Case study: Workforce engineering for AI-enabled R&D expansion

Client overview

Our client, a global pharmaceutical organisation, was undertaking a multi-site expansion of its AI-enabled and digitally integrated R&D capabilities, spanning discovery, development and translational science. The programme aimed to accelerate innovation velocity while maintaining regulatory precision, data integrity and global consistency across sites.

The challenge

The organisation faced several interrelated workforce and execution challenges during R&D site expansion. These included:

• Risk of mis-hiring frontier digital roles before platform and operating models were fully mature
• Potential loss of critical institutional and regulatory knowledge during transformation
• Uneven capability readiness across sites as AI and digital systems scaled
• Limited visibility into which roles should be retained, retrained, recruited or automated
• Growing execution risk as workforce complexity increased faster than headcount

Company leadership required a systematic, evidence-based framework to align talent decisions with digital ambition and site-level execution realities.

Our solution

BioTalent applied a Talent Science™ workforce blueprint, using the Retain, Retrain, Recruit, Automate (4R) framework across 2,000 R&D employees. This allowed our client to:

Segment roles by urgency, automation feasibility, skills adjacency and attrition risk

• Identify which roles needed to be retained, due to high knowledge density and regulatory dependency
• Target retraining pathways for adjacent roles transitioning into AI-enabled workflows
• Create precise recruitment profiles for capabilities that couldn’t feasibly be developed quickly internally
• Select automation road-mapping for repeatable, rules-based R&D activities

The Talent Science™ framework was aligned to platform maturity and site ramp-up timelines, ensuring workforce actions supported execution rather than disrupting it.

The results

• Clear prioritisation of retain vs retrain vs recruit vs automate decisions at enterprise scale
• Reduced risk of capability loss during AI and digital transformation
• Faster time-to-productivity for new and expanding R&D sites
• Avoidance of premature or misaligned external hiring
• Measurable reduction in workforce-driven execution risk across digital programmes

The approach enabled our client to scale digital R&D capability without inflating headcount or destabilising delivery.

Why it worked

• Systematic role classification: decisions based on execution dependency, not job titles
• Skills adjacency logic: retraining focused where transition probability was highest
• Automation discipline: applied where risk and feasibility thresholds were met
• Platform synchrony: workforce actions sequenced to digital system maturity
• Enterprise visibility: leadership gained a unified view of workforce readiness across sites

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