AECOM is seeking a Product Management Lead to own the product strategy, delivery sequencing, and outcome realization for AI- and agentic-enabled capabilities embedded into day-to-day enterprise workflows across core business operations.
This role is accountable for deciding where and how AI should be applied, including defining appropriate levels of autonomy, human-in-the-loop (HITL) designs, trust thresholds, and Responsible AI guardrails. The Director ensures AI products are not only delivered, but adopted, trusted, and generating measurable business value at scale.
Operating within enterprise product standards and governance, this role partners closely with Process Intelligence & Discovery, Responsible AI, Architecture, Data, Engineering, Delivery, and Operations teams. The Director leads Product Managers and serves as the primary steward of AI product decisions, trade-offs, and outcomes across the Operational AI portfolio.
This is a product leadership role, not a technical architecture or program management position.
This position offers flexibility for hybrid work schedules to include both in-office presence and some flexibility for virtual/telecommute work to be based in Houston or Dallas, TX.
Key Responsibilities
- Own and maintain the Operational AI and agentic product portfolio roadmap, defining where AI should assist, advise, or act autonomously, aligned to enterprise product and Responsible AI standards
- Translate process discovery insights and business demand into AI-ready product backlogs, including autonomy levels, HITL workflows, guardrails, and success metrics
- Lead AI-specific product trade-off decisions, balancing value vs. risk, autonomy vs. control, model cost vs. quality, and reuse vs. time-to-impact
- Define product hypotheses, evaluation criteria, and trust thresholds for AI capabilities (e.g., accuracy, containment, correction rates, explainability, user confidence)
- Partner with Architecture, Data, and Engineering to shape feasible AI solutions that meet business intent, Responsible AI requirements, and operational constraints—without owning technical design
- Own adoption and behavior-change strategy for AI products, incorporating rollout feedback, user corrections, and realized outcomes into roadmap evolution
- Establish and operate autonomy governance with Responsible AI, Legal, Risk, and Operations to determine when AI may act, advise, or must defer to human approval
- Lead and develop Product Managers, strengthening AI product craft across outcome definition, HITL design, evaluation planning, and risk stewardship
- Communicate product status, risks, and performance using AI-relevant evidence, including adoption, trust signals, correction rates, cost per interaction, and value realization