproduct of transformation success. It is one of its most powerful accelerators.

The CIO’s role in closing the alignment gap

Consider this:40% of chief operations officers identify AI as the capability with the greatest potential to drive revenue growth. Nearly half of CFOs (49%) consider cost optimization the single biggest driver of transformation initiatives.

These expectations are signals technology leaders cannot afford to ignore. In driving technology forward, CIOs must ensure the connection between their effort and the organization’s business goals is clear, credible and understood across the executive team — from the CEO to the finance, risk and HR executive. To do so, tech leaders should consider several foundational shifts in how they think and operate: 

• Reinvent the IT operating model for value-based prioritization — with ROI, value streams and objectives and key results (OKRs) as the primary measures of success. This ensures other business leaders can see and validate technology’s value — driving alignment and accelerating maturity.
• Engage in enterprise prioritization decisions, not just implementation. Position technology investments and their sequencing in the context of long-term enterprise strategy, such that the CEO, board of directors and shareholders can all get behind them.
• Become an arbiter of AI use, ensuring AI investments are tied to ROI, cost discipline and business outcomes. Redefine AI governance to include common language around value, consistent reporting and cross-functional accountability.

In an era of accelerating technology change and growing investment, executive cohesion around what outcomes matter and how to measure them is more than a nice-to-have. It is a business capability as essential as data governance or cybersecurity. It is the discipline that helps determine whether technology investments lead to higher maturity and a competitive edge or dissolve into fragmentation and unrealized potential. The technology leaders who can connect technical activity to strategic outcomes, translate progress into the language of enterprise value and build shared confidence across the executive team will do more than deliver successful transformations. They will help define what leadership looks like in the next generation of digital enterprises.

A few months ago, during a break at an industry conference, I ended up in one of those side conversations that I kept thinking about long after the conference ended. I was talking with several sales leaders about how AI was beginning to reshape the way they worked — from the CRM and business intelligence tools they relied on every day to the broader enterprise applications that supported their sales process. None of them asked for another dashboard. They didn’t want another tab open in the CRM. They wanted to know, in plain language, which accounts needed attention before the next call. I had spent years leading initiatives that built the reporting infrastructure to answer exactly that question — just spread across three or four different screens. That was the moment I realized the question had changed. Sales teams no longer wanted another place to look. They wanted an answer.

The question no dashboard could answer

For most of my career in enterprise business intelligence, my role has gone well beyond translation. I have led initiatives that brought together data from across the business, helped design the enterprise data architecture underneath executive reporting and worked with sales, finance and operations leaders to turn a business question into a report, a KPI or a dashboard living inside one enterprise application or another. The unspoken assumption behind almost every dashboard I helped build was that the user would go find it, open it, read it correctly and act on it, in the middle of an already full day.

During planning sessions over the past couple of years, I started noticing something I had never heard five years earlier. It was not that the dashboards were wrong. Clicking through three separate systems to prepare for one client call had become a tax nobody had time to pay, and the teams I supported started raising it in one-on-ones and quarterly reviews. At first, I read that feedback as an adoption problem, something a better onboarding session or a cleaner interface could fix. I no longer believe that.

I remember sitting with one of our sales leaders while we walked through his workflow before an important client meeting. We opened the CRM, then a separate reporting application, then a pricing tool, then a forecasting dashboard. Halfway through, he looked at me and asked, “Why can’t one system just tell me what I need to know?” I did not have a good answer for him that day. I have been building toward one ever since, and that single question has reframed how I think about every enterprise application my team touches.

From navigating systems to asking questions

I have spent most of my career supporting sales and partner operations, so I saw this shift first inside sales teams. One request I started hearing repeatedly surprised me. Representatives no longer wanted a better report. They wanted a single conversational entry point that could pull opportunity data, check it against pricing or forecasting numbers, pull in something from an HR system if the deal touched staffing and hand back a recommendation instead of a raw export.

Months later, when Gartner published its prediction that 40% of enterprise applications would carry task-specific AI agents by the end of 2026, up from under 5% a year earlier, it did not surprise me. I had already started seeing exactly that inside the sales organizations I support, well before I saw the number attached to it.

What matters here, and what I have watched happen firsthand, is that the underlying systems of record are not disappearing. The CRM still holds the opportunity data. The HCM platform still owns workforce records. What is changing is the layer sitting on top of the business systems people use every day. CIO’s Bill Doerrfeld’s own reporting on agentic AI backs this up, noting that agents are already updating CRM fields automatically from client interactions, with agent-enriched deals moving through pipeline stages meaningfully faster than the rest. I have watched a version of that same pattern play out with the teams I support. The value is not a flashier report. It is closing the gap between having a question and getting an answer people actually trust.

That word, trust, is where my earlier work on data foundations and this shift toward conversational interfaces meet. A layer that sits across a sales technology stack is only as good as the data underneath it, and a system that can now act on a recommendation, not just display one, raises the stakes on getting that foundation right. I have written before about how AI models fail when the data feeding them was never properly governed. A conversational layer spanning multiple business systems does not reduce that risk. It multiplies it, because the system is no longer just reporting a number back to a human who can apply judgment. Increasingly, it is taking the next step itself.

What this shift asks of BI leaders

I do not think the job of enterprise BI leadership disappears in this shift. I think it moves. For years, a meaningful share of my time went into dashboard design and enterprise data architecture, choosing which metric goes where, how a chart should read and which filters a user needs. Some of that work still matters, but a growing share of my attention now goes into questions that used to sit further down my list. Which systems should an AI layer be allowed to query? What happens when two systems disagree about the same customer? Who is accountable when an agent takes an action instead of simply surfacing a report?

I have started telling my own team something I did not fully believe five years ago. The organizations getting real value from this shift are the ones that treated integration and governance as part of the rollout from day one, not something bolted on once adoption took off. That matches what I have seen leading enterprise analytics for a national network of sales and partner relationships. Connecting a CRM, an HCM platform and a forecasting tool through one conversational layer is a real technical challenge, but it is usually solvable. The harder problem is deciding in advance what that layer is allowed to do once it has access to all of it.

I would tell any BI leader watching this shift the same thing I have started telling my own team. Stop measuring success by how many dashboards get built or how many people log into a portal. Start asking whether the people you support are getting trustworthy answers faster than they were a year ago, inside the tools and language they already use. It has changed how I evaluate success. I no longer ask whether we can build another dashboard. I ask whether we’re helping someone make a better decision faster. If the honest answer is no, the fix is probably not a better dashboard. It is rethinking what sits between your people and the applications they have been navigating on their own for too long.

I still believe in the discipline that built my career. Clean data. Clear ownership. Dashboards that earn a leader’s trust before they earn a login. What has changed is where that discipline gets applied.

It used to live inside the report. Increasingly, it lives inside the conversation. Business intelligence stops being something you check periodically and becomes something that responds to you in real time. The organizations that succeed won’t be the ones that build the most dashboards. They’ll be the ones whose people stop asking where to look because the answer already knows.

Every other week, there’s a new story about AI solving a math problem that took mathematicians decades to touch. Most recently, it was a conjecture that had sat unsolved since 1939, cracked with an assist from an AI model. Meanwhile, the typical team still can’t ask ChatGPT what we sold last week without pulling three dashboards and arguing over whose number is right. That begs the question: why is AI intelligent enough to outsmart MIT professors, but it can’t outsmart the sales intern?

Here’s part of the answer. AI doesn’t get to guide a real business decision until it can answer with real accuracy, not 95%, not 99%, all the way. Getting there means clearing three hurdles: context, determinism and cost. Solve those three and you get accuracy. Right now, none of them are solved, which is why accuracy, not intelligence, is the actual blocker nobody wants to admit.

Grounding is the first wall

Ask an AI model a question about your company and it doesn’t actually know your company. It doesn’t know who owns which decision. It doesn’t know why your warehouse manager overrides the forecast every October, or which of two conflicting reports your team trusts. A new hire picks this up by working somewhere long enough. AI needs it handed over, deliberately, in layers.

The first layer is your teams, roles and workflows: who does what, and who is allowed to change it. The second is your industry and business context; the reason a service outage means something different for a bank than it does for a media company. The third is your data itself, the tables, definitions and history that make an answer true rather than plausible. All three have to be handed over deliberately. None of them show up on their own.

What I have observed is that companies skip straight to buying an agent and skip this grounding work entirely, which doesn’t end well. The agent still sounds confident. It’s also wrong in ways nobody catches until a decision has already been made on top of it.

Businesses do not want a coin flip

A client once told me something I have not stopped thinking about. We proposed solving their facility allocation problem with a classic optimization algorithm, deterministic and auditable, the kind where the same input always produces the same output. They were disappointed, stating, “I hired you because you are AI experts. Classic optimization gives us deterministic results. We want AI that gives us indeterminate results.”

I understood what they meant. They wanted something that felt like AI. But indeterminate is not a feature you want in a system telling you how much inventory to order. In the pursuit of using AI for the sake of using AI, it is easy to lose sight of the outcome the business actually needs.

That conversation still shapes how I scope every new engagement. When a client asks for AI without naming the decision it needs to support, I ask what happens the day it’s wrong. If the answer is a bad number in a board deck, we’re not talking about the same kind of AI they think they’re asking for. Sometimes that conversation ends the engagement before it starts. More often, it reshapes it into something smaller and more useful, an agent that handles the easy 80% of questions and flags the rest for a human, instead of one system trying to do everything at once.

Here is a number to think about. Anthropic recently published how it automated its own internal business analytics queries using Claude, and even with a purpose-built system, aggregate accuracy landed around 95%. That is one of the best AI labs in the world, building for its own internal use, still getting roughly one answer in twenty wrong. Tell any CFO that number and watch how fast they walk back to their deterministic BI dashboard.

I don’t think 95% is close enough. Not when the number ends up in a board deck. Not when a wrong answer becomes a real decision with real money behind it. In my experience, executives will forgive AI for being slow to learn. They will not forgive it for being confidently wrong. AI doesn’t earn a seat at the decision table by being right most of the time. It earns it by being right every time, the same way the deterministic system it’s replacing was. That’s not a popular thing to say in a market excited about what AI can do, but popularity was never the bar a business decision needed to clear.

Cost swings before the ROI math holds still

The variance in the cost of running an AI agent is enormous. I have watched the same agent perform the same task use up to 30 times as many tokens in one run as in another. Try defending a Well Architected Framework, or any ROI model, against a cost that swings that hard.

The trend lines pull in opposite directions at the same time. The price per token has generally been falling, which should make agents cheaper over time. At the same time, multi-agent architectures are burning through more tokens to do the same job. And an agent genuinely grounded in your teams, industry context and data — the grounding I described above — will use even more tokens than a shallow one. Full accuracy costs more, not less. The good news, if the trend holds, is that this improves rather than worsens over time. But “if the trend holds” is doing a lot of work in that sentence.

Not every question your business asks needs the same level of AI maturity. How many customers are in the system is easy, and I’ll trust an agent’s answer. How revenue has changed over time is medium. Forecasting future demand by product, week and store gets harder. Figuring out how to allocate demand across factories and manufacturing lines is super hard. Crafting a strategy that takes into consideration the three layers of context plus macroeconomic trends is, honestly, impossible for AI or a human to answer with certainty. The higher you climb that ladder, the more a wrong answer costs you, and the less I’m willing to accept anything short of fully right.

The mistake I see most often is a company jumping from easy straight to hard, expecting an agent that can answer “how many customers do we have” to also handle causal questions like “why did we stop selling a product.” Context requirements, accuracy requirements and token cost all climb together as you move up that ladder. That’s also the difference between handing an agent a task and handing it a role; the judgment a planner, buyer or analyst brings to a job every day sits at the top of the ladder, not the bottom.

The mathematician who cracked that 87-year-old conjecture and the CFO who wants last week’s sales are asking for different things. The mathematician wanted a collaborator: someone to try a thousand wrong paths and surface one interesting idea, where a 5% hit rate is a triumph. The CFO wants a number she can put in front of a board, where a 5% miss rate is a liability. AI has earned the first seat. It hasn’t earned the second.

It will, but not by getting smarter. It gets there when someone does the unglamorous work of grounding it in the company’s context, constraining it to be right the same way twice, and paying for that at a cost the ROI math can survive. Intelligence was never the blocker. Accuracy is. So, until an agent can clear all three hurdles, give it the easy 80% of the ladder, keep a human on the rest and don’t let the fact that it disproved a conjecture convince you it’s ready to order your inventory.

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.”

Most AI investment strategies fail not because the tool or platform underperforms, but because organizations didn’t clearly define what success looks like before they started building.

Through a new approach to measuring value, Zoetis chief digital and technology officer Keith Sarbaugh and his business partners have leveraged a value-driven framework to scale AI solutions across research, manufacturing, and customer experience. And they measure every investment against goals before, during, and after the deployment.

In addition, his team rolled out a model-agnostic gen AI platform now used by nearly 95% of employees, which turned early experimentation into enterprise-wide adoption. Sarbaugh’s current focus now is partnering with Zoetis’ CHRO to advance the $9 billion global company’s capabilities in managing organizational AI adoption.

How are you integrating AI into your growth plans at Zoetis?

We have an umbrella program we call AI@Zoetis, where we unify our AI work under an enterprise purview, which spans research and development, manufacturing, commercial operations, customer and colleague experience, and other business functions. We manage AI collectively to enable grassroots innovation.

For example, we made our generative AI platform available to everyone, so as many people as possible can experiment and innovate. Our colleagues have access to 10 different LLMs, and we’ve seen over 95% adoption rate among our user community, and more than 11,000 colleague-built agents.

One popular AI use case is helping colleagues build their own development plans. The agent guides a colleague through a conversational, coach-like experience to map out their career aspirations against Zoetis’ competency framework, which was key to its wide adoption. This idea came from people not in HR, illustrating the point that some of the best use cases come from our broader employee base.

What’s an AI use case that directly impacts customers?

We have millions of customer interactions across our channels. Our sales force is out talking to them, who are also in our digital platforms, and we receive thousands of calls through customer service. We’ve been using the industry standard Net Promoter Score (NPS) to measure customer loyalty and satisfaction, but NPS is a measure that can take longer to generate. Zoetis has accelerated our awareness of customer feedback into real-time listening, using AI to understand these customer interactions in a holistic way, and at a scale we couldn’t achieve before. NPS still matters for tracking long-term trends and maintaining a consistent industry benchmark. We simply use AI to listen, learn, and act quicker.

Our AI customer experience platform also lets us look across all our customer touchpoints like calls, emails, and websites in real time, immediately identify issues and insights, and then be smart about how we address them. We now have more than 10 times the feedback signals we had in the past. We see trends sooner and act faster, and as humans, we can’t do that without AI.

How are you deciding where to make your AI investments?

We use different lenses. One is AI for the masses, which is our generative AI platform for colleagues; second is our middle lens, where we drive value in a particular function; and the third is enterprise-wide transformation, the big bets that’ll fundamentally change our business. We don’t do many, but we do them in a smart way.

With the transformative investments, we focused on both our commercial business and R&D, which we knew had the highest probability of serious returns. We started with seven golden use cases and knew that if we hit on two or three, it would be a big deal. Of the original seven use cases, six of them exceeded their value target and went from PoC to scale.

Since those earlier days, we’ve broadened to include manufacturing and supply chain, and our enabling functions.

Overall, we didn’t go out of the gate looking for productivity gains. We thought about business transformation right from the start. Today, we’re scaling up those first-mover investments and continue to leverage our value-driven framework to identify more use cases.

Are you creating new value frameworks so you and the rest of the ELT are unified in your investment strategy?

We developed a business value realization framework, which isn’t as sophisticated as it sounds. Before we make a tech investment, we ask what category of benefit we expect to receive, whether it’s revenue uplift, cost reduction, productivity, or whatever. We predict what success will look like and how we’ll measure it. 

Because with AI, we’re trying to move fast. We put a value case together at this early stage and do a PoC, and if it hits its target, we update the value case and decide whether to scale. A key element of the framework is real-time measurement. Are we seeing what we wanted, and if not, how do we pivot for more value?

The speed of iteration and scaling decisions make AI investments unique, so we can’t use our traditional value frameworks for digital investments, generally. Measuring outcomes post-implementation has become even more important.

How is your CHRO partnership impacting AI value?

Our CHRO and I partner closely to ensure enterprise enablement. When driving new ways of working that impact your workforce, you need a comprehensive approach, clear communications, and genuine buy-in. Colleagues adopt faster when they help shape the change. We’re prioritizing our workforce strategy, understanding what AI means for jobs at Zoetis, identifying skills that matter most, and building a plan to upskill people.

Another focus area is organizational change management (OCM). We reviewed our first AI investments to learn from our mistakes, and one consistent theme was that we shortchanged OCM. We thought naively that what we build will be so compelling, adoption will just come. But we didn’t do the right communication and stakeholder management. We recognize our need to develop OCM as a core competency, so our CHRO and I are building an enterprise playbook for AI change.

What’s your pragmatic advice to other CIOs when it comes to OCM?

When you’re wrapped up in a change program, you know what’s coming, but no one else does. When you impact your entire workforce, you need a smart approach to stakeholder management and communications. Involving colleagues in the creation of something new will aid in adoption. Have a deliberate and intentional communications plan and cadence, and have the discipline and objectivity to measure and learn. Our first tries weren’t perfect, but we listened to feedback and pivoted, and those pivots drove further commitment.

Has your communication at the board level changed?

When I talk to my peers about their board conversations, half focus on risk and compliance, and the other half talk about transformation and revenue generation. I’m fortunate that our board cares about both and has great energy around generating revenue, and how AI will give us a competitive advantage. By managing risk and compliance, we can spend our time focusing on potential drug candidates and getting to market quicker. Our board conversation is both about enablement and compliance.

What advice would you give to tomorrow’s CIOs?

The role is now about orchestration, understanding the business, and realizing value from technology investments. If you want to work with the best technology and bleeding-edge innovation, you’ll get some of that as a CIO, but the focus is broader, centered much more on processes and complex business problems than ever.

My advice is if you love working with technology, you’ll get that as a CIO. If you love delivering meaningful outcomes for the business and the customers you serve, it’s a truly rewarding role, and you’ll be an even more successful CIO.

The concept of digital transformation (DX) has been around for quite some time now. Many companies are adopting digital technologies such as AI, IoT and the cloud to transform their businesses, operations and organizational cultures, striving to improve productivity and create new value.

However, on the other hand,

• They have introduced IT tools and systems, but their operations haven’t changed
• They keep repeating proof-of-concept (PoC) projects, but these remain at the PoC stage and do not lead to full-scale deployment
• Frontline staff and all employees do not view DX as something that directly concerns them

These are just a few of the many challenges frequently reported.

Why does this happen?

The fundamental cause lies in viewing DX merely as a project to introduce digital technology.

The essence of DX is not the D (digital), but the X (transformation).

And throughout history, it has always been people who carry out that transformation.

No matter how brilliant or well-crafted a strategy may be, or how advanced the AI introduced, without the talent capable of mastering it, executing it and turning it into results, the strategy will remain nothing more than a pipe dream, and DX will not move forward. I believe that in the coming era, one of the most important roles required of a CIO is to develop talent capable of executing DX.

DX talent is not simply ‘people who are knowledgeable about IT’

First, let’s clarify what DX talent actually means.

When we hear DX talent, we tend to imagine highly specialized professionals such as data scientists, digital consultants and AI engineers. Of course, such expertise is important. However, it is not enough on its own to truly drive DX forward.

What is truly important in DX is

• Understanding management and operational challenges
• Considering value from the customer’s perspective
• Utilizing digital technology as a tool
• Driving transformation while engaging others

In other words, DX talent is not merely IT talent.

They are individuals who can apply the equation and apply it to actual business operations, organizational structures and customer experiences.

The Kansai Electric Power Group has also set a goal to transform into an AI-first company by rebuilding operations on the premise that AI exists. However, to achieve this, we need talent who can treat AI not merely as a convenient tool to try out, but as a weapon to master and utilize to the fullest, and who can embed it into the organization’s DNA and operating system.

A DX talent strategy is a business strategy, not merely a training or HR initiative

First and foremost, it is crucial not to confine the DX talent strategy to training initiatives or HR measures.

DX talent development, by its very nature, asks the questions:

• What kind of company do we aim to become?
• What competitive advantages we want to build
• What value do we want to provide to customers and society?

In other words, it is intrinsically linked to the business strategy itself.

For example,

• We want to use AI to dramatically improve operational productivity
• We want to use data-driven approaches to enhance the quality of management decision-making
• We want to enhance the customer experience and improve customer satisfaction and NPS

If so, you must define a DX talent strategy that can make this a reality and strategically advance the development of such talent.

When formulating a DX talent strategy, there is one principle I personally keep in mind. It is to align people and organizations with the strategy through vertical consistency and horizontal coherence.

First, vertical consistency refers to:

1. Management philosophy (mission, vision, values) and business strategy
2. The DX strategy as the means to achieve them
3. The DX talent strategy for developing the people capable of executing them
4. Organizational culture, which significantly influences strategy execution

This consistency refers to ensuring that the approach, interpretation, rules and actions regarding these four major areas are maintained along the same policy and logic from start to finish.

Next, horizontal alignment refers to the alignment of the following elements, which tend to have a particularly strong influence on the DX talent strategy:

• The organization’s hierarchy (vertical) and departments (horizontal), rules, responsibilities and authority, as well as employee communication styles and engagement
• The HR systems, which consist of the cycle of recruitment → placement → development → evaluation → compensation

Horizontal alignment also ensures these elements do not contradict the DX talent strategy and are logically consistent with it.

If there is even the slightest flaw in this vertical consistency and horizontal coherence, the listener will feel a sense of unease — thinking, “something doesn’t quite add up” or “can I really trust this?” — before even considering the content itself.

• Have your policies or arguments drifted off course without you realizing it?
• Are there any contradictions between departments, materials, statements and actions?

Only when both of these elements are in place does an explanation become persuasive, and trust in the organization and its people begins to build. That is precisely why I believe it is crucial, when communicating, to carefully verify the vertical consistency within the flow of policies and arguments, as well as the horizontal coherence among stakeholders, information and actions.

DX won’t move forward just by knowing

There is another pitfall people often fall into when developing DX talent. It is when acquiring knowledge becomes the goal in itself.

For example, someone who:

• Took a DX training course
• Learned how to use AI tools through e-learning
• Attended an external DX seminar

These are certainly necessary. However, they alone will not bring about change in people or organizations.

What matters is not knowing but being able to act. In fact, in many organizations,

• Participants understood the material during training
• Got excited during the seminar
• But nothing changes on the ground

This is because there is a significant barrier between knowledge and action.

As part of its DX and AI strategy, the Kansai Electric Power Group has defined and publicly announced its DX Talent Strategy, which clarifies the ideal talent profile, the number of employees to be developed and the training framework.

• Target participants are classified into three tiers: Advanced DX Talent, DX Promoters and All Employees
• We formulated a DX Talent Strategy that defines skills and mindsets based on the Digital Skills Standard (DSS) established by the IPA. As talent development measures tailored to each talent profile and proficiency level, we will offer a total of 31 training courses in fiscal year 2025
• Through further expansion of training content and other measures, we aim to develop approximately 70 Advanced DX Talents and approximately 5,000 DX Promoters across all departments by the end of fiscal year 2028

A key aspect of implementing this DX talent strategy is

• Measuring and visualizing the number of employees across the entire organization for each talent profile and proficiency level
• I believe that rather than simply stopping at attending training sessions, applying what you learn to your actual work allows you to advance your proficiency through the stages of knowing → being able to do → being able to teach.

That is my belief.

Praise is the best tool you can use right away

At the Kansai Electric Power Group, we’re moving forward while being highly conscious of the alignment between our DX talent strategy and our HR systems — that is, the recruitment → placement → development → evaluation → compensation cycle. Among these, the ideal that people with high DX skills who have achieved results in business and operations receive high financial compensation is something everyone can imagine, but the reality is that there are very high hurdles to overcome when actually trying to implement it.

Even in such circumstances, the most efficient and immediately implementable recommendation is to utilize internal and external recognition programs — in other words, praise.

At Kansai Electric Power, as part of our internal recognition program, we hold an annual DX event called KANDEN Digital Day, attended by approximately 1,100 members of the Kansai Electric Power Group. At this event, we honor individuals who have excelled in DX and achieved results as DX Pioneers, and we also recognize those who have created and utilized outstanding custom GPTs through our Custom GPT Contest.

We also actively apply for external awards in fields such as IT and DX. Recently, we have been selected for as many as eight awards; most recently, we were selected as the first electric power company to be included in the DX Stocks 2026 initiative, jointly organized by the Ministry of Economy, Trade and Industry, the Tokyo Stock Exchange and the Information-technology Promotion Agency (IPA).

When we are selected for awards through such recognition programs, the people implementing those DX initiatives not only receive social recognition for their work and gain the psychological reward of joy, but this also transforms into self-esteem and confidence, becoming a further source of motivation and fulfillment and leading to personal and organizational growth — thus setting a virtuous cycle in motion.

Another major effect of this initiative is the feedback loop from evaluation and rewards to recruitment. When we win an award through an external recognition program, positive word-of-mouth about Kansai Electric Power’s DX efforts spreads online. Students and people working on DX at other companies who see this might think Kansai Electric Power is quite advanced in DX. It seems like they’re doing all sorts of cutting-edge and interesting things, so I’d definitely like to join the company and work on this together! In other words, we aim to create a world where the evaluation and rewards provided by these awards generate positive feedback that leads to recruitment.

Furthermore, in the age of AI, we believe that Kansai Electric Power should not only be chosen by people but also chosen by AI.

• Based on the latest information, please create a ranking of companies in the energy industry that are making progress in DX. Please also provide the rationale for your assessment.
• Based on the latest information, please create a ranking of Kansai-based companies that you would recommend for DX professionals seeking employment. Please also provide the rationale behind your assessment.

I intend to strive for continuous improvement and growth every day so that, when these prompts are entered into generative AI, Kansai Electric Power will remain the kind of company that receives the response: Kansai Electric Power is number one.

A CIO is the leader responsible for developing talent for strategic execution and organizational transformation

As discussed so far, developing DX talent is not merely a matter of conducting training and education. It is interconnected with the company’s management philosophy, strategy, business operations and organizational culture.

That is precisely why CIOs are expected to serve not as leaders responsible for technology adoption, but as leaders responsible for talent development to execute strategy and drive organizational transformation. Especially in the age of AI, I strongly believe that a company’s future competitiveness will hinge on how many people it can cultivate — not merely people who can use AI, but people who can collaborate with AI to execute strategy and create value and people who can drive organizational transformation to make that a reality.

Talent development is the greatest investment in DX

In the world of DX, it’s easy to get caught up in the latest technologies and new tools. However, ultimately, it is people who drive change in companies — in every era.

• Identifying challenges
• Consider customer value
• Master the use of AI
• Engage those around you
• Execute the transformation

Only when the number of such talented individuals increases will DX truly take root in an organization.

Developing talent capable of executing DX is not merely about teaching skills. It is, in essence, the very act of building an organization that can continue to evolve.

In this age of AI, the value of people is actually increasing. Isn’t it true that CIOs are expected not only to master technology but also to believe in, nurture and unleash human potential?

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.”

Each year the CIO 100 Awards showcase outstanding IT initiatives, and every year they illustrate the power and potential of technology to transform how people work, how organizations perform, and the value they offer to customers.

The 2026 cohort of winners is no different. Each one demonstrates how IT executives and their teams successfully move from ideation to deployment to scaling a solution for the future, overcoming challenges and driving adoption along the way to ensure their organization gets a return on its investment.

[ Interested in meeting and learning from all CIO 100 winners? Join us next week at CIO 100 Awards & Conference in Frisco, TX. Limited seats remain! Register here ]

The winning initiatives come from a range of industries and utilize a host of technologies to achieve their goals, as is the case annually. A growing proportion of these stand-out projects leverage artificial intelligence, raising the bar on the art of the possible for all IT departments.

The following 10 award-winning projects serve as representatives for the outstanding work done by all the 2026 honorees.

ABB democratizes AI agent creation and deployment

Organization: ABB

Project: ABBY — AI Agentic Platform for Workforce Transformation

IT leader: Vikke Kandell, CIO

IT leaders at ABB, a manufacturer, had some big hurdles to clear when it came to building an AI strategy.

They had to overcome employee fears that AI would take away jobs, the potentially high cost of AI vendor licenses, and pressure from investors, customers, and executives to advance the use of AI in the enterprise.

“We looked at this and asked, ‘How do we address all this?’ and build something that the company is proud of,” says Babu Kuttala, vice president of data analytics and AI.

The answer is ABBY, an AI agentic platform that enables employees to create and deploy specialized AI agents for specific business tasks.

To build ABBY, Kuttala and his team used best-of-breed LLMs (about 25 in total). They built a centralized orchestration layer using generative AI that integrates internal knowledge bases with external ecosystems, creating a unified platform where agents can access enterprise data, understand required actions, and execute tasks across multiple systems. And they created preconfigured skills so that employees could build agents tailored to their workflows without having to code.

ABBY was rolled out in 2025 to 100 users but is now used by 63,000 (more than 75% of the company’s workforce, Kuttala notes) with an average of 10,000-plus workers using it daily. IT continues to add LLMs and capabilities to expand use of ABBY even further, Kuttala says.

Belcorp modernizes manufacturing with Smart Factory

Organization: Belcorp

Project: QPlant — Smart Factory

IT leader: Venkat Gopalan, Chief Digital, Data, and Technology Officer

Legacy processes were limiting Belcorp’s ability to scale and compete. Its manufacturing relied on ERP-driven processes with limited shop-floor automation and weak connectivity across production, packaging, quality, and maintenance. The company depended heavily on manual records and post-process reconciliation, resulting in fragmented data, limited real-time insight, inefficiencies, and higher risks for errors.

Smart Factory changed all that. The IT initiative reimagined how manufacturing teams work “by creating a connected, data-driven environment where production, quality, maintenance, and operations are aligned around real-time information and standardized execution,” says Venkat Gopalan, chief digital, data, and technology officer.

At Smart Factory’s core is a manufacturing execution system that orchestrates production workflows, quality processes, and operational execution, he explains. IoT-enabled equipment integration and a centralized SCADA platform provide real-time visibility into shop-floor operations, while electronic batch records digitize production execution, strengthen traceability, and reinforce compliance by design.

Integrating those operational technologies with the company’s enterprise platforms was another critical component of success, Gopalan says, creating a trusted flow of real-time data across manufacturing, quality, maintenance, and business systems. “This connected architecture transformed isolated data into actionable insights, enabling faster decision-making, greater operational visibility, and continuous improvement across the manufacturing lifecycle,” he adds.

The initiative generated more than $1 million in financial benefits in its first year alone.

“Most importantly, Smart Factory established the digital foundation for the future of manufacturing at Belcorp,” Gopalan says. “With real-time operational data and connected systems now in place, we’re well positioned to accelerate advanced analytics, AI-driven optimization, predictive maintenance, and other Industry 4.0 capabilities that will continue delivering value for years to come.”

Cohesity replatforms post-acquisition for commercial growth

Organization: Cohesity

Project: Lead to Cash Replatforming Program (Veritas Integration)

IT leader: Brian Spanswick, CIO

Cohesity set an ambitious objective: Complete an enterprise-scale lead-to-cash replatform in under six months.

That’s a tight timeline for any replatforming initiative, but Cohesity’s project had another layer of complexity. It followed Cohesity’s December 2024 acquisition of Veritas, a company twice its size in revenue, leaving Cohesity to integrate the majority of a global enterprise revenue engine into its own operating model without disrupting customers, partners, or sellers.

“We had to bring the two companies together, merge the workforces together, and create an overall harmonized organization and operating infrastructure platform,” says Eric Brown, who as CFO and COO led the project.

The program migrated heavily customized CRM, CPQ, PRM, ERP, and subscription platforms (some of which were “very brittle, very bespoke,” Brown says) to a unified SaaS CRM, CPQ, and ERP environment with uninterrupted selling, billing, and partner operations.

This was no lift-and shift, Brown stresses. “It was a business process optimization project as well. We want to run very efficiently, so we questioned everything and used the migration process to simplify and streamline the business in every possible respect.”

The initiative enabled continuity for 13,000-plus customers, protected revenue during integration, and established a scalable commercial foundation for future growth.

Brown cites several factors that contributed to success. First, leadership was upfront about what it would take to meet the deadline, a process that involved carefully prioritizing the capabilities that would appear in the first iteration. Leadership also streamlined decision-making, establishing office hours that “ran with military precision” to handle issues. And the company selected a specialized partner, requiring its top talent be assigned to Cohesity.

Dairyland Power goes agentic to protect field crews

Organization: Dairyland Power Cooperative

Project: ODIN — Organizational Effectiveness Agentic AI

IT leader: Nate Melby, VP and CIO

Dairyland Power Cooperative had amassed a large collection of field observations, incident reports, near-misses, safety rules, and work methods that could yield insights into processes and practices that could help protect its workers.

But the insights were essentially out of reach, trapped in siloes.

Dairyland’s organizational effectiveness team turned to CIO Nate Melby for help unlocking those insights. Melby then turned to agentic AI, recognizing that the technology could address the team’s need to make better use of its data.

“This was about finding insights on how to work more safely,” Melby says. “It’s about preventing incidents.”

The collaboration between the two teams created ODIN, the first agentic AI implementation of its kind in the electric utility industry.

Focused on worker safety, ODIN autonomously connects the collective safety knowledge of the organization and delivers actionable insights directly to field crews at the moment work is planned.

ODIN was developed through a hybrid approach that combined an agentic AI platform and Dairyland’s internal private generative AI platform called VoltWrite. ODIN leverages LLMs, retrieval-augmented generation, and a coordinated swarm of autonomous agents.

Agents work together to analyze internal safety data, performance history, work practices, and safety rules and then synthesize the information into clear guidance on the safest way to perform specific tasks.

ODIN has produced results, including a reduction in OSHA recordable injuries and improvements in the quality and consistency of pre-job safety briefings.

ODIN was deployed in early 2025 for use by Dairyland’s workers in transmission construction and electrical maintenance, which are the highest-risk work areas. Dairyland is looking to expand ODIN’s use to other teams.

Dow’s digital sustainability ledger drives low-carbon sales

Organization: Dow

Project: Carbon Footprint Ledger

IT leader: Deb Bauler, Chief Information and Digital Officer

Executives at Dow consider the Carbon Footprint Ledger (CFL) as more than a technology or innovative carbon accounting methodology. According to Senior Global IT Director Jeremy Preston, CFL is “a digital business capability that enables Dow to translate sustainability investments into customer value.”

CFL transformed how Dow uses greenhouse gas emissions data. It combines a methodology aligned to international standards with an enterprise-scale digital platform. It also integrates manufacturing, supply chain, commercial, and sustainability data to generate product carbon footprints under enterprise-level governance and management at scale.

In doing so, Preston says it creates “a trusted, traceable link between low-carbon processes and raw materials implemented across its manufacturing network and the lower-carbon products customers seek.”

The technology team worked closely with sustainability and business teams, collaboratively developing the capabilities needed to reconstruct product genealogy, maintain end-to-end data lineage, track low-carbon attributes across interconnected manufacturing processes, and generate product carbon footprints that can support customer offerings and commercial transactions.

CFL was built on Dow’s Integrated Data Hub and in partnership with Boston Consulting Group and Databricks.

The core CFL platform is fully deployed and supports commercial transactions today.

Preston says CFL “enables Dow to turn sustainability investments into customer value, commercial differentiation, and new growth opportunities.” Dow reports that it has driven hundreds of millions of dollars in low-carbon product sales in 2025 and 2026.

The company is now expanding its use. “We are extending adoption across additional products, manufacturing networks, business segments, and customer use cases while continuing to enhance automation, analytics, and integration with commercial processes,” Preston says.

J&J transforms quality management with AI

Organization: Johnson & Johnson

Project: Q&C Strategy

IT leader: Michael Comprelli, Vice President, Head of Technology, Technical Operations, and Risk; Joel O’Connor, Head of Technology, Medtech Quality, and Compliance

Johnson & Johnson is using AI to transform quality management through its Q&C Strategy.

QuIn is an AI-powered digital assistant that fuses human expertise with machine learning, automation, and data-driven insights to boost efficiency, reliability, and worker impact. By embedding gen AI into core quality management systems processes, QuIn proactively gathers actionable insights, increases operational efficiency, and allows teams to focus on high-value, patient-centric work.

Cora is an innovative generative AI platform that provides regulatory intelligence monitoring, impact analysis, and augmented content revision. Cora assists with document analysis, compliance comparison, stakeholder analysis, policy/standard creation, procedural/document updates, and document comparison. Cora is purpose-built for regulated environments, validating outputs against source material and offering a user experience that instills trust in the outcome.

QuIn and Cora, which automate time-intensive tasks and democratize information access, are on track to deliver significant value, with J&J reporting more than $62 million in documented true cost savings by 2028 from QuIn alone. Cora delivered $2 million in cost efficiency in 2025 and will deliver a documented cost savings of $25 million by 2028.

“Our teams proved responsible AI can be applied meaningfully in a highly regulated environment without compromising the rigor, accountability, or human judgment that quality requires,” says Michael Comprelli, vice president, head of technology, technical operations, and risk.

He continues, saying that J&J “moved these ideas beyond experimentation and into products that employees use in their daily work. We did that by bringing together Quality expertise, product management, data engineering, architecture, cybersecurity, user-experience design and AI engineering around a common purpose.”

JLL brings intelligent automation to business services

Organization: JLL

Project: Business Service Digitization

IT leader: Pinak Dash, Global Head of JLL Business Services and Legal Technologies

JLL launched its digitization initiative to drive process redesign as well as systematic AI and RPA deployment across JLL Business Services (JBS).

The initiative was designed to address inefficiencies that hampered scalability and competitive positioning. It was also designed to eliminate manual processes that consumed thousands of hours across finance, HR, legal, procurement, marketing, research, IT, and lease administration.

Pinak Dash, global head of JBS and legal technologies, says the digitization initiative had a dual-strategy combining traditional digitization with generative AI innovation to hundreds of processes.

JLL lists three innovations critical to the program’s success.

First is a hybrid platform that integrates RPA with JLL’s proprietary AI platform called Falcon, which created intelligent automation that adapts and learns. It enables real-time process automation, intelligent document processing with automated extraction/validation, and smart decision-making for continuously optimizing workflows.

The second innovation is its use of ProHance for real-time process monitoring and enabling of data-driven optimization. Sensors capture granular productivity metrics, identify bottlenecks, and provide actionable insights for continuous improvement across automated and manual processes.

Third is its custom AI assistants and transaction agents. Falcon-powered assistants provide intelligent knowledge search while specialized agents execute complex transactions across enterprise SaaS platforms. These handle multisystem workflows, reducing human touchpoints while maintaining accuracy and compliance.

Dash says the initiative has delivered quantifiable benefits through improved efficiency, accuracy, and quality of services provided to clients.

“The initiative delivers on our business goals, makes us more efficient, provides customers better service, and it opens up the capabilities and bandwidth of our people to do what they like to do and to find innovative ways to serve our business,” he adds.

Nationwide partnership platform delivers efficiencies, business growth

Organization: Nationwide

Project: Enterprise Digital Platform (EDP)

IT leader: Michael Carrel, EVP and CTO

Nationwide’s new Enterprise Digital Platform (EDP) gives the company “a scalable way to connect with external partners quickly, securely, and consistently across all areas of our business,” says company EVP and CTO Michael Carrel.

He explains that “instead of treating every integration as a custom effort, EDP creates a common front door for digital products, documentation, onboarding and governance.”

That innovation has produced better experiences for the company’s partners. It saves time for Nationwide teams, partners, and customers. And it supports faster launch times for new products and enables growth across the business.

“EDP changed the model from fragmented, point-to-point integrations into an enterprise platform built around reusable digital products. That shift lets us support a range of integration options in one governed environment, meet partners at different stages of technical maturity, and add new capabilities over time without redesigning every relationship from scratch,” Carrel explains.

EDP uses cloud-native microservices, role-based access control, and advanced analytics. Nationwide IT created modular microservices to make EDP more scalable, resilient, and adaptable. And IT decoupled it from infrastructure-specific dependencies so that it would be a platform-agnostic developer portal. That, Carrel says, reduced operational constraints across environments.

Additionally, IT shifted from a user-specific model to role-based access, which improved security, simplified administration, and better served the needs of different audiences.

Meanwhile, robust analytics delivers visibility into platform usage and performance, which Carrel says helps ensure Nationwide continuously evolves the platform based on measurable outcomes.

The core platform is fully deployed, with Nationwide planning to expand it.

“Our Enterprise Digital Platform is more than a piece of technology,” Carrel notes, “it represents a strategic enabler to support growth objectives across Nationwide’s businesses.”

PITT Ohio fast-tracks shipment requests with AI assist

Organization: PITT Ohio

Project: No Touch Email (N@TE AI)

IT leader: Scott Sullivan, President and CEO (formerly CIO)

As PITT Ohio started its AI journey in 2024, the mandate was clear: Use the technology to solve “real problems,” says Ryan Carner, director of enterprise IT solutions.

“We wanted to hit the ground running and find a problem that was solvable,” Carner says, noting that the company also wanted to use the experience to build in-house AI skills. “The idea was to find a business case for AI that would be our first but not the only one.”

PITT Ohio leaders decided to tackle what Carner describes as a “mundane but very important task for how our business operates”: handling emails to the customer service team.

The need was significant. Customer service representatives were manually processing hundreds of pickup request emails daily, each requiring five to 15 minutes to interpret and re-enter shipment details into the company’s transportation management system (TMS). The emails were complicated, containing a lot of information submitted in nonstandardized ways and varying formats. This repetitive task consumed valuable time, introduced errors, and delayed customer response.

N@TE uses generative AI and natural language processing to transform unstructured email content into structured pickup orders automatically and in real-time. N@TE scans incoming emails, extracts key shipment data, and creates orders directly in the TMS via API integration. It operates seamlessly within existing workflows, requiring no change in customer behavior or retraining of staff.

PITT Ohio deployed N@TE in 2025, and the company also secured a patent for the product that year. N@TE has produced a 30-60X increase in processing speed, 99% accuracy in extracting and populating order data, and a 70% reduction in handling costs per pickup order.

SMU builds AI adoption through grassroots ambassador program

Organization: Southern Methodist University

Project: Scaling AI Without Scaling AI: Organizational AI Scaling Through Willingness

IT leader: Jason Warner, Associate CIO

Like executives in most organizations, leaders at Southern Methodist University encountered mixed attitudes about AI. Some workers had little interest in using the tech, others were afraid it would take jobs, still others were curious about what it could do.

Associate CIO Jason Warner and other leaders decided to leverage that last group, believing the best way to get SMU faculty and staff to embrace AI was to use enthusiasts to help smooth the way.

So, instead of treating AI as a conventional technology rollout, Warner and his colleagues built opt-in communities of practice known as the AI Coalition of the Willing and Operation Copilot.

The goal, Warner says, was to build institutional capability, reduce risk, and generate momentum.

“We knew the fastest way to scale AI was to scale the willingness of people to use the technology, and not talking to people about cost savings and the like,” Warner says, adding that willing users as great ambassadors and evangelists who showcase in formal and informal ways the technology’s potential for hesitant or skeptical colleagues.

Participating faculty members have access to a licensed ChatGPT account as long as they use it. Staff members have access to Copilot accounts after taking a self-paced training course and likewise must use it to keep that access.

Warner says these willing workers are demonstrating the benefits of AI (significant time reclamation, reduced cognitive load, improved quality of outputs, expanded professional capacity).

SMU is now moving to a single solution and scaling AI, confident that its use will deliver returns following in the footsteps of the early adopters.

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Petco Park consistently ranks among MLB’s top ballparks for fan experience. That doesn’t happen by accident, and it didn’t wait for a star-studded roster or a deep postseason run.

According to Padres CIO Ray Chan, the club made a deliberate choice more than a decade ago to run its tech organization as if every seat were full and the team was playing in October every year. The philosophy was simple — build a World Series-level digital foundation so when the on-field product caught up, the elite fan experience would already be there.

More than a ballpark

Most people know Petco Park as the home of the San Diego Padres, which it is, but the venue was designed to be more than that. In a typical year, it hosts 81 regular-season home games, plus potential postseason contests, and then adds concerts and private events in renovated premium spaces.

By Chan’s count, that totals to nearly 400 annual events, often with more than one on the property in a single day. Different parts of the venue may host different audiences simultaneously, with IT expected to turn spaces quickly and support the unique digital requirements of each event.

The multi-use model puts a premium on flexibility and speed. Spaces are designed to be reconfigured quickly, and the underlying technology stack must adapt just as quick.

An always‑on network

When Chan arrived 15 seasons ago, Petco Park looked very different from a connectivity standpoint. On sellout nights, fans often couldn’t place a call or send a text once they were inside the building. There was no real concept of a digital fan journey.

The first major shift came with deploying a full-venue managed distributed antenna system (DAS) from Verizon, and an Extreme Networks Wi-Fi solution, providing fans, staff, and baseball operations with reliable connectivity throughout the ballpark. That network has since become the converged backbone for almost everything that happens at Petco, including digital ticket entry via the MLB Ballpark app, security and operations, tech like instant replay and dugout tablets used by coaches and players, and in‑venue IPTV and signage, all riding on the same IP infrastructure.

For fans, the network is invisible. For Chan’s team, it’s non‑negotiable. “None of this stuff works without the infrastructure in place,” he says.

Consolidated convenience

The Padres have leaned heavily into the league-standard MLB Ballpark app, which provides a consistent digital experience across all 30 venues, while allowing clubs to customize the local section. At Petco specifically, that app becomes the fan’s control center for digital ticketing, ballpark navigation, and a built-in payments and discount wallets tied to offers like Padres Pay and contactless options.

The result is a highly digitized journey, and for many fans, their first and last interaction with the ballpark occurs on their mobile device, and that’s by design.

Toward frictionlessness

Chan and his team are already looking beyond digital barcodes to facial-authentication-based entry, leveraging MLB’s Go Ahead Entry program rolling out at several parks. In that model, fans enroll once in the app with a selfie, then simply walk through a designated lane while overhead cameras verify identity and automatically scan tickets.

The promise is a hands-free, eyes-up experience where fans no longer need to take out their phones at the gate. Chan says this is the most frictionless way to enter a ballpark, and it even enables personalized greetings by name at the turnstile, another small but memorable touch to create a World Series-caliber experience.

Concessions are another example of how Petco’s IT modernization seamlessly enhances the fan journey. Petco is now a fully cashless venue, so fans pay with credit cards, mobile wallets, or the dedicated Padres Pay capability integrated into the Ballpark app. This reduces transaction friction, speeds lines, and improves security by minimizing cash handling.

IPTV everywhere

The expanding IP television footprint is another hallmark of Petco’s fan experience strategy. New screens throughout the venue serve multiple roles to ensure game coverage is never lost, even when fans leave their seats.

They also show real-time updates and wayfinding, an L-bar format that combines live video with adjacent ad inventory and informational content, and full-screen takeovers during concerts or special events, letting the venue transform its look and feel to match what’s happening on the field or stage.

Because it’s all IP-based, game-day operations and marketing teams can reskin the park on the fly, turning screens into a flexible engagement and monetization channel rather than relying on fixed signage.

Constant modernization

Despite opening in 2004, Petco Park doesn’t feel like a 22-year-old venue. Chan says that’s intentional and points to a continuous program of infrastructure upgrades and capital projects to redo suites, unify premium spaces such as the Western Metal rooftop and loft, and find areas to transform into new experiences.

Beneath those visible changes lies ongoing modernization of the network and systems in terms of upgrading switches, faster Wi-Fi, and backend platforms to support the latest apps and services. As Chan puts it, the goal is to make the park look and feel no older than a couple of years, which requires consistent ownership commitment and alignment between IT and operations.

Perhaps the most important part of Chan’s playbook, however, is cultural rather than technical. He describes the Padres as a listening organization that actively solicits and incorporates fan feedback to refine the experience across seasons.

That mindset is shaping the club’s approach to AI, so instead of chasing it for its own sake, Chan is focused on use cases that improve customer service by using chatbots or AI-assisted voice lines to free staff for higher-value interactions, and solve specific operational problems such as using AI to match lost-and-found queries with a database of found items.

The bigger IT picture

The way Petco Park manages its technology operations offers patterns that can apply beyond sports venues, starting with establishing a converged, resilient backbone. Connectivity is a shared utility layer that everything else depends on, rather than a series of isolated projects. That makes it easier to add new capabilities later without rearchitecting every time.

Chan’s philosophy of building as if every seat were filled also applies across all e-commerce peaks, clinical surges, and manufacturing seasonality. Capacity planning, observability, and failover should be set at Black Friday, not an average Tuesday. And treat your environment as a multiuse and continuously modernizing platform. Petco’s “more than a ballpark” mindset reflects the shift toward mixed-use destinations or campuses that blend learning and events, and offices that evolve into collaboration hubs that chip away at legacy infrastructure. IT leaders across sectors can apply the same rolling-renovation model to networks, identity, observability, and edge infrastructure, keeping technical debt manageable.

It’s no secret that the CIO role is changing in Canadian enterprises. Driving operational performance and increasing efficiency are, of course, still a big part of the job. But CIOs are now expected to do more than cut costs and keep the lights on. Increasingly, boards of directors expect the CIO to play a key role in creating value and enabling their organizations to outperform their rivals. Recent data backs this up. The overwhelming majority (91%) of Canadian technology leaders believe advanced technology will be the primary driver of competitive advantage over the next three years, according to KPMG’s “The 8 execution imperatives for Canadian tech leaders,” a white paper based on the firm’s 2026 Global Tech Report.

The race to capture value from emerging technologies has intensified, leaving little room for organizations that remain on the sidelines. Yet in Canada, just 27% of organizations consider themselves innovators or early adopters, while 72% are fast or slow followers. Nearly all (85%) believe they’ll need to take more risks with emerging technologies just to remain relevant.

The traditional, operational-first mindset among CIOs has become a liability. CIOs are expected to be strategic players, but meeting that expectation requires taking thoughtful, well-considered action across key imperatives.

AI success depends on data

AI is not the only advanced technology that organizations are working to deploy, but it’s certainly garnering the most attention from C-suite executives, boards of directors, and investors. To enable AI at scale, the data foundations enterprises have built over the past decade will be necessary, but they are not sufficient on their own. CIOs must make modernizing the state of enterprise data a top priority.

In most large organizations, data often remains locked inside legacy systems where the system of record is also the system of engagement, and that significantly limits accessibility for AI workloads. Data should be treated as the strategic asset that it has become. The quality, timeliness, and governance of that data vary widely, and reconciling those inconsistencies is one of the most persistent barriers to enterprise-level deployment.

Scaling pilots into enterprise programs also requires closing the gap between AI-native talent, who have the skills to move quickly, and those with deep institutional knowledge, who understand how their organizations work and what they need to achieve. These two groups must work hand in hand. Otherwise, organizations may accelerate toward the wrong goals, which won’t move the business forward.

“The barriers to scaling AI aren’t just technical,” says Sanjay Pathak, partner and national leader, technology strategy and digital transformation services, KPMG Canada. “CIOs need to truly and deeply understand the value chain of what their organizations do. Those who get there will have the imagination, the courage, and the foresight to use AI to transform their organizations.”

Communicating ROI requires the right framing

Beyond scaling, simply communicating the value of AI also poses a significant challenge. Just over half (53%) of Canadian organizations surveyed say they struggle to demonstrate or communicate AI value to stakeholders. Part of the problem is that CIOs are making the wrong argument in the wrong room because they’re framing ROI as a technology metric rather than a business outcome.

“Any CIO who doesn’t truly understand what their business does is missing a beat around how innovation is going to help the organization achieve ROI,” says Pathak. “Understanding how to deploy AI inside your value chain will give you a head start and a competitive advantage in unlocking real business benefits.”

A formal performance measurement framework that tracks customer experience, revenue growth, and employee adoption alongside cost metrics gives CIOs a far more accurate picture of long-term value. Linking funding decisions to those strategic outcomes makes sustained investment easier to justify.

There is also a compliance dimension that often goes unacknowledged in these conversations. CIOs who bring business, technology, and compliance leaders together to design innovative processes that are “compliant by design” from the start are protecting future value as much as they are delivering value today.

“You need to assemble that multi-dimensional cohort of business, technology, risk, and compliance leaders at the same table, envisioning compliance by design,” says Pathak. “The winners in this space are going to be the ones who really think about business ambition holistically and focus on efficient delivery, operations, and compliance.”

Building disciplined innovation governance

An organization’s approach to governance makes an enormous difference in how quickly and confidently it can deploy and take advantage of advanced technologies. As noted above, almost three-quarters (72%) identify as fast or slow followers, and 85% say they need to move more aggressively to embrace new technologies. Canadian organizations aren’t lacking ambition. What they lack are the conditions required to innovate with confidence: clear ownership, defined risk thresholds, and shared accountability between technology, risk, and business teams.

“You can be an innovator, but if your innovation is not directly connected to strategic business ambition and safety guardrails such as risk management, governance, and compliance, you’re creating labware,” says Pathak. “Being an early adopter means you’re comfortable with the technology. To make it truly viable, you must embrace all dimensions of enterprise value.”

Strong governance does not slow innovation down but instead provides a structure that builds confidence and resilience at every level of the organization, from the board to project teams. A tiered governance approach that takes risk into account allows organizations to advance low-risk, incremental improvements and high-reward initiatives in parallel.

Expanding partnerships to accelerate innovation

Innovation isn’t a single-player game, and Canadian organizations know it. Ninety-seven percent of respondents say they plan to expand their external ecosystems. To date, a significant portion of those relationships have been transactional and focused on a specific capability or problem. But savvier organizations are moving toward multi-party innovation, where partners pool capabilities to share both risk and reward. This model requires a different kind of commitment because organizations are betting on a partner’s long-term viability, not just their current capability. Together, they must build the integration and governance infrastructure that makes these ecosystems work for all participants.

“Moving to multi-party innovation ecosystems is an investment in integration and data,” says Pathak. “If you’re going to look at best of breed and stitch those together, what must be true for that to work is the ability for those different ecosystems to integrate and interoperate. And that creates a much stronger need for safety and governance.”

Cybersecurity is another dimension that grows more important with every new partner added to the ecosystem. Security should be proactively built in, not imposed after a breach has already occurred.

“The more ecosystem-based partnerships you have, the more opportunity you create along with the threat you have to deal with,” says Pathak. “You expand the attack surface, and you become more of a target, so governance and cybersecurity must be designed in from the start, not bolted on later.”

Canadian government incentives, including Scientific Research and Experimental Development (SR&ED) tax credits and AI-focused clusters, offer a way to share some of the cost and risk, particularly during periods of economic uncertainty.

By leveraging these funding frameworks alongside robust ecosystem governance, forward-thinking organizations can safely scale their networks to turn shared risks into sustainable competitive advantages.

In closing

For organizations navigating this environment, KPMG Canada emphasizes that the most consequential decisions ahead are not purely, or even mostly, technical. They are about how CIOs choose to lead, partner, measure, and govern in a period that rewards both ambition and discipline in equal measure.

The data points are clear. CIOs who lead with both strategic ambition and disciplined execution will elevate their organizations above their competitors. By paying attention to the quality of their data, aligning technical priorities with critical business goals, and instituting strong governance and cybersecurity, they will set a higher standard for what Canadian competitiveness looks like in the years ahead.

To learn more, read the full whitepaper: The Top 8 Execution Imperatives for Canadian Tech Leaders.

As an aviation industry veteran with over 30 years of experience, Alaska Airlines CIO Charu Jain is all too familiar with the technology integration process that often follows a big airline merger.

By her count, she’s been involved in four such projects. But none, she says, has brought her greater satisfaction than leading the overhaul of Alaska’s PSS following its $1.9 billion acquisition of Hawaiian Airlines in September 2024.

“This is one of the biggest milestones in any merger work done between airlines,” says Jain, speaking from her company’s Seattle offices just two months after Alaska and Hawaiian completed their transition to a shared PSS.

In its simplest terms, a PSS is an all-encompassing software suite used by airlines to record and manage a passenger’s journey, from booking tickets and checking in baggage at the airport, to boarding the aircraft and accessing the in-flight menu. “A PSS touches almost every function of an airline from employees to guests,” says Jain.

Two brands, one system

At the time of the merger, Alaska and Hawaiian each had its own PSS. No sooner had the ink dried on the deal than the cutover project got underway to bring both airlines’ systems under a single operating platform.

According to Jain, the two airlines agreed from the get-go that they’d retain their own unique historic brands, both with a combined history of close to 200 years, which would be reflected through the system.

“It had never been done before, developing capabilities to enable two brands on one platform,” adds Jain, who also serves as Alaska’s SVP of merchandising and innovation. “We didn’t want a situation where a passenger travelling from Spokane to Seattle on an Alaska-branded flight, and then onto Honolulu on a Hawaiian-branded flight, would have to navigate two separate systems. So we thought about how to make that experience more seamless.”

After settling on a PSS, developed by travel software manufacturer Sabre, Jain and her colleagues began work on migrating the airlines’ millions of bookings and passenger information, while also updating their various guest- and employee-facing tools for the new system.

Selling cutovers and mock flights

Executing a system cutover on such a large scale is a delicate balancing act, not least in a live-environment where, for a major airline, any form of disruption to the passenger experience can be bad for business. So there was no attempt to rush the project.

“To make sure we didn’t have any issues with customers’ bookings, we really took a risk-optimized approach with a phased deployment and a phased cutover,” says Jain.

Much of this hinged on what Alaska refers to as a selling cutover. Starting in October last year, all new bookings were made on the new PSS, which allowed the group to drain old bookings from the legacy system, and start selling tickets six months in advance of the official transition date; the average booking curve for an airline is around six months.

“There was no migration of millions of records and bookings,” says Jain. “This meant when our customers checked in on the first day [of the PSS], it was as if the booking had been made on the native system.”

While this was going on, however, Alaska was hit by a sizeable IT outage that grounded flights across the country and impacted the travel plans of nearly 50,000 passengers. It followed a previous IT outage in July. However, Jain says the disruptions didn’t impact the project in any way.

So in the final months leading up to the cutover completion, Alaska carried out several dress rehearsals to test the system, including mock flights for domestic and international routes in anticipation of the recent launch of several non-stop services to Europe.

This involved real guests arriving at the airport, completing check-in, going through security, and taking their seats as if they were about to take off. Leaving no stone unturned, the simulation also accounted for baggage collection, pets, wheelchair users, and onboard hospitality, stopping just short of passengers being served actual food.

Alaksa completed five such mock rehearsals in all. “The fifth one was when everything worked without any medium or high issues, and gave us the confidence we were ready,” says Jain.

As part of the airline’s scenario planning, it also set up command centers in various locations, including Honolulu and Seattle, to plan for unforeseen and unrelated problems on the day of the cutover.

A dedication to collaboration

A project is only ever as a good as its people, and Jain is quick to hail the collaborative spirit that Alaska and Hawaiian brought to the table. As a PSS involves both the operational side of an airline’s business — touching on everyone from pilots, flight attendants, and baggage handlers — and commercial departments responsible for policies and pricing, this was more than a purely technological undertaking.

“This was about people coming together from two companies to make this one big thing happen,” says Jain.

When Alaska started making bookings on the new PSS last fall as part of the selling cutover, it also began training employees how to use system. It was around that time as well, says Jain, that the airline was confident the transition would be completed by April 2026, just in time for the busy summer travel season.

Since the PSS has been up and running, the company has also introduced a single mobile app to replace Alaska and Hawaiian’s separate existing ones, allowing passengers to personalize their experience to the airline brand they’re more familiar with.

“It’s a much more seamless experience now that there’s no confusion knowing which app to go on, or why they have two booking numbers,” says Jain. Alaska’s employees are also just as happy with their new tools, she adds.

Astute leaders recognize that many times, automation and technology failures aren’t really about the technology itself. Rather, failures occur because the necessary underlying infrastructure linking people, processes, and technology isn’t in place. For example, consider organizations that try to partner together but don’t take the time to align their tech implementations in a way that match the real-world outcomes they want to achieve. Without trust infrastructure and meaningful alignment, such collaborations are doomed to fail.

Creating a foundation of trust

AI adoption brings its own unique opportunities and challenges to both internal and external collaborations, especially in the way it disrupts existing workflows and encourages new forms of risk-taking.

An analysis by the Center for Creative Leadership notes that leaders should build cultural foundations of trust so new technology implementations strengthen rather than erode that trust. Creating psychological safety in the workplace occurs when leaders model learning rather than feign certainty about AI changes, and seek honest involvement and feedback from team members while being transparent about intentions and trade-offs associated with AI use.

After all, it’s hard to build a cultural trust infrastructure when one day everyone’s told how much they’re valued, and the following day, thousands are laid off because of AI restructuring.

When your internal team can’t trust your approach to intelligent automation, outside organizations you partner with may also develop trust barriers. How can they trust your organization to treat them fairly and with transparency if they’re concerned you’re planning to use tech in a way that will undermine a partnership?

Extending trust to digital spaces

In addition to using the foundation of cultural trust in communicating efforts to implement AI, the idea of trust can directly impact how these tools are used and set up. Because of this, intelligent automation needs a solid trust infrastructure in place to succeed. Partners sharing digital resources need to clearly define how they configure and manage their tech, as well as the real, measurable KPIs they want to achieve through implementation. Processes and procedures for sharing data in a secure and timely manner gives both sides the necessary information to leverage tech in the way intended.

A lack of trust — particularly fear of the unknown — can often undermine cross-enterprise collaborations, and this is especially true of tech implementations. A shared foundational infrastructure helps improve cultural trust through increased transparency, which in turn can improve buy-in among the individuals who interact with that tech on a day-to-day basis.

Digital trust infrastructure also enables AI tools to be more effective, so when team members interact with the AI to get insights, recommendations, or data reports, they can trust what the tech tells them. Instead of trying to create their own workarounds to avoid using the tools, they become adopters and promoters, closing the gap in data and insights that so often plague other collaborations.

AI won’t fix what’s broken

Unfortunately, many organizational leaders seek AI implementation to be a cure-all for problems. Astute leaders recognize if their organization doesn’t have a solid trust infrastructure in place, AI will magnify those problems, not fix them.

“Think of AI as a piece of world-class, designer furniture,” says Jary Carter, co-founder and CRO of B2B-focused commerce platform OroCommerce. “If you put a $20,000 Italian sofa in a home filled with clutter, dust, and bad flooring, it doesn’t make the house look better. It just further highlights your mess. In business, that mess is legacy systems, fragmented data, and siloed teams. If you haven’t built a unified digital infrastructure first, AI will only accentuate your flaws. It also won’t improve your customer experience by highlighting inefficiencies directly to customers.”

While throwing AI at existing data gaps won’t solve your collaboration problems, the automation isn’t necessarily to blame. Trust-building needs to happen alongside tech implementations because otherwise, the right data won’t be shared, people and machines won’t have access to the info they need, and the entire collaboration will falter.

Laying the foundation for better collaboration

The emergence of gen AI means that when we talk about trust infrastructure, we can no longer consider cultural and organizational trust alone. Leaders must also consider what that trust infrastructure looks like for their digital applications and automations.

By taking proper steps to build a dependable infrastructure based on transparency, aligned values, and clearly defined goals, leaders can increase their digital trust. This will yield greater buy-in of automated intelligence within the organization, and improve its applications during cross-enterprise collaborations.

The scenario isn’t hypothetical: Some of the companies that went furthest in replacing people with AI have had to backtrack.

For example, in 2024 Klarna became a European benchmark for what AI could do for a company. Its AI assistant handled two-thirds of customer service chats in its first month, performing the equivalent of 700 full-time agents. As a result, company leadership decided to freeze hiring, and the workforce shrank from around 5,000 to 3,800 employees.

Just a year later, Klarna’s CEO admitted the company had gone too far in replacing people with agents, which had negatively impacted both the service and the product. In fact, the company reversed course, rehiring human agents to ensure customers could always speak to a person.

The interesting point here isn’t that AI failed. The problem was something else: understanding the customer service function solely in terms of productivity and costs, without considering the bigger picture.

If measured by response times and equivalent FTEs, automation was optimal. Measured by satisfaction, perceived quality, and the ability to resolve complex cases, the result was different — and ultimately forced a reversal.

For CIOs, this disconnect presents a leadership opportunity: Management and other departments need precisely the comprehensive technical and business process perspective CIOs can bring to the table.

AI is redesigning how a function is delivered

It’s tempting to read Klarna’s AI journey (and back) as a customer service story. But the pattern affects every business function. Introducing AI agents isn’t just adding another tool: It reshapes decision-making, day-to-day learning, and ultimately, how service is delivered.

If you only think in terms of productivity (what’s automated, how much is saved, how many equivalent FTEs are freed up), it’s easy to lose sight of the deeper implications. It’s easy to discover too late that what’s being delivered is no longer the same, even if on paper more is being produced.

This is difficult to see at first. A function can perform worse and still show better operational metrics for months. The consequences appear in other areas, far removed from the automated function: in reputation, lost customers, or poor decisions.

CIOs see this pattern earlier and more strongly. When an agent used by IT — often among the earliest adopters — ceases to be a helpful assistant, the changes have quick and significant impact. They influence which alerts reach the operations team, which code modifications are proposed to developers, which incidents are prioritized by security personnel. This goes beyond simply speeding up work: It determines what the team sees and doesn’t see, and it shifts the decision-making environment.

Agents don’t just execute. They change how they detect problems, how they respond, and even how they learn. If this phenomenon is evaluated solely with performance metrics, it runs the exact same risk Klarna faced internally: gaining speed and losing perspective.

The paradox: More capacity for action, less direct vision

Many IT managers are beginning to notice the paradox inherent in AI agent use. The organization can act faster, deliver more volume, and automate more decisions, but at the same time lose touch with the complexity of reality.

Previously, a support team learned not only by resolving incidents, but also by identifying where integrations failed or what user behaviors revealed a deeper problem. If that work is now automated, the organization can continue to resolve issues, but employees lose valuable learning opportunities.

The risk the team faces is that AI will work well enough to push knowledge and capabilities about how a business unit should operate out of the foreground.

The CIO opportunity

This is where the CIO’s role needs to change. CIOs must move beyond being those who simply automate processes to become those who provide, both within and outside their department, a comprehensive understanding of how AI impacts a business function. This means going beyond productivity gains and contributing other, less visible aspects, such as enhanced experience, business perspective, and changes in service delivery, whether for employees or customers.

This perspective is invaluable both at the senior management level and in other areas such as operations, customer service, and, of course, human resources. In the current climate, with its constant announcements of workforce reductions, the conversation tends to focus on cost and time savings. The CIO is well-positioned to provide the other side of the coin: where strong oversight is necessary, what can be delegated to AI, and where it’s essential to plan for the reversal of automation that, on paper, appears to be working.

That ability to recover is, in fact, one that the organization cannot afford to lose. Not all organizations can regain capabilities as quickly as they are lost.

Your mission: To present a clear-eyed view of AI’s business impact

The CIO’s mission, therefore, is to help clarify what can be delegated to AI and what should not be relinquished without losing the capacity to intervene. In some cases, the answer will be clear: repetitive tasks, initial classification, draft generation, or technical searches. In others, the boundary may be more delicate: prioritizing risks, deciding on exceptions, changing legacy systems, or acting on processes without sufficient oversight.

This will be one of the most important services in the CIO’s role over the next few years. Beyond advancing the adoption of agents, they will have to provide, both within and outside of IT, the necessary understanding of the impact of agents on a business function. And, finally, they must retain the ability to reverse course when the expected results aren’t being delivered, no matter how good the metrics look.

One of the biggest challenges companies like Aetna face every year is an annual HEDIS review of its records to identify gaps in care. For large national payors, the scale of the challenge is immense. So Aetna has deployed a gen AI-driven document intelligence platform that has reduced the need for manual review by 65%.

“We have a large group of amazing trained medical coders who do this every day,” says Nathan Frank, chief digital and technology officer at Aetna. “This is about making it easier for them by speeding up the process. Something that might have taken weeks or months we can now do in days.”

The Healthcare Effectiveness Data and Information Set (HEDIS) is a range of performance measures for the managed care industry. Developed and maintained by the nonprofit National Committee for Quality Assurance (NCQA), the first version of HEDIS was released in 1991.

Under the HEDIS measures, large managed care providers like Aetna review more than 10 million medical records annually to identify gaps in care. These gaps are missed or overdue preventative care or chronic disease management tests including missed cancer screenings, blood sugar tests for diabetics, eye exams, and immunizations. Closing these gaps improves patient outcomes, and health plans are measured in how well they perform. But processing medical records is no easy task.

“We’re talking about medical charts that have white space filled with handwritten notes,” Frank explains. It’s not just structured data, it’s lots of physical clinical documentation.”

Adding up the numbers

Frank says industry benchmarks for large providers indicate an annual review process that requires about 50,000 work weeks, equivalent to nearly 1,000 dedicated full-time employees. It would take a team of 50 reviewers more than 20 years to complete a single annual review using fully manual processes.

Enter AI Medical Chart Review, a platform developed by Aetna that leverages cloud services and gen AI to automatically extract clinically relevant data from records, and prioritize records based on the likelihood of measure closure and evidence strength.

“Large language models and gen AI give us the ability to train a model to decipher the charts, identify the high value codes, and build correlations,” Frank says.

In the space of about six months, Frank’s team ideated the platform, and designed and trained a PoC that was able to process millions of records in just two weeks. As a result, AI Medical Chart Review has earned Aetna a CIO 100 Award in IT innovation.

“Now we’ve gone through 14 million documents,” Franks says. “We’re seeing a reduction of manual effort, which is now being transitioned into other areas like quality control and making sure the automated chart review is working as expected.”

Behind the curtain

Using gen AI, the platform automatically ingests and analyzes unstructured medical records and clinical documents. And as part of that process, it identifies and extracts clinically relevant information for specific HEDIS measures like diagnosis codes, medication records, lab results, and visit documentation. With this data, the platform generates a prioritized set of records based on the likelihood of measure closure and strength of clinical evidence, which is then passed to human employees for review and validation.

Frank says the platform has increased gap closure rates (leading to improved Star Ratings and higher reimbursement), streamlined workflows, and enabled teams once dedicated to manual record review to shift focus to higher-value activities.

Frank says much of the speed and success in building the platform comes down to a shift in the way it approached the design and build process. Rather than exhaustively writing specifications and requirements, Aetna created a team that included engineers and subject matter experts who worked together to build out capabilities iteratively.

“It allowed us to move much faster, and having a business subject matter expert sitting in the same virtual or physical room with us got us a much better outcome,” Frank says. “The product model, our cloud compute model, and our AI governance model allow for quick reviews to make sure we’re using AI responsibly with the right guardrails. It’s increased the speed to get from product launch to go-live.”

He adds that small teams that don’t have to deal with a lot of bureaucracy are key to moving quickly.

“You need to design with security, compliance, and a responsible use of AI as core principles from day one,” he says. “Everything we do from a new build standpoint starts with thinking about how we make it cloud native, how we build with the right elasticity and speed, and how we optimize the cost.”

The most important element of all, he says, is a good relationship with your subject matter experts.

“You can have a great product manager and engineer, but you really need that business subject matter expert who’s excited about it, and who has a passion for transforming the process,” Frank says. “Once you put those three together, you’ll see amazing things like this happen all the time.”

Many VMware customers assumed the most disruptive effects of Broadcom’s acquisition were already behind them. Licensing changes and pricing shifts required attention, but infrastructure strategy largely stayed the same. That assumption is about to be tested.

By October 2027, VMware customers must migrate to VMware Cloud Foundation (VCF) 9.[1] What sounds like a routine upgrade has much larger implications. The timeline is compressed. Hardware and operational changes may be required. And organizations are being pushed to rethink platform strategy at a time when they are balancing modernization, cost control, and new demands such as artificial intelligence (AI) and cloud-native development.

Infrastructure transitions rarely happen quickly. Moving workloads, retraining teams, validating interoperability, and maintaining resilience all take time. When migration is mandated rather than optional, the risk profile shifts. CIOs must act within a certain window of time while still supporting critical systems and ongoing initiatives.

A shift in trust and long-term strategy

Industry analysts expect more than one-third of VMware workloads to move to alternative platforms by 2028.[2] That projection reflects a reassessment of vendor dependency and long-term cost predictability, pressures intensified by ongoing supply chain delays and rising hardware costs. Pricing changes and bundled licensing have introduced uncertainty into infrastructure planning, prompting many leaders to reconsider whether maintaining the status quo still makes sense.

At the same time, modernization pressures are building. Hybrid cloud has become foundational and application teams increasingly develop in containers. AI workloads are moving from experimentation into production. These shifts require platforms that can support traditional virtualization alongside modern application models, without adding operational complexity.

 “No responsible IT person would put a new workload on VMware.”

              – Lee Caswell, SVP, Product and Solutions Marketing, Nutanix 

Some enterprises are responding by containing their existing VMware environments while directing new workloads elsewhere. Others, according to Lee Caswell, SVP of Product and Solutions Marketing at Nutanix, are evaluating full platform transitions to regain flexibility and cost control.

Turning a required migration into a strategic advantage

The VCF 9 deadline is more than a compliance milestone. It offers a chance to rethink infrastructure design for the next decade.

A modern platform should enable organizations to:

• run virtual machines, containers, and AI workloads side by side
• operate consistently across on-premises, cloud, and edge environments
• preserve hardware investments and team expertise
• maintain resilience, security, and operational simplicity

The Nutanix Cloud Platform supports this transition by providing an enterprise virtualization foundation that integrates hybrid cloud operations, container orchestration, and AI readiness within a single operating model. Built-in migration tools and flexible deployment options can help reduce switching friction while giving organizations control over the pace of modernization.

As infrastructure decisions become harder to reverse and modernization pressures accelerate, CIOs face a clear choice. They can treat the VMware deadline as a forced disruption or use it as a catalyst for transformation. For organizations willing to act early, the difference may shape operational agility and innovation capacity for years to come.

Learn more by visiting www.nutanix.com/vmware-alternative/transition.

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[1] Facing CIO backlash, VMware extends support and slows down release cycles, Gartner, Inc, Gyana Swain, July 18, 2025

[2] The CIOs Guide to Broadcom’s Acquisition of VMware, Gartner, Inc, Julia Palmer, Mike Cisek, Tony Harvey, April 3, 2024.