AI in Insurance: Why Building an Agent Factory May Be the Wrong Strategic Bet

Executive Summary

The question is not whether insurers will use AI

The insurance industry is entering a period where Artificial Intelligence will become as fundamental as databases, workflow engines, document repositories, and cloud infrastructure. The key strategic question is not whether insurers will use AI. The question is where AI capabilities should originate.

Some platform vendors are pursuing an “agent development framework” strategy that encourages insurers to build, configure, test, deploy, and maintain custom AI agents. While attractive on the surface, this approach risks shifting insurers away from their core competencies and into the business of AI engineering.

This paper argues that the long-term winner will not necessarily be the platform vendor that provides the most extensive agent-building toolkit. Rather, it will be the vendor that embeds AI directly into insurance workflows and enables business users to achieve measurable productivity gains without requiring a new generation of AI specialists.

We propose that the future belongs to workflow-native AI, not agent-construction platforms.

What this brief covers

The strategic difference between AI as a tool and AI as a product
The hidden cost, productivity, catch-up, and economic problems of build-your-own agents
A practical Cognition+ vision: AI agents embedded directly in insurance workflows

The Strategic Difference

AI as a tool vs. AI as a product

An agent development strategy positions AI agents as products that insurers must design, configure, test, govern, and deploy. These are fundamentally different strategies.

The agent development perspective

“Customers should build their own insurance agents.”

The business-driven view

“Customers should use AI-enhanced insurance workflows.”

Insurers do not purchase policy administration systems because they aspire to become software companies. Similarly, most insurers do not want to become AI engineering organizations.

Their objective is to:

Issue policies faster
Handle claims more efficiently
Improve customer service
Reduce operating expenses
Improve underwriting quality
Improve accounting accuracy

Platforms already structured around insurance workflows:

Policy, claims and contact management
Accounting and reporting
Task and reinsurance management
Document management
Workflow automation

Should AI improve these existing insurance processes, or should customers be expected to construct and maintain agents themselves?

The Hidden Cost

The “build your own agent” model

Agent development sounds simple. An AI agent editor enables — and requires — input for agent configuration, knowledge base assignment, MCP server setup, prompt creation, model selection, and a testing environment. At first glance this appears straightforward.

However, once deployed at enterprise scale, organizations require a far broader operating discipline:

Agent governance, security review, and compliance review
Prompt engineering and knowledge management
Version control, testing procedures, and approval processes
Monitoring and production support

This creates demand for entirely new job categories

Director of AI Engineering
AI Operations Manager
Prompt Engineer
AI Governance Lead
Agent Architect
AI Quality Analyst

The irony is that many insurers are simultaneously attempting to reduce administrative workload.

Should AI improve these existing insurance processes, or should customers be expected to construct and maintain agents themselves?

The Productivity Paradox

Software should reduce specialized work

The historical purpose of insurance software has been to reduce specialized work. Cognition+ already includes automated task assignment, workflow-driven policy processing, claims workplans, integrated accounting, reporting tools, document templates, and automated reserves and payment processes.

These capabilities allow frontline users to do more without deeper technical expertise

CSRs
Underwriters
Adjusters
Accounting staff

The agent-development paradigm reverses this. Instead of making a CSR more effective, the organization must first build an agent capable of supporting the CSR.

Why not simply provide AI directly to the CSR?

If an insurer must hire AI specialists before realizing business value, the implementation burden may exceed the productivity benefit.

The Catch-Up Problem

Competing with global AI platforms

A custom agentic framework bears strong conceptual similarity to broader AI orchestration platforms already available in the market: low-code agent creation, knowledge source integration, tool orchestration, multi-model support, and testing and deployment. A domain-specific software vendor faces an enormous challenge competing against global technology providers whose entire business revolves around AI platforms.

Investment areas required simply to maintain parity

Model integration
Governance
Security
Scalability
Evaluation tooling

Monitoring
Developer tooling
Copilot experiences
Enterprise integrations

As AI evolves, platform providers focused entirely on AI will continue innovating across a much larger customer base.

This creates a perpetual catch-up dynamic.

The Economics Problem

Scale economics favour hyperscale AI

There is also an infrastructure challenge. Large AI platforms benefit from enormous economies of scale.

Hyperscale advantages

Larger shared compute clusters
Higher utilization rates
Purchasing power for GPU capacity
Shared platform operations
Massive user bases

Costs a niche platform absorbs

Infrastructure costs
Platform engineering costs
AI governance costs
Support costs
Model integration costs

Those costs must ultimately be recovered through customer pricing. From a strategic standpoint, it may become difficult for niche providers to consistently match the economics of hyperscale AI ecosystems.

The result could be a higher effective AI cost per transaction compared to solutions built on broader enterprise AI platforms.

Domain Expertise

Insurance knowledge is not AI infrastructure

The strongest argument for industry-specific platforms is domain expertise. That argument remains valid. Insurance systems vendors possess expertise in policies, endorsements, renewals, claims, reinsurance, billing, accounting, and workflow rules. Cognition+ demonstrates this through deeply integrated functionality across policy, claims, contact, accounting, reporting, reinsurance, document, and task management capabilities. However, domain expertise does not necessarily imply ownership of the AI platform layer.

The most sustainable strategy

Use best-of-breed AI infrastructure
Apply insurance domain expertise on top of it
Deliver embedded business outcomes

An unsustainable strategy

Building and maintaining an entirely separate AI ecosystem

A Practical Vision

Cognition+ AI agents embedded in workflows

Rather than positioning AI as a toolkit customers must assemble and maintain themselves, Cognition+ will deliver targeted AI agents that are purpose-built for specific insurance workflows and installed directly into the platform experience.

The first example is a Cognition+ Help Agent designed with the Cognition+ help file as its primary knowledge source. Users can ask natural-language questions about platform functionality, workflow steps, configuration options, and day-to-day system usage, with answers grounded in approved Cognition+ documentation rather than generic web content.

Uses the Cognition+ help content as the authoritative knowledge source.
Answers user questions in plain language from within the Cognition+ platform.
Supports users across workflows, configuration, policy processing, accounting, claims, and reporting.
Reduces reliance on manual document searches, informal knowledge transfer, and support escalation for routine product questions.

This capability does not ask insurers to become AI builders. It packages Cognition+ product knowledge into an accessible assistant that helps users work more confidently.

A Practical Vision

Risk Intelligence Agent

A second example is an AI agent created and used through an API-based integration pattern. The Cognition+ Platform makes calls to the agent as part of the normal user workflow, sending relevant policy content for risk analysis and receiving structured results presented back to the user within the Cognition+ interface.

Calls are initiated from the Cognition+ Platform rather than requiring users to leave the system.
Policy content is evaluated for risk observations, inconsistencies, exposure considerations, and other underwriting-relevant signals.
The resulting analysis is returned in a structured format that renders clearly in the Cognition+ UI.
Supports workflow-native risk analysis, where AI is embedded into the business process instead of existing as a separate destination.

This approach aligns AI investment directly with insurance productivity. Customers don't need to design prompts, configure an orchestration layer, manage model behaviour, or maintain a library of custom agents. This API-based risk analysis feature is planned for delivery in an upcoming Cognition+ Platform release.

Use AI infrastructure where it makes sense, apply Cognition+ domain knowledge to specific workflows, and deliver outcomes inside the Platform Experience.

Conclusion

Build AI, or benefit from AI?

The emergence of agent frameworks represents an important evolution in insurance technology. However, there is a meaningful distinction between enabling insurers to build AI and enabling insurers to benefit from AI.

The risk of an agent-centric strategy

Shifts responsibility for AI success onto the customer.
Creates demand for new AI-specialized roles.
Introduces ongoing governance and maintenance burdens.
Competes against global AI platform providers with enormous scale advantages.
Focuses on constructing agents rather than improving insurance productivity.

A workflow-centric strategy offers a different path

AI embedded directly into policy and claims workflows.
AI embedded into accounting and task management workflows.
Measurable productivity improvements without requiring organizations to become AI engineering firms.

In the long run, insurers may discover that they never wanted an agent-development framework. They simply wanted their people to be more productive.