Formation Bio

BD Platform Vision

The BD Platform vision is about scaling business development operations more efficiently across Formation Bio. By consolidating opportunities, data, and communications into a single system, it transforms fragmented processes into a collaborative hub. AI agents play a central role in this vision by searching across sources, analyzing information, and surfacing insights in context so teams can focus on decision-making rather than manual work. This vision serves as a guidepost, helping us focus on what matters most first, build gradually, and deliver early impact while staying aligned on long-term strategy.

Design Lead (me)
Product Lead
Director of BD
BD Associate

Team
Timeline

Ongoing

Shifting Business Model
As Formation Bio transitioned from SaaS into a pharmaceutical company, the goals, workflows, and success metrics changed completely. Building a portfolio of assets quickly was critical. Without assets, there would be no pipeline, no products, and ultimately no company.

Context

Limited Visibility
The process of finding and evaluating assets was entirely manual. Teams relied on conferences, emails, Excel trackers, and constant meetings. With no centralized system, there was little visibility into deal flow, progress was slow, and valuable opportunities risked slipping through the cracks.

Untapped Potential of AI
At the same time, a tech paradigm shift was underway: moving from traditional software to AI-native systems. This created the opportunity to design a platform where AI agents could search across sources, analyze information, and consolidate insights into a single hub — transforming BD into a scalable, collaborative, and insight-driven function.

How might a platform powered by AI transform a slow, manual, and fragmented process into one that scales asset evaluation with the speed and clarity needed to rapidly build a drug portfolio?

Design Approach

Mapping the Current State

1

We started by documenting how BD was actually operating: scattered spreadsheets, email threads, conference notes, and constant meetings. This revealed the core challenges — no single source of truth, limited visibility, and evaluation that often stretched across days.

Establishing a Baseline System

2

The first design priority was structure. We envisioned a central CRM-style platform where the team could backfill historical data, track what everyone was working on, and consolidate deal flow. This provided immediate visibility and patterns across opportunities.

Codifying Evaluation Criteria

3

The next challenge was judgment. BD described their evaluation process as “an art, not a science.” To bring more consistency, we codified how assets should be assessed: biological fit, modality relevance, clinical impact, market attractiveness, and strategic alignment and broke each into detailed sub-questions to guide decision-making.

Building Mental Models

4

To make evaluation actionable, I created mental models that mapped different ways of combining criteria (e.g., Indication × Modality, Indication × Target). These models helped surface hidden opportunities and reframe how the team could think about prioritization.

Facilitating Team Workshops

5

I ran workshops with the BD team to test these frameworks. Together we ideated around search strategies, then prioritized the approaches that could create the biggest impact. This process aligned the team around shared language and evaluation principles.

Partnering on System Design

6

With this foundation, I collaborated with the Director of BD to sketch early system diagrams. These mapped how an AI-powered investment model could connect to sourcing tools, diligence workflows, and financial models — setting a vision for how everything could scale together.

Exploring AI-Native Possibilities

7

Finally, we looked outward. I studied potential competitors and adjacent tools to understand how others were applying AI, and used this to refine our own direction. From there, I prototyped interfaces that imagined AI agents continuously scanning for opportunities, auto-flagging matches, and consolidating insights into a single hub for the BD team.

What this unlocks for us

One connected CRM with shared visibility

Structured, collaborative thinking documented and shared

Historical reasoning and data now reusable and searchable

Clear problem framing led to the start of a product roadmap

Experimenting with a shared goal

A roadmap

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