Program Manager, AI Automation Enablement

About SPG

SPG is a specialty insurance holding company focused on acquiring and scaling Managing General Agents (MGAs) and Wholesalers in the E&S and specialty insurance markets. We are building a portfolio of best-in-class operations supported by centralized excellence in innovation, data, and operational transformation. Our commitment to innovation and operational discipline enables our operating divisions to compete and grow in dynamic markets.

Position Summary

The Program Manager AI Automation Enablement is a leader responsible for defining how artificial intelligence and automation create measurable value across our underwriting, sales, operations, finance, and claims functions. This role manages the AI use-case pipeline, pilot execution, and field adoption—ensuring AI investments deliver business results and are embraced by our field teams. Serving as the primary business owner for AI enablement, you will partner closely with Corporate IT on platforms and governance, and with Delivery teams on scaling approved initiatives.

Key Responsibilities

• AI Strategy & Roadmap Development

  • Define and maintain a 12–24-month AI and automation roadmap aligned to enterprise priorities.
  • Establish strategic point of view on how AI supports underwriting leverage, operational efficiency, and scalability across portfolio companies.
  • Translate emerging AI capabilities into practical, business-ready opportunities.
  • Provide executive leadership with regular updates on priorities, progress, outcomes, and ROI.

• Use-Case Management & Prioritization

  • Lead structured intake process for AI and automation opportunities from underwriting, sales, operations, finance, and claims across operating companies.
  • Define and apply qualification criteria for AI-ready use cases (business value, technical feasibility, adoption readiness, risk profile).
  • Quantify expected benefits including capacity creation, cycle-time reduction, cost avoidance, and scalability impact.
  • Own and manage the enterprise AI use-case backlog and prioritization framework.

• Field Partnership & Adoption Enablement

  • Design and facilitate structured focus groups with field underwriting and operations teams.
  • Partner with business leaders across operating companies to validate problem statements, pressure-test workflows, and identify adoption barriers early.
  • Build and maintain an AI Champion Network across the field to drive engagement and change readiness.
  • Ensure field feedback directly informs pilot design, iteration, and scale decisions.

• Pilot Design, Execution & Decision Authority

  • Design, execute, and evaluate time-boxed AI pilots (typically 30–90 days).
  • Define pilot success metrics, adoption thresholds, and clear exit criteria.
  • Evaluate pilot results using standardized evaluation framework and make formal recommendations to scale, iterate, defer, or stop initiatives.
  • Serve as the decision gate between experimentation and enterprise implementation.
  • Apply rigorous assessment across business value, technical performance, adoption readiness, and risk.

• AI Evaluation Framework Ownership

  • Develop and maintain comprehensive evaluation framework for assessing AI opportunities from intake through scale decision.
  • Create qualification scorecards, pilot design standards, success criteria, and scale/stop decision thresholds.
  • Create repeatable methodology for measuring accuracy, business impact, adoption signals, and ROI.
  • Define production monitoring standards including accuracy drift detection, adoption tracking, and value realization measurement.
  • Ensure disciplined, evidence-based decision-making that protects AI investment and accelerates outcomes.

• Enterprise Alignment & Governance

  • Partner with Head of AI & Automation (Corporate IT) to align pilots with enterprise strategy, approved platforms, security standards, and governance requirements.
  • Collaborate with VP, Digital Strategy to ensure alignment with enterprise architecture and data strategy.
  • Coordinate with Compliance, Legal, Risk, and IT Security on responsible AI practices.
  • Support development of AI usage standards, guardrails, and human-in-the-loop controls.
  • Maintain documentation to support audit and regulatory readiness.

• Transition to Delivery

  • Partner with VP, Delivery & Implementations to transition approved initiatives into operational execution.
  • Provide clear business requirements, target workflows, adoption plans, and success metrics to scale implementations.
  • Maintain appropriate separation between pilot ownership and large-scale delivery execution.

Required Qualifications

• Bachelor’s degree in business, Insurance, Operations Management, or related field; MBA or advanced degree preferred.
• Leverage Six Sigma Green Belt certification required, with demonstrated application to process redesign or automation initiatives; Black Belt a plus.
• 7–10 years of progressive experience in insurance operations, underwriting operations, or business transformation.
• Demonstrated experience operationalizing AI tools (Claude, OpenAI/ChatGPT, or similar LLMs) to streamline business processes with measurable results.
• Hands-on experience building workflow automation or RPA solutions (e.g., Power Automate, UiPath, or similar) in operational environments—not solely managing automation vendors or IT delivery teams.
• Experience developing and applying AI evaluation frameworks including: Business case validation (ROI, capacity impact, cost-benefit analysis), Technical feasibility assessment (data readiness, integration complexity, accuracy thresholds), Adoption risk evaluation (change impact, user experience, training requirements), Compliance and risk assessment (regulatory implications, bias detection, audit requirements).
• Wholesale, MGA, or E&S insurance experience strongly preferred.
• Proven history leading: Process redesign or operational efficiency initiatives enabled by AI/automation, Automation, RPA, or digital enablement programs from pilot through adoption, Cross-functional change initiatives with measurable results, Strong facilitation, stakeholder management, and executive communication skills, Demonstrated ability to influence senior leaders and field teams without direct authority.
• Practical, applied understanding of AI capabilities and limitations: LLM use cases (document summarization, data extraction, decision support), Agentic AI and workflow automation design, Human-in-the-loop controls and quality assurance frameworks, Prompt engineering and AI output validation.

Preferred Qualifications

• Experience building or scaling AI-powered solutions using Claude, OpenAI API, Microsoft Copilot, or similar enterprise AI platforms.
• History of moving AI from experimentation to production use with documented adoption and ROI.
• Deep exposure to underwriting workflows, submission intake, clearance, policy servicing, or claims operations.
• Experience designing and running pilots or proofs of concept with clear scale/stop decision frameworks.
• Understanding of insurance regulatory requirements and compliance frameworks.
• Familiarity with AI governance, bias detection, and responsible AI principles.