Director, Sales AI Transformation Digital & Agent Experience Product Manager

UnitedHealthcare is simplifying the health care experience, creating healthier communities, and removing barriers to quality care. The Director, AI Transformation, Medicare Sales Digital & Technology Experience will lead the strategy, transformation, and delivery of AI-enabled products across Medicare shopping, sales, agent, partner, telesales, and support experiences. The role will unify fragmented capabilities, improve cross-channel journeys, establish responsible AI practices, and lead multidisciplinary product and technology teams.

Responsibilities:

• Define and execute a unified product strategy for AI-enabled Medicare shopping, sales, agent, partner, and support experiences
• Own the integrated portfolio vision, product principles, sequencing, dependencies, and measurable outcomes across digital direct-to-consumer, Jarvis, eAlliance, telesales desktop, and broker-help desk experiences
• Translate business strategy, user needs, technology capabilities, and market signals into a clear multi-year transformation roadmap and near-term delivery priorities
• Rationalize overlapping capabilities, reduce duplicated solutions, and identify where shared platform services should replace channel-specific implementations
• Establish clear portfolio governance, decision rights, investment recommendations, and executive communications
• Own the product strategy for AI Voice and conversational IVR capabilities that support Medicare education, guided decision support, intent capture, navigation, and appropriate routing
• Design web and mobile experiences that allow shoppers to initiate or schedule a voice connection with telesales, including browser-based calling or comparable digital-to-voice pathways where supported by the approved technology ecosystem
• Ensure relevant shopper progress, preferences, questions, and completed steps can be made available to the receiving telesales workflow, subject to consent, privacy, security, and platform constraints
• Define experiences in which an AI voice assistant can educate or guide a shopper, collect approved information, resolve appropriate needs, or transition the interaction to a licensed human agent
• Create clear escalation, opt-out, authentication, consent, disclosure, and recovery patterns so users understand when they are interacting with AI and can reach a person when needed
• Partner with contact center, telephony, Amazon Connect, CRM, digital, data, architecture, and operations teams to align call routing, journey context, agent desktop integration, and service reliability
• Apply conversational design, speech recognition, natural language understanding, voice user-interface, latency, accessibility, and call-quality considerations to product decisions
• Measure containment or completion only alongside customer understanding, successful routing, handoff quality, agent readiness, compliance, and overall journey outcomes
• Create a coherent end-to-end experience that allows consumers and sales professionals to move across digital, agent-assisted, partner, and service touchpoints without unnecessary loss of context or repeated data entry
• Define cross-channel journey patterns for education, needs capture, provider and prescription inputs, plan comparison, assistance, enrollment, follow-up, and servicing transitions
• Ensure each experience applies the right persona, permissions, language, context, skills, and workflow for the user while drawing from a common intelligence foundation
• Partner with experience design and research leaders to continuously observe users, identify friction, validate improvements, and protect simplicity as the ecosystem grows
• Champion human-centered experiences that make AI helpful, explainable, accessible, and easy to recover from when confidence is low or human support is needed
• Lead the evolution of common knowledge services and reusable AI skills that deliver consistent, current, and governed answers across channels
• Define how persona-specific context, journey state, business rules, permissions, and interaction history are applied without creating separate intelligence stacks for every tool
• Guide orchestration patterns that support intent recognition, retrieval, task completion, next-best-action recommendations, and handoffs between AI and human-assisted experiences
• Create product requirements for context continuity and interoperability so relevant information can follow the journey across approved systems and channels
• Partner with data, architecture, AI, compliance, and operations teams to establish feedback loops, evaluation methods, confidence thresholds, and human-in-the-loop controls
• Introduce modern product and engineering practices that increase delivery speed, product quality, learning velocity, and accountability
• Build durable partnerships with engineering, architecture, platform, data, design, research, and operations leaders, aligning teams around outcomes rather than isolated feature delivery
• Use AI-assisted software development tools and practices, including coding agents such as Claude Code and Codex, to improve discovery, implementation, testing, review, documentation, and release readiness where enterprise standards permit
• Create clear product increments, acceptance criteria, evaluation plans, release gates, and observability requirements for AI-enabled capabilities
• Reduce handoff friction across business, product, design, engineering, and technology teams through shared artifacts, rapid decisioning, and transparent ownership
• Coach teams toward smaller releases, faster feedback, automated quality practices, disciplined experimentation, and continuous delivery
• Establish a common product operating system spanning intake, discovery, prioritization, roadmaps, delivery, release, adoption, support, and lifecycle management
• Define outcome metrics across consumer and agent experience, answer quality, speed to resolution, self-service, handoff success, adoption, conversion support, productivity, and operational reliability
• Use product telemetry, user research, interaction analysis, and frontline feedback to identify friction and prioritize improvements
• Create transparent portfolio reporting that connects delivery progress to user and business outcomes
• Build mechanisms to identify, contain, and correct inaccurate, low-confidence, inconsistent, or high-friction AI experiences
• Ensure AI-enabled product experiences align with Responsible AI principles and applicable legal, regulatory, compliance, privacy, security, accessibility, and records requirements
• Partner with business and technology controls teams to integrate governance into the product lifecycle rather than treating review as a final-stage activity
• Ensure clear human escalation, override, auditability, feedback, and issue-management pathways within critical shopping and selling journeys
• Lead readiness across training, communications, support, release management, monitoring, and peak-season operations
• Balance transformation speed with the reliability and trust required for Medicare sales experiences
• Lead and develop a high-performing multidisciplinary product organization with strong product judgment, technical fluency, delivery discipline, and customer empathy
• Create clarity across teams by defining mission, outcomes, roles, decision rights, and shared ways of working
• Influence senior business and technology leaders across a matrixed organization and resolve competing priorities through evidence and transparent tradeoffs
• Attract modern product and delivery talent while elevating the capabilities of existing teams through coaching and hands-on leadership
• Model urgency, curiosity, accountability, humility, and a willingness to challenge legacy assumptions in service of users and outcomes

Requirements:

• • Bachelor's degree or equivalent practical experience
• • 5+ years of demonstrated experience in artificial intelligence (AI), machine learning, generative AI, AI agents, or related technologies. Expertise may be acquired through professional experience, graduate research, academic achievement, open-source contributions, certifications, entrepreneurial ventures, or independent projects
• • Demonstrated success transforming a digital product organization or complex product portfolio within a modern, technology-enabled company
• • Experience leading multiple customer-facing and employee-facing digital products through strategy, delivery, adoption, optimization, and lifecycle decisions
• • Proven ability to connect product experience, platform architecture, data, AI, and engineering delivery into a cohesive roadmap
• • Solid technical fluency with modern software delivery, APIs, cloud platforms, data products, AI architectures, product telemetry, experimentation, and quality practices
• • Working knowledge of conversational AI and voice experience concepts, including IVR, contact center integration, speech recognition, natural language understanding, call routing, digital-to-voice handoffs, and agent desktop connectivity
• • Hands-on familiarity with AI-assisted development tools and agentic engineering workflows, such as Claude Code, Codex, or comparable coding agents
• • Experience leading cross-functional product, design, engineering, data, and operations teams in a large, matrixed environment
• • Exceptional ability to simplify complexity, make decisions with incomplete information, and communicate clearly with executives and delivery teams
• • Demonstrated user-first product judgment, delivery discipline, and success improving speed without sacrificing quality or trust
• • 10+ years of experience in digital product management, software product development, technology transformation, platform leadership, or related roles
• • Experience in healthcare, Medicare, insurance, financial services, travel, retail, or another complex and highly regulated consumer industry
• • Experience modernizing digital shopping, marketplace, commerce, customer service, contact center, CRM, or sales enablement ecosystems
• • Experience building multi-persona platforms that adapt context, permissions, workflows, and recommendations by user type
• • Experience with conversational AI, retrieval-augmented generation, agentic workflows, shared knowledge platforms, recommendation systems, or next-best-action capabilities
• • Experience designing seamless digital-to-human and human-to-digital handoffs across channels
• • Experience with AI Voice, conversational IVR, browser or app-initiated calling, contact center platforms, telephony integration, and voice-to-agent escalation patterns
• • Experience leading platform consolidation, capability reuse, shared services, or migration from fragmented product stacks
• • Experience establishing AI evaluation, observability, human-in-the-loop, or model and content quality practices
• • Experience operating high-volume or peak-season digital and contact-center experiences
• • Passion for improving health care simplicity at scale, even if prior health care experience is limited