Creating repeatable processes in insurance

Insurance agencies often manage or struggle with multiple processes, policies, and people. Without standardized workflows, inefficiencies can creep in, creating bottlenecks and frustrating both employees and clients. Here’s how to bring clarity and consistency to your agency's workflows: 1. Identify Repetitive Tasks Start by mapping out your current processes. What tasks come up repeatedly? For example: Claim processing. Policy renewals. Client onboarding. By recognizing these, you can create templates or step-by-step guidelines to streamline execution. 2. Build a Standard Operating Procedure (SOP) Document your workflows into a clear, easy-to-follow SOP. Include: Step-by-step instructions for each process. Decision points where teams might need additional approval. Clear roles and responsibilities. Tools like Trello or Asana can help digitize and track these SOPs. 3. Leverage Automation Where Possible Why spend hours on manual work? Automate repetitive tasks like: Sending reminders for policy renewals. Emailing claim status updates. Generating reports on sales or claims metrics. Automation saves time and reduces human error. 4. Train Your Team Thoroughly Even the best workflows will fail without proper training. Conduct sessions to familiarize staff with: The new standardized procedures. Tools or software introduced to support these workflows. Encourage questions and feedback to make the process user-friendly. 5. Continuously Monitor and Improve Standardization is not a “set it and forget it” approach. Regularly review and refine workflows based on: Employee feedback. Client satisfaction data. Process outcomes. Iterative improvements will keep your workflows effective as your agency evolves. Consistency isn’t just good for efficiency—it’s also a key driver of client trust and satisfaction. Are you currently working to standardize your agency’s workflows? Let’s chat about the challenges or tools you’re considering! 

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

Too many clinics treat Insurance verification like a checklist item: ✅ Verify benefits ✅ Note the copay ✅ Move on But here’s the truth: If your verification process isn’t repeatable, proactive, and built to scale, you’re setting your team (and your patients) up for frustration. When verification breaks down: Patients are blindsided by unexpected bills 💸 Front desk staff spend hours chasing payments 🕰️ Clinicians get pulled into admin fires 🔥 Trust erodes—and so does plan-of-care completion The best-run clinics have insurance workflows that are consistent – same process every time. 🔍 Thorough – includes deductibles, visit limits, and auth requirements 📞 Proactive – done before the patient shows up 📊 Documented – clear tracking and accountability 🤝 Patient-friendly – so the financial conversation is honest and easy Want fewer cancellations? Better collections? Start by tightening the one process that touches every new patient from day one. Insurance verification isn't just ops—it's part of your patient experience.

By now, everyone has heard of the MIT study stating 95% of generative AI pilots are failing. We believe this is a combination of unrealistic expectations of generative AI, and the operating system of the business (process) is missing from the plan. Agents act on tasks, but value is created across well defined business processes. If you don’t model the flow end to end, you get local wins that harm global outcomes. I liken it to RPA’s early days, repeated with bigger stakes. 𝗪𝗵𝗮𝘁 𝘁𝗼 𝗱𝗼 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗠𝗼𝗱𝗲𝗹 𝗯𝗲𝗳𝗼𝗿𝗲 𝗮𝗴𝗲𝗻𝘁𝘀 Turn SOPs, Visio, and tribal knowledge into BPMN that reflects reality. Use AI to draft, humans to correct. Your goal is an explicit blueprint that agents can safely live inside. You can use SPADE (https://lnkd.in/dbR-wcr9) to do this. 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗲 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗮𝗿𝗲𝗮𝘀 𝘁𝗼 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲 Simulation exposes bottlenecks, rework, and queue time. Executives care about ROI, cycle time, throughput, so show how these move under different configurations. You do not need perfect data to see signal. 𝗚𝗼𝘃𝗲𝗿𝗻 𝗳𝗼𝗿 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀, 𝗻𝗼𝘁 𝗮𝗰𝘁𝗶𝘃𝗶𝘁𝘆 Autonomous agents are a non-starter in the enterprise. Use human-in-the-loop and clear guardrails. Tie every change to North Star metrics so no one confuses experiments with progress. 𝗔 𝟲𝟬-𝗱𝗮𝘆 𝗽𝗮𝘁𝗵 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀 𝗗𝗮𝘆𝘀 𝟭–𝟮𝟬: Rapid discovery across 10 candidate processes. Produce current-state maps, baselines, and improvement opportunities. 𝗗𝗮𝘆𝘀 𝟮𝟭–𝟯𝟬: Rank with quick simulations and ROI scoring. 𝗗𝗮𝘆𝘀 𝟯𝟭–𝟰𝟬: Deep-model the winner. Define where agents belong and where humans stay in the loop. 𝗗𝗮𝘆𝘀 𝟰𝟭–𝟲𝟬: Pilot to prove it with hard outcomes. 𝗣𝗿𝗼𝗼𝗳 𝘁𝗵𝗮𝘁 𝗽𝗿𝗼𝗰𝗲𝘀𝘀-𝗳𝗶𝗿𝘀𝘁 𝘄𝗶𝗻𝘀 One insurance claims client of ours achieved 120 to 30 days cycle time, 15 to 288 claims per agent per day, and $14,000 daily savings. The client hit a five-year growth goal in a single year. This work won IBM’s AI for Business North America award. 𝗔𝗻𝘁𝗶-𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝘁𝗼 𝗮𝘃𝗼𝗶𝗱  • Automating a broken process  • Over-modeling edge cases during discovery  • Betting on agent autonomy without governance  • Prioritizing by gut feel over ROI and throughput sensitivity 𝗜𝗳 𝘆𝗼𝘂 𝗵𝗮𝗱 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁 𝗠𝗼𝗻𝗱𝗮𝘆  • Convert one SOP to BPMN.  • Run a what-if simulation on queue time and staffing.  • Identify one agent-sized task inside the process and test with human-in-the-loop.  • Report ROI, cycle time, and throughput impacts against your North Star. Where is your organization currently weakest: modeling, simulation, or governance? Full blog here: https://lnkd.in/eK9gxaXt #AI #AgenticAI #DigitalTwin #BPMN #Automation #ProcessImprovement #Operations #ROI