Director of Product Management, Enterprise AI

Job description

About Analog Devices Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, AI, and software technologies into solutions that combat climate change, reliably connect humans and the world, and help drive advancements in automation and robotics, mobility, healthcare, energy and data centers. With revenue of more than $11 billion in FY25, ADI ensures today's innovators stay Ahead of What's Possible. Learn more at www.analog.com and on LinkedIn and X. Empowering the Intelligent EdgeOur solutions help customers transform raw data into actionable insights and make connected devices smarter and more responsive. With our analog and mixed-signal devices, power management, radio frequency offerings, and edge processors and sensors, we harness and activate the data that bridges the physical and digital worlds. Whatever breakthroughs are next—in aerospace, automotive, sustainable energy, communications, digital healthcare, industrial automation, instrumentation, or consumer, ADI will be there to keep you ahead of what’s possible. The RoleADI is seeking a Director of Product Management to lead product strategy and execution for a portfolio of enterprise AI agents and assistants. Reporting to the Head of Enterprise AI, this leader will build and develop a high-performing team of product managers and establish a consistently high standard for product thinking, written requirements, prioritization, and partnership with science and engineering. The role will own the vision, roadmap, and investment choices for enterprise agents and assistants. It will determine which use cases ADI should pursue, sequence, defer, or decline, grounding those decisions in business value, user needs, technical feasibility, risk, and the evolving capabilities of AI. The role will serve as the bridge between business stakeholders and delivery teams, translating real workflows and pain points into clear requirements, success criteria, and executable scope. This is a strategically important role in ADI's Enterprise AI agenda. It will ensure that AI investment is directed toward valuable, adoptable products with measurable outcomes, and that launches are supported by effective alignment, communication, and change management across the organization. Key ResponsibilitiesProduct 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. Experience12+ 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 cre