Closing the AI Explainability Gap in Insurance
A conversation on AI, compliance, governance, and accountability in insurance communications
Recently, I had the opportunity to host a broadcast for MHC Automation with Rao Tadepalli and Emily Washington on the explainability of AI in insurance communications.
This is one of the most important questions facing regulated industries as AI moves deeper into customer-facing and operational workflows. The issue is not simply whether AI can make processes faster, more efficient, or more scalable. The question is whether the organization can explain what happened, why it happened, who approved it, what data was used, and how the outcome can be defended.
Insurers operate in an environment where customer communications are tied directly to compliance, governance, claims, coverage, billing, policyholder experience, and regulatory accountability. As AI becomes part of those workflows, explainability becomes more than a technical feature. It becomes an operating requirement.
A black box may be acceptable in some experimental use cases. It is much harder to defend when the output affects a regulated customer communication, a policyholder decision, or a process that may later need to be reviewed by auditors, regulators, or legal teams.
That was the focus of this conversation: how insurers can use AI effectively while still maintaining the guardrails required in highly regulated communication environments.
The full broadcast is available here:
If your organization has a story to tell about AI, customer communications, compliance, digital transformation, or the future of regulated industries, I can help bring that story to market through hosted conversations, podcasts, webinars, event coverage, and thought leadership programs.
Contact me at treelinepress.com to discuss future collaborations.