Damco’s team, after three decades in insurance technology, built intelligent, governed AI agents that turn fragmented insurance data into real-time intelligence and act on it. But within the guardrails defined by your compliance team.
Introduction: A Typical Monday in Insurance
It started, as most of these stories do, with a Monday morning question.
The CEO of a mid-sized MGA had just walked into a meeting and asked something that should have been simple: “Which of our products is bleeding margin this quarter?” But the answer didn’t arrive until Thursday.
And that took an analyst pulling three reports, cross-referencing two dashboards, and building a spreadsheet from scratch. By the time it landed in CEO’s inbox, it was already 48 hours late. The decision had already been made on Tuesday based on instinct.
This is not an isolated incident. It is, in many ways, the dominant story of enterprise technology in 2026. Data exists, on-prem systems are running, and SaaS platforms have been implemented. And yet, intelligence remains locked inside these systems, just out of reach of the people who need it most.
This is precisely where AI was expected to change things, giving insurance businesses faster access to insights and a cleaner path to decisions. But for most, the gap remains. Not because insurers are forbidden from using AI. Many are already using LLMs to streamline day-to-day operations. But strict regulatory requirements and real governance risks in insurance make a safer verification layer non-negotiable, which existing publicly accessible AI assistants don’t offer. As a result, AI never moves past the pilot stage.
This blog post covers what regulators expect of insurance businesses today and how insurers can build an intelligent, governed AI layer engineered to their workflows that drives productivity without compromising compliance.
The AI Story: High Adoption, Low Business Impact
Enterprise AI is having a moment. Generative AI spending surged to $37 billion in 2025, and 78% of large organizations now report using AI in at least one business function, up from 55% just two years ago. The insurance industry is no exception. The global AI market in insurance is projected to grow from USD 13.45 billion in 2026 to USD 154.34 billion by 2034. The adoption curve looks extraordinary. But adoption and impact are not the same thing.
Source: Blog- Navigating the Trust Paradox | Damco
A 2025 S&P Global survey of 1,000+ enterprises across North America and Europe found that 42% had abandoned most of their AI initiatives, up dramatically from 17% in 2024. Nearly half of all AI proof-of-concepts never made it to production. Separate research from IDC found that 88% of observed POC don’t make the cut to widescale deployment.
Primary triggers? Messy data, missing governance, and disconnected workflows. As a result, businesses aren’t able to maximize value out of their AI investments.
“The high number of Al POCs but low conversion to production indicates the low level of organizational readiness in terms of data, processes, and IT infrastructure,” IDC’s authors report.
The gap between a promising AI pilot and actual deployment is simply wider than most organizations anticipated. And no sector feels that more than insurance. 70% of insurance CEOs believe that the lack of clear AI regulation could become a direct barrier to their organization’s success.
Key takeaway:
AI spending is growing, but so is the pilot-to-production gap. For insurance, the barriers are both technical and regulatory.
What Regulators Say: If You Can’t Explain What Your AI Did, You Shouldn’t Deploy It
Insurance has a requirement that most other industries don’t: every decision must have an audit trail. Most AI systems were never built with that in mind.
Regulators have been explicit about this. In December 2023, the National Association of Insurance Commissioners (NAIC) Model Bulletin was adopted, since taken up by 24 states. It requires insurers to establish governance programs, maintain audit documentation, and test AI models for bias. Full accountability remains with the insurer, regardless of where the AI originated.
The New York Department of Financial Services (NYDFS) went further. NYDFS Circular Letter No. 7 requires that AI models be actuarially sound, regularly audited, and that carriers provide written explanations when AI influences adverse underwriting outcomes.
This is the market sentiment, and it is straightforward. It is a structural requirement to mandate accountability, prevent biasness, and protect consumer data. If you cannot explain what your AI agents did and why, you cannot deploy it commercially.
And yet, when a CIO asks how an AI tool arrived at a claims triage decision, the answer in most cases is ambiguous. Or worse, a confident-sounding explanation that has nothing to do with the actual computation. AI can hallucinate, reflect bias, and increase data privacy concerns. This is why explainability and governance are critical to the success of an AI model in insurance. These cannot be an afterthought.
Key takeaway:
In insurance, AI governance isn’t optional. It must be built into every core workflow.
30 Years In – Here Is What Insurance Transformation Actually Takes
Our insurtech experts have built technologies that make insurers’ lives easier across 900+ engagements in 32 countries, including the Caribbean, the eastern United States, and the UK. So when the team turned its attention to enterprise AI, not as an experiment but as something that must work in production, that depth of context shaped everything.
Our team identified the same pattern: insurers have data sitting in fragmented systems. What they lacked was a practical way to turn that data into insights without routing it through an analyst, multiple dashboards, and a two-day wait. Businesses did not shy away from investing in AI and automation. 90% of insurance executives prioritize AI to improve financial and operational performance. Yet most initiatives performed well only in isolated settings. As soon as they met real workflows and real data, they fell short. Only one in five companies has AI solutions running in production today.
One of the major concerns remains regulatory compliance. In almost every AI initiative our team reviewed, compliance had been treated as a finishing step. Legal teams were introduced after the system was already built, flagged issues with the architecture, and suddenly a six-month project stretched to eighteen months. That pattern pointed to one conclusion: governance cannot be added afterwards. It must be an extension of the foundation, present before the first query runs and every one that follows. What team needed was a standard that could make that governed connection possible. Model Context Protocol (MCP) provided exactly that.
Key takeaway:
Insurers have a data goldmine and have been actively investing in AI to generate real-time intelligence. But what’s missing is governance built into the foundation.
Enters MCP: A Universal Standard Market Was Waiting For
In November 2024, Anthropic released the Model Context Protocol (MCP). It was built to solve a specific problem: how do you let an AI assistant connect to external systems, databases, APIs, and business tools in a structured, secure, and governed way, without writing a custom integration for every combination?
The simplest way to understand it: MCP is to AI what USB-C is to devices. A universal standard that lets any AI assistant connect to any data source through a common language. Before MCP, every new AI tool needed its own custom connector.
Adoption has been swift. Within a year, OpenAI, Microsoft, Google, and AWS had all embraced the standard. Vercel followed suit, launching its MCP server in August 2025, allowing AI tools to access deployment logs, project metadata, and documentation directly within a developer’s existing environment. The pattern is clear: connect your existing systems once, and any AI tool can use them. The Damco team saw the same opportunity in insurance.
Key takeaway:
MCP servers act as a universal connector for AI, helping them securely connect to external data sources without requiring custom integrations for every new tool.
Meet InsureEdge Agentic Actions: Built for How Insurance Works
Our engineers designed InsureEdge Agentic Actions, a governed AI software that helps turn your systems of record, including Guidewire, Salesforce, InsureEdge, and legacy platforms, into systems of insight and action. These custom AI agents connect to your core systems and LLMs of your choice (Claude, ChatGPT, or Gemini), pull the right data, and act in real-time within the guardrails your compliance team defines.
It is not an off-the-shelf product. We bring a reference architecture, a working compliance engine, and a library of insurance capabilities and install it into your on-prem tech stack.
Unlike a reporting tool or a chatbot, these AI agents don’t just answer questions but also act. A CEO can open Claude and ask: ‘Which of my team members are losing the most renewals this quarter?’ The response arrives in under 200 milliseconds, drawn from your live production database rather than a language model’s training data. No dashboards or waiting on analysts for reports.
InsureEdge Agentic Actions supports over 20 live governed capabilities across three core domains: policy and agent performance, claims and fraud, and account intelligence. A claims manager can ask ‘Has this account made similar claims in the last 24 months?’ and get an immediate answer with full source attribution. An underwriter can ask ‘What is the claim ratio in ZIP code 33101 over the last two years?’ and know whether to write the risk before the quote expires.
And that is only part of it.
The Governance Engine: Every AI Action Passes Through Four Mandatory Checks
What makes InsureEdge Agentic Actions fundamentally different is one design decision: a governance engine sits between the LLM and your data before any action runs. It is built to stay compliant with NAIC, NYDFS, and other regulatory requirements from the ground up.
Between the AI agents deciding what to do and anything happening, four checks run automatically:
- RBAC verification: Is this person allowed to complete the task? Before acting on any request, InsureEdge Agentic Actions checks whether the user has permission to perform it against predefined role-based access controls. If unauthorized, nothing happens.
- Data sensitivity classification: How sensitive is this action? Each action gets tagged as PII, financial, or a write before it runs. This classification always takes place before execution, not after.
- Audit entry: Every action has an audit trail. A timestamped record is created before any action runs, capturing the user identity, the capability invoked, the parameters passed, and the IP address. Even requests that were refused are logged.
- Scope validation: Are the parameters within the user’s licensed data scope? Is the date range valid? If not, the call is declined, and that is also logged.
So, when a regulator asks how a claims decision was reached, why a particular agent’s data was accessed, or who queried fraud scores on a specific account, the audit log has the answer. This is what AI governance actually means in insurance. It is a technical guarantee that unauthorized access is blocked before it happens, and every action is documented before it completes.
Key takeaway:
Every action runs through four mandatory compliance checkpoints, each logged. So, when a regulator asks, answers are already there.
What This Looks Like Across Your Organization
The underwriting manager who used to spend 40 minutes pulling renewal reports can now ask “Which agents have the most renewals due in the next 30 days?” and receive a ranked list, with premium values and agent contact details in seconds. The same manager can follow up immediately: “Which of those agents also has the highest non-renewal rate?” InsureEdge Agentic Actions chains the queries automatically, with no context lost.
The claims director reviewing a large commercial account can ask, “Summarize the claims on policy #7732 over the last three years,” and receive a structured brief, with dates, amounts, loss cause codes, and open versus closed status in seconds.
The CEO preparing for a board meeting can ask, “What is my loss ratio by product this quarter, and how does it compare to the same period last year?” and get a response drawn from your live production database, with a citation and a tagged compliance note.
In every case, InsureEdge Agentic Actions records the interaction, which tool was used, which sensitivity level was accessed, how long it took, from which IP address, and under which user account.
The Bottom Line
Enterprise AI is growing fast, but governance, compliance, and security remain the biggest barriers to growth for insurance businesses. The demand for a safer way to retrieve information and act across insurance workflows without breaking compliance has never been clearer. InsureEdge Agentic Actions is built to meet that demand. We have spent over three decades learning the domain deeply enough to build it correctly.
You do not need a big bang transformation. Just one workflow that finally runs itself. Pick the single workflow that costs your team the most time. Whether it is a growing claims backlog or renewals slipping through the cracks, we can build custom governance software, which requires no rip-and-replace or migration. These AI agents integrate with your existing enterprise systems, stay compliant with your rules, and are audit-ready from day one.
Request your 30-minute consultation now.
