AI won’t save your strategy. Imagination will

While companies deploy AI for efficiency, the leaders who win markets will use it to expand what’s possible.

“Sean,” the CEO of a health technology startup, invited Garry to join what he called a strategy offsite. By mid-morning, it had become something else. Six people, a whiteboard, a Series A closing in ninety days. The question on the table: where do we invest next? More engineers? A bigger sales team? Another content hire?

Garry set down his coffee and asked a different question. “What if we stopped asking what we can afford to build, and started asking what we can now imagine building—that we genuinely couldn’t have a year ago?”

The room shifted. So did the conversation. And that reframe—from resource allocation to possibility expansion—is the strategic inflection point that most leadership teams have not yet reached, and it is the one that will separate the companies that use AI to compete from the ones that use it to lead.

The research reinforces this point. A recent report from Deloitte finds only 34% of organizations are using AI to deeply transform their work. Another report from McKinsey confirms that companies using AI to pursue growth are 3.5x more likely to achieve enterprise-level impact than those focused on cost reduction.

Drawing on our work with senior leaders—Jenny as an executive coach and leadership development expert, and Garry as a physician-entrepreneur who mentors and advises early-stage startups—we have identified three strategies for leading innovation in the age of AI.

1. Start with the Friction, Not the Platform

When Garry first met with the founding team of “Axiom Health,” a startup focused on next-generation cardiometabolic biomarker analytics for early disease detection, their conversations were almost entirely about technology. The platform was front and center; the patient was not.

He pushed them to slow down. Before talking about what to build, he asked them to map the real tension their future patients and physicians were living with: Where were clinicians hitting walls? What workarounds revealed unmet needs? And what would need to be true for a better solution to be trusted at scale?

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