Principal Artificial Intelligence (AI) Product Manager - REMOTE (with Travel)

FERGUSON

3d • $11,633 - $18,617 • Remote

About The Position

Ferguson is building its AI product capability from the ground up. This is the first dedicated product management hire on the AI team and is charged with finding the highest-value problems across a $30B distribution business, building AI products that solve them, and driving adoption of those products at scale. The Principal AI Product Manager reports to the VP of AI Enablement and operates with full ownership of the product lifecycle, from early discovery through scaled deployment and measured business impact. This is not a backlog management role; it’s a builder role. You will spend time in the field understanding how work actually happens, define what to build and what to say no to, ship fast, prove value, and use that momentum to unlock the next investment. The right candidate has built products in high-growth or high-velocity environments where speed, ambiguity, and direct accountability for outcomes were the norm. You’ve shipped products that moved real business metrics, not just features that checked roadmap boxes. You know how to operate with a small team and make outsized impact. And you’re energized by the idea of bringing that approach into a Fortune 500 company where the opportunity is enormous and the AI product function is yours to build.

Requirements

• 8+ years in product management with increasing scope and ownership, ideally including time at a high-growth software company, SaaS platform, or AI-native product organization where you shipped products with direct revenue or efficiency impact.
• Demonstrated track record of owning products end-to-end — not just writing requirements, but driving outcomes from discovery through adoption.
• Demonstrated ability to thrive in fast‑moving, ambiguous environments—proactively solving problems and creating structure rather than waiting for defined processes.
• Experience with AI/ML product development; must have genuine curiosity about the technology and the ability to go deep enough to have informed opinions about feasibility and approach.
• Sharp product instincts, able to look at a messy, ambiguous problem space and see the product that should exist, then build the case and the plan to make it real.
• Strong business sense with the ability to build credible financial models, size opportunities, and speak the language of P&L impact.
• Analytical rigor paired with intellectual honesty — following the data and reforming opinions, hypotheses and approaches in light of new information.
• Comfort with process mapping, workflow analysis, and identifying where AI can augment or automate work in ways that people will actually adopt.
• Enough technical depth to assess AI/ML capabilities, understand architecture trade-offs, challenge engineering assumptions, and have a point of view on build vs. buy decisions.
• Understanding of enterprise data landscapes, system integration complexity, and the realities of building on top of legacy infrastructure.
• Familiarity with agile development practices and a strong preference for iterative, data-informed delivery at pace.
• Exceptional business customer management in sophisticated, matrixed organizations, building trust/credibility and knowing how to get things done through influence rather than formal authority.
• Executive presence along with presenting to C-suite audiences clearly, confidently, and with integrity.
• Strong facilitation skills, able to walk into a room of people with conflicting priorities and walk out with alignment and a path forward.
• A bias for direct communication, giving candid assessments on scope, timelines, and trade-offs.

Nice To Haves

• Experience in B2B, distribution, wholesale, construction, industrial, or supply chain environments is a strong plus.

Responsibilities

• Embed in Ferguson’s operations — branches, field teams, sales offices, contractor job sites — to build a first-person understanding of how work happens and where AI can create step-change value.
• Lead discovery through direct observation, customer interviews, workflow analysis, and data exploration, prioritizing observed behavior over reported behavior.
• Map the full landscape of problems worth solving across the value chain, from supplier through sales, the branch, fulfillment, and service teams to project owner, engineer, contractor and end customer.
• Build and maintain a living view of the opportunity set that evolves as you learn, not a static document that sits on a shelf.
• Prioritize tenaciously based on business impact, technical feasibility, and organizational readiness — not business customer volume or executive visibility.
• Make explicit trade-off decisions and communicate them clearly, so business customers understand not just what was chosen but why alternatives were deprioritized.
• Reassess priorities continuously as new information surfaces, market conditions shift, and the team’s understanding of the problem space deepens.
• Build compelling, evidence-based narratives that make the problem real to engineering teams, executives, and business partners — connecting every initiative to measurable business outcomes.
• Secure and maintain executive sponsorship by framing AI value in terms leadership cares about: revenue growth, customer experience, and associate productivity.
• Navigate contending priorities across a sophisticated, matrixed organization, building coalitions and resolving conflicts before they become blockers.
• Translate fluently between business and technical contexts, ensuring both sides have what they need to make good decisions fast.
• Own the product end-to-end: vision, strategy, roadmap, backlog, and shipped outcome.
• Define MVP scope that increases learning while delivering enough value to build organizational confidence and fund the next iteration.
• Drive decisions on whether to build internally, purchase externally, or collaborate with partners based on strategic value, speed-to-market, and long-term capability building.
• Set technical direction on decisions that affect product outcomes (latency, data quality, model selection, UX) while respecting engineering’s ownership of implementation.
• Write clear user stories and acceptance criteria, participate in sprint ceremonies, and make real-time scope and priority trade-offs with the delivery team.
• Ship fast, learn fast. Default to iteration over perfection. Get products in front of users early and let real usage data drive the next move.
• Define success metrics before development starts and instrument products to measure adoption, usage, and business impact
• Own the numbers: this role is responsible for adoption, retention, time-to-value, and business outcomes.
• Run meticulous post-launch accountability loops: did it move the metric, what did we learn, what do we adjust, what do we kill.
• Build the organizational habit of measuring what matters and making decisions based on evidence rather than intuition or seniority.
• Own the product-specific go-to-market strategy: pilot design, rollout cohort selection, product training, success criteria, and the path from initial pilot to scaled deployment.
• Build the product-level narrative that drives adoption, making the case to end users for why this specific tool makes their work better and helps them better serve their customers, and owning whether they actually use it.
• Create internal case studies, proof points, and success stories that build momentum and reduce resistance across the organization.
• Partner with the AI Enablement and Adoption Manager on broader organizational readiness, using their AI fluency and change management programs to accelerate product-specific adoption while providing product wins as proof points for the wider enablement effort.
• Contribute to executive updates, board communications, and organizational storytelling as a visible champion of AI product impact.
• Design and maintain systematic feedback loops, including in-app feedback, usage analytics, satisfaction measurement, and regular user panels.
• Continuously validate product assumptions against actual user behavior in the field, closing the gap between what we think is working and what we know is working.
• Feed insights from each initiative into the next, building institutional knowledge about what works at Ferguson and compounding the team’s effectiveness over time.
• Maintain an ongoing practice of tracking AI developments across vendors, competitors, and adjacent industries — translating market signals into actionable roadmap decisions.
• Evaluate emerging AI capabilities and assess their relevance to Ferguson’s product portfolio and strategic priorities.
• Lead the partner and vendor ecosystem: evaluate third-party AI capabilities, hold vendors accountable for delivery, and know when to make a change.
• Establish the practices, rituals, and playbook for how product management operates within Ferguson’s AI organization.
• Mentor and develop junior PMs as the team grows, building critical thinking, collaborator management, and technical proficiency across the team.
• Create scalable, repeatable frameworks for discovery, prioritization, delivery, and measurement that will serve as the foundation for a growing AI product organization.

Benefits

• health, dental, vision, paid time off, life insurance and a 401(k) with a company match
• mental health coverage
• gender affirming and family building benefits
• paid parental leave
• associate discounts
• community involvement opportunities

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: No Education Listed