Most life sciences companies don't have an AI pilot problem - they have a P&L problem 

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The pilots work. The demos impress. Teams are energized. And twelve months later, the CFO asks the same question: what did we actually get for it?

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The pattern is not unique to our industry. MIT NANDA’s 2025 study of enterprise generative AI found that only about 5% of pilots achieve rapid revenue acceleration; most stall with little or no measurable P&L impact. In life sciences, where validation, data privacy and regulatory scrutiny add weight to every step, the gap is often wider.

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LS-EATA helps leadership teams assess their current enterprise AI capabilities, identify critical gaps and establish priorities for moving from fragmented initiatives towards sustainable enterprise transformation.​​​​​​

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A 30-minute, no-obligation conversation to discuss your enterprise AI priorities, challenges and opportunities to create a practical pilots to P&L sprint approach​​​​​​​​​​​​​​​​​

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Why pilots stall before they reach the P&L

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Having run a global P&L and built a Life Sciences practice from a few hundred people to several thousand, I see the same three gaps again and again:

• Strategy without ownership. AI priorities are set at the top, but no one owns turning each one into a business result.

• Value measured too late. The value case is written after the pilot, if at all, rather than before it is funded.

• Governance that arrives at the wrong moment. GxP validation, privacy and EU AI Act questions surface just as a pilot is ready to scale, and momentum dies.

 

None of these is a technology problem. Better models will not fix them.

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Three shifts that change the outcome

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1.    Start from the P&L, not the use-case list. Most AI portfolios grow bottom-up: a promising idea, a willing sponsor, a pilot. The companies that see results reverse the order. Every initiative has to earn its place against a line the CFO already tracks.​​

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2.   Make the hard calls. Not every pilot deserves to scale. Some need a specific blocker removed. Some are worth keeping, but only at minimal cost. And some should stop, so their budget and people can go to the initiatives that matter. Few organizations make these calls explicitly, and the portfolio grows wider instead of deeper.​

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3.   ​Build governance into the decision, not after it. In life sciences, the questions that stop an AI initiative from scaling are usually predictable. Asking them at the start turns governance from a brake into a steering wheel.

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What changes when you get this right

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Leaders who make these shifts tend to end up with:

• A clear, defensible call on every AI initiative

• A value case their finance team will sign, expressed in terms the board recognizes

• A short list of blockers, each with an owner

• A realistic plan for scaling the few initiatives that matter most

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A focused way to start

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We have built this thinking into a focused, 4-week engagement for life sciences leaders: the Pilots-to-P&L Sprint. It works with your own data and your finance team’s baselines, asks only a few hours of your team’s time each week, and ends with a decision rather than a report.

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If your AI portfolio has more pilots that results, we would welcome a 30-minute conversation about where yours stands.​​​​​​​​​