AI Product Manager

Role purpose

Define, prioritize, and deliver AI and GenAI products that solve high-value business problems while satisfying enterprise requirements for risk, security, compliance, usability, adoption, and operational readiness. The role connects business strategy, AIRP platform capabilities, citizen development, and measurable outcomes.

Client-specific emphasis

• This is primarily AI for business, with use cases spanning KYC, credit underwriting, governance tracking, pitch book generation, Banker 360, Customer 360, deal library intelligence, financial crime quality, and sanctions screening.
• The Product Manager should understand AIRP as the enterprise AI-ready platform and coordinate with engineering on AWS, Terraform/IaC, DevOps pipelines, security, and governance dependencies.
• Responsible citizen development through Power Platform, Copilot Studio, Power Apps, Power Automate, and Power BI is part of the AI-native bank agenda and should be reflected in adoption and roadmap planning.

Primary ownership

• AI product vision, AIRP roadmap alignment, use-case prioritization, business case, and success metrics.
• Product discovery, MVP definition, pilot planning, production rollout, adoption, measurement, and lifecycle management.
• Cross-functional delivery alignment across business, AI engineering, research, AIRP platform, cloud engineering, risk, compliance, legal, cybersecurity, data, UX, and citizen-development teams.

Key responsibilities

• Define product vision, strategy, roadmap, and success metrics for AI and GenAI products across business and enterprise functions.
• Identify and prioritize use cases based on value, feasibility, data readiness, risk profile, platform readiness, AWS/cloud dependencies, user impact, and strategic alignment.
• Translate business needs into product requirements, user stories, acceptance criteria, operating requirements, governance requirements, and measurable outcomes.
• Manage product lifecycle from discovery, experimentation, MVP, pilot, production rollout, adoption, monitoring, and continuous improvement.
• Partner with AI engineers, researchers, cloud/platform teams, architects, UX, risk, compliance, legal, cybersecurity, data owners, and business stakeholders.
• Define KPIs such as adoption, productivity gain, cost reduction, cycle-time reduction, accuracy, risk reduction, user satisfaction, operational efficiency, and control effectiveness.
• Ensure AI products include appropriate governance, human oversight, explainability, auditability, security, data privacy, and Responsible AI controls.
• Drive AI-native adoption through change management, training, communication, feedback loops, executive reporting, and responsible citizen-development enablement.

Must-have candidate profile

• Experience in product management, digital transformation, AI/ML products, data platforms, enterprise technology, or business transformation.
• Strong understanding of AI, GenAI, LLM capabilities, limitations, risks, and enterprise adoption considerations.
• Ability to translate business problems into AI-enabled product opportunities, roadmaps, requirements, and measurable outcomes.
• Experience working with engineering, data science, research, architecture, risk, compliance, cybersecurity, legal, and business stakeholders.
• Strong roadmap management, prioritization, stakeholder engagement, business-case development, delivery governance, and adoption-management skills.
• Working knowledge of AI platform dependencies, including cloud delivery, data readiness, governance approvals, security reviews, and production support.

Preferred experience

• Banking, financial services, fintech, risk, operations, compliance, customer service, banker productivity, or enterprise productivity background.
• Experience delivering AI copilots, knowledge assistants, automation tools, analytics products, AI platforms, data products, or citizen-development programs.
• Familiarity with AIRP-style platform models, AWS-hosted AI platforms, Power Platform, Copilot Studio, Power Apps, Power Automate, Power BI, AI governance, model risk, data privacy, cybersecurity, and Responsible AI requirements.

Initial screening questions

• What AI product have you taken from concept to production?
• How would you prioritize KYC, credit underwriting, pitch book generation, Customer 360, deal intelligence, and sanctions-screening use cases?
• How do you balance business value, data readiness, risk, platform readiness, and AWS/cloud implementation constraints?
• What KPIs did you define and what measurable impact was achieved?
• How did you work with risk, compliance, security, engineering, and business teams?
• How would you drive responsible citizen development adoption using Power Platform and Copilot Studio?