Lead Product Manager AI productivity and adoption, Azure

PricewaterhouseCoopers•Raleigh, NY

About The Position

As an AI productivity and adoption Manager in PwC’s Advisory practice, you will guide product strategy, shape delivery priorities, and support the development of AI-enabled solutions across the software product lifecycle. You will lead teams, coach junior professionals, manage client accounts, and exercise autonomous judgment on complex workstreams while using technology and innovation to support delivery and build lasting client relationships. You will collaborate across Azure Native Development & Testing and Cloud Engineering, Data & Analytics to advance product innovation, support adoption of AI productivity tools, and translate business needs into practical product capabilities. The role includes up to 80% travel. The salary range is $99,000–$232,000, with actual compensation depending on skills, experience, qualifications, location, and applicable employment laws. Hired individuals are eligible for an annual discretionary bonus.

Requirements

At least a bachelor’s degree.
At least 4 years of experience.

Nice To Haves

Experience leading AI productivity adoption initiatives across Azure-native products.
Background translating product strategy into prioritized delivery roadmaps.
Familiarity with AI-human collaboration, rapid experimentation, and digital tooling.
Experience managing cross-functional product teams through software delivery cycles.
Skill using analytical thinking for requirements, testing, and release decisions.
Experience guiding client conversations on adoption, value, and workflow change.

Responsibilities

Lead AI product strategy and roadmap development for Azure-native applications and productivity solutions.
Manage product discovery, requirements definition, and prioritization across client-facing software initiatives.
Guide cross-functional teams through planning, build, test, and release activities across the software development lifecycle.
Translate business needs into functional requirements, user stories, and acceptance criteria for engineering teams.
Evaluate AI and automation opportunities to improve developer productivity, adoption, and delivery speed.
Coordinate client discussions on product vision, scope, tradeoffs, and delivery milestones.
Apply analytics, rapid experimentation, and feedback loops to refine product direction and feature decisions.
Review technical solution options across cloud architecture, application development, CI/CD, and testing practices.
Mentor junior team members and support their growth in product management and delivery execution.
Monitor delivery risks, dependencies, and issue resolution across multiple workstreams.