The usual approach pharma leaders take in building next year's budget breaks down completely when it comes to AI. Nobody knows where the frontier will be by April 2027, let alone 2028. Yet we see orgs locking in roadmaps this autumn that run to March 2028 because their planning loop has a two-year horizon. So, how to budget more appropriately for AI projects?

A pharma company that builds its own model to predict compound toxicity owns something valuable, but it'll be out of date within a few years. The same company likely also owns several things it will keep using after it retires the model, and it would struggle to replace any of them. These are assets worth investing in.

Pharma leaders running AI-assisted drug discovery are assuming that their contracts – with frontier labs and model providers – will keep their data safe. The truth is, even if the contract itself is solid and the vendor has the best of intentions, there is still significant risk to your data.

AI labs are pumping out models faster than anyone can meaningfully evaluate them. If you treat every viral launch as a priority, you’ll get exhausted, and your team will get distracted. Decide in advance which launches get your attention and which you let pass. Base your decision-making on the problems your team actually needs to solve, not how loud the launch was.

Frontier AI labs are making massive leaps. There’s an explosion of research, accelerating in every industry. Then you look around at your own AI org, and progress feels... underwhelming. It’s easy to lay the blame on bad data, small budgets, or the wrong headcount, but the real problem is your org’s AI maturity — whether or not you understand the level you’ve reached, and what you need to work on next. We've built a tool to give you a clear picture of exactly these things — the AI Org Maturity Assessment.

ML infrastructure is painfully finicky, and using it entails managing a dozen irrelevant technical details just to do basic research. There are ideas that researchers don’t let themselves have, because engaging with them would mean weeks of infrastructure-wrangling before anyone gets to learn a thing. Sooner or later, your infrastructure starts setting your research agenda. We measure this as Research Agility; and it might be the most important metric in your organization.

An uncomfortable claim: In pharma, biotech, and any serious applied AI, projects almost never fail because of the model. They fail because of the data. Everyone agrees that data matters, but not everyone is interested in funding the work needed to keep it useful. Data work has to run as a standing program, owned, scheduled, and budgeted, not as a one-time cleanup at the start of a project.

For any model tackling any problem, the hardest part of evaluation is keeping the metric aligned with reality. An evaluation is just a measurement tool, and tools can be miscalibrated, biased, or simply pointed at the wrong thing. The urgent question is less: “Are my evals lying to me?” (They are, or they soon will be.) Instead, it’s: “When my evals do lie, how will I know — and what can I do about it?”