Companies across industries are racing to adopt AI, with Stanford estimating that 78% of organizations now use AI in at least one business function. But I’m watching a different trend emerge from my conversations with executives and investors in lower Manhattan boardrooms. The real question isn’t whether companies are deploying artificial intelligence. It’s whether they have leadership teams capable of making those deployments matter.
AI is being embedded into workflows, products, and decision-making processes faster than most organizations anticipated. Deployment and adoption, however, are not the same thing. In recent months, I’ve noticed the conversation shifting among leadership teams at public companies, private firms, and private equity-backed businesses. Nobody’s debating whether to adopt AI anymore. They’re wrestling with where it should go first, how fast to move, and how to ensure it actually improves outcomes without introducing risk the business isn’t ready to absorb.
The Federal Reserve’s latest Business Trends Survey indicates that nearly 62% of companies report difficulty translating AI experimentation into measurable business value. That gap between experimentation and execution is where leadership becomes the differentiator. According to McKinsey’s 2024 State of AI report, organizations with dedicated executive oversight of AI initiatives are three times more likely to see returns on their investments than those without clear leadership accountability.
Identifying promising AI use cases is often the easy part. In many companies, AI experimentation is a grassroots effort. Product groups test new features, operations teams automate workflows, and individual departments explore ways to improve efficiency. While these experiments can generate meaningful insights, translating them into organizational capabilities requires something far more difficult at the leadership level.
Without leadership coordination, initiatives quickly become fragmented. Teams adopt tools independently and governance struggles to keep pace. This is where organizational leadership becomes the differentiator. Scaling AI requires someone at the top who can align multiple functions around shared priorities, establish clear guardrails for experimentation, and build trust in how AI is deployed across the business.
The World Economic Forum’s Future of Jobs Report 2024 highlights that 68% of executives cite leadership capability gaps as the primary barrier to successful AI integration. Not technology limitations. Not budget constraints. Leadership gaps. The companies that ultimately succeed with AI are not simply those that adopt the most tools. They are those that build the leadership bench capable of integrating AI into the fabric of how the business operates.
As AI moves from science project to enterprise integration, board conversations are becoming more strategic. Rather than debating which tools to adopt, directors are focusing on how AI should influence the company’s strategy, operating model, and long-term advantage. In many cases, the conversation centers on several critical decisions that I’ve heard repeatedly in investor meetings and executive briefings.
Do we build proprietary AI capabilities, or is off-the-shelf good enough? And if we build, where do we start? Do we go after the low-hanging fruit first, or tackle the challenge that everyone knows needs solving but has been avoided? How do we sequence investments so early wins build toward something scalable, rather than a collection of disconnected experiments?
What governance and risk structures do we need before AI systems have access to more data, more processes, and more decisions? These questions reflect a broader shift in how boards and leadership teams view AI. The challenge is no longer simply adopting new technology. It is determining how AI fits into the company’s operating model, competitive strategy, and risk framework as it becomes embedded across the business.
Deloitte’s 2024 Global Technology Leadership Study found that 71% of boards now include AI strategy as a standing agenda item, up from just 34% two years ago. For boards, answering these questions often begins with evaluating whether the leadership team has the right capability and risk appetite to guide the organization through these decisions.
Despite the rapid pace of innovation, many leaders recognize that the public narrative around AI adoption often runs ahead of enterprise reality. One example of this gap is the growing attention around the Chief AI Officer role. While larger enterprises have introduced this position, organizations across most industries remain uncertain whether it represents a permanent leadership model or a temporary response to a rapidly evolving technology landscape.
In many organizations, the more immediate question is where AI strategy should reside. Companies are still determining whether responsibility should sit within technology organizations, product leadership, data teams, or the business units themselves. Until those questions are resolved, introducing a new executive role dedicated solely to AI can create more ambiguity than alignment.
Harvard Business Review’s analysis of AI organizational structures found that companies with centralized AI leadership reported 40% faster time-to-value on AI projects compared to those with distributed responsibility. But centralization only works when the person at the center has credibility across functions and the authority to make strategic tradeoffs.
Fundamentally, enterprise-wide AI transformation still faces a practical challenge that I encounter in nearly every executive conversation I have. Trust. For AI systems to meaningfully augment or replace human decision-making, they must consistently demonstrate measurable and frictionless improvements over existing processes. That’s harder than it sounds when you’re dealing with legacy systems, change-resistant cultures, and risk-averse compliance departments.
As a result, many companies are using a more surgical approach in deploying AI rather than attempting broad transformation all at once. These deployments are frequently embedded within products or services where the value is clearer and easier to measure. Over time, those targeted applications can create the foundation for broader adoption, but the path to enterprise scale is typically more gradual than headlines suggest.
Historically, succession planning often prioritized operational continuity. Leadership teams sought executives who deeply understood the company’s existing business model and could sustain performance within it. While that perspective remains important, it is no longer sufficient on its own. Today, the evaluation has expanded in ways that would have seemed unthinkable just five years ago.
CEOs, investors, and leadership teams are also asking whether their next generation of executives has the perspective and resilience to guide the organization through sustained technological change. AI represents one major shift, but it will not be the last. Companies will continue to face waves of innovation, regulatory developments, and geopolitical pressures that reshape their industries.
This reality has placed greater emphasis on forward-looking leadership evaluation. Past performance still matters, but what leadership teams and investors are increasingly focused on is a candidate’s ability to guide the organization through its next phase of transformation. Maintaining alignment and momentum as new technologies potentially reshape the overall business model requires a different kind of executive than what succeeded in more stable environments.
What I keep hearing consistently across board and investor conversations is this reality. Technical AI expertise alone is not what defines the next generation of successful leaders. The quality that keeps coming up is adaptability. Over the next five to seven years, organizations will face repeated waves of technological change that will make today’s AI transformation look like a warm-up exercise.
AI capabilities will evolve in ways we can’t fully predict. Regulatory frameworks will mature and likely become more complex. New competitors will continue to reshape markets with business models that didn’t exist eighteen months ago. Leaders who succeed in that environment will be those who can continuously reassess strategy, adjust operating models, and maintain organizational momentum despite uncertainty.
AI may be the most visible catalyst for change today, but the broader leadership challenge extends beyond any single technology. What leadership teams and investors are ultimately searching for are executives who can guide organizations through tectonic shifts while building the capabilities required for what comes next. That’s a fundamentally different skill set than what was valued in previous generations of executive leadership.
The companies that win with AI won’t be the ones with the most advanced technology. They’ll be the ones with leadership teams who can influence those around them to lean into AI not as a tool, but as a new way of operating. If your organization isn’t hiring with that lens, you’re already behind. And the gap between those who get leadership right and those who don’t will only widen as AI becomes more deeply embedded in how businesses compete and create value.
