About the Role

We are looking for a Senior Product Manager – AI Adoption & Business Value to accelerate AI adoption across Flutter Brazil and ensure our AI investments translate into measurable business outcomes.

You will work across the organization to identify high-value opportunities, transform business problems into AI use cases, and drive them from discovery through adoption and measurable impact.

This is a highly cross-functional and hands-on role, acting as a bridge between Business, Product, AI Engineering and Data. Beyond delivering individual use cases, you will help establish the product lifecycle, development practices and operating model required to build AI products consistently and at scale.

What You'll Be Doing

AI Opportunity Discovery & Business Partnership

• Partner with business teams to understand workflows, pain points and opportunities where AI can generate meaningful value.
• Build and maintain a pipeline of AI opportunities across Operations, Customer Service, Commercial, Marketing, CRM, Risk, Finance, Product and Technology.
• Translate business problems into clear AI use cases, hypotheses and expected outcomes.
• Prioritize opportunities based on ROI, feasibility, scalability, adoption potential and strategic relevance.
• Challenge opportunities where AI does not provide sufficient incremental value compared with simpler solutions.

AI Use Case Delivery & Value Realization

• Own AI use cases from discovery → prioritization → prototype → evaluation → rollout → adoption → impact measurement.
• Build business cases and define baselines, expected outcomes and success metrics before development.
• Work with AI Engineering, Data Science and Platform teams to rapidly prototype and validate solutions.
• Define MVPs and experiments that validate value before significant investment.
• Track outcomes including hours saved, cost reduction, productivity, automation rate, quality improvement, revenue uplift, adoption and cost-to-serve.
• Compare realized impact against expected ROI and recommend whether solutions should be scaled, improved, redesigned or discontinued.

AI Product Lifecycle & AI SDLC

• Help design and continuously evolve the company's AI Product Lifecycle and AI Software Development Lifecycle (AI SDLC).
• Define repeatable stages, standards and quality gates from problem discovery and experimentation to production, monitoring and continuous improvement.
• Establish practices for prototyping, prompt and context development, model selection, Evals, testing, deployment, observability and production monitoring.
• Define clear Definition of Ready / Definition of Done and production-readiness criteria for AI products and agents.
• Partner with Engineering, Security, Data and Governance teams to embed privacy, security, responsible AI and compliance requirements into the development lifecycle.
• Establish feedback loops connecting user behavior, Evals, production performance and business outcomes to continuous product improvement.
• Develop reusable playbooks, templates, frameworks and best practices that help teams build AI products faster and more consistently.
• Identify opportunities to automate parts of the AI SDLC itself, improving development velocity, testing, quality and deployment.

AI Adoption & Change Management

• Drive adoption of AI products, agents and capabilities across business teams.
• Redesign workflows where necessary rather than simply adding AI to existing processes.
• Define onboarding, enablement, documentation and feedback mechanisms.
• Identify barriers related to usability, trust, behavior or process and work with teams to increase adoption.
• Identify successful use cases that can be transformed into reusable capabilities and scaled across the organization.

AI Quality & Continuous Improvement

• Define Evals and acceptance criteria covering accuracy, reliability, quality, latency, safety, cost and business effectiveness.
• Partner with technical teams on AI Agents, LLM applications, RAG, MCP, tool calling and workflow automation.
• Use AI observability, user feedback and business metrics to continuously improve deployed solutions.
• Monitor AI economics, including model and inference costs, ensuring solutions remain sustainable at scale.

What You'll Bring

• Strong Product Management experience, ideally with AI, automation, data or platform products.
• Proven ability to discover business problems and transform them into technology-enabled solutions.
• Experience defining or improving Product Development Lifecycles, SDLCs or AI development practices.
• Strong analytical and commercial mindset with experience building business cases and measuring ROI.
• Understanding of Generative AI, LLMs, AI Agents, RAG, Evals and AI Product Lifecycle practices.
• Strong experimentation and discovery skills.
• Ability to translate effectively between business stakeholders and technical teams.
• High autonomy and comfort operating in ambiguous, fast-changing environments.
• A pragmatic mindset focused not simply on building AI, but on ensuring AI is adopted, scalable and generates measurable value.
• Advanced/Fluent English

Success Measures

Success will be measured by realized AI ROI, adoption and business impact of deployed use cases, productivity and efficiency gains, time from opportunity to measurable value, conversion of experiments into scalable solutions, and improvements in the maturity, quality and velocity of Flutter Brazil's AI Product Lifecycle and AI SDLC.