I recently met with Tia Cheang, a brilliant, forward-thinking Chief AI Officer, about how the role has changed, and why so many companies are having to rethink what AI leadership really means in 2026.
Because a fundamental shift is happening in how CEOs are building organisations. There’s less headcount, and more AI. Across the board, traditional business models are being rebuilt.
How the role has changed
This is where the Chief AI Officer steps in - working in close partnership with the CEO and CIO - not to chase innovation, but to run AI as a business system.
As Tia explained, the early versions of this role were focused on momentum: deliver a platform, prove a few use cases and build a team.
The Chief AI Officer of 2026
Fast forward to 2026 and AI is now embedded into core operations. It’s driving efficiency, automating work, and influencing headcount decisions. And when that happens, resilience becomes non-negotiable.
It’s no longer enough to build AI. You have to sustain it, manage it in real time and prevent the slow abandonment that often follows six months of early enthusiasm.
That’s where the Chief AI Officer of today steps in, to take your AI planning out of the ‘hype’ stage and into real, tangible systems embedded within your business.
The Chief AI Officer of 2026 looks very different to the early hype-driven versions of the role you may have seen a few years back.
Tia’s advice for implementing big AI projects
So, when it comes to implementing these big AI projects, where do you start? Here was Tia’s advice:
Start with an Interim Chief AI Officer for six months to focus on one or two high-value areas where inefficiency is obvious and time is being wasted. Small, contained pilots. No big-bang rollouts.
Early AI projects are learning exercises. You’re unlikely to see huge ROI on the first attempt, and that’s fine. The value comes from understanding what works, what breaks, and what your organisation is actually ready to support.
Then onboard a Data Governance person, someone who understands data, GDPR, and AI regulation. This might be the least glamorous part of building AI, but it needs to be done early to stop trouble later on,
Engineering comes next. Data and AI engineers. And finally full-stack engineers, but only once the foundations are solid.
For some organisations, a board advisor or NED with AI experience can also provide a safety net, particularly if you already have a strong CTO.
Contact Ellis Seder for a confidential chat and further information about hiring AI talent.