Sr. Manager, Product Development (AI & Automation Product Management)

The Sr. Manager of Product Development (AI & Automation Product Management) leads the strategy, design, and delivery of both AI-driven and rules-based automation products. This role is responsible for advancing intelligent automation across the enterprise by combining advanced AI/ML capabilities with traditional workflow, rules, and process automation solutions.

The leader will drive a cohesive product strategy that integrates AI-enabled decisioning with deterministic automation to deliver scalable, efficient, and compliant operational outcomes. This role requires strong product leadership, technical fluency across AI and automation paradigms, and the ability to guide cross-functional teams from ideation through deployment and continuous optimization.

Key Responsibilities

Product Strategy & Vision (AI + Automation)

• Define and execute a unified product strategy that spans AI/ML solutions and non-AI automation (e.g., rules engines, workflow orchestration, RPA)
• Identify and prioritize opportunities to optimize processes using the right approach (AI vs. deterministic automation vs. hybrid)
• Develop integrated product roadmaps aligning business goals, operational efficiency, and customer experience
• Drive build vs. buy vs. partner decisions across AI platforms and automation tooling

Product Development & Delivery

• Lead end-to-end lifecycle for AI and non-AI automation products: ideation, requirements, design, build, launch, and continuous improvement
• Translate business and operational needs into product requirements, user stories, and automation logic
• Deliver:
  • AI-driven solutions (e.g., NLP, document intelligence, predictive models)
  • Rules-based automation (e.g., decision engines, workflows, straight-through processing)
• Ensure scalability, resiliency, and maintainability across automation solutions

AI, Data & Automation Integration

• Partner with data scientists, engineers, and automation teams to implement:
  • Machine learning models and LLM-based capabilities
  • Workflow automation engines and orchestration platforms
  • Document processing and structured/unstructured data pipelines
• Design hybrid solutions combining:
  • AI inference + business rules
  • Human-in-the-loop workflows where needed
• Ensure proper model lifecycle management (MLOps) alongside automation lifecycle governance

Required Domain & Product Ownership

• AI and automation products including:
  • Automated Case Creation (IntelliPath Connect)
  • IntelliPath Clinicals
  • Automated Approval (M0)
  • Automated Fax / Unstructured Data Processing (ORCA)
  • Clinical Review Intelligence (CRI)
  • Real-Time Document Approval (RTDA)
  • UPADS/Pathway related workflow and decision automation
• Deeply understand and optimize:
  • End-to-end workflows across intake, review, decisioning, and document processing
  • Integration points between AI solutions and deterministic automation systems
  • Identify opportunities to:
• Increase straight-through processing rates
  • Reduce manual intervention
  • Improve accuracy, cycle time, and operational scalability

Customer, Operations & Workflow Focus

• Analyze user workflows and operational bottlenecks to determine optimal automation approaches
• Balance accuracy, explainability, and efficiency in AI vs. rule-based decisioning
• Ensure seamless user experiences across automated and human-driven processes
• Drive adoption through intuitive design, transparency, and trust in automation outputs

Governance, Risk & Responsible Automation

• Ensure all AI and automation solutions meet regulatory, compliance, and audit requirements
  • Establish governance frameworks for:
  • AI model risk (bias, drift, explainability)
  • Rules engine integrity and change control
  • Automation failure handling and exception management
• Partner with risk, compliance, and legal to maintain responsible and ethical solutions

Leadership & Stakeholder Management

• Lead and mentor product managers across AI and automation domains
• Collaborate with business, clinical, operations, IT, and data teams
• Influence executive stakeholders with clear product vision, roadmap, and ROI-driven outcomes
• Manage vendor relationships across AI platforms and automation tools

Performance, Metrics & Continuous Improvement

• Define KPIs across both AI and non-AI automation, including:
  • Automation rate / straight-through processi