How is AI transforming day-to-day operations in biopharma BD and partnering?
Every BD team is asking the same question right now: how do we use AI, and how fast do we move? The pressure to adopt is real but so is the risk of getting it wrong. The teams moving deliberately right now are building workflows, habits, and data foundations that will be significantly harder to replicate in 12 months.
This white paper cuts through the noise. Drawing on interviews with senior BD and partnering leaders at Merck, Galen, and Vitrivax, it examines where AI is delivering measurable value in day-to-day partnering operations and where human judgement remains irreplaceable.
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What's inside the AI white paper
Real-world perspectives on AI adoption across biopharma BD and licensing operations, alliance management and partner oversight, landscaping, triage, and conference preparation, governance, data security, and responsible rollout, and five principles for clean AI adoption from practitioners already navigating the shift.
Who this resource is for
This white paper is designed for BD and Alliance Management leaders who need to move faster on external opportunities without sacrificing decision quality. Build internal AI capability without creating new bottlenecks. Understand what responsible, enterprise-level AI adoption looks like in practice.
Contributors
The insights contained within this resource were given by the following pharma BD leaders...
Martin Svorc
Director, Business Development & Licensing, Merck Group
Natasha Guy
Business Development & Licensing Manager, Galen
Damien Dessis
Chief Business Officer, Vitrivax
Robin Knight
Chief Product Officer, Inpart
The five principles of AI adoption
The teams seeing the most value from AI are not moving the fastest. Here are the five principles separating deliberate adopters from those who will spend next year catching up.
1. Start narrow, prove value, then scale
Pick one well-defined process and do it well. A targeted pilot builds the internal credibility needed to expand. Broad mandates rarely land.
2. Build on strong data foundations before deploying AI
AI amplifies what already exists in your data infrastructure. If your data is fragmented or inconsistent, your AI outputs will be too.
3. Treat AI as decision support, not decision-making
AI informs decisions. People make them. In high-value partnering, trust and negotiation nuance cannot be modelled
4. Invest in people, not just tools
AI adoption fails when it is treated as a technology rollout rather than a capability shift. Fluency is now a baseline competency, not a specialist skill.
5. Govern proactively, especially around data security and IP
Internal valuations, negotiation strategies, and partner information are among your most competitively sensitive assets. Governance frameworks are not optional.
The need for AI in biopharma partnering
AI is no longer a future consideration for biopharma partnering teams. Teams that delay adoption risk falling behind competitors who are already moving faster, screening more effectively, and making better-informed decisions. Waiting for perfect solutions or complete certainty is itself a strategic risk. The question is no longer if AI belongs in partnering, but how it is applied.
The evidence is clear on where AI delivers. Not at the negotiation table, but behind it. Data management, harmonisation, landscaping, and triage are where AI excels, transforming fragmented information into connected, actionable intelligence. By automating the heavy analytical lifting, AI frees teams to focus on strategy, judgement, and relationships.
But discipline matters. Trust, transparency, data quality, and governance are non-negotiable, particularly when sensitive partner data is involved. AI systems are powerful but brittle without strong data foundations, and dangerous if treated as decision-makers rather than decision-support tools.
The future of biopharma partnering is not machine-led. It is augmented. Those who embrace this balance early will not just move faster. They will partner more effectively.