Pharmaceutical innovation is a sequentially vulnerable enterprise: discovery, development, and launch depend on a tightly coordinated chain of specialist contributions that can be disrupted by even small gaps in talent, knowledge, or collaboration. As pipelines diversify [e.g., biologics, competitive generic therapy (CGT), or RNA], global trials intensify, and compliance expectations rise, the people dimension (i.e., who stays, who speaks up, how teams learn) has become a first‑order determinant of time, cost, and quality. In this context, preserving tacit knowledge, sustaining engagement, and fostering psychological safety are not ‘soft’ goals; they are prerequisites for reliable execution and timely patient impact.[1], [2], [3], [4]

The workforce challenge is two‑sided. First, many organizations face retention risk among late‑career experts whose departures can create knowledge vacuums during crucial chemistry, manufacturing, and controls (CMC) or late‑phase activities. Second, there is recruitment and mobility pressure for artificial learning (AI)/machine learning (ML), computational biology, and data engineering talent, where external demand is acute and internal role designs are still maturing. Treating these as separate problems leads to piecemeal fixes; treating them as an integrated, analytics‑informed talent‑system issue better reflects how work actually gets done in pharma R&D.[5], [6]

Here, we reposition people analytics not as a field separate from industrial-organizational (I-O) psychology (IOP) but as its measurement, strategy and decision layer: analytics tells us where and when problems are emerging; I‑O theory explains why and how to intervene.7 Therefore, we emphasize two primary lenses (psychological safety and JD‑R) and use supporting theories (goal setting, social exchange theory, and behavioral reasoning) selectively to match real pharma pain points [e.g., site activation bottlenecks, Data Monitoring Committee (DMC) meeting dynamics, or knowledge capture before retirement].[8], [9], [10] To establish this integration, we first introduce these theories at a high level before linking them to the pharmaceutical context.