Lead ongoing customer discovery efforts: conduct interviews, site visits (virtual or in-person), and workflow observation sessions with clinical and administrative end users
Develop and maintain a deep understanding of how healthcare organizations use answering services, operator consoles, and AI-augmented communication — including the pain points, workarounds, and compliance constraints that matter most
Synthesize input from cross-functional teams, market and customers into product insights; distinguish signal from noise when stakeholder demands conflict
Define user personas and jobs-to-be-done for the AI platform’s primary audiences: clinical staff, contact center agents, patients, and healthcare administrators
Use modern AI tools to accelerate your own product work: generate prototypes and wireframes, run rapid concept tests, and explore technical feasibility before committing engineering resources
Design and run structured product experiments — A/B tests, pilot programs, shadow deployments — to validate hypotheses before broad rollout
Define clear acceptance criteria and success metrics for every feature, including AI-specific measures such as containment rate, deflection accuracy, latency, and user-perceived quality
Actively evaluate emerging AI capabilities (new LLMs, voice models, orchestration frameworks) and recommend adoption decisions based on product value and technical fit, not novelty
Ensure AI features are designed with clinical workflow reality in mind: how nurses, physicians, schedulers, and operators interact with systems under time pressure
Identify and manage product-level compliance and safety considerations including HIPAA, minimum necessary data principles, and the appropriate use of AI in clinical decision-adjacent contexts
Collaborate with Clinical and Operations teams to ensure AI behavior aligns with care protocols, escalation paths, and patient safety standards
Own the relationship between product requirements and engineering delivery — writing precise, well-scoped specifications that reduce ambiguity without over-constraining implementation
Serve as the product accountability owner for the outsourced development partner, reviewing deliverables against acceptance criteria and escalating quality issues promptly
Collaborate with Marketing and Customer Success on go-to-market sequencing for new AI features, including packaging, positioning, and customer communication
Represent the product perspective in executive conversations; communicate roadmap priorities, tradeoffs, and AI adoption risks clearly to non-technical stakeholders
Define the measurement framework for AI product performance: establish baselines, set targets, and drive quarterly reviews of key metrics with engineering and executive leadership
Build a culture of learning from production data: use real-world call outcomes, error logs, and user feedback to continuously improve AI model performance and product design
Identify and act on leading indicators of customer churn or dissatisfaction related to AI feature quality