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ABM Program Manager, AI, Data & Operational Solutions, ABM Global Practice Team

Job in Greater London, London, Greater London, W1B, England, UK
Listing for: Amazon Inc.
Full Time position
Listed on 2026-09-27
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), AI Business & Operations, Business Systems & Technology Analysis, Change Management
Salary/Wage Range or Industry Benchmark: 90000 - 130000 GBP Yearly GBP 90000.00 130000.00 YEAR
Job Description & How to Apply Below
Location: Greater London

ABM Program Manager, AI, Data & Operational Solutions, ABM Global Practice Team

Job :  | AWS EMEA SARL (UK Branch)

The ABM Solutions & Implementation team within the AWS Global ABM Practice is chartered to define, build, deploy, govern, and enable the solutions that empower 200+ ABM practitioners globally to operate efficiently and  create, manage, and support the end-to-end delivery of ABM capabilities — from AI-powered technologies to process automation to content management — ensuring our marketers spend less time on manual execution and more time on strategic customer impact.

We are seeking an ABM Program Manager to own and scale the AI, data, and operational solutions that support our ABM community. This role sits at the intersection of marketing and technology: you will take complex, ambiguous problems and deliver AI-based solutions end-to-end — from problem definition through design, build, testing, and global rollout. You will act as the key partner between ABM stakeholders, cross-functional teams (Martech teams, Marketing Operations, and our Integrated Demand Center), and external vendors, translating strategic ABM objectives into scalable solutions.

This role combines strategic thinking with a builder mindset. You will not simply route requirements to a cross-functional support team — you will assess ABM requirements, and where a solution can be built using out-of-the-box AI tools (such as Amazon Quick, MCP integrations, and Asana), you will build it and enable ABMers to use these solutions effectively. Success means ABM practitioners have an integrated, easy-to-use set of AI-powered tools embedded in their daily workflow that adapt as their accounts evolve — delivered with rigor, field-tested adoption, and measurable impact.

Key

job responsibilities
  • Serve as the business lead for ABM AI, data, and operational solutions end-to-end — define the vision, program strategy, requirements, and success criteria for AI-based ABM solutions with limited guidance, operating in problem spaces where the solution is not yet defined.
  • Design and build AI-powered tools and automation flows from problem definition through production — including customer journey engines, Quick Skill automations, MCP integrations across enterprise platforms (CRM, Highspot, Asana), and reactive signal-monitoring layers that keep solutions current as accounts evolve.
  • Drive delivery and support of complex, cross-functional solutions — including design, App Sec compliance, build, user acceptance testing with field practitioners, documentation (SOPs and training guides), product demonstrations, and global rollout.
  • Serve as the key partner between marketing stakeholders, cross-functional teams (Martech teams, Marketing Operations, and our Integrated Demand Center), and external vendors — translating business requirements into technical specifications, and communicating technical constraints back to marketing stakeholders.
  • Drive AI enablement and adoption across a globally distributed practitioner community — assess AI maturity, build leveling frameworks, deploy Quick Skills tooling, and coach practitioners (e.g., via a Champions program) to scale adoption across all Geos.
  • Spot risks and drive appropriate trade-offs — understand design approaches within the ABM technology domain (AI/ML frameworks, MCP integrations, data sources, agent orchestration, UI/UX) and ask the right questions to ensure sound decisions.
  • Define, track, and report on program KPIs (adoption, usage volume, practitioner impact, time savings, delivery quality). Use data to identify bottlenecks, drive improvements, and inform investment decisions.
  • Create new processes and mechanisms — AI solution generation workflows, orchestration patterns, coaching…
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