Everything is changing, from data pipelines and technology platforms, to vendor selection and employee training — even core business processes — and CIOs are in the middle of it to guide their companies into the future.
In 2024, tech leaders asked themselves if this AI thing even works and how do you do it. Last year, the big question was what the best use cases are for the new technology. This year will be all about scaling up and starting to use AI to fundamentally transform how employees, business units, or even entire companies actually function.
So whatever IT was thought of before, it’s now a driver of restructuring. Here are seven ways the CIO role will change in the next 12 months.
Enough experimenting
The role of the CIO will change for the better in 2026, says Eric Johnson, CIO at incident management company PagerDuty, with a lot of business benefit and opportunity in AI.
“It’s like having a mine of very valuable minerals and gold, and you’re not quite sure how to extract it and get full value out of it,” he says. Now, he and his peers are being asked to do just that: move out of experimentation and into extraction.
“We’re being asked to take everything we’ve learned over the past couple of years and find meaningful value with AI,” he says.
What makes this extra challenging is the pace of change is so much faster now than before.
“What generative AI was 12 months ago is completely different to what it is today,” he says. “And the business folks watching that transformation occur are starting to hear of use cases they never heard of months ago.”
From IT manager to business strategist
The traditional role of a company’s IT department has been to provide technology support to other business units.
“You tell me what the requirements are, and I’ll build you your thing,” says Marcus Murph, partner and head of technology consulting at KPMG US.
But the role is changing from back-office order taker to full business partner working alongside business leaders to leverage innovation.
“My instincts tell me that for at least the next decade, we’ll see such drastic change in technology that they won’t go back to the back office,” he says. “We’re probably in the most rapid hyper cycle of change at least since the internet or mobile phones, but almost certainly more than that.”
Change management
As AI transforms how people do their jobs, CIOs will be expected to step up and help lead the effort.
“A lot of the conversations are about implementing AI solutions, how to make solutions work, and how they add value,” says Ryan Downing, VP and CIO of enterprise business solutions at Principal Financial Group. “But the reality is with the transformation AI is bringing into the workplace right now, there’s a fundamental change in how everyone will be working.”
This transformation will challenge everyone, he says, in terms of roles, value proposition of what’s been done for years, and expertise.
“The technology we’re starting to bring into the workplace is really shaping the future of work, and we need to be agents of change beyond the tech,” he says.
That change management starts within the IT organization itself, adds Matt Kropp, MD and senior partner and CTO at Boston Consulting Group.
“There’s quite a lot of focus on AI for software development because it’s maybe the most advanced, and the tools have been around for a while,” he says. “There’s a very clear impact using AI agents for software developers.”
The lessons that CIOs learn from managing this transformation can be applied in other business units, too, he says.
“What we see happening with AI for software development is a canary in the coal mine,” he adds. And it’s an opportunity to ensure the company is getting the productivity gains it’s looking for, but also to create change management systems that can be used in other parts of the enterprise. And it starts with the CIO.
“You want the top of the organization saying they expect everyone to use AI because they use it, and can demonstrate how they use it as part of their work,” he says. Leaders need to lead by example that the use of AI is allowed, accepted, and expected.
CIOs and other executives can use AI to create first drafts of memos, organize meeting notes, and help them think through strategy. And any major technology initiative will include a change management component, yet few technologies have had as dramatic an impact on work as AI is having, and is expected to have.
Deploying AI at scale in an enterprise, however, is a very contentious issue, says Ari Lightman, a professor at Carnegie Mellon University. Companies have spent a lot of time focusing on understanding the customer experience, he says, but few focus on the employee experience.
“When you roll out enterprise-wide AI systems, you’re going to have people who are supportive and interested, and people who just want to blow it up,” he says. Without addressing the issues that employees have, AI projects can grind to a halt.
Cleaning up the data
As AI projects scale up, so will their data requirements. Instead of limited, curated data sets, enterprises will need to modernize their data stacks if they haven’t already, and make the data ready and accessible for AI systems while ensuring security and compliance.
“We’re thinking about data foundations and making sure we have the infrastructure in place so AI is something we can leverage and get value out of,” says Aaron Rucker, VP of data at Warner Music.
The security aspect is particularly important as AI agents gain the ability to autonomously seek out and query data sources. This was much less of a concern with small pilot projects or RAG embedding, where developers carefully curated the data that was used to augment AI prompts. And before gen AI, data scientists, analysts, and data engineers were the ones accessing data, which offered a layer of human control that might diminish or completely vanish in the agentic age. That means the controls will need to move closer to the data itself.
“With AI, sometimes you want to move fast, but you still want to make sure you’re setting up data sources with proper permissions so someone can’t just type in a chatbot and get all the family jewels,” says Rucker.
Make build vs buy decisions
This year, the build or buy decisions for AI will have dramatically bigger impacts than they did before. In many cases, vendors can build AI systems better, quicker, and cheaper than a company can do it themselves. And if a better option comes along, switching is a lot easier than when you’ve built something internally from scratch. On the other hand, some business processes represent core business value and competitive advantage, says Rucker.
“HR isn’t a competitive advantage for us because Workday is going to be better positioned to build something that’s compliant” he says. “It wouldn’t make sense for us to build that.”
But then there are areas where Warner Music can gain a strategic advantage, he says, and it’s going to be important to figure out what this advantage is going to be when it comes to AI.
“We shouldn’t be doing AI for AI’s sake,” says Rucker. “We should attach it to some business value as a reflection of our company strategy.”
If a company uses outside vendors for important business processes, there’s a risk the vendor will come to understand an industry better than the existing players.
Digitizing a business process creates behavioral capital, network capital, and cognitive capital, says John Sviokla, executive fellow at the Harvard Business School and co-founder of GAI Insights. It unlocks something that used to be exclusively inside the minds of employees.
Companies have already traded their behavioral capital to Google and Facebook, and network capital to Facebook and LinkedIn.
“Trading your cognitive capital for cheap inference or cheap access to technology is a very bad idea,” says Sviokla. Even if the AI company or hyperscaler isn’t currently in a particular line of business, this gives them the starter kit to understand that business. “Once they see a massive opportunity, they can put billions of dollars behind it,” he says.
Platform selection
As AI moves from one-off POCs and pilot projects to deployments at scale, companies will have to come to grips with choosing an AI platform, or platforms.
“With things changing so fast, we still don’t know who’s going to be the leaders in the long term,” says Principal’s Downing. “We’re going to start making some meaningful bets, but I don’t think the industry is at the point where we pick one and say that’s going to be it.”
The key is to pick platforms that have the ability to scale, but are decoupled, he says, so enterprises can pivot quickly, but still get business value. “Right now, I’m prioritizing flexibility,” he says.
Bret Greenstein, chief AI officer at management consulting firm West Monroe Partners, recommends CIOs identify aspects of AI that are stable, and those that change rapidly, and make their platform selections accordingly.
“Keep your AI close to the cloud because the cloud is going to be stable,” he says. “But the AI agent frameworks will change in six months, so build to be agnostic in order to integrate with any agent frameworks.”
Progressive CIOs are building the enterprise infrastructure of tomorrow and have to be thoughtful and deliberate, he adds, especially around building governance models.
Revenue generation
AI is poised to massively transform business models across every industry. This is a threat to many companies, but also an opportunity for others. By helping to create new AI-powered products and services, CIOs can make IT a revenue generator instead of just a cost center.
“You’re going to see this notion of most IT organizations directly building tech products that enable value in the marketplace, and change how you do manufacturing, provide services, and how you sell a product in a store,” says KPMG’s Murph.
That puts IT much closer to the customer than it had been before, raising its profile and significance in the organization, he says.
“In the past, IT was one level away from the customer,” he says. “They enabled the technology to help business functions sell products and services. Now with AI, CIOs and IT build the products, because everything is enabled by technology. They go from the notion of being services-oriented to product-oriented.”
One CIO already doing this is Amith Nair at Vituity, a national physician group serving 13.8 million patients.
“We’re building products internally and providing them back to the hospital system, and to external customers,” he says.
For example, doctors spend hours a day transcribing conversations with patients, which is something AI can help with. “When a patient comes in, they can just have a conversation,” he says. “Instead of looking at the computer and typing, they look at and listen to the patient. Then all of their charting, medical decision processes, and discharge summaries are developed using a multi-agent AI platform.”
The tool was developed in-house, custom-built on top of the Microsoft Azure platform, and is now a startup running on its own, he says.
“We’ve become a revenue generator,” he says.
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