Transform the Integrated Services Product Team Operating Model: Identify, redesign, and scale AI-enabled workflows across discovery, customer research, Product Requirement Document (PRD) roadmaps, prioritization, launch readiness, executive reviews, analytics, PMO, go-to-market, and post-launch learning.
Build AI Agents, Automation and Infrastructure: Create, configure, test, and maintain AI agents, tools, workflows, and integrations using APIs, retrieval, orchestration, agent instructions, approved AI platforms, enterprise systems, and related technical patterns.
Prototype, Pilot and Scale: Rapidly prototype with real users, validate workflow fit, identify failure modes, and move successful solutions from idea to prototype to pilot to scaled adoption, working through technical, operational, security, privacy, and change‑management barriers.
Create Reusable Capabilities and Playbooks: Build repeatable agents, templates, standards, workflows, and best practices for common product, PMO and GTM work, including customer insight synthesis and journey mapping, requirement generation, PRD review, competitive analysis, roadmap tradeoffs, launch planning and execution, risk reviews, business case development, and executive communication.
Measure Impact: Define and track outcomes tied directly to business and customer value creation, including adoption, time saved, cycle‑time reduction, decision speed, employee experience, quality improvements, rework reduction, revenue generation and business or customer outcomes from AI enabled workflows.
Drive Adoption and Behavior Change: Train teams, coach leaders, create champions, document best practices, and make AI-enabled workflows easy, safe and useful enough to become the default way of working.
Establish Quality, Governance and Trust: Partner with security, legal, privacy, IT, PMO, go-to-market and engineering to ensure workflows are secure, compliant, auditable, explainable, and appropriate for enterprise use.
Design Human‑In‑The‑Loop Systems: Define where AI should act independently, where humans must review or approve, and how teams should manage risk, quality and accountability in AI-enabled workflows.
Increase Leverage: Identify and automate repetitive, manual, duplicative, or low‑value work so teams can spend more time on customer insight, product judgment, and execution.
Evaluate Emerging AI Patterns: Stay current on AI agent frameworks, tooling, security models, governance practices, workflow automation approaches, and enterprise patterns, then translate the most practical opportunities into Ford use cases.
Requirements
Strong technical fluency and a hand‑on builder mindset, with familiarity across modern AI workflow patterns including APIs, retrieval, agent instructions, orchestration, evaluations, and human‑in‑the‑loop flows.
Experience building AI‑enabled workflows, automations, internal tools, or agent‑based systems that were adopted by real users, not just prototypes.
Strong product management fluency including discovery, customer research, PRDs, roadmaps, prioritization, launch readiness, stakeholder alignment and product analytics.
Experience redesigning knowledge‑work workflows or operating models at team or enterprise scale.
Ability to rapidly prototype, test with users, measure outcomes, and iterate.
Comfort working within enterprise constraints, including security, privacy, legal, IT, data access, and governance.
Ability to influence cross‑functional teams and drive adoption without relying solely on formal authority.
Strong communication, documentation, training, storytelling, and collaboration skills, with the ability to bring skeptical teams along.
Even better, you may have…
Experience in automotive, digital product, software, platform, or large‑scale enterprise organizations.
Enterprise implementation experience, especially in environments with security, privacy, identity, legal, compliance, procurement, IT and data‑access constraints.
Experience creating training, enablement, champion networks and reusable playbooks for scaled adoption.
Background in workflow automation, developer tools, internal tooling, product operations, design operations, or AI enablement.
Experience building tools for teams rather than only consumer‑facing applications.
Experience evaluating AI platforms, vendors, agent frameworks, and emerging enterprise AI tooling.
Understanding of AI safety, data governance, access control, auditability, and responsible AI practices.
Experience working with product, engineering, design, analytics, business operations, legal, security, and IT teams in a complex matrixed environment.