Director of Product Management, Enterprise AI

Analog Devices•San Jose, CA

20h•$268,640 - $369,380•Onsite

About The Position

ADI 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.

Requirements

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.

Nice To Haves

an advanced degree is preferred, or equivalent practical experience.

Responsibilities

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.
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.
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.
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.
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.
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.