An ungoverned AI is like a chaotic, unpredictable forest. To achieve consistent business value, AI must be 'farmed'—a process of applying governance, organization, and boundaries to cultivate predictable results. This regulated approach is key to harnessing AI for reliable revenue generation.

When reporting on AI experiments to the board, avoid using "learning" as a primary KPI, as it can sound like an excuse for failure. Instead, translate those learnings into tangible outcomes and demonstrable progress toward goals, showing what impact the learning has and promises.

Leaders must resist the temptation to deploy the most powerful AI model simply for a competitive edge. The primary strategic question for any AI initiative should be defining the necessary level of trustworthiness for its specific task and establishing who is accountable if it fails, before deployment begins.

If your team cannot articulate the specific business outcome of their AI usage in a single sentence, you don't have an AI strategy. You simply have 'token maxing'—usage for the sake of usage. This framework forces a direct link between AI spend and business results.

For companies adopting AI reactively, governance frameworks are more than risk mitigation. They enforce strategic discipline by requiring clear business objectives, performance metrics, and resource tracking, preventing wasteful spending on duplicative tools and unfocused initiatives.

While every AI pilot may be individually justifiable, they often fail to be collectively coherent. A true strategy exists when initiatives build on one another, creating compounding value—like when clinical data feeds medical affairs AI. A collection of disconnected projects cannot be governed effectively.

A significant gap exists between companies stating an AI strategy (44%) and those with a formal governance framework (13%). This suggests firms prioritize value extraction over establishing ethical guardrails, risking a loss of investor and consumer trust.

Leaders who say "we need an AI strategy" often reveal a lack of a clear core business strategy. AI should be a component that enables the overall company vision, not a separate initiative. This mindset shift grounds AI efforts in tangible business value tied to your unique differentiators.

When facing top-down pressure to "do AI," leaders can regain control by framing the decision as a choice between distinct "games": 1) building foundational models, 2) being first-to-market with features, or 3) an internal efficiency play. This forces alignment on a North Star metric and provides a clear filter for random ideas.