Why BD teams cannot afford to lose their memory & how AI can help

Business development in biopharma is a compounding game. Every screen, diligence report, and partnering meeting offers signal: why we passed, what we learned, nuances of a therapeutic area, and catalysts to re-engage.

But here’s the reality: BD teams are drowning in evaluation tasks. In-licensing decks flood inboxes. Conference partnering schedules stack 15–20 meetings back-to-back. New therapeutic areas emerge while old ones cycle back into vogue. The deluge of information never stops.

And without organizational memory, that overload compounds. Teams don’t just evaluate new assets, they re-evaluate old ones without realizing it. They rebuild slides and trackers that already existed. They repeat outreach to partners who’ve already heard “no.” What should be a forward-looking process becomes a loop of forgotten context and duplicated effort.

At Merck, Yael Weiss saw how a ‘record every interaction’ database let new hires pick up relationships without recreating history. That kind of continuity doesn’t just save time, it transforms overload into leverage. That’s still a lot of information for new hires to read, process, and remember, of course. But even with databases, access to prior knowledge often breaks down. The antidote is simple to name but hard to sustain: build durable organizational memory.

Why access to prior knowledge keeps breaking down

Even great business development teams struggle to answer basic, but high-stakes, questions:

• Have we already diligenced this asset, target, or company?
• What were our reasons to pass or pause? What has changed since we made that decision?
• Who on our side knows this team, and what’s the relationship history?
• Are we about to duplicate outreach, or worse, contradict a prior position?

Why it’s so hard to create organizational memory:

1. Fragmented capture. Evaluations live in decks, emails, meeting notes, and people’s heads.
2. Shifting strategy. Therapeutic focus and stage preferences evolve; yesterday’s “no” might be today’s “yes-if” (but only if they remember to circle back).
3. Headcount churn. People inevitably move on, taking knowledge and context with them.
4. Exploding landscapes. Cycles of what’s hot (like gene therapy, CRISPR, and GLP-1s) widen the surface area to cover.
5. Throughput pressure. Screening has to be broad by design, so BD teams can turn over rocks without missing diamonds in the rough.

The cost of not having a historical backlog

• Redundant work and slower cycles. Teams repeat screens and re-open old questions while new hires spend months re-learning tribal knowledge.
• Partner friction. Inconsistent messages to the same outside party (“didn’t you pass on this last year?”).
• Strategic blind spots. Forgotten red flags (toxicity, intellectual property (IP) gaps, weak pharmacokinetics (PK)) re-emerge late.
• Missed timing. You can’t act on “right science, right time” if you can’t quickly recall what changed.

Turn the firehose into a filter

Every BD team knows the pressure to find a blockbuster. But without a system of memory, you’ll keep getting overwhelmed and continue to carry yesterday’s blind spots forward.

AI doesn’t shrink the torrent of data, but it does remember every drop, so your team doesn’t have to. Instead of starting from scratch, the system can tell you: “We looked at this program 18 months ago. Here’s why it was a ‘yes-if,’ here’s what changed since, and here’s the next decision point.”

Each new evaluation is faster because it’s built on the scaffolding of every prior one. The quantity of data coming in doesn’t decrease, but with memory, teams can focus on what matters most, at the right time.

A Business Development Memory System (BDMS)

AI can build a BDMS: a living, searchable system of record for evaluations and interactions. It’s not a static tracker; it’s a memory flywheel.

Core elements of a BDMS include:

• Canonical evaluation record. One structured entry for every screen: identity, decision state, rationale, risks, triggers, provenance.
• Relationship timeline. A stitched history of every touchpoint, so newcomers see the movie, not just still frames.
• Unified taxonomy. Common tags for modality, target, indication, and risk to enable retrieval and portfolio-level analysis.
• AI layer on top. Semantic search, similarity detection, trigger monitoring, dedupe checks, and just-in-time recall.

Why AI makes this finally work

Before AI, some BD teams tried to build a BDMS manually, but the process was slow, incomplete, and exhausting. With AI, though, what used to take weeks or months to compile can now be done far faster.

With an AI-powered portfolio analysis system, BD teams can:

• Run semantic recall: “Find assets like the TLR7 program we passed on due to PK variability.”
• Get just-in-time briefings: Updated briefings based on new outreach information, bundled with prior decisions and updated context.
• Track Yes-If triggers: Automatically trigger alerts based on custom yes-if triggers, such as a Phase 1 readout or a new IP filing.
• Generate coverage maps: “We’ve met 70% of CRISPR players; here are the gaps.”
• Flag duplication and inconsistency: “Your note today conflicts with the reason for passing last year.”

The result: screening compresses from weeks to hours, freeing teams to focus human bandwidth on nuanced diligence.

How to build a BDMS

Define the foundation

• Choose a system of record.
• Lock schema and taxonomy.
• Set governance and provenance rules.

Backfill recent history

• Import the last 24–36 months of evaluations and partner meetings.
• Link decks, notes, and virtual data rooms (VDRs).
• Assign owners to clean up fuzzy entries.

Layer on AI

• Enable semantic search and Yes-If trigger monitoring.
• Configure dedupe alerts.

Change team habits

• Require evaluation records for every screen.
• Hold weekly 15-minute “memory reviews” with BD teams to ensure new data enters the system.
• Make memory visible in every readout.

The BD memory flywheel

1. Capture: Every interaction becomes structured insight.
2. Connect: AI links assets, risks, and relationships.
3. Recall: Context surfaces at the moment of decision.
4. Decide: Faster, more consistent evaluations.
5. Compound: Each decision enriches the system for the next one.

Memory as strategy

Patients don’t benefit when we forget. In biopharma BD, memory is strategy, because it lets teams act with speed and conviction when science, need, and timing align.

The mountain of asset evaluations isn’t going away. Modalities will keep cycling, landscapes will keep expanding, inboxes will keep filling. But overload doesn’t have to mean paralysis. With a BDMS and AI as the steward of organizational memory, teams can transform volume into clarity and repetition into compounding advantage.

That’s how BD teams escape rework and focus on what matters most: finding the next cure and shaping the partnerships to deliver it.

Photo by Markus Winkler on Unsplash