Who Should Lead AI in Your Company? (Hint: It’s Not Just IT)

AI is no longer just a technical upgrade – it’s a broader business transformation. Across the mid-market, we’re seeing a shift: the most impactful AI adoption often begins not in IT, but with operational leaders – those in HR, finance, operations, or sales.

This isn’t to downplay the role of IT or the CTO. Their expertise is essential. But increasingly, AI success hinges on the people who live inside the processes every day.

The Evolving Role of IT and the CTO

As AI gains traction, it’s not just workflows that are being reimagined – leadership roles are evolving too. Nowhere is this more evident than in IT. Traditionally, the CTO or Head of IT was the guardian of infrastructure, responsible for uptime, compliance, and integration. Today, those same leaders are being asked to drive innovation and guide AI strategy.

The CTO of global financial technology company Broadridg put it simply: “AI really makes chief technology officers and CIOs become the chief problem solvers.” In this new reality, their job is no longer just about keeping systems running. It’s about identifying which business problems AI can solve–and how to do that safely, effectively, and at scale.

Several technology leaders describe the same shift in mindset. Modern IT leaders are less focused on day-to-day operations and more on orchestration:

• Which challenges require human insight?
• Which tasks can be accelerated with AI?
• Which tools should be built, and which can be bought?

The CTO may now be tasked with choosing a vendor for AI-driven customer service automation, while also reskilling IT staff to manage AI workflows. Their influence increasingly touches product development, compliance, and even forecasting and financial planning.

This expanded remit makes IT a strategic partner to every business function. Finance might ask for AI-driven forecasting. HR might lean on AI to streamline onboarding. Ops leaders might seek predictive tooling to manage inventory or staffing. The IT team becomes the connective tissue enabling this transformation.

There’s a cultural dimension too. As AI enables flatter decision-making and greater autonomy at the edges, the CTO’s leadership style needs to shift from command-and-control to coaching and enablement. AI takes on many routine tasks, giving technical teams space to experiment. Several CTOs have described how they now spend more time mentoring engineers, encouraging creativity, and supporting cross-functional innovation.

Ultimately, the best IT leaders blend technical fluency with business understanding and empathy. They support the organisation not just in what gets built–but in how people adapt and succeed with it. This makes the role more expansive, and more essential, than ever.

Where the Best AI Pilots Actually Begin

Some of the best AI implementations don’t begin with a formal strategy. They begin with a team. A real problem. And permission to try something small.

These early wins often come from:

• Finance leads tired of month-end delays
• Ops managers looking to reduce manual ticket triage
• HR teams experimenting with AI-generated interview summaries

They start by:

• Identifying a slow or repetitive workflow
• Running a time-boxed pilot
• Measuring what changed (speed, accuracy, effort)

And crucially, they loop in IT not to run the project – but to help implement it safely, securely, and scalably.

Shaping a Culture That Supports AI

Technology alone won’t transform a company; people have to embrace the change. A recurring theme in our conversation was the importance of culture and leadership in AI adoption. Leaders need to cultivate an environment where teams feel confident experimenting with AI, without fear of failure or job loss.

Without a culture that supports exploration and learning, AI adoption will stall.

Functional leaders can help shape that culture by:

• Framing AI as a co-pilot, not a replacement
• Highlighting early wins and credible results, no matter how small
• Encouraging teams to try new tools with clear boundaries and learning goals

This balanced approach – encouraging innovation and insisting on clear rationale – helps prevent polarisation between AI enthusiasts and the skeptics on your team. Your pro-AI folks learn to tie their proposals to outcomes, and your cautious folks see that AI is being used thoughtfully, not recklessly.

One of the most powerful things leaders can do is incorporating AI into your own workflows and sharing the experience. Something as simple as using an AI assistant to summarise weekly reports or brainstorm ideas can signal to your team that you’re embracing these tools (and learning from them). It demystifies AI when people see their leaders actively using it.

What We’ve Learned at Brim

Many of the most successful AI implementations we’ve supported haven’t started in IT. They’ve started with a motivated business unit, a real problem, and the ability to try something quickly.

Teams that have seen strong early outcomes often:

• Understand the problem deeply
• Have access to the data or systems involved
• Are empowered to test and learn

When these teams collaborate with IT–bringing process knowledge and domain context into the room–results improve quickly. Trust grows. New ideas emerge. And adoption spreads organically.

Final Thoughts

AI doesn’t need a massive strategy document or a centralised task force to get started. It needs leaders. particularly functional leaders. who understand the work and are willing to take a first step.

If you’re a functional leader wondering whether you should lead your company’s AI efforts, the answer is: you already can.

• Choose a small problem worth solving
• Run a pilot with clear goals
• Collaborate with IT to make it safe and scalable
• Share the results, refine, and build from there

The role of IT in this journey is critical. But the insight–the "what should we improve and why" – often comes from the business. Together, that’s where real transformation begins.

That’s the AI opportunity: not just to deploy tools, but to reshape how work gets done – one team, one problem, one win at a time.

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