Building the Case for Organizational AI Investment
Most AI pilots in biotech produce good local results and then die. A team does something impressive, writes up the savings, and presents it at an all-hands. Then nothing changes organizationally. Six months later, that team's AI usage has drifted back toward occasional, and the broader organization hasn't moved.
The failure mode is almost always the same: the case for AI was made in efficiency terms when it needed to be made in investment terms.
What an investment case looks like
A compelling organizational AI investment case has four components:
1. The current cost of the status quo. Not in hours, but in program impact. "Our current regulatory submission process adds 14 weeks of avoidable delay to NDA preparation. At our current burn rate, that delay costs $X million in runway." This is the baseline.
2. The specific intervention. Not "we should use AI more" but "we will implement three specific workflows — parallel dossier drafting, automated literature synthesis, and AI-assisted CMC writing — with defined rollout and success criteria."
3. The projected impact, conservatively stated. "Based on our 90-day pilot, these three workflows recover 18 hours per week across the regulatory and clinical writing teams. Conservatively, this moves our NDA preparation timeline from 18 months to 14 months."
4. The required investment. Tool licenses, training time, workflow redesign effort, and the initial implementation cost. If the investment is $X and the value is 4 months of IND acceleration, the math usually isn't close.
The governance layer
One reason AI investments don't stick: they're treated as individual tools rather than organizational infrastructure. The companies that get compounding returns from AI treat it like they treat their ELN, their CTMS, their regulatory submission system — as infrastructure with defined ownership, maintenance, and improvement cycles.
This means someone owns the prompt library. Someone owns the AI tool stack decision. Someone is accountable for measuring outcomes quarterly. Without that governance layer, AI stays in the "interesting experiments" category and never becomes a competitive advantage.
The final module of this curriculum walks through how to build that governance layer for your organization.