Head of AI Product Validation & MVP Scale

Head Of Ai Product Validation & Mvp Scale-Up, Early Adopter Validation & Mvp Scale-Up

The Head of AI Product Validation & MVP Scale-Up, Early Adopter Validation & MVP Scale-Up is a highly seasoned product leader who validates enterprise product value propositions with early adopters, converts real-world learning into product direction, and prepares successful MVPs for repeatable scale across the enterprise.

Operating with significant autonomy, you will address highly complex, ambiguous, and cross-functional product challenges by testing assumptions with real users, clarifying what drives adoption, and surfacing the business, process, data, and change requirements needed to expand an MVP beyond a pilot. You will influence senior stakeholders through insight, judgment, and demonstrated evidence of market and business value.

• Define and own the validation strategy for new digital or AI-enabled products, ensuring the value proposition is tested with the right early adopter segments.
• Translate customer, user, and workflow insights into a clear product direction that sharpens differentiation and business relevance.
• Guide prioritization decisions based on evidence of desirability, feasibility, and business value rather than internal assumptions alone.
• Lead structured engagement with early adopters to test assumptions, gather feedback, and understand what drives willingness to adopt, expand, and advocate.
• Design and run iterative learning cycles that convert interviews, pilots, usage data, and stakeholder feedback into concrete product and commercialization decisions.
• Distinguish signal from noise by identifying the patterns that are relevant for broader enterprise adoption versus one-off requests from pilot users.
• Establish and monitor validation metrics that demonstrate value realization, user traction, process fit, and readiness for broader rollout.
• Surface the product, process, data, integration, support, and change management requirements required to move from MVP to enterprise scale.
• Create evidence-based recommendations on whether to persevere, refine, reposition, or stop an MVP based on observed outcomes and scaling potential.
• Partner closely with business leaders, product teams, data scientists, engineers, IT, operations, and change leaders to ensure early adopter feedback informs the right enterprise decisions.
• Act as a consultant, aligning stakeholders around what has been validated, what remains uncertain, and what must be true to scale successfully.
• Communicate trade-offs, learning, adoption barriers, and scale implications to senior stakeholders and sponsors.
• Define, prioritize, and refine epics, experiments, and user stories required to validate the core value proposition and remove barriers to adoption.
• Ensure delivered MVP capabilities are fit for learning, measurable in use, and aligned to the questions the business needs answered before scaling.
• Review outcomes from each release and translate them into backlog decisions that improve both product value and scale readiness.
• Build the scale-up case for successful MVPs by clarifying repeatable value, implementation requirements, support needs, and operating model implications.
• Balance scope, timeline, cost, and learning objectives to maximize both near-term validation and long-term enterprise viability.
• Identify opportunities to accelerate adoption by removing friction in onboarding, integration, governance, training, and stakeholder ownership.
• Lead, manage, and develop product managers or cross-functional product resources, setting clear expectations for customer validation, learning velocity, and business impact.
• Provide coaching, mentorship, and performance feedback to strengthen product judgment, discovery practices, and stakeholder effectiveness.
• Assign work based on capability and experience while enabling team members to operate with appropriate autonomy.
• Serve as an escalation point for complex product, adoption, and scale-readiness decisions.
• Foster a culture of accountability, evidence-based decision-making, collaboration, and continuous improvement.

• 10+ years of progressively increasing responsibility in product management, product strategy, innovation, venture incubation, digital transformation, or related enterprise roles.
• Bachelor's degree required in business, technology, engineering, computer science, chemistry, or a related field.
• Advanced degree preferred; master's or Ph.D. in chemistry or a related scientific discipline.
• Demonstrated success validating AI system value propositions across enterprise functions, with particular depth in science- and technology-oriented domains.
• Experience leading cross-functional teams and influencing stakeholders through ambiguity to move complex AI systems from MVP to broader enterprise adoption; strong familiarity with R&D, applied science, or adjacent technical environments preferred.