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GSK is seeking an AI Transformation and Enablement Director to define how agentic AI becomes the way Development Operations delivers high-quality work. You will shape the multi-year strategy and roadmap, and build the investment foundation behind it.
You will lead adoption and drive exponential machine capability across the function, influencing operating models, tooling choices, and governance with senior stakeholders in R&D and enterprise teams.
As AI Transformation and Enablement Director, you will drive how agentic AI becomes the way all people across Development Operations deliver high quality work, and you will own what that means for how the function works. You will define the strategy and its multi-year roadmap, and build and defend the investment behind it. You will own what adoption, and exponentially increasing machine capability, mean for how work evolves across Development Operations and for the operating model: where work sits, which roles change, and which processes get fundamentally reinvented rather than automated as they stand. You will set the bar on what gets taken from ground-up build into production, and hold the view on which agentic tooling the function should be using, from the frontier labs through to open-weight and locally run models. You will be Development Operations' voice into the R&D-wide and enterprise agentic AI agenda. This role reports into the Head of Asset Acceleration and AI.You will shape how GSK uses AI to measurably improve clinical development delivery and improve outcomes for patients.
Drive the agentic AI transformation and enablement strategy for Development Operations, its multi-year roadmap and the prioritisation across it, anchored to measurable clinical trial outcomes.
Build and defend the investment case for transformation at scale, with clear ROI and payback logic, and enforce stage-gate discipline, including the call to stop or reshape what does not demonstrate value.
Own what widespread agentic adoption and exponentially increasing machine capability mean for how Development Operations is organised: where work sits, which roles change, and how the function operates.
Define which processes get fundamentally reinvented rather than automated as they stand.
Carry those changes with functional leaders as their peer, so that adoption changes how the work is actually done.
Hold the view on which agentic tooling Development Operations should be using, across frontier labs, open-weight and locally run models, judged on adoption, enablement, cost, output quality and compliant deployment.
Keep that view current against a fast-moving landscape, and influence central decision making so that Development Operations ends up with appropriate, effective usage, with the evidence and the experiments behind it.
Make frontier agentic tools routine practice across every group, and set the direction for the learning and capability teams and the Champion Network that deliver it.
Hold the function accountable for adoption: who is using what, how deeply, where uptake has stalled, and what changes as a result.
Set the quality bar for ground-up build, and decide what gets productionised and what stays as local self-serve tooling.
Act as Development Operations' named counterpart into the R&D-wide and enterprise agentic AI agenda, securing the sponsorship that scaled deployment needs and setting the deployment approach with the enterprise teams behind it.
Lead a team, and hold the delivery of the Development Operations AI portfolio through it, including how the value of both ground-up and top-down efforts is attributed.
Leaders across Development Operations and R&D: shape strategy and priorities, secure sponsorship, and agree what changes in the operating model as adoption lands.
Frontline users and the Champion Network across Development Operations: make sure what gets built solves real problems, and that the strongest ground-up work is found and scaled.
Central AI, enterprise technology, security, regulatory and quality teams, plus the frontier labs and providers: influence central decisions, enable compliant deployment at scale, and keep a first-hand read on each option.
Bachelor's degree in Life Sciences, Engineering, Computer Science, Business, or a related field.
Strong and proven experience in Pharma strategy, applied AI or technology, or transformation, gained at a top-tier strategy consultancy, a lea