The Challenge

A mid‑sized biotech company had invested heavily in data management but was not realising sufficient value. Leadership wanted an assessment of the entire data landscape to identify opportunities for AI‑driven improvements across the drug‑development pipeline.

Our Approach

We audited the company’s data landscape, built predictive models and developed a strategic roadmap to embed AI into drug development.

• Data audit & opportunity identification: Documented the existing data management infrastructure across five departments and identified eight opportunities where AI could accelerate drug development.
• Predictive modelling: Developed proof‑of‑concept models to predict purification success and improve lead designs using generative AI.
• Strategic roadmap & implementation: Delivered a three‑year implementation plan, created an in‑silico sandbox and assembled a cross‑disciplinary team while assessing FAIR practices.

The Outcome

• Improved lead candidate selection by predicting success probabilities early in the process.
• Integrated proof‑of‑concept models into standard procedures for candidate prioritisation.
• Created an in‑silico sandbox enabling engineers to experiment with digital twin simulations.
• Delivered a three‑year, step‑wise strategy and identified eight AI implementations to transform the company’s data culture.

Why It Matters

The project demonstrates how careful auditing and targeted AI initiatives can deliver immediate benefits and lay a foundation for long‑term digital transformation. By tying AI to clear business questions, the company improved decision‑making and set a course for data‑driven drug development.