Who Owns Your AI Transformation?

I get asked who should own AI strategy a lot, and the interesting thing isn’t the question itself – it’s the way different people ask it. When the CIO asks, they’re usually looking for confirmation that it belongs in IT. When the CMO asks, they’re making the case that customer-facing AI is really a marketing function. Operations leaders assume it’s theirs because that’s where the processes live. And in more than a few organizations, the answer has been “let’s form a committee,” which is corporate-speak for “nobody owns it.”

I wrote about this same question years ago in the context of digital strategy, and the answer hasn’t changed – just gotten more urgent. The right owner isn’t defined by which department they sit in. They’re defined by what they can see, what they can connect, and how they think about their job.

Every article in this series has been building toward this question, even if it wasn’t obvious at the time. Starting with the change requires someone who can see the change clearly across the whole business. Understanding the gap between knowing and understanding requires someone who can assess that gap across multiple functions. Leading with empathy requires someone who’s done the internal work to connect with what people are going through. Thinking strategically requires someone who can hold the “what you do, where you play, how you win” frame across the Building Blocks. And establishing an Environment of Possibilities requires someone who can stack the deck and hold people accountable for results.

That’s a lot of “someones” – and if you read the list carefully, you’ll notice they’re all describing the same person.

The Wrong Answers

Let me walk through the ownership models I’ve seen fail, because you’ll recognize at least one of them.

When IT owns AI strategy, it becomes a technology project. The team evaluates platforms, builds infrastructure, stands up governance frameworks, and delivers technically sound solutions that the business doesn’t adopt because nobody asked them what problem they were trying to solve. IT is essential to AI transformation – you need their expertise in data architecture, security, integration, and scalability – but when they own the strategy, the conversation gravitates toward what the technology can do rather than what the business needs it to do.

When Marketing owns it, it becomes a campaign. AI gets applied to customer segmentation, content personalization, ad targeting – all legitimate use cases, but all confined to one slice of the business. The operational improvements, the product intelligence, the internal process transformation – none of that gets the same attention, because it’s not what Marketing thinks about when they think about AI.

When Operations owns it, it becomes process optimization. The focus narrows to efficiency, automation, cost reduction – important work, but it misses the strategic questions about how AI might change what you sell, how you relate to customers, or what competitive advantages you build. Operations tends to think about doing the same things better, not doing different things entirely.

And when a committee owns it – when the answer to “who’s accountable?” is “the AI Steering Committee meets monthly” – nobody owns it. Committees are excellent at reviewing progress, debating priorities, and requesting updates. They are terrible at making the hard calls, moving fast when opportunities appear, and maintaining the relentless daily focus that transformation requires. A committee is a governance mechanism, not an owner.

Each of these models captures a real piece of what AI transformation requires, but none of them captures the whole thing – and the gap between the piece and the whole is where most AI strategies quietly die.

What the Role Actually Requires

The person who owns AI transformation needs to be fluent across domains without being deep in all of them. That’s a specific and uncommon skill set, and it’s worth being precise about what it includes.

They need business case literacy – the ability to describe benefits and ROI in terms that make the CFO nod, while also knowing when a projected return is realistic and when someone’s inflating numbers to get a project approved. If your AI strategy person can’t build a credible business case, they’ll never get past the first budget conversation. If they can’t spot an inflated one, they’ll approve projects that disappoint and erode trust in everything that follows.

They need process design instinct – an understanding of how work actually flows through an organization, where the bottlenecks are, where information gets lost between handoffs, and where the real value gets created versus where people are just managing the friction of systems that don’t connect. This is the Operations and Data Building Blocks in practice, and it’s where most AI opportunities live.

They need to be technical enough to know when someone is overcomplicating the architecture or dismissing the cloud, or when a vendor is selling capability their product doesn’t actually have. They don’t need to write code, but they need to sit in a technical review and know which questions to ask – and more importantly, know when the answers don’t add up.

They need interpersonal communication skills that go well beyond “good presenter.” AI transformation introduces fundamentally different ways of working, and this person will spend a significant amount of their time questioning established norms and changing closely held beliefs. That requires the kind of credibility that comes from understanding someone’s world before you try to change it – which is the empathy work from Article 3.

And they need to be comfortable leading cross-functional teams where they don’t have direct authority over most of the people involved. The AI transformation owner will work with IT, operations, sales, marketing, product, finance, and HR – often in the same week – and they need to be credible in every one of those rooms. Not expert-level credible, but fluent enough to know when the tech group is overengineering a solution, when marketing is chasing a shiny use case that doesn’t connect to strategy, or when operations is sandbagging because they’re afraid of what the change means for their team.

That’s a rare combination – the kind of person who’s spent enough time across enough functions to develop pattern recognition that doesn’t live in any single department. And it’s exactly why so many organizations struggle with this role – they try to fill it from within a single function, and they get the blind spots that come with that function’s worldview.

Flip the Pyramid

Here’s the part that’s going to sound a little counterintuitive: the person who owns AI transformation shouldn’t be leading from the top – they should be leading from the bottom.

We all grew up with the same mental model of how organizations work. CEO at the top, executives below, managers below that, and the people who actually do the work at the bottom. We reinforce it constantly – we talk about “reporting up,” “cascading objectives,” “drilling deep into the organization.” The whole structure is designed to push information upward, because that’s what the hierarchy demands.

But think about what that structure says about the people closest to your customers – the ones doing the actual work, making the actual products, having the actual conversations. They’re at the bottom of the pyramid. The structure literally puts the most important people in the company – the ones at the point of impact – in the lowest position.

Now flip it. What if the person who owns AI transformation saw their primary job not as directing from above, but as serving from below? What if their fundamental question wasn’t “what should you be doing?” but “what can I do to make your work easier, more effective, and more impactful?”

This is the idea behind servant leadership, and it’s not soft-skills idealism – it’s a practical operating model for AI transformation specifically, because everything in this series has been about creating conditions for other people to succeed. Article 1: create the conditions for change-first thinking. Article 2: create the conditions for deep understanding. Article 3: create the conditions for trust through empathy. Article 4: create the conditions for strategic thinking. Article 5: create the conditions for an Environment of Possibilities. None of that is top-down. All of it is service-oriented – enabling the people closest to the work to do their jobs better with AI, rather than dictating from above how they should use it.

The AI transformation owner who operates as a servant leader asks different questions than the one who operates as a commander. Instead of “what’s our AI roadmap?” they ask “what problems are you trying to solve, and how can AI help?” Instead of “why isn’t your team adopting the new tools?” they ask “what’s getting in the way, and what do you need from me to fix it?” Instead of “here’s the strategy, go execute” they ask “does this strategy reflect what you’re seeing on the ground, and what are we missing?”

This isn’t about abdicating leadership. The owner still sets direction, makes hard calls, holds people accountable, and moves the organization forward. But they do it with their attention focused outward and downward – toward the people doing the work and the customers who benefit from it – rather than upward toward the executive suite. The board gets their updates. The CEO gets their dashboards. But the energy goes toward the point of impact.

The Thread That Connects Everything

We’ve covered a lot of ground in six articles, and I want to be honest about what ties it all together, because it’s not complicated – it’s just hard to do consistently well.

Leading AI into your business is fundamentally a human challenge that requires technical literacy, not a technical challenge that requires human sensitivity. The technology is the easy part – it’s accessible, it’s improving constantly, and there’s no shortage of vendors eager to sell it to you. The hard part is everything we’ve talked about in this series: seeing the change clearly before you pick the tools. Understanding your work deeply enough to teach AI what matters. Leading with empathy for what the transition feels like from the inside. Thinking strategically about where AI creates real value in your specific business. Establishing an Environment of Possibilities where experimentation produces learning, not just demos. And putting someone in charge who can hold all of that together with a servant’s heart and a strategist’s mind.

The Five Building Blocks give you a map – Operations, Customer Connections, Product Intelligence, Data Mastery, and Team Dynamics. These six articles give you the leadership approach to navigate that map. But maps and approaches only matter if someone picks them up and uses them.

If you’re the person reading this and thinking “that sounds like my job” – whether you have the title or not – you already know the hardest truth about this work: you can’t do it alone. The cross-functional fluency, the pattern recognition across domains, the ability to spot the real problem underneath the obvious one – these take time to develop and they take experience to sharpen. The leaders I’ve worked with who do this well have almost always had someone in their corner who’d seen the pattern before, who could ask the right question at the right moment, and who had no agenda other than helping them succeed.

That’s the kind of support I built JazzAI to provide – experienced executive guidance for the leaders navigating exactly these challenges. Not a consultant selling a methodology or an AI tool with a chatbot interface, but a thinking partner with forty years of pattern recognition who’s been in the room where these decisions get made.

But whether you use JazzAI or not, the work described in this series is real, it’s urgent, and it’s not going to wait for you to feel ready. The organizations that figure out AI transformation won’t be the ones with the best technology – they’ll be the ones with the best leadership. And if you’ve read this far, there’s a decent chance that leadership starts with you.

If you want to keep building on these ideas – practical frameworks, experienced perspective, no vendor hype – join our mailing list and let’s keep the conversation going.