Director of Product Management, Enterprise AI

What You'll Do

• Product Management Leadership & Team Development Build a strong product management organization that combines rigorous product practice with credible partnership across science, engineering, and the business.
• Recruit, lead, coach, and retain a high-performing team of product managers responsible for enterprise AI products.
• Set clear standards for product thinking, discovery, written requirements, prioritization, decision quality, and product reviews.
• Develop the team's ability to work credibly with data science, ML engineering, software engineering, design, and business stakeholders.
• Create clear accountability, career development, and operating mechanisms that enable the team to perform consistently at a high level.
• Agent & Assistant Product Strategy Define the vision, portfolio strategy, and roadmap for enterprise agents and assistants, making disciplined choices about where ADI should invest.
• Establish a clear product vision and multi-horizon roadmap for a portfolio of enterprise AI agents and assistants.
• Decide which use cases to pursue, sequence later, defer, or decline based on user value, business impact, technical feasibility, risk, and cost.
• Distinguish between capabilities that can be delivered reliably today and those that depend on emerging technical maturity.
• Maintain a coherent portfolio view that balances near-term delivery with longer-term platform and capability development.
• Business Partnership & Product Definition Build active partnerships with business stakeholders and convert real workflows and pain points into precise product direction.
• Lead discovery with business stakeholders and users to understand workflows, decisions, pain points, constraints, and unmet needs.
• Translate insights into crisp product requirements, success criteria, scope, user journeys, and acceptance conditions.
• Ensure science and engineering teams can execute without repeated reinterpretation by resolving ambiguity early and documenting decisions clearly.
• Challenge requests constructively, separating underlying user problems from proposed solutions and avoiding low-value or poorly defined use cases.
• Impact Measurement & Portfolio Decisions Define how product success is measured, reviewed, and used to redirect investment across the Enterprise AI portfolio.
• Establish a balanced product scorecard covering adoption, task completion, user trust and satisfaction, grounding, relevance, and other quality measures.
• Connect product performance to business outcomes such as time saved, productivity improvement, cost avoided, risk reduced, or service quality improved.
• Run a regular review cadence that makes progress, underperformance, risk, and learning visible to product teams and executives.
• Use performance evidence and user feedback to scale successful products, adjust roadmaps, improve weak experiences, or stop investments that are not delivering value.
• Cross-Functional Product Delivery Create alignment across the functions required to design, build, launch, and improve enterprise AI products.
• Drive shared priorities and decisions across data science, engineering, design, content, platform, data, security, legal, and go-to-market or enablement teams.
• Lead product planning from discovery through requirements, build, evaluation, release readiness, launch, and iteration.
• Clarify ownership, dependencies, decision rights, and trade-offs so teams can move quickly without compromising quality or responsible AI expectations.
• Communicate roadmap choices, delivery progress, constraints, and risks clearly to executives and other stakeholders.
• Launch, Adoption & Change Management Ensure that shipped products are understood, adopted, and incorporated into the workflows they are intended to improve.
• Own launch planning and coordinate readiness across product, technology, content, communications, enablement, support, and business teams.
• Define target users, rollout sequencing, adoption plans, feedback channels, and support requirements for each launch.
• Partner with business leaders and change teams to embed agents and assistants into real workflows rather than treating release as the end point.
• Use adoption and usage evidence to identify friction, refine the experience, strengthen enablement, and improve product value over time.
• Experience 12+ years of experience in product management, including 6+ as a people leader Significant experience leading product management teams responsible for complex software, AI, data, platform, or enterprise technology products.
• Demonstrated success defining product vision, portfolio strategy, roadmaps, and investment priorities in technically complex environments.
• Strong product discovery skills, with experience converting business workflows and user pain points into clear requirements, success criteria, and executable scope.
• Experience partnering closely with data science, machine learning, and engineering teams to deliver products from concept through production and continuous improvement.
• Working knowledge of generative AI, large language models, retrieval-augmented generation, agents, assistants, evaluation, grounding, and the practical limits of current AI capabilities.
• Proven ability to create prioritization and governance mechanisms that balance user value, business impact, feasibility, cost, risk, quality, and responsible AI considerations.
• Experience defining and reviewing product metrics across adoption, completion, trust, satisfaction, quality, and measurable business outcomes.
• Strong written communication and product documentation skills, with the ability to create clarity for technical and non-technical audiences.
• Experience leading launches, adoption programs, and organizational change for enterprise products or new ways of working.
• Executive-level communication and influencing skills, with the judgement to explain trade-offs, challenge assumptions, and align senior stakeholders.
• A leadership style that combines strategic direction, customer and user focus, rigorous decision-making, talent development, and strong cross-functional partnership.
• Bachelor's degree in business, computer science, engineering, design, or a related field is required; an advanced degree is preferred, or equivalent practical experience.
• What does success look like? A high-performing product management team is established, with consistently strong product thinking, written requirements, prioritization, and partnership with science and engineering.
• ADI has a clear, credible, and actively managed roadmap for enterprise agents and assistants, with explicit decisions on which use cases to pursue, sequence, defer, or decline.
• Business workflows and user problems are translated into clear, stable product requirements and success criteria that delivery teams can execute without repeated reinterpretation.
• Product performance is reviewed through a trusted scorecard that connects adoption, task completion, trust, satisfaction, grounding, and relevance to measurable business outcomes.
• Launch and change management practices lead to sustained adoption, and performance evidence is used to redirect investment toward the products and capabilities creating the greatest value.
• For positions requiring access to technical data, Analog Devices, Inc. may have to obtain export licensing approval from the U.S.
• Department of Commerce - Bureau of Industry and Security and/or the U.S.
• Department of State - Directorate of Defense Trade Controls.
• As such, applicants for this position – except US Citizens, US Permanent Residents, and protected