AI Will Transform Pharma Strategy—But Only If the Foundation Is Right

Pharma and Biotech companies are entering an era where AI will fundamentally reshape how strategic decisions are made. For AI to add real value in portfolio strategy, the tools we use must have the right foundation.

First, we need structured and comprehensive inputs. Many systems hold project plans, but to truly leverage AI we must go further—capturing uncertainties in timelines and costs, commercial dynamics, competitor entry, and even project dependencies. With this level of structure, AI can interact with the data directly, making it possible to update assumptions across a portfolio with a single prompt.

✨ Low-hanging fruit: instantly adjust assumptions portfolio-wide instead of making manual updates.

Second, we need project models that capture the essence of drug development. A simple stage-gate checklist is not enough. Dynamic models should reflect how projects actually behave—accelerations, delays, pivotal points, and interactions. For AI, these models become blueprints of project dynamics, enabling it to reason about “what happens if” scenarios with accuracy and speed.

✨ Low-hanging fruit: see in real time how a delay in one trial impacts the entire portfolio.

Third, we need output that reflects the uncertainty of the future. A single average outcome will not do. Instead, we should generate thousands of simulated futures for each project and portfolio. AI thrives on this scale of data—finding patterns, linking inputs to outcomes, and helping us answer the questions that matter most, such as: what needs to happen to reach our revenue target, or which portfolio mix best balances risk and reward?

✨ Low-hanging fruit: immediate insights into risk levels, prioritization, and portfolio composition.

If we want AI to truly support decision-making in Pharma, these three elements—structured inputs, dynamic models, and large-scale simulations—are essential.