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Director, AI Strategy

Job in Berkeley Heights, Union County, New Jersey, 07922, USA
Listing for: Hub International
Full Time position
Listed on 2026-06-03
Job specializations:
  • IT/Tech
    AI Engineer, Business Systems/ Tech Analyst
Job Description & How to Apply Below
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 Director, AI Strategy & Automation Enablement is a business leader responsible for defining how artificial intelligence and automation create measurable value across our underwriting, sales, operations, finance, and claims functions. This role owns the AI strategy, 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. This role is intentionally positioned upstream of delivery, with authority to test, prioritize, and stop initiatives before enterprise rollout.

This is a strategy, enablement, and business decision-making role not a software engineering or IT delivery position. Success requires deep understanding of insurance operations, hands-on experience operationalizing AI tools, and the ability to translate AI capabilities into adoption-ready solutions that field teams embrace and use.

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.

* Establish 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 &…
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