Most insurance teams don't adopt AI as a single product. They assemble a stack — a few tools that each own a layer of the submission-to-bind and claims workflow, working together. The practical question for 2026 isn't only which platform is best (we rank those in our guide to AI tools for insurance claims processing), but also which layers your stack actually needs, and how few tools you can get away with. And should you be interested in more broad overview of AI for underwriting, check our big AI for underwriting guide.

Here's the short version: a complete underwriting-and-claims AI stack has roughly seven layers, from an insurance-native workflow core to document extraction, orchestration, and reporting. The more of those layers a single insurance-native platform covers, the less integration work you inherit. FurtherAI is designed to be that core, absorbing several layers other teams stitch together from point tools.

Key takeaways

• An insurance AI stack is the set of tools that automate underwriting and claims across layers: a workflow core, document extraction, general-purpose LLMs, orchestration, robotic process automation (RPA), customer-service agents, and business intelligence (BI).
• You rarely need every layer as a separate product. An insurance-native core like FurtherAI consolidates the workflow, extraction, and policy-checking layers, so you bolt on fewer point tools.
• The layers still matter individually: pick best-of-breed where the core doesn't reach, and use orchestration tools to connect them.
• Real outcomes come from the stack, not any one tool. FurtherAI customers report intake up to 30x faster, audit time down roughly 45%, and up to 646% ROI on document-heavy workflows.
• Insurers expect more than 20% cost savings from AI over the next two years, per EY — but only if governance and integration are built in from the start.

What is an insurance AI stack?

An insurance AI stack is the combination of AI tools an underwriting or claims team uses to move work from a broker's messy submission or a first notice of loss (FNOL) all the way to a bound policy or a settled claim. Each layer does one job well: reading documents, reasoning over them, moving data between systems, handling routine customer questions, and reporting on results.

The reason to think in layers is simple. When you know which layer a tool belongs to, you can see overlaps, avoid paying twice for the same capability, and spot the gaps a single platform leaves behind. It also makes the build-versus-buy call clearer, because you're deciding layer by layer rather than all at once.

The layers of an insurance AI stack

The table below maps the seven core layers, the tools that lead each one, and whether an insurance-native platform like FurtherAI covers it natively. Vendor performance figures are self-reported unless otherwise cited.

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Below, each layer uses the same structure so you can compare like for like.

Layer 1: Insurance-native workflow core

• What it does: runs the actual underwriting and claims workflows — submission intake, SOV mapping, policy comparison, underwriting audit, and claims intake — with audit trails and human review built in.
• Leading tools: FurtherAI, the AI workspace purpose-built for commercial insurance. It supports customers writing more than $15 billion in premium and is used by insurers including Accelerant, MSI, and Leavitt Group.
• Best for: carriers, MGAs, wholesalers, and brokers that want one insurance-native layer across the submission-to-bind and claims lifecycle instead of a dozen point tools.
• Watch-out: it's built for commercial and specialty lines, so very small agencies or personal-lines direct-to-consumer teams may not be the target fit, and deep legacy-core integration still takes implementation work.

"After evaluating several vendors, we chose FurtherAI for its performance, insurance expertise, and partnership approach. The forward deployed engineer model makes a big difference — they work directly with our teams and help us get results quickly." — Doug Alexander, VP of Digital Delivery, Upland Capital Group

Layer 2: Document extraction

• What it does: converts unstructured documents — ACORD forms, statements of value, loss runs, policy wordings — into structured, machine-readable data.
• Leading tools: Azure AI Document Intelligence and Google Document AI for general extraction. An insurance-native core handles this layer with insurance-specific understanding of ACORD and SOV structures, so many teams don't need a separate extraction product.
• Best for: teams with high volumes of varied or handwritten documents that generic optical character recognition (OCR) can't reliably parse.
• Watch-out: horizontal extraction tools output data but don't act on it; you still need a workflow layer to turn extracted fields into quotes, proposals, or claim decisions.

Layer 3: General-purpose LLMs

• What it does: provides flexible reasoning for drafting coverage letters, summarizing long policy documents, and turning messy FNOL notes into structured detail.
• Leading tools: ChatGPT Enterprise, Anthropic's Claude, Google Gemini, and Microsoft Copilot. Some teams run more than one frontier model to compare outputs or route sensitive work to the strongest data-handling option.
• Best for: individual underwriters and adjusters handling ad hoc tasks, and product teams prototyping on top of frontier models.
• Watch-out: these models have no native understanding of insurance documents, no audit trail, and no core-system integration, so anything near a bind or claim decision needs guardrails and human review.

Layer 4: Workflow orchestration

• What it does: connects the SaaS tools an insurance team uses daily — email, customer relationship management (CRM), rating engines, and e-signature — and moves data between them.
• Leading tools: Make (visual, friendly to non-technical ops teams), n8n (developer-oriented and self-hostable), and Zapier (the broadest no-code connector catalog).
• Best for: operations teams that need to wire the stack together quickly without standing up an engineering project.
• Watch-out: none of these understand ACORD forms or SOVs, and usage-based pricing can climb fast at high volume; governance and audit logging are thinner than regulated environments usually require.

Layer 5: Robotic process automation

• What it does: automates repetitive, rule-based work in legacy core systems, such as keying data, reconciling records, and moving files between applications.
• Leading tools: UiPath, the enterprise RPA standard, now adding an agentic layer that pairs bots with AI agents. UiPath reports that roughly 40% of underwriting work is administrative.
• Best for: large carriers and service centers automating legacy core systems without replacing them.
• Watch-out: classic bots are brittle and break when a source screen or document format changes, implementation is consulting-heavy, and pricing is out of reach for most small MGAs.

Layer 6: Customer service and FNOL agents

• What it does: resolves routine policyholder and first-notice-of-loss questions — claim status, billing, certificate requests — at the top of the funnel.
• Leading tools: Intercom Fin, one of the most deployed no-code service agents; Intercom reports it resolves an average of 67% of queries. Gartner expects agentic AI to autonomously resolve 80% of common customer-service issues by 2029.
• Best for: customer service, policyholder support, and FNOL teams that want a production-grade agent with little engineering lift.
• Watch-out: service agents aren't built for coverage analysis, reserve-setting, or underwriting judgment, and resolution quality depends heavily on the knowledge base behind them.

Layer 7: Business intelligence and reporting

• What it does: turns policy, claims, and premium data into dashboards for loss-ratio monitoring, underwriting performance, fraud signals, and portfolio risk. This layer matters: US insurance fraud costs at least $308.6 billion a year, and fraud-signal reporting is one way teams catch it.
• Leading tools: Power BI (cheaper, tight Microsoft integration) and Tableau (more sophisticated visualization).
• Best for: analytics, actuarial, and finance teams that need shareable reporting on top of underwriting and claims data.
• Watch-out: BI tools surface insights but don't act on them, and both need a well-designed data warehouse and reliable pipelines underneath.

The build-your-own option: Agent frameworks

• What it does: lets engineering teams build custom AI agents from scratch — a submission-triage agent, a claims fraud-signal agent, or a treaty summarizer.
• Leading tools: LangChain, with LangGraph for orchestration and LangSmith for observability.
• Best for: teams with strong engineering resources that need something proprietary and deeply integrated with internal systems.
• Watch-out: everything from prompts to production monitoring stays on your team, build times run into months, and without insurance domain expertise it's easy to end up with a prototype that never ships. We cover this trade-off in our build versus buy guide.

How the layers work together

The value shows up when the layers connect. A claims example: an FNOL arrives by email, the service agent captures the basics, the extraction and workflow core structure the documents and route the claim, an LLM drafts the acknowledgment, RPA updates the legacy claims system, and BI tracks cycle time and reserves. See our AI claims intake framework and FNOL automation guide for that flow in depth.

An underwriting example runs the same way: a broker submission lands, the core extracts and standardizes the SOV and ACORD data, runs appetite and policy checks, and produces a proposal, while orchestration syncs the CRM and BI monitors hit ratios. For a tools-only view of that side, see our AI tools for commercial underwriting.

How much of the stack can one platform cover?

The fewer separate tools you integrate, the faster you get value and the less you spend maintaining glue code. An insurance-native core consolidates the workflow, extraction, and policy-checking layers, and reaches into the LLM and claims-intake layers, so most teams only add orchestration, RPA, or BI where they already have investments.

The results come from that consolidation. FurtherAI customers report submission clearance dropping from about 32 minutes to about one minute at roughly 99% accuracy, underwriting audit time falling about 45% (from roughly 200 hours to 110 hours per MGA), 400% ROI on policy checking with up to a 95% reduction in manual review, 646% ROI on complex property SOV intake, and 90%+ automation of claims intake. Across the stack, FurtherAI integrates with more than 100 enterprise systems, including Applied Epic, Salesforce, AMS360, and Guidewire.

Governance across the whole stack

Every layer that touches an underwriting or claims decision needs to be explainable and auditable. Align the stack with the NIST AI Risk Management Framework and the NAIC Model Bulletin on the use of AI systems by insurers, which expects a written AI systems program and compliance with existing law when AI affects consumers. The cleanest way to keep governance intact is to minimize the number of tools handling regulated decisions and to favor layers that capture every output as structured, citable data. Our guide to AI governance in insurance covers this end to end.

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REFERENCES 

^{Coalition Against Insurance Fraud. "The Impact of Insurance Fraud on the U.S. Economy." insurancefraud.org}

^{EY. "Gen AI in Insurance: Key Survey Findings." ey.com}

^{FurtherAI. "Customers." furtherai.com}

^{FurtherAI. "Product." furtherai.com}

^{Gartner. "Gartner Predicts Agentic AI Will Autonomously Resolve 80% of Common Customer Service Issues Without Human Intervention by 2029." gartner.com}

^{National Association of Insurance Commissioners. "NAIC Members Approve Model Bulletin on Use of AI by Insurers." naic.org}

^{National Institute of Standards and Technology. "AI Risk Management Framework." nist.gov}

DISCLAIMER 

This article is for general informational purposes only and does not constitute legal, regulatory, compliance, underwriting, or other professional advice. The content reflects information available as of the date of publication, and FurtherAI undertakes no obligation to update it as laws, regulations, or AI technologies evolve.