Sr. Product Manager, AI Products

Sr. Product Manager, AI Products

Department: Product
Location: Remote
FLSA Status: Exempt
People Leader: No
Travel: Up to 5%
Summary
Company Summary
Accuity partners with hospitals and health systems through a technology-enabled, physician-led model that improves clinical documentation integrity, coding accuracy, reimbursement optimization, and quality outcomes.
Job Summary
The Sr. Product Manager, AI Products, will own a strategic product area focused on transforming Accuity’s clinical revenue cycle platform through AI-enabled automation, agentic workflow orchestration, decision support, and human-in-the-loop productivity tools.
This role will define and deliver products that use AI agents, clinical intelligence, workflow automation, analytics, and operational feedback loops to improve documentation accuracy, coding quality, revenue integrity, throughput, and client performance. The Sr. Product Manager will partner closely with engineering, data science, clinical operations, compliance, client success, and commercial teams to move Accuity from technology-enabled services toward a scalable AI-first clinical revenue cycle platform.
The ideal candidate has strong product judgment, healthcare workflow depth, and hands-on experience building or managing AI-enabled products. This person must be able to translate complex clinical, operational, and financial workflows into well-designed agentic systems that augment expert users, reduce manual work, improve consistency, and create measurable business value.
Responsibilities
Product Strategy and Roadmap
• Own product strategy, roadmap, and measurable outcomes for AI-enabled and agentic workflow capabilities across a defined product area.
• Identify high-value clinical revenue cycle workflows where AI agents, automation, decision support, or workflow orchestration can improve productivity, accuracy, quality, speed, margin, or client performance.
• Prioritize roadmap investments based on business impact, user value, model readiness, data availability, implementation complexity, risk, compliance considerations, and scalability.
• Translate clinical, operational, compliance, and client needs into clear product requirements for AI-assisted workflows.
• Ensure roadmap decisions are grounded in client value, operational leverage, model performance, risk management, and business outcomes.
AI Workflow and Product Design
• Define agentic workflow patterns, including task intake, prioritization, routing, recommendation, escalation, exception handling, audit trails, and human-in-the-loop review.
• Define product requirements for AI agents, including user goals, data inputs, model outputs, confidence thresholds, guardrails, feedback mechanisms, monitoring requirements, and success metrics.
• Translate manual expert workflows into scalable AI-assisted or agentic product experiences that augment expert users, reduce manual work, improve consistency, and create measurable business value.
• Drive human-in-the-loop design to ensure expert users remain appropriately engaged in review, validation, escalation, and final decision-making.
• Support product experiences that are usable, explainable, measurable, operationally safe, and trusted by users.
Cross-Functional Execution
• Partner closely with Engineering, Data Science, Design, Analytics, and Clinical Operations to develop and scale AI-enabled product capabilities.
• Lead discovery with internal operators, clinical experts, client users, implementation teams, and commercial stakeholders to validate workflow opportunities and product priorities.
• Lead cross-functional execution across Product, Engineering, Data Science, Operations, Clinical, Compliance, Client Success, Sales, and Implementation teams.
• Make pragmatic tradeoffs between innovation, risk, usability, speed, implementation complexity, and business value.
• Provide guidance to Product Managers and product team members on AI product practices, agentic workflow design, experimentation, measurement, and responsible deployment.
Measurement, Quality, and Responsible AI
• Define and monitor KPIs for AI and agentic workflow performance, including adoption, automation rate, override rate, exception rate, accuracy, productivity, quality, user trust, revenue impact, and margin improvement.
• Build feedback loops that improve AI performance over time through user input, operational outcomes, quality assurance findings, and client-specific patterns.
• Partner with Compliance, Security, Privacy, and Clinical leadership to ensure AI capabilities meet regulatory, audit, PHI, clinical safety, and client trust requirements.
• Ensure product releases include clear measurement plans, feedback loops, monitoring processes, operational readiness, and appropriate controls.