Vice President, Product - AI Solutions

Title and Summary

Vice President, Product - AI Solutions

The Vice President, AI Product Management will drive the strategy, roadmap, and delivery of AI-enabled products and enterprise capabilities for Mastercard. This leader will define how artificial intelligence, generative AI, machine learning, and agentic capabilities are applied across the payments ecosystem to improve customer experiences, reduce risk, accelerate product innovation, and create measurable business value.

This role requires a product leader who can operate at the intersection of AI, payments, data, technology, risk, and commercialization. The VP will partner closely with engineering, data science, design, legal, privacy, security, compliance, regional business teams, and customer-facing organizations to deliver scalable, trusted, and responsible AI solutions.

• Product Strategy & Roadmap
  • Translate market trends, customer needs, emerging AI capabilities, and business priorities into clear product strategies and investment recommendations.
  • Prioritize product opportunities based on customer value, business impact, feasibility, risk, regulatory considerations, and enterprise readiness.
  • Develop business cases for AI investments tied to revenue growth, cost optimization, risk reduction, customer experience, productivity, and speed to market.
  • Create a product roadmap that supports experimentation, agent build, certification, deployment, monitoring, and commercialization at scale.
  • Partner with engineering, architecture, data, security, legal, compliance, risk, and business teams to align AI platform priorities to enterprise strategy.
• Product Delivery & Execution
  • Lead end-to-end product management from discovery and concept development through launch, adoption, scaling, and lifecycle management.
  • Own product requirements, roadmap tradeoffs, release planning, adoption strategy, and success metrics for AI-enabled products and platforms.
  • Partner with engineering, data science, architecture, and design teams to deliver secure, scalable, reliable, and user-centered AI solutions.
  • Establish clear operating rhythms for roadmap reviews, customer feedback, executive updates, launch readiness, performance tracking, and post-launch optimization.
  • Ensure AI products are designed for enterprise scale, global deployment, local market needs, and the security standards required in financial services.
• Governance, Risk & Compliance
  • Embed responsible AI, data governance, privacy, security, and compliance requirements into the product lifecycle.
  • Partner with legal, privacy, security, compliance, risk, and audit teams to define practical controls for production AI agents.
  • Ensure every production agent has a named owner, risk classification, approved data sources, approved tools, evaluation evidence, telemetry, and a support model.
  • Define approval gates and production-readiness criteria based on agent autonomy, data sensitivity, tool access, regulatory exposure, and business impact.
  • Drive consistency across AI governance, model governance, data governance, and enterprise access-control policies.
• Customer Adoption & Commercialization
  • Drive adoption of the Agent Factory across internal product, engineering, data, and business teams.
  • Create product experiences, onboarding guides, documentation, templates, and enablement programs that make it easier for teams to build agents through the platform.
  • Develop metrics to track usage, adoption, reuse, quality, cost, risk, and business value.
  • Support the evolution of the Agent Factory from internal platform capability to commercializable AI product foundation.
  • Partner with business and product teams to identify AI agent capabilities that can be embedded into customer-facing products, partner solutions, or monetizable services.

• Experience in product management, product strategy, platform product management, fintech, data products, AI/ML products, or enterprise technology.
• Experience leading product teams, cross-functional teams, or complex product portfolios.
• Strong understanding of artificial intelligence, machine learning, generative AI, large language models, agentic AI, data products, model lifecycle management, and responsible AI practices.
• Experience working in regulated, security-conscious, or highly governed environments such as financial services, payments, banking, insurance, healthcare, or large enterprise technology.
• Ability to translate complex technical capabilities into clear product strategy, roadmaps, executive narratives, and customer-facing value propositions.
• Strong executive communication skills with the ability to influence senior leaders across product, engineering, data, security, risk, compliance, legal, and business functions.
• Experience defining product metrics, OKRs, adoption goals, platform KPIs, and business value measures.
• Strong technical fluency with cloud, APIs, platform architecture, data governance, identity/access management, observability, and CI/CD concepts.