AI Product Manager

Baylor Scott & White Health•Remote,

About The Position

The AI Product Manager supports delivery of BSWH’s priority AI use-case roadmap in partnership with Senior AI Product Managers and cross-functional teams. The role helps translate customer, clinical, operational, and business problems into product requirements, user stories, acceptance criteria, delivery plans, success measures, and launch-readiness materials for AI-enabled capabilities. It works across multiple use cases, helping maintain execution discipline, resolve issues, align stakeholders, improve documentation, and track outcomes rather than independently owning a full product area. The position contributes to discovery, requirements definition, backlog management, testing coordination, adoption support, and continuous improvement for AI-enabled customer and operational experiences. It helps ensure capabilities are useful, trustworthy, measurable, and operationally sustainable, while supporting safe and responsible implementation across BSWH use cases. The role coordinates product delivery across product, engineering, architecture, data, ontology, analytics, design, clinical, operational, privacy, security, and compliance stakeholders. It also supports launch planning, adoption measurement, reusable AI platform capabilities, and common delivery practices across the AI use-case portfolio.

Requirements

Bachelor’s degree in Business, Product Management, Engineering, Computer Science, Data Science, Healthcare Administration, or a related field; an equivalent combination of education and relevant experience may be considered.
4–7 years of experience in product management, digital product delivery, technology delivery, business analysis, product operations, data products, healthcare technology, or a related field.
Experience supporting digital products, AI-enabled capabilities, automation solutions, analytics products, customer experiences, operational workflows, or technology-enabled services.
Experience translating customer, business, clinical, or operational needs into product requirements, user stories, acceptance criteria, prioritization inputs, and delivery artifacts.
Experience working with cross-functional teams, including product, engineering, design, analytics, data, operations, and business stakeholders.
Familiarity with AI-enabled product concepts, including large language models, AI agents, orchestration, retrieval, personalization, automation, evaluation, guardrails, and human-in-the-loop workflows.
Ability to work with technical teams to document requirements involving APIs, integrations, enterprise systems, data products, analytics, platform capabilities, and workflow automation.
Strong comfort using product management and collaboration tools, roadmaps, backlogs, user stories, release plans, and product documentation practices.
Ability to interpret product analytics, adoption trends, user feedback, operational metrics, quality signals, and business outcomes.
Strong written communication skills and ability to create concise, well-structured documentation for technical and non-technical stakeholders.
Ability to turn ambiguous ideas into clear requirements, user stories, delivery plans, and success measures.
Ability to support multiple AI use cases simultaneously while maintaining organized documentation, follow-up, decision tracking, and stakeholder alignment.
Practical understanding of AI-enabled product development and willingness to learn how AI capabilities, data, workflows, evaluation, and operational guardrails interact.
Strong analytical, communication, collaboration, ownership, attention-to-detail, and follow-through skills.
Comfort operating in a regulated or high-trust environment where safety, privacy, compliance, auditability, and customer trust are important.

Nice To Haves

Healthcare, life sciences, financial services, or other regulated-industry experience.
Experience supporting AI-powered customer experiences, digital assistants, conversational interfaces, recommendation systems, search experiences, workflow automation products, or decision-support capabilities.
Experience applying Jobs-to-Be-Done, customer-centered design, design thinking, agile product development, product operations, or outcome-based product management methods.
Experience in regulated environments involving sensitive data, safety requirements, auditability, and compliance considerations.
Experience with product analytics, experimentation, user research, stakeholder interviews, customer journey mapping, process mapping, or operational workflow analysis.
Experience using AI tools responsibly and effectively to accelerate research, synthesis, documentation, prototyping, analysis, or product delivery tasks.

Responsibilities

Support Senior AI Product Managers in translating priority AI use cases into product requirements, epics, user stories, acceptance criteria, dependencies, and release plans.
Maintain and organize product documentation, decision logs, issue lists, delivery artifacts, stakeholder inputs, and product-readiness materials across assigned use cases.
Coordinate with product, engineering, architecture, data, analytics, design, clinical, operational, privacy, security, and compliance stakeholders to keep delivery aligned and progressing.
Identify delivery risks, open questions, dependency gaps, and decision points requiring escalation.
Participate in discovery sessions with customers, operators, clinical teams, journey leaders, and business stakeholders to understand problems, workflows, pain points, and desired outcomes.
Document jobs-to-be-done, process flows, user needs, use-case hypotheses, functional and non-functional requirements, and measurable success criteria.
Translate user and operational feedback into structured product inputs for roadmap refinement and prioritization.
Support market, user, competitor, and internal capability research to inform AI product decisions and identify reuse opportunities.
Support backlog grooming, sprint planning, roadmap updates, release coordination, and cross-functional status reporting for assigned AI use cases and capabilities.
Help ensure user stories and requirements are clear, testable, prioritized, and aligned with customer value, implementation feasibility, and business impact.
Track delivery progress, milestones, blockers, risks, stakeholder feedback, and dependencies across product, engineering, data, ontology, and operational teams.
Prepare concise updates, decision materials, meeting notes, action trackers, and launch-readiness materials for product and stakeholder forums.
Help define expected AI-enabled behaviors, escalation paths, human review points, usability expectations, guardrails, and operational considerations.
Partner with AI architecture, engineering, quality, data, and clinical or operational stakeholders to document how AI capabilities should operate in real-world workflows.
Support responsible AI implementation by ensuring requirements consider privacy, compliance, auditability, transparency, reliability, safety, and customer trust.
Connect use-case needs to reusable AI platform capabilities, data products, ontology assets, integration patterns, and evaluation approaches.
Support launch planning, stakeholder readiness, operational handoffs, change-management materials, training inputs, release notes, and adoption tracking.
Assist in defining and monitoring product metrics, usage signals, quality indicators, feedback loops, adoption measures, and operational outcomes.
Gather and synthesize user, stakeholder, operational, and product analytics feedback to identify opportunities for iteration and improvement.
Help ensure each use case has clear outcomes, ownership, measurement routines, and an ongoing improvement plan.
Identify common requirements, recurring pain points, reusable patterns, and shared capability needs across multiple AI use cases.
Document opportunities to reuse similar requirements, workflows, data needs, evaluation approaches, or platform capabilities to prevent duplication.
Support collaboration across AI Product Managers, Senior AI Product Managers, Data Product Managers, Ontology teams, Engineering, Architecture, Quality Engineering, Analytics, and Operations.
Contribute to common product templates, intake materials, documentation standards, readiness checklists, and repeatable product delivery practices for the AI use-case portfolio.