Staff (Director), AI Product Management (Global Functions)

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 own a major slice of our Global Functions portfolio — across HR, Legal, Compliance, Procurement, Finance, and Corporate Communications — act as the strategic counterpart to the engineers and digital workers in your pod, and set the forward deployed strategy they execute against. This is a senior individual contributor role for someone who can identify and shape high-value AI opportunities across Global Functions, build the case to fund and deploy against them, prototype hands on, and ensure engineers are working on what matters most. You will not manage engineers; you will shape what they build in your domain and how we measure whether it worked.

Responsibilities

• Hunt for high-value AI opportunities across Global Functions — record-to-report, hire-to-retire, source-to-pay, contract-to-signature, and message development and approval — and develop a structured pipeline of AI products ranked by impact, feasibility, and time-to-value.
• Quantify value rigorously by building business cases that survive finance and senior business unit scrutiny — cycle time, cost-to-serve, risk reduction, hours returned, and spend under management.
• Develop domain pattern recognition in functional workflows — including how work flows between shared services, centers of expertise, and business partners — so you can spot AI-shaped problems the business itself hasn't yet articulated.
• Shape the opportunity portfolio in your domain, maintaining a living view of in-flight, queued, and de-prioritized opportunities, with the rationale for each decision, and feeding it into the broader Enterprise AI portfolio.
• Make trade-off recommendations — defining success metrics, running the analysis, and recommending where engineers go when business sponsors compete for the same resources.
• Push back, with evidence, on opportunities that won't move scalable outcomes, protecting engineering focus.
• Be the strategic counterpart to the forward deployed, full-stack, and data engineering leads in your domain, 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 review, drafting, triage, reconciliation, monitoring, and query handling — where engineers and business users stay in the loop, and how evaluation and oversight make that safe.
• Design AI that is deployable in functional work, building SOX and financial controls, legal privilege and confidentiality, employee data privacy and works council requirements, anti-bribery and third-party due diligence, disclosure obligations, and EU AI Act high-risk requirements into every solution.
• Operate fluently with VP-level business and functional leaders, running conversations that turn fuzzy ambitions into concrete, fundable AI engagements, and moving between functions with very different cultures, risk appetites, and ways of working.
• Build sponsor coalitions, securing the data access, change-management commitment, and air cover your engagements require to succeed.
• Communicate impact clearly, in language that connects AI work back to patients, science, and enterprise outcomes.
• Apply the value-realization framework, using Enterprise AI's framework to measure every engagement in your domain and improving it with what you learn.
• Run honest post-mortems — capturing what worked, what didn't, and feeding those learnings back into how your domain selects opportunities and designs engagements.

Basic Qualifications

• 10 or more years of professional experience across some combination of pharma/life sciences, AI/ML product strategy, and product management or strategy roles in technology-led organizations.
• Track record of taking at least one technology or AI initiative from problem framing through funding and into delivery.
• Direct involvement in, or close partnership with, at least one Global Function — Legal & Compliance, HR, Finance, Corporate Communications, or Procurement — including its core end-to-end processes, such as record-to-report, hire-to-retire, source-to-pay, contract-to-signature, or message development and approval.
• Hands-on work with modern AI/ML concepts — including LLMs, agentic systems, and digital workers, the ML lifecycle, and data and evaluation patterns — demonstrated through prototyping.
• Prior responsibility for operating as an individual contributor without direct reports and influencing engineering and business teams through frameworks and documented decision criteria.
• Background in product management or product strategy, including opportunity sizing, roadmap development, prototyping, and stakeholder alignment.
• Working knowledge of the enterprise systems and data behind Global Functions, such as SAP, Workday, Ariba or Coupa, contract lifecycle management (CLM) tools, and case management or knowledge tools — including data ownership, quality, permissioning, and the unstructured documents and text where much of the value sits.
• Track record of identifying AI value in high-volume, knowledge-intensive work, such as review, drafting, triage, reconciliation, monitoring, or query handling.
• Working knowledge of the control and regulatory constraints on AI in functional work, including SOX and financial controls, legal privilege and confidentiality, employee data privacy and works councils, anti-bribery and third-party due diligence, disclosure obligations, and the EU AI Act's high-risk categories.
• Direct involvement in partnering with senior functional leaders, such as the CFO, CHRO, General Counsel, Chief Procurement Officer, or Chief Communications Officer, to turn transformation priorities into funded, measurable AI programs.

Preferred Qualifications

• Experience working across more than one Global Function.
• Experience navigating GxP, regulatory, and data-governance requirements in pharma AI deployments.
• Experience codifying product management and user research practices into AI-native tools — such as skills, plugins, and markdown files — and using the command line to automate daily work.