How pharma leaders should budget for AI.
Most function heads build next year’s AI budget by asking their teams for ideas and assigning numbers nobody can defend. Finance then puts a bought tool, a model experiment and a data cleanup on one line called “AI spend”, so leaders fund three different risks as if they were one. In this free guide, we show you how to bring your planning committee a plan in three parts instead of one number.
What you’ll learn.
Written for heads of clinical operations, R&D, regulatory, medical affairs and other function leaders at biopharma companies who are building next year’s AI budget now.
Start from what AI is for. Decide whether you expect AI to speed up your team’s current work or let them do work they’ve never done. Then size your team and set your payback timeline to match.
Spread your risk on purpose. Fund some safe bets and a few long shots. Give each experiment a small budget, a fixed period and a stop date; hold money back to scale the ones that work.
Test with your best people. When leaders staff a test with the cheapest team, they learn only that the cheapest team failed. Put a senior team on your biggest bet for six months so you can stop or scale with confidence.
Buy commodities, build what sets you apart. Buy models and general drafting tools. Build on your own trial data, your own tests and the way your people make decisions; no vendor can sell those to your competitors.
Pay for groundwork early. Give data cleanup and cost tracking their own line, so your teams stop cleaning the same records for every new project.
Fund problems, not techniques. Tie your experiment budget to problems you need to solve, like trials that keep missing enrollment targets. Leave the choice of technique until spring.
Bring the committee
a plan in three lines.
An operating line. Staff training, shared practices and the tools you buy. Keep it fairly flat and measure it by time saved.
An investment line. Experiments with stop dates, plus money you hold back to scale the ones that work. Measure each against the problem it targets.
A groundwork line. The data cleanup and tracking every later project needs.
Name an owner for each line, including whoever will own its cost in year two.
Plan next year’s AI budget with us.
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