In boardrooms around the world, a familiar question keeps surfacing: Who should lead our AI efforts? It’s a reasonable question. As generative AI reshapes the business landscape, leaders are rightly trying to determine who is best positioned to guide their companies through the shift. In response, some have rushed to appoint chief AI officers (CAIO), as if one brilliant hire could unlock AI’s full promise. But this approach often misfires. The CAIO arrives with fanfare. Pilots begin. A few flashy demos surface. And then? Not much. Projects stall. Teams don’t adopt. The CAIO departs. The board starts asking tough questions. It’s not because the CAIO wasn’t smart or capable. It’s because the role itself, at least as it’s currently imagined, is overloaded and misaligned. The assumption is that one individual can bridge innovation and operations, oversee compliance and infrastructure, and deliver fast wins across the enterprise. That’s an impossible job description. The companies making real progress are building ecosystems of leaders involved in the AI journey. The Lone-Leader Trap Too often, the CAIO is tucked under the CTO or siloed in a strategy group. They’re told to experiment, to be bold, but also to avoid risk, deliver immediate value, and ensure enterprise-wide transformation. In one global-services firm, the AI lead created a promising prototype using a large language model. But the project went nowhere. Business units hadn’t been brought in early, training never happened, and the new tool sat unused. Even in better-aligned orgs, the pressure on a single leader to drive AI transformation is intense. Boards want their investment in AI to quickly translate to top-line revenue. Legal asks for guardrails. Operations wants automation. Marketing wants personalization. It’s not that these goals are wrong. But expecting one person to deliver them all is setting them up to fail. An Ecosystem Approach In companies where AI is taking root, the best leadership is distributed, with many executives working in sync. Holmes Murphy, a leading commercial-insurance brokerage-and-services company, offers an early example. One individual has been elevated to orchestrate the overall AI program, but the responsibility for AI-driven change is distributed across all levels of the organization. It starts with an AI leadership team made up of deeply engaged senior executives (including the CEO, CIO, COO, and chief legal officer), each selected for their deep understanding of the business, commitment to innovation, and ability to drive top-down operational change. Supporting them is a small, cross-functional AI Center of Excellence (COE) composed of five or six individual contributors and manager-level employees who share a passion for innovation and willingness to challenge the status quo. The COE is tasked with developing deep expertise in AI capabilities, staying current on emerging trends, and translating those capabilities into business-relevant applications. Over time, the COE will serve as a key driver of internal innovation, with the broader goal of embedding an AI-forward mindset across the entire organization. Some leaders in your AI ecosystem should focus on building. These are your engineers, heads of digital, or product teams experimenting with generative tools. Others should focus on integration: the COO rethinking workflows, the customer-support lead reorganizing teams to leverage new AI systems. Still others should serve as connectors: the CFO tying investments to outcomes, the general counsel evaluating risk, the CEO ensuring that AI is embedded in how the organization learns and adapts. This doesn’t require a complex bureaucracy. The structure matters less than the mindset: AI isn’t a department. It’s a capability the whole leadership team needs to own. The CEO’s Critical Role Among those leaders, the CEO plays a unique and outsized role. The CEO holds the power to set strategy, align incentives, and shape the organization’s cultural response to change. AI transformation, when done well, is not about the technology itself. It’s about the strategic and operational shifts it enables. AI strategy is your business strategy. That’s why CEOs cannot delegate AI leadership entirely. Their words and, more importantly, their actions, signal how seriously their companies are taking the transition, and whether the future includes a place for every employee. The most effective CEOs lead with direct involvement, authenticity, clarity, and a growth mindset. Authenticity means mapping AI adoption to their own personality and strengths. A technical CEO might prototype new workflows and share the results. A non-technical CEO might co-create new processes with internal builders and show that AI is for everyone, not just experts. Direct involvement means showing up, not just as a sponsor, but as a peer in critical working sessions. It means engaging with pilots, attending hackathons, and giving feedback as real work happens. That behavior was on full display recently at ITAGroup, an 800-person employee-recognition and events company. The CEO, Brent Vander Waal, along with the COO, CIO, and CFO, spent hours in cross-functional sessions with managers and frontline staff. Together, they mapped how each unit operates and pinpointed opportunities for AI to improve employee and customer experience. The initiative culminated in a company-wide hackathon chaired by the executive team. Leadership watched, listened, offered feedback, and judged the results. The signal was clear: This transformation belongs to everyone. Clarity and a growth mindset should define how the CEO communicates. Policies must be clear. Messaging must be consistent. And the underlying frame should be opportunity-driven. It’s about scaling work, evolving roles, and creating space for more human creativity and judgment. Who Else Should Be at the Table? Start with a builder. Someone curious, energized by possibility, and willing to test new ideas even when the outcome is uncertain. This might be someone already inside the organization, a head of innovation, a product lead, or an engineering director quietly experimenting with AI tools. But that builder needs a partner. Someone grounded in the business, who understands where work happens, where friction happens, and what’s worth solving. Without somebody who can view experiments through an operational lens, they will often remain disconnected from real impact. Finally, you need a strategist. Someone who can zoom out, assess which initiatives align with long-term goals, and steward limited resources to their highest use. These leaders don’t need identical resumes. But they do need to all be collaborative. Curious. Grounded in reality but open to reimagining how things work. They can tolerate ambiguity but know when to focus and ship. Some of the most impactful AI champions emerge organically through hands-on exposure rather than formal selection. We’ve observed that many organizations are still in the early stages of AI maturity and lack a practical understanding of what today’s tools can realistically accomplish. An effective way to bridge this gap is through ideation sessions with leadership that demystify core concepts, showcase live examples, and—crucially—encourage hands-on experimentation with tools such as ChatGPT Pro and Claude. These early interactions often shift mindsets and spark more grounded, creative thinking about AI’s role in the business. These early sessions often ignite curiosity. In at least two recent cases, participants who had no prior AI experience got inspired and became deeply engaged after seeing use cases tested live. They began experimenting independently, generating new ideas, and encouraging peers to do the same. This kind of early empowerment has helped surface unlikely champions—people who, once inspired, lead by doing rather than by title. It’s tempting to look inward and tap the existing AI team to lead the company-wide shift. But as Brendan Hopper, the CIO of Commonwealth Bank, notes, “Many of the people who’ve been working with AI the longest may not be the right ones to lead enterprise transformation. They’re brilliant in the lab, but transformation happens in the business.” That’s a crucial distinction. Technical expertise is necessary but not sufficient. Leading transformation requires business fluency, emotional intelligence, and the ability to galvanize others across functions. Consider how JeffMaggioncalda, Coursera’s former CEO, seized on ChatGPT not as a lab exercise but as a business imperative. He dove into the tool himself, then launched “Project Genesis,” a cross‑functional team that mapped AI pilots—involving such tasks as translations, coaching, and content generation—against clear impact metrics. He rewrote OKRs to bake AI goals into every department, held open forums to surface concerns, and built human‑in‑the‑loop feedback to catch bias and manage risk. That blend of technical curiosity, business fluency, and change‑management muscle is exactly what transforms isolated experiments into company‑wide breakthroughs. Designing for Durability AI efforts stall when coordination breaks down. A promising model might need new data flows, process redesign, legal input, change management, and training. If each of those sits in a different part of the org, each with its own roadmap and priorities, the friction can be fatal. One of us (John) outlined this in a previous HBR article on the systematic adoption of AI. The key isn’t just experimentation. It’s designing a repeatable system for evaluating, integrating, and scaling good ideas. That means clarifying roles: who builds, who integrates, who decides. It means standing up lightweight infrastructure, centers of excellence, working groups, playbooks, that create flow between departments rather than barriers. It also means avoiding several dangerous pitfalls. Notably: Ignoring change‑management. A candidate who can articulate how neural networks work but can’t craft a narrative, negotiate risk, or upskill thousands of colleagues will leave cultural resistance untouched and ROI unrealized. Gatekeeping of ideas. When AI planning is limited to a tight inner circle, the team misses the lived‑in knowledge of frontline employees. Strong leaders keep the channels wide open—through idea portals, rotating workshops, even factory‑floor office hours—and run a clear process for sorting, testing, and scaling the best suggestions. Misreading the risk landscape Choosing someone blind to ethics, privacy, and regulatory exposure can spark reputational or legal crises that overshadow any early gains, eroding executive and board confidence in the whole program. Lead Together, or Not at All It’s tempting to believe the right hire will unlock AI. But AI isn’t a solo act. It’s not even a duet. It’s an enterprise-wide shift that touches every function. The companies that figure this out won’t be the ones who find the perfect CAIO, if such a person exists. They’ll be the ones who build a leadership model flexible enough and durable enough—and staffed by leaders curious enough—to navigate what comes next. Hiring matters. But the real work starts when the whole team leans in.
