Fractional Chief AI Officer for a mid-market insurance carrier
Stepped in as Halberd Mutual's fractional Chief AI Officer. Set company-wide AI strategy, governed AI/ML investments across the enterprise, and led execution on the high-impact initiatives the carrier could not afford to staff with a full-time hire.
To maintain client confidentiality, the company and industry in this case study have been anonymized. The underlying solution is the same.
The problem
Two production AI systems in, Halberd Mutual hit the next problem on the maturity curve: scattered ambition without strategic ownership.
The claims-payment automation was running. The operational data platform was running. Leadership saw what was possible and wanted more. Underwriting wanted to test generative AI for application review. Customer service wanted agent-assist tooling. The fraud team had been pitched by three different vendors. Each business unit was building its own AI plan, none of them talking to each other, and no one was responsible for making sure the pieces fit together or that the risks were managed.
Hiring a full-time Chief AI Officer for a mid-market carrier is a 9-to-12-month search, a high six-figure base, and a long ramp before they ship anything useful. Halberd needed AI leadership now, not eventually.
What we built
A fractional Chief AI Officer function: embedded in Halberd’s executive team, with the authority and accountability of a full-time CAIO and the senior depth that came from having built the production systems we now governed.
The strategic deliverable was a 24-month AI roadmap tied to specific business outcomes (claim cycle time, expense leakage, underwriting capacity, agent retention), with sequencing and decision points the executive team could act on. Alongside the roadmap, we put a risk-tiered governance framework in place for evaluating, deploying, and monitoring AI/ML systems, aligned to insurance regulatory expectations and the carrier’s auditor relationships. Every production system has documented model cards, eval baselines, and escalation paths.
Execution wasn’t theoretical. We led direct delivery on three new initiatives selected from the roadmap: an underwriting application triage assistant, a fraud signal model running against claim intake, and an adjuster-side agent for routine correspondence drafting. Each shipped under the same governance layer rather than as a one-off pilot.
We also evaluated 11 AI/ML vendor pitches across the business units. Killed 7 that didn’t survive a real eval, greenlit 2 that did, deferred 2 into the roadmap as decisions for later. And we handed back a staffing plan for the eventual AI/data function the carrier will need to hire, with role definitions and sequencing for when a permanent CAIO joins.
Results
Five production AI systems now run under unified governance: two we built before the CAIO engagement (claims-payment automation, operational analytics), three shipped under it. Annualized program value across all five sits north of $5M, traced and defensible at the line-of-business level rather than as a single soft number.
The first new production system shipped within 8 months of the CAIO engagement starting. Vendor spend that would have gone into seven unproductive pilots got redirected to the two that survived eval. AI risk and compliance are documented in a form the carrier’s auditors and regulators accept rather than as a slide deck nobody reads.
More importantly: Halberd’s AI program is a coherent program now, not a stack of disconnected projects. The executive team makes AI decisions with the same rigor they apply to underwriting and reserving. The day they hire a permanent CAIO, that person inherits a running program with a roadmap, governance, and shipped systems, not a backlog of half-finished pilots.