Staff, AI Product Manager

About the Role:

GSK’s Enterprise AI team, a highly skilled applied AI group, translates the promise of AI into measurable impact across every major group at GSK — R&D, Medical, Commercial, Supply Chain, HR, Legal, Finance, and beyond. Bionic pods — AI product managers, forward deployed engineers, full-stack engineers, data engineers, and AI agents and workers — embed directly with scientific and business teams to build AI products that change outcomes.

We are hiring a Staff, AI Product Manager to own the Orchestration and Automation platform tower: the low and medium code platforms where GSK builds agents and automations, and the enablement model that lets thousands of non-engineers use them safely. The estate spans workflow and agent builders such as Power Platform, Copilot Studio and n8n, alongside the adjacent orchestration tooling that sits with them. You will own the roadmap and the governance model, and act as the platform consultant business teams come to when they need to turn demand into working automations.

Why This Role Exists

GSK runs various low and medium code platforms, and demand from business teams outpaces any central delivery team. This role exists to take consolidation decisions, and to build the self-enablement layer — guardrails, templates, training, support — that lets citizen developers build safely without engineering review on every change.

In this role you will be the person who:

• Owns the Orchestration and Automation platform tower and its roadmap.

• Takes consolidation decisions across the low and medium code platform estate.

• Acts as the platform consultant for business teams building on the estate.

• Owns the governance model that defines what a citizen developer may build unsupervised.

How This Differs from Traditional Product Management

This is not a traditional enterprise product role managing a source system’s roadmap, releases, and backlog. As an AI Product Manager in Enterprise AI, you:

• Build new AI products from ambiguous, unsolved business problems.

• Work with probabilistic systems, defining evaluations, acceptable error, and human oversight.

• Help lead a bionic pod, deciding which work goes to engineers and which to AI agents and workers, and how they hand off.

• Prototype hands on and are measured on business outcomes.

Key Responsibilities

Platform Portfolio & Consolidation

• Map capability against actual usage across the whole low and medium code estate, and recommend what consolidates, what stays, and what retires.

• Quantify value rigorously. Build credible business cases that survive finance and senior business unit scrutiny — cycle time saved, manual effort removed, licence cost avoided, automations running in production.

• Develop deep automation pattern recognition so you can spot the workflows worth automating and the ones that should be fixed at source instead.

Strategic Prioritization & Portfolio Shaping

• Shape the platform roadmap. Maintain a living view of in-flight, queued, and de-prioritized platform work, with the rationale for each decision, and feed it into the broader AI Platforms & Tools portfolio.

• Make trade-off recommendations. When two business sponsors both want platform investment, define the success metrics, run the analysis, and recommend where effort goes.

• Protect engineering focus. Push back — with evidence — on opportunities that won’t move scalable outcomes.

Engineering Partnership & Enablement

• Consult on platform fit and design. Advise business teams and delivery squads on which platform fits, how to structure the build, and what it takes to reach production. Translate ambiguous requests into crisp problem statements and success metrics.

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

• Surface re-use and platform leverage when a one-off solution should become a reusable capability.

• Design the human and agent split. Decide which tasks the pod’s AI agents and workers own, where engineers and business users stay in the loop, and how evaluation and oversight make that safe.

Stakeholder Alignment & Communication

• Operate fluently with VP-level business and scientific leaders. Run conversations that turn fuzzy ambitions into concrete, fundable AI engagements.

• Build sponsor coalitions that secure data access, change-management commitment, and air cover for your engagements.

• Communicate impact clearly in language that connects AI work to patients, science, and enterprise outcomes.

Measurement, Learning & Iteration

• Apply Enterprise AI’s value-realization framework to every engagement and platform in the tower, and improve it with what you learn.
• Run honest post-mortems and feed lessons back into how the tower selects platforms and prioritizes demand

Required Skills:

• 16+ years of progressive experience in enterprise platform leadership, with a meaningful part of it spent owning a platform or platform estate end to end: roadmap, architecture direction, operations, and lifecycle.

• Demonstrated track record of taking a platform from fragmented or underused to well run: consolidation calls made, adoption grown, and the operating model that sustained it.

• Experience running a platform in a regulated enterprise, with working knowledge of at least one part of the pharma value chain and the data, decisions, and constraints inside it.

• Solid technical fluency in modern AI/ML, covering LLMs, agentic systems, AI agents and workers, data and evaluation patterns, sufficient to set architecture direction, challenge engineers, scope realistically, and avoid hype-driven choices.

• Experience leading engineers directly while also leading a far larger citizen developer community through influence, frameworks, and quality of thinking.

• A platform product heritage: roadmap shaping, lifecycle and version management, vendor and licensing ownership, service levels, and stakeholder alignment across a large and varied user base.

Preferred skills:

• Deep, hands-on Microsoft Power Platform experience is mandatory: Power Apps, Power Automate and Dataverse at enterprise scale, including environment strategy, ALM, licensing, and the governance model around them.

• Has owned a low or medium code automation platform at enterprise scale and run a citizen developer or self-service programme covering thousands of users, with guardrails that held under pressure.

• Fluent with the wider low and medium code agent and workflow stacks that sit alongside Power Platform, such as Copilot Studio, n8n, and the equivalents from the major cloud vendors.

#AGTech

Skills

AI Platforms, Change Management, Data Analysis, Microsoft ALM, Microsoft Power Apps, Microsoft Power Automate, Microsoft Power Platform, Roadmap Development, Statistical Analysis, User Needs Analysis

Why GSK?

Uniting science, technology and talent to get ahead of disease together.

GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases – to impact health at scale.

People and patients around the world count on the medicines and vaccines we make, so we’re committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients, accountable for impact and doing the right thing is the foundation for how, together, we deliver for patients, shareholders and our people.

Inclusion at GSK:

As an employer committed to Inclusion, we encourage you to reach out if you need any adjustments during the recruitment process.

Please contact our Recruitment Team at IN.recruitment-adjustments@gsk.com to discuss your needs.

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