AI elevated IT – Now CIOs have to make it real through people

Over the past two years, AI has done something few technologies manage to do. It’s raised the profile of IT to the highest levels of the enterprise.

CIOs are no longer viewed simply as the people who keep systems running, manage vendors, and protect the architecture. McKinsey’s Global Tech Agenda 2026 describes a “structural shift” in which CIOs are becoming “strategy architects,” shaping how their companies compete by weaving AI and data into operating models.

That visibility matters. But there’s no time for CIOs to rest on their laurels.

Because getting AI onto the agenda isn’t the same as making AI real in the business. The first phase of AI gave CIOs a bigger seat at the table. The next will determine whether they keep it.

The first phase of AI was the easy part

The early wave of AI adoption followed a familiar pattern. Organizations experimented quickly. They licensed tools. They ran pilots. They gave teams access. They started finding use cases. In many cases, they produced enough momentum to make AI feel real.

But experimentation isn’t transformation.

The hard part starts when leaders have to move from excitement to operating reality. Deloitte’s 2026 State of AI in the Enterprise shows that organizations increasingly believe they’re strategically prepared for AI, but they feel less prepared operationally across infrastructure, data, risk, and talent. That gap matters because it explains why many companies can talk confidently about AI while still struggling to scale it in ways that are measurable, governable, and useful.

This is where the CIO challenge changes.

The next phase isn’t really about technology

As AI moves deeper into the enterprise, the questions get harder:
 

• Where does AI solve meaningful business problems instead of creating more noise?
• How do you scale beyond pilots and isolated use cases?
• How do you govern decisions, data, risk, and accountability?
• How do you tie AI to business value instead of novelty?

Those aren’t purely technical questions. They’re operating model questions.

That’s why the old IT mindset starts to break down here. AI doesn’t just change systems. It changes how people work, how decisions get made, how processes move, and how accountability gets distributed. In other words, the real transformation challenge isn’t just technological. It’s organizational.

For CIOs, that creates an opening. Not to own every transformation decision, but to recognize that AI success increasingly depends on whether the organization can connect technology choices to workforce realities: skills, roles, work design, decision rights, process ownership, change adoption, and trust. That’s why the next chapter of AI leadership runs through people as much as it runs through technology.

The transformation triangle still applies

CIOs have spent years immersed in transformation projects that used some version of the people, process, and technology framework. AI doesn’t replace that framework. In fact, it makes it more important.

People: From users to collaborators

AI isn’t just another software layer people have to learn. It changes how work gets done and how employees interact with information, recommendations, and decisions in the flow of work.

The people challenge of AI is much bigger than access or adoption. It now includes judgment, trust, skills, role redesign, and clear expectations for how humans and AI should work together.

This is why people can’t be treated as the downstream recipients of AI change. They’re central to whether AI becomes useful, trusted, and scalable in the business at all. For CIOs, that raises new questions: which roles need new skills, where should human oversight stay firmly in place, and how should accountability work when AI increasingly informs decisions?

That pressure makes partnership with HR and business leaders much more important. If CIOs want AI to scale, they have to help shape not just the technology behind it, but the workforce readiness around it.

Process: From workflows to decisions

Many organizations are still layering AI onto existing workflows and calling it transformation. That may create isolated gains, but it rarely changes how the business runs.

Real value comes when leaders redesign processes around faster decision-making, clearer accountability, and better coordination across functions. That’s harder work. It requires the CIO to operate less like a technology owner and more like a cross-functional architect.

Technology: From tools to platforms

Point solutions were fine for the experimentation phase of AI. They’re much less useful when the goal is scale, control, and trust.

As AI becomes more embedded in business operations, fragmented systems create fragmented intelligence. Data gets harder to govern. Context gets lost. Measurement gets weaker. Integration becomes a constant tax on speed.

This is where platform thinking starts to matter much more.

The HCM platform is an overlooked but critical lever

HCM is a key example of where the old CIO playbook starts to break down.

For years, many CIOs could treat their company’s HCM platform as a support domain. Keep it secure. Keep it stable. Help HR get what it needs. Delegate much of the day-to-day to HR or the head of enterprise applications. Move on.

That playbook makes less sense now.

If AI is pushing companies to rethink how work gets done, then the systems that hold workforce data, skills data, role structures, service interactions, scheduling logic, compliance workflows, and employee experience signals matter much more than they used to. They’re no longer just administrative systems. They’re becoming part of the foundation for how the enterprise understands work, governs change, and prepares people to adopt new ways of operating.

It’s difficult to build credible workforce intelligence on top of fragmented employee data. It’s difficult to govern AI-enabled decisions when the systems behind those decisions are stitched together from disconnected tools. And it’s difficult to redesign work when the core platform can’t give the business a coherent view of workforce capacity, skills, performance, and experience.

If you’re exploring how the right HCM foundation can support AI at scale, register for our May Solution Spotlight to see how a single platform with a single data model can help reduce complexity, strengthen data confidence, and create the clarity needed to move from AI ambition to action.

Hold my spot

For CIOs, this doesn’t mean choosing consolidation at all costs or treating interoperability as a failure. It means taking a much closer look at the HCM landscape and being more deliberate about where the business needs true platform depth, where interoperability is sufficient, and where fragmentation has started to erode trust, intelligence, and readiness for change.

The CIO who sees that early has a chance to move from support role to strategic partner. The one who doesn’t may still own the plumbing, while someone else defines how work truly evolves.

The new CIO mandate

The next phase of CIO leadership isn’t about deploying more AI. It’s about helping the business turn AI into a workable, governable, scalable model for how people and technology operate together. Or to put it more directly: CIOs must now co-own people readiness for AI adoption, with a strong HCM platform as the scaling factor.

That raises the bar for what CIOs have to pay attention to. Not just models and tools, but the systems that shape workforce reality underneath them.

That means building stronger foundations for trustworthy data; creating clearer governance around workflows, decisions, and accountability; connecting AI investments to measurable business outcomes; and paying closer attention to the platforms that influence workforce readiness and adoption.

Phase one of AI brought the CIO role into the spotlight. Phase two will test whether they can translate that visibility into lasting influence.

If CIOs want to play a bigger role in shaping how AI transforms work, they need to look just as closely at the people platform underneath it. Read our related blog to explore the difference between a truly single HCM platform and a “unified” system built from cobbled-together point solutions.

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