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From AI ambition to operational reality in R&D

Establishing a baseline and aligning on shared direction to accelerate drug discovery, clinical development, and submission timelines

The why

A mid-sized pharmaceutical company set a clear ambition: accelerate cycle times, increase probability of success, and shorten overall timelines across R&D. Leadership wanted to harness AI and digital across target identification, molecule discovery, clinical trial design, and real-world evidence.

However, in a rapidly changing AI and digital landscape, translating aspiration into operational impact required clarity on where the R&D organisation stood and where it was going. The goal was clear, but how to get there was not.

The challenge

Digital maturity varied widely across R&D. Clinical Development had strong data capabilities and data infrastructure, while parts of Research and Pharmacovigilance relied on fragmented systems and dispersed expertise. AI initiatives in areas such as molecule discovery and protocol optimisation were emerging, but largely ad hoc. There was no shared baseline across strategy, governance, data architecture, talent, and ways of working to guide prioritisation and scalable investment.

How we helped

We benchmarked digital maturity across the R&D value chain against established industry standards, covering Strategy & Vision, Technology & Data Infrastructure, and Organisation & Talent. In addition, we carried-out deep-dives into each function, incl. Research, Clinical Development, and Pharmacovigilance processes. Through structured workshops with functional teams and R&D leadership, we identified capability gaps, resource constraints, and scalability barriers, and translated them into targeted initiatives designed to address these barriers and enable stronger pipeline impact, shorter cycle times, and improved submission readiness.

Outcome

A clear cross-functional R&D digital maturity baseline
R&D leadership gained a shared, evidenced-based view of digital maturity across Research, Clinical Development, and Pharmacovigilance. The assessment made clear both strengths (e.g., advanced biometrics capabilities and strong scientific data generation) and key challenges (e.g., in strategy translation, resource alignment, and change execution).
Clarity on digital capabilities and gaps
We created a holistic overview of digital capabilities across R&D. This included the number and capacity of AI- and data-skilled staff, current digital tools and usage, and stated future aspirations. Capability concentration, fragmentation, and gaps became visible, as did the scalability challenges the organisation faced.
A structured path from ambition to execution
We translated findings into a path forward. This included sharpening the AI strategy to align with pipeline priorities, defining enabling initiatives such as data platform harmonisation and targeted upskilling, and identifying quick wins to demonstrate AI value in key areas and maintain momentum.

What moved the needle

The assessment worked because it was grounded in operational reality.
A framework built for the client’s R&D context We developed our approach and digital maturity framework to fit the client’s portfolio, organisational size, and growth ambition. We worked directly with scientific and clinical teams to anchor the analysis in real workflows, from target validation to submission planning.
From maturity assessment to R&D decisions Strong engagement of R&D leadership ensured that findings translated into decisions on priorities, governance, and resource allocation. This allowed to move beyond a maturity score, into a decision-making process for scaling AI and digital across R&D.
Partner

Daniel Schmidt

Daniel works with life science executives to design strategies, operating models, and transformation programs. He brings both functional depth and board-level perspectives, helping senior leaders navigate complexity and make clear choices. He formerly led the life science practice at Deloitte Denmark.

Daniel holds a B.Sc. in International Business and an M.Sc. in Economics and Business Administration from Copenhagen Business School.

Selected experience
  • TA strategy for immunology portfolio
  • Strategy for application and value realisation of AI across R&D
  • Business model design for new innovative business unit
  • Global digital transformation across commercial functions
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Daniel Schmidt