The hype is real! Well, maybe… Over the last few weeks I don’t think there’s a leader in the Global 2000 and beyond that hasn’t been asked or thought:
“What can ChatGPT or any of the latest hype of AI technologies do for our business?”
or almost just as likely,
“Just how risky are these technologies?”
Both are important questions and are rarely answered with an applied‑AI or practical‑outcome lens. More importantly, the tools and processes we’ve been using to introduce technology have already taught us the broad‑brush strokes of managing risk, creating committees, and connecting teams. Lovingly, these create great slide decks but seldom move the business forward; instead they bias toward observation and risk management. To take advantage of AI, enterprises need to change how they assess and get AI into production.
The real role of AI leadership
Today, the majority of ownership for AI lands with IT, InfoSec, HR, and other usually horizontal teams (Baker McKenzie). It’s understandable—AI is nerdy, creates risks, and is complex to understand. It doesn’t help that technology companies explaining and selling AI struggle to start with a business outcome and, for their sins, try to translate any question as quickly as possible into you purchasing their platform.
Said no CEO ever, “I’m so glad I have the perfect AI platform! My strategy is done.”
I’m guilty of this sin in previous lives, but AI leadership needs to step back from technology and instead think in new ways about how to drive practical value. This can only come by identifying transformational use cases that start with a business statement.
The real role of AI leadership is to understand the most critical business problems in ultimate detail and translate those into a portfolio of AI initiatives for the organization, while managing C‑suite buy‑in and alignment, and reporting back the performance of each investment.
So right about now you’re probably asking: Why does this need to be a new C‑suite role? Surely a CFO, CIO, CTO, or another role should be able to achieve this. Maybe—but AI has been on executive radars for the last 10 years, yet according to Deloitte’s State of AI 2022 report, 50 % of projects lack executive buy‑in (Deloitte). We’ve all heard some version of a stat about just how few AI projects make it to production. I’d argue this is because organizations use traditional methods to assess and progress AI adoption. There’s probably a sprinkle of Conway’s law in these dynamics, meaning company‑wide transformation is doomed from the start. To kick‑start AI transformation, the AI charter needs to report directly to the CEO and be responsible for bringing all senior leaders together in a common plan.
So what next? You’ve decided to establish the Chief AI Officer (CAIO) role—what should they do? How do we ensure we’re not just creating expensive slide decks or experiments that don’t reach production? As a leader, how do you measure success of this role?
There are four common dimensions to track to understand the success and impact of adding a CAIO to your business:
Company‑wide AI transformation & initiative roadmap
This is the hardest step to aligning the organization. We’ve been doing roadmaps wrong: fancy room, muffins, a bunch of ideas dropped into a deck, presented weeks later to a room of people who mostly agree with you—fail. Invest in a roadmap that includes education programs and AI projects; turn it into the framework that aligns all teams, establishes principles for prioritizing and scoring impact, and becomes a living backlog. Review and ideate regularly.Impact register & transformation status
Starting an AI project is easy; finishing one is hard. A good CAIO calls out the desired impact up front and continuously focuses the org on delivering that outcome in production. A great CAIO tracks the real impact over time and updates the business.
Just as important is ensuring the overall organization evolves alongside the initiatives. Don’t underestimate the antibodies resisting change. As you can see from the AI‑generated image above, some of those antibodies might be right. Show both project status and progress in change management.Competitive landscape
This one gets forgotten—and underestimated. As a member of the C‑suite, the CAIO must not only drive transformation and impact but also understand and communicate how competitors might leverage AI to disrupt your business. A successful CAIO provides an exposure‑and‑control model for potential competitor initiatives and crafts contingency plans.AI program management
We all recognize the importance of managing bias, fairness, auditability, etc. But CAIO‑led program management must extend up and down the organization—regular board updates and regular team updates. Company‑wide transparency and an education program for every level. During those sessions, you’re likely to discover your next big AI idea.
See? It’s not that hard—well, maybe a little. Nothing worth doing is easy. You can wait for the easy button, but what’s to stop someone else from figuring it out first?