AI Strategy Director, Global Clinical Development Operations

GSK

21h•Hybrid

About The Position

This role sits within DP&I (Development Operations Platforms & Innovation), part of Development Global Clinical Operations at GSK. DP&I is building the capability to deploy AI at scale across clinical operations and the wider Development Operations function, with a mandate to help reshape how GSK plans, delivers, and continuously improves clinical trials. DP&I develops and deploys scalable, AI-native capabilities, shared foundations, and new ways of working across Development Operations. Working across clinical operations, technology, product, data, and external partners, the team builds the platforms, products, and partnerships needed to turn innovation into measurable value. As AI Strategy Director, you will drive the AI-native Development Operations strategy: defining where AI can create meaningful value across Development Operations, testing concepts hands-on using frontier tools, and shaping how priority capabilities move from concept to scaled deployment. You will work directly with business SMEs and enterprise technology teams to prototype, pressure-test, and deploy AI-enabled workflows. Where external capability is needed, you will lead vendor evaluation and shape build-buy-partner decisions with commercial rigour. This role combines strategy, hands-on solution testing, and deployment judgement. It requires structured problem-solving, strong commercial and enterprise credibility, and hands-on fluency with frontier agentic development tools. This role reports into the Head of Asset Acceleration and AI. You will shape how GSK uses AI to measurably accelerate trial delivery and improve outcomes for patients.

Requirements

Bachelor’s degree in Life Sciences, Engineering, Computer Science, or a related field.
Extensive experience in Pharma strategy, applied AI/technology, or related roles.
Deep understanding of clinical operations and wider clinical development, gained in industry, consulting or other relevant settings.
Strong knowledge of industry trends and technologies shaping the future of clinical trials.
Demonstrated experience building business cases and delivering AI-enabled transformation programmes with measurable business outcomes.
Practical fluency across AI, data and technology topics to work effectively with technical teams.
Strong commercial judgement and experience shaping vendor decisions in complex enterprise settings.
Exceptional written and verbal communication, with the ability to translate complex strategic and technical topics for senior, non-technical audiences.
Hands-on experience with AI-native development tools, including frontier agentic development tools (e.g. Claude Code, Codex), and rapid prototyping practice.

Nice To Haves

Advanced degree (PhD, MBA, MSc, or equivalent) in a relevant discipline.
Experience in top-tier strategy, venture build, applied AI, or digital consulting environments.
Track record of driving AI-enabled value beyond pilot through to scaled adoption and measurable portfolio-level impact.
Broad knowledge of Pharma industry processes, particularly within R&D, clinical development, or drug discovery, and familiarity with the regulatory, scientific, and operational challenges unique to the sector.
Knowledge of GxP, regulatory pathways, or compliance frameworks relevant to deploying AI in clinical operations.

Responsibilities

Drive the AI-native Development Operations strategy and roadmap, anchored to measurable clinical trial outcomes.
Lead prioritization for AI opportunities across Development Operations in line with the AI-native Development Operations strategy and R&D priorities.
Continuously scan the external landscape for emerging AI capabilities, frontier tools, vendor offerings, and new ways of working, translating relevant developments into clear implications for Development Operations strategy, portfolio priorities, and build-buy-partner choices.
Define the build-buy-partner approach for each priority capability, including vendor and partner evaluation where needed, balancing speed, differentiation, risk, cost, and long-term platform value.
Build and defend business cases with clear ROI, payback logic, and success criteria; enforce stage-gate discipline, including the decision to stop or reshape initiatives that do not demonstrate value.
Shape the deployment strategy and roadmap with enterprise AI, technology, data, security, regulatory, and quality teams to ensure scalable and compliant delivery using shared foundations rather than one-off point solutions.
Drive cross-functional alignment and endorsement for the AI-native Development Operations strategy across the enterprise.
Partner with leaders across R&D to identify priority workflow bottlenecks, define measurable outcomes, and shape AI-native capabilities.
Lead 0-to-1 incubation for priority opportunities: frame hypotheses, use AI-native development tools to run hands-on experiments with technology teams and business SMEs, and define the evidence required to scale, reshape, or stop.
Define adoption strategy for priority AI-native capabilities, including workflow redesign, user readiness, and change interventions required for scaled deployment.
Stakeholder Management: Leaders across R&D to identify priorities, secure sponsorship and align on value. Leaders across enterprise AI, technology, security, and quality teams to enable scalable deployment. Strategic relationships with external vendors, consulting partners and technology providers to assess capabilities, shape partnerships and deliver value. Frontline users across Development Operations to ensure solutions solve real problems and value is realised at scale.