Staff (Director), Commercial Enterprise Product Mgmt. - E2E Marketing Enablement

GSK • Enterprise AI
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 digital workers — embed directly with scientific and business teams to build AI products that change outcomes.
We are hiring a Staff, Marketing Automation & AI Product Manager to own a major slice of our marketing enablement opportunity portfolio — marketing automation, content supply chain and assembly, the marketing experience layer, campaign management, and performance measurement and act as the strategic counterpart to the engineers and digital workers in your pod, setting 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 within marketing enablement, build the case to fund and deploy against them, and ensure FDEs 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.
Why This Role Exists
Forward deployed teams are most powerful when paired with sharp opportunity selection. Enterprise AI has no separate strategy layer: the AI Product Manager sets the forward deployed strategy, and the pod executes it. This role closes that gap at this level by ensuring the engineers we deploy are working on the right things.
In this role you will be the person who:
Owns a clear domain or capability area within the Enterprise AI opportunity portfolio.
Identifies, sizes, and shapes the highest-value AI opportunities in that domain.
Partners daily with forward deployed, full-stack, and data engineering leads to scope and steer engagements end-to-end.
Builds the business and executive alignment the pod needs to land and scale.
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 rather than evolving an established platform.
Work with probabilistic systems — defining evaluations, acceptable error, and human oversight instead of fixed functional requirements.
Help lead a bionic pod, deciding which work goes to engineers and which to digital workers, and how they hand off.
Prototype hands on and are measured on business outcomes, not features shipped or system uptime.
Key ResponsibilitiesOpportunity Identification & Value Sizing
Hunt for high-value AI opportunities across marketing automation, content supply chain and assembly, the marketing experience layer, campaign management, and performance measurement; develop a structured pipeline of opportunities ranked by impact, feasibility, and time-to-value.
Quantify value rigorously. Build credible business cases that survive finance and senior business unit scrutiny — cycle time saved, decisions accelerated, revenue uplift, cost avoided, scientific productivity gained.
Develop deep domain pattern recognition so you can spot AI-shaped problems in marketing enablement workflows the business itself hasn’t yet articulated.
Strategic Prioritization & Portfolio Shaping
Shape the opportunity portfolio in your domain. Maintain a living view of in-flight, queued, and de-prioritized opportunities, with the rationale for each decision, and feed it into the broader Enterprise AI portfolio.
Make trade-off recommendations. When two business sponsors both want an FDE pod, defines the success metrics, run the analysis and recommend where engineers go.
Protect engineering focus. Push back — with evidence — on opportunities that won’t move scalable outcomes.
Engineering Partnership & Enablement
Be the strategic counterpart to forward deployed, full-stack, and data engineering leads in your domain. Translate ambiguous business problems into crisp problem statements, prototypes, success metrics, and decision frameworks engineers can build against.
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–digital split. Decide which tasks the pod’s digital 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 in your domain and improve it with what you learn.
Run honest post-mortems and feed lessons back into how your domain selects opportunities.
Additional information:
Reporting line: VP, Commercial AI/ML Transformation
Position level: Director
Team management: N/A
Primary location: London New Oxford Street (UK)
Secondary locations: Philadelphia (PA, US), Boston (MA, US), Durham (NC, US)
Working model / presence onsite requirement: Hybrid
Business travel: 25% travel between the UK, USA, and India
Application end date: Sunday, November 8, 2026 EOD GMT
What You BringRequired Functional Experience
10+ years of progressive experience spanning some combination of pharma/life sciences, AI/ML product strategy, and senior product management or strategy roles in technology-led organisations.
Demonstrated track record of identifying, scoping, prototyping, and shepherding high-value technology or AI initiatives from problem framing through funding into delivery.
Strong working knowledge of pharma or other regulated industry marketing enablement — particularly how content supply chain, marketing automation, and the HCP-facing experience layer operate end-to-end: content assembly, campaign management, channel activation, and performance measurement — and the data, decisions, and constraints inside them.
Solid technical fluency in modern AI/ML — LLMs, agentic systems, and digital workers, ML lifecycle, data and evaluation patterns — sufficient to leverage evidence from rapid prototyping to challenge engineers, scope realistically, and avoid hype-driven choices.
Experience operating as a senior individual contributor — leading through influence, frameworks, and quality of thinking rather than headcount.
A product management or product-strategy heritage: discovery, opportunity sizing, roadmap shaping, prototyping, and stakeholder alignment — ideally on products whose core behaviour is AI rather than conventional software.
Required Domain Experience
Experience in building workflows for creative asset development and content submission workflows, including MLR/MAC readiness, to enable timely deployment across channels.
Working knowledge of content lifecycle integrity and traceability — ensuring derivative content stays linked to its approved source throughout its lifecycle.
Experience with omnichannel campaign execution and the performance measurement practices used to track channel and content effectiveness.
How You Operate
You think in terms of value, not activity.
You convert ambiguity into clarity engineers and digital workers can act on.
You bias toward small, rapid prototyping, sharp documents and decisions over slideware and process.
You hold strong views, loosely held, and welcome being proven wrong by data.
You take the long view on patient impact while caring deeply about shipping things this quarter.
Skills Profile
The following skills define what strong performance looks like in this role across seven domains. Each descriptor reflects the scope, autonomy, and impact expected at this level.
AI Opportunity Identification
Opportunity Discovery: Leads opportunity discovery across a major domain (e.g. R&D, Medical, Commercial, Supply Chain, or an enterprise function). Builds and maintains a structured pipeline ranked by impact, feasibility, and time-to-value.
Problem Framing: Frames problems at the domain level — defining the shape, scope, and strategic fit o