CIOs must actively lead AI experimentation rather than simply governing its use
If you’re a CIO waiting on the “perfect” AI strategy, stop. That mindset slows you down while everyone else is moving. You don’t need to know everything before you start. You just need to start.
Technology, especially AI, doesn’t wait for approvals or roadmaps that take months to finalize. It moves fast. Too many leaders hesitate because they think one mistake could set them back. The truth is, failing to act is a bigger risk. You can’t learn anything new by standing still. Learning comes from doing. You have to experiment with AI, learn from what works, and from what doesn’t. That’s what builds internal advantage.
There’s historical precedent here. The skepticism around online shopping, or SaaS before it became the default, shows how early resistance costs companies speed and insight. We’ve seen this before. AI is no different. The gap between users and observers will widen fast. CIOs who lead from the front will find themselves with a team that’s ready, systems that have been tested, and a roadmap built from lived experience, not just theory.
The CIO’s role is shifting from a technology gatekeeper to an innovation enabler
The era of IT as a control center is over. You’re not just approving tools. You’re shaping how your company adapts to change. AI doesn’t fit into neat processes. It learns by use. Your job is to make that possible across the organization.
SaaS showed us what happens when technology becomes more accessible. Anyone with a login could suddenly build, test, and deploy in ways that used to require dedicated infrastructure. AI shifts that even further. It’s not about flipping a switch, it’s about embedding new capabilities into the everyday flow of work. That’s where adoption happens.
Take Workday as an example. They didn’t start with a giant plan. They put AI in the tools employees already use, low friction, high relevance. When your people can try AI without needing permission or complicated onboarding, they discover what works in their context. That’s when things scale, when adoption is driven from within, not forced from above.
As CIO, your responsibility is to champion that shift, not slow it down. You still need governance, no doubt. But if your strategy is mostly guardrails, you’ll miss the opportunity altogether. Give people access. Then enable them to explore, use, and create. That’s the real value you’re being asked to lead now.
Building employee trust and fluency with AI is essential for widespread adoption
You can give people access to AI tools, but access alone doesn’t create results. Most employees won’t adopt something they don’t trust or understand. That’s where most companies stall.
Workday handled this the right way. They selected AI Champions, internal advocates who weren’t just technical experts but members of different teams. These people led by example. They didn’t lecture, they showed their teams how AI could actually help in specific roles. Real situations, real improvements. It wasn’t a top-down mandate. It was peer-driven learning, and that made AI feel relevant, not theoretical.
If you’re serious about integrating AI across your business, start with culture. Get early users involved who can navigate both the tech and the organization. Make AI training contextual. When employees see their peers using AI to simplify reports, speed up decisions, or surface insights they didn’t have before, they listen. This removes friction and builds momentum.
Don’t confuse deployment with adoption. You can roll out AI applications, but unless people feel ownership, usage stays low. You need trust, and trust comes from real-world benefits and peer influence, not PowerPoint decks or strategy memos.
Traditional ROI models do not fully capture the value of AI initiatives
You can’t measure AI readiness with yesterday’s metrics. Traditional ROI tools focus on immediate dollars, how fast something pays you back. That doesn’t work with AI. You need to measure value in terms of speed, learning, and downstream opportunity.
At Workday, they put together an AI Advisory Council to guide these kinds of decisions. One small team built a tool that streamlined parts of the earnings reporting process. It wasn’t forecasted. And it didn’t require major resources. But it delivered fast value and sparked broader thinking on how AI could impact financial planning. That kind of result wouldn’t have shown up in a standard ROI model.
Executives need to change how they evaluate projects. Not every early-stage AI experiment will drive revenue in quarter one. But if it increases learning velocity, informs future deployments, or opens up new capabilities, it’s already valuable. The mistake is holding AI to the same criteria as matured, low-risk systems. That approach filters out the learning you need right now.
You’re not looking for perfect results; you’re looking for progress you can build on. Teams need room to try, fail fast, and find signal through use. Every productive misstep is another step toward usable scale.
Companies must foster a hands-on culture of learning and experimentation across all employee levels
If you want AI to matter in your organization, people need real time with it, not more instructions. The best way to demystify AI is to open it up. When employees interact with the tools directly, writing prompts, testing features, training small models, they begin to understand how these systems work and where they can be useful. That kind of learning is practical, not abstract.
Some companies are letting employees train chatbots or experiment with prompt engineering. That’s smart. It removes the guesswork. People stop seeing AI as something external and start seeing how it can align with their specific tasks. This builds natural confidence in the tools and lays the groundwork for scalable adoption.
It doesn’t matter what level someone is at, executive or contributor. If they can see how AI supports their goals, usage will grow. Encouraging experimentation sends the right message: that everyone has a role in shaping how the company adapts to AI. You don’t need every project to succeed at once. But you do need engagement, and hands-on use is what gets you there.
You also start to surface employees who get excited about translating AI into solutions. That’s how you find your internal experts. Not through titles, through action. These people become force multipliers. Teach enough of them to operate well in this space, and you’ll have internal capacity growing faster than external hiring ever could.
Waiting for AI maturity is a strategic error; timely action fuels innovation
There’s a common but dangerous idea making the rounds, that you need to wait for AI to mature before using it meaningfully. That mindset gets you nothing. AI evolves by market demand and user feedback. If you’re not engaging now, you’re losing both reference points and momentum.
Early exploration isn’t about looking perfect. It’s about creating strategic readiness. Every experiment gives you new data. Every misstep uncovers insights that polished models don’t provide. The companies learning today are the ones that will scale outcomes tomorrow, because they know what works inside their own operations.
Workday saw this in real time. Small initiatives driven by curiosity often outpaced larger efforts in terms of usefulness and speed. The results weren’t always immediate wins in dollar terms, but they were real, and they informed future roadmaps. Delaying these kinds of efforts means missing that feedback loop entirely.
If you’re a decision-maker, this is the time to move, not cautiously, but deliberately. Create space for small experiments. Prioritize systems that show uptake. Drop the assumption that the market will wait. It won’t. And every step you take now makes it easier to scale when the real shift hits.
Key executive takeaways
- CIOs must lead AI by doing: Waiting for the perfect AI strategy delays critical learning. CIOs should enable experimentation now to develop internal capability and maintain competitive momentum.
- Shift IT from control to enablement: CIOs should move beyond gatekeeping and focus on unlocking AI access across teams. Embedding intuitive tools into daily workflows accelerates grassroots adoption and innovation.
- Build trust through peer-led learning: Deploy internal champions who can show how AI supports real work. This bottom-up approach drives adoption faster than top-down mandates.
- Rethink ROI to prioritize speed and insight: Traditional investment models overlook early-stage AI value. Leaders should measure success through learning gains, development velocity, and influence on future planning.
- Make AI hands-on for all levels: Encourage experimentation across functions by letting employees train models, write prompts, and deploy simple tools. This builds fluency and surfaces unexpected value.
- Don’t wait for maturity, act now: AI grows through use. Leaders must enable rapid testing and small-scale deployment today to shape scalable impact tomorrow.
