Principal (Senior Director), AI Product Management (Portfolio Systems)

Business Introduction
GSK remains committed to achieving bold commercial ambitions for the future. By 2031, we aim to deliver £40 billion in annual sales, leveraging our existing strong performance momentum to significantly increase our positive impact on the health of billions of patients globally. Our Ahead Together strategy is centered on early intervention to prevent and alter the course of disease, thereby protecting people and supporting healthcare systems. Our diverse portfolio consists of vaccines, specialty medicines, and general medicines.

Through continuous innovation and a dedicated focus on scientific and technical excellence, we strive to develop and launch new, groundbreaking treatments that address critical health challenges.

Position Summary
GSK's Enterprise AI team exists to translate the promise of AI into measurable impact across every major group at GSK — R&D, Medical, Commercial, Supply Chain, HR, Legal, Finance, and beyond. You will act as the strategic compass for driving a portfolio of AI solutions that transform how GSK runs its technology and R&D portfolios — from intake, prioritization, and funding stages through resource allocation, benefits tracking, and portfolio reviews — and own the forward deployed strategy each pod executes against.

This is a senior individual contributor role for someone who can see across GSK's value chain, identify where AI will create disproportionate value, prototype hands on, and ensure engineers are always deployed against the highest-impact opportunities. You will not manage engineers; you will shape what they build, where they go next, and how we measure whether it worked. Forward deployed teams are most powerful when paired with sharp opportunity selection: you will identify, size, and prioritize the AI opportunities that matter most to GSK, pressure-test proposed engagements against value, feasibility, and strategic fit, and build the case, framing, prototype, and executive alignment needed for the pod to land and scale — replacing stale, periodic portfolio reporting with continuous, trusted insight leaders are willing to act on.

Responsibilities

This role will provide YOU the opportunity to lead key activities to progress YOUR career. These responsibilities include some of the following:

• Hunt for high-value AI opportunities across the portfolio management and value-realization lifecycle — intake, prioritization, funding stages, resource allocation, benefits tracking, and portfolio reviews — and develop a global portfolio of AI products ranked by impact, feasibility, and time-to-value.

• Quantify value rigorously by building business cases that hold up with finance and executive sponsors — cycle time saved, decisions accelerated, cost avoided, and capacity returned to portfolio and delivery teams.

• Develop domain pattern recognition in portfolio management workflows — including what leadership actually needs from a portfolio view and why most portfolio reporting is stale before it is published — so you can spot AI-shaped problems the business itself hasn't yet articulated.

• Own the Enterprise AI opportunity portfolio, maintaining a living view of in-flight, queued, and de-prioritized opportunities, with the rationale for each decision.

• Make hard trade-off calls — building the framework, defining success metrics, running the analysis, and recommending where engineers go when business units compete for the same resources.

• Say no, with evidence, to opportunities that look attractive but won't move scalable outcomes, protecting engineering focus.

• Be the strategic counterpart to the forward deployed, full-stack, and data engineering leads, translating ambiguous business problems into crisp problem statements, prototypes, success metrics, and decision frameworks engineers can build against.

• Shape engagements end-to-end — defining scope, success criteria, exit conditions, and scaling pathways before kick-off, then staying close as engineers execute.

• Surface re-use and platform leverage, spotting when a one-off solution should become a reusable capability and working with engineering to make that case.

• Design the human-digital split, deciding which tasks the pod's digital workers own — such as gathering status, reconciling data across sources, flagging risks, and drafting analysis — where engineers and business users stay in the loop, and how evaluation and oversight make that safe.

• Redesign the portfolio operating model around automation, defining decision rights, gates, cadences, roles, and how work moves between people and digital workers, rather than bolting automation onto a manual process.

• Build a reusable skills library, establishing shared agent skills, prompts, templates, and connectors that multiple teams contribute to and reuse, with clear versioning, quality standards, ownership, and discovery.

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