Product Manager, Generative AI & Analytics

What Success Looks LikeUser Adoption

• Increase active engagement across business users, analysts, developers, and operational teams.
• Demonstrate sustained growth in product adoption and usage.

Accuracy and Trust

• Deliver reliable AI experiences with measurable improvements in response quality, relevance, and user satisfaction.
• Establish evaluation frameworks that ensure transparency and confidence in AI-generated outputs.

Productivity Impact

• Improve business, analyst, and developer productivity through AI-powered automation and insights.
• Reduce manual effort and accelerate access to information and decision support.

Scalability and Enablement

• Enable onboarding of additional domains, functions, and teams through reusable AI capabilities and improved metadata quality.
• Increase organizational readiness for enterprise AI adoption.

Product Strategy and Vision

• Define and execute the product strategy, roadmap, and long-term vision for AI enablement capabilities.
• Identify opportunities where AI can improve productivity, automation, insight generation, and user experience.
• Align product priorities with business objectives and enterprise digital transformation strategies.

Product Discovery and User Experience

• Conduct research with business users, analysts, engineers, developers, and operational stakeholders.
• Identify user pain points and opportunities through data-driven discovery and experimentation.
• Translate customer needs into product requirements, user stories, epics, and measurable success criteria.

AI Product Delivery

• Partner closely with engineering, data science, UX, and platform teams to develop and launch AI-powered products.
• Guide development of conversational interfaces, AI assistants, natural language analytics, and developer productivity tools.
• Define experimentation frameworks and evaluation methodologies for AI capabilities and model performance.

Metrics and Business Value

• Establish KPIs and success measures focused on adoption, engagement, productivity, quality, trust, and operational efficiency.
• Monitor product outcomes and continuously optimize based on user feedback and behavioural insights.
• Develop business cases and value realization frameworks for AI investments.

Governance and Responsible AI

• Champion Responsible AI principles, governance frameworks, and compliance requirements.
• Promote metadata quality, knowledge management, explainability, and trustworthy AI practices.
• Partner with security, legal, and governance stakeholders to ensure policy compliance.

Stakeholder Management

• Build strong partnerships across business functions, technology organizations, and senior leadership.
• Communicate product vision, strategy, roadmap, risks, and outcomes effectively to executive and technical audiences.
• Drive organisational adoption and change management activities for AI-powered capabilities.