How to surface relevant data in insurance workflows

In my role leading data in insurance, I’ve spent the last couple of years watching how we actually use AI — not how we talk about it. Like many others, we went through the phase of prompt engineering. Better prompts. Smarter instructions. “Act as a senior underwriter…” “Think step by step…” It was useful. But it was never the real lever. What really changes outcomes is how the work is structured around the model. I’ve seen clear evidence that older AI models, when embedded into well-designed workflows, consistently outperform newer models used in isolation. That resonates strongly with what I see across claims, underwriting, and operations. ⸻ A concrete insurance example If I ask AI: “Design a claims automation strategy.” The response is usually polished — and largely theoretical. But when we approach it as a flow instead: • Start with historical claims data across markets • Segment by product, severity, and complexity • Identify steps suitable for straight-through processing vs human judgment • Use AI for FNOL triage, document extraction, and summarisation • Route edge cases with full context and confidence scores • Measure impact on cycle time, leakage, expense ratio, and customer outcomes The result becomes operational. Same AI. Very different outcome. ⸻ The shift is subtle but important. We’re moving from prompting AI to managing AI systems. From one-off interactions to repeatable decision flows. From experimentation to production-grade operating models. In insurance terms, this means building digital underwriters and digital claims handlers — with controls, escalation paths, auditability, and governance built in from day one. That’s a leadership problem, not a tooling problem. ⸻ The real risk isn’t that AI is moving too fast. The real risk is spending another year perfecting prompts while the actual advantage comes from workflow design, integration, and scale. We’re no longer chatting with AI. We’re managing it. And the leaders who learn to do that well will quietly pull ahead.

𝐈𝐧𝐬𝐮𝐫𝐚𝐧𝐜𝐞 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐫𝐢𝐩𝐞 𝐟𝐨𝐫 𝐀𝐈 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 1/3 🡪 Claims processing In most insurance organizations, the front-end of claims processing is the slowest, least structured part of the value chain. • Incoming claims often arrive with incomplete or unstructured documentation. • Teams spend hours manually reading, classifying, and routing cases. • This introduces delays, inconsistencies, and operational inefficiencies. Contrary to popular belief, the most valuable AI transformation in this workflow isn’t in final approval or fraud detection. It’s in the document intake and triage process. 1️⃣ 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐈𝐧𝐭𝐚𝐤𝐞 AI-powered document processing enables insurers to: ➢ Classify unstructured documents like scanned PDFs, discharge summaries, and invoices ➢ Extract relevant data points (e.g., treatment date, policy number, diagnosis, claimed amount) ➢ Flag missing or duplicate documents based on claim type or policy rules The result is faster, more accurate claim setup and reduced manual effort at intake. 2️⃣ 𝐂𝐥𝐚𝐢𝐦𝐬 𝐓𝐫𝐢𝐚𝐠𝐞 AI models trained on historical claim outcomes can: ➢ Score claims for complexity, urgency, and potential risk ➢ Route low-risk claims for straight-through processing ➢ Prioritize complex or inconsistent cases for manual intervention This improves team efficiency, reduces bottlenecks, and speeds up processing time, especially for straightforward claims. ----------------------------- In one recent engagement at : • Automating document intake and triage reduced overall processing time by 40% • Human teams shifted from manual reviews to exception handling • The insurer was able to increase first-time-right submissions and improve turnaround times for clean claims For insurers serious about using AI to improve operations, claims triage and document intake are the right places to start. These processes have clear data inputs, defined decision points, and a measurable impact on cycle time, cost, and customer satisfaction. #insurance #aiforinsurance #insurtech

The best AI for insurance is built for reality. It’s not about isolated intelligence. It’s about building smart systems that fit into how decisions are actually made. Here’s what I’ve learned while building intelligent systems for claims processing, particularly in healthcare insurance: Classification makes a difference. When a claim runs over 20 pages, organizing it into structured buckets—clinical, financial, contextual (we use 33 categories)—dramatically cuts search and review time. Visual triage speeds things up. A document looks like what it is. A CNN model can recognize SSN cards, payment summaries, or handwritten notes instantly, helping teams sort, route, and prioritize with greater speed. Context isn’t optional. Using domain-specific keyword models to extract terms like “oncology” or “chemotherapy” brings the right insights to the surface, enabling faster, more confident decisions. The takeaway? AI is most effective when it respects the workflow, reduces friction, and gives every user, from nurse to analyst, a faster path to clarity. Efficiency isn’t the result of AI. It’s the result of AI designed with intent. #AI #Insurance #WorkflowIntelligence #DesigningForClarity

LlamaIndex just unveiled a new approach involving AI agents for reliable document processing, from processing invoices to insurance claims and contract reviews. LlamaIndex’s new architecture, Agentic Document Workflows (ADW), goes beyond basic retrieval and extraction to orchestrate end-to-end document processing and decision-making. Imagine a contract review workflow: you don't just parse terms, you identify potential risks, cross-reference regulations, and recommend compliance actions. This level of coordination requires an agentic framework that maintains context, applies business rules, and interacts with multiple system components. Here’s how ADW works at a high level: (1) Document parsing and structuring – using robust tools like LlamaParse to extract relevant fields from contracts, invoices, or medical records. (2) Stateful agents – coordinating each step of the process, maintaining context across multiple documents, and applying logic to generate actionable outputs. (3) Retrieval and reference – tapping into knowledge bases via LlamaCloud to cross-check policies, regulations, or best practices in real-time. (4) Actionable recommendations – delivering insights that help professionals make informed decisions rather than just handing over raw text. ADW provides a path to building truly “intelligent” document systems that augment rather than replace human expertise. From legal contract reviews to patient case summaries, invoice processing, and insurance claims management—ADW supports human decision-making with context-rich workflows rather than one-off extractions. Ready to use notebooks https://lnkd.in/gQbHTTWC More open-source tools for AI agent developers in my recent blog post https://lnkd.in/gCySSuS3