What is decision provenance and why does drug discovery need it now?
A practical definition of what it means to make scientific decisions traceable, and why this is critical in the AI era.
The backbone of a successful drug program is a series of decisions. The reasoning behind each decision is the most valuable asset a company has, and today it has no home. Organizations are carrying the most valuable resource they own entirely uninsured.
The biggest risk used to be institutional knowledge walking out the door when a scientist leaves. Now it’s much worse. The race to put AI in the loop is quietly moving decades of judgment into models that companies don’t own. Manage that wrong, and the edge becomes someone else’s product.
Understanding how and why previous decisions were made is equally as challenging as making new decisions. This challenge shows up in questions such as:
Should we still pursue our lead target after our competitor has released phase 1 toxicity data?
What additional indications is our asset suitable for that are in line with our commercial strategy?
Why do we believe that biological pathway X is a key driver of indication Y?
This challenge is omnipresent because the reasoning behind those decisions was never captured in a way anyone can retrieve.
What is decision provenance?
Decision provenance is the why behind the what, captured the moment a decision is made and kept alive so it can be questioned, defended, and reused long after the room has cleared.
These are the tradeoffs and heuristics that go unspoken as a result of decades of experience. The indication specific nuances. Concessions that scientists arrived at during an in-person meeting that remain hidden in slide 44 of a SharePoint.
For key opinion leaders and industry vets this knowledge becomes tacit, second nature. For the rest of the organization, this tacit institutional knowledge becomes elusive and slips out the door as soon as scientists leave the organization.
Capturing this logic and making it accessible to cross-functional teams is the core substrate of decision provenance. However, just capturing it is not enough. This knowledge needs to be operationalized to drive future decisions and eliminate repeated mistakes.
Imagine sending a self driving car to a specific location, and every time it returned, the left side of the car was completely totalled. Instead of looking back at the data and route history, you repair the car and send it back down the same route, only for it to return totalled once again.
This is exactly what it’s like to make high-stakes decisions without decision provenance.
The cost of relearning what you already knew
When it’s time to revisit a previous decision, weeks are spent repeating analyses and doing detective work.
A target gets deprioritized. At the time, it’s a sensible call. Two years later, a competitor publishes promising phase 1 data, and someone in portfolio review asks the obvious question. Why was this program killed?
The reasoning was never written down. It lived in a meeting and a deck that no longer exists. So a team spends the next quarter reconstructing an argument that took an afternoon to produce. They re-run the analyses, interview whoever is still around, and arrive back at a version of the same reasoning, now stale, having burned time and R&D resources.
This is not a one off. Every revisited portfolio decision must be re-derived. New hires inherit conclusions without the reasoning behind them, so they either trust the call blindly or redo the work to believe it. The time cost is not the occasional fire drill. It is a tax on every decision the organization ever wants to look at twice.