Why Insurers Should Embrace Data and Algorithms

Most insurance companies don’t have a product problem. They have a 𝐬𝐢𝐠𝐧𝐚𝐥 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. Trouble shows up early for customers… and late for leadership. McKinsey’s 2025 analysis shows that only a small fraction of insurers capture meaningful value from AI and the reason isn’t model quality. It’s because 𝐝𝐚𝐭𝐚 𝐬𝐢𝐭𝐬 𝐢𝐧 𝐬𝐢𝐥𝐨𝐬 across underwriting, claims, support, and policy servicing. Another study highlights that predictive analytics when actually integrated can reduce loss ratios, speed up claims, and improve risk accuracy. But most insurers never reach that stage because their systems can’t surface early patterns. So what happens? A spike in confusion calls. Customers misusing features. Renewal expectations not matching policy reality. Claim friction rising quietly for weeks. By the time these signals hit dashboards, the damage is already in motion: lower NPS, rising churn, operational load, regulatory exposure. This is why insurance needs an 𝐈𝐂𝐔 - 𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐂𝐨𝐫𝐫𝐞𝐜𝐭𝐢𝐨𝐧 𝐔𝐧𝐢𝐭. A team that: 1. Connects disparate data into a single, queryable layer. 2. Builds early-warning models for churn, fraud, sentiment, and claims delay. 3. Flags mismatches between expectation and experience in real time. 4. Routes insights directly into underwriting, ops, and customer teams. When insights arrive early, transformation doesn’t arrive late. And in insurance, 𝐭𝐡𝐞 𝐞𝐚𝐫𝐥𝐢𝐞𝐬𝐭 𝐬𝐢𝐠𝐧𝐚𝐥 𝐢𝐬 𝐭𝐡𝐞 𝐮𝐥𝐭𝐢𝐦𝐚𝐭𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐭𝐨 𝐰𝐢𝐧. #InsuranceIndustry #DataAnalytics #CustomerExperience #PredictiveAnalytics

I’ve seen many insurers experimenting with AI - but only a few are realizing transformational value. In our latest report, which I had the pleasure of co-authoring, we examine what truly separates AI leaders from the rest. The results were striking: 📈 Over the past five years, insurers leading in AI achieved 6.1x the total shareholder returns of AI laggards. This is more than a technology advantage, it’s a strategic imperative. So, what sets the AI leaders apart? ✅ They take an enterprise-wide approach to AI—not isolated pilots. ✅ They rewire their core processes: underwriting, claims, distribution, and customer service. ✅ They build a modern capabilities stack—scalable infrastructure, high-quality data, and reusable components. ✅ They invest just as much in change management and workforce enablement as they do in technology. ✅ They view gen AI and agentic AI not just as tools, but as differentiators capable of reasoning, empathy, and creativity. AI is becoming the defining force of competitive advantage in insurance, and the gap between leaders and laggards is widening fast. 📘 Explore our perspective here: https://lnkd.in/ekaV_Jyy #Insurance #AILeadership #GenAI #DigitalTransformation #FutureOfInsurance #AgenticAI #InsureTech #McKinseyInsight #FinancialServices

The industry with 6x the TSR vs. the average 2–3× is… insurance. Insurers that lead with AI aren’t just keeping pace, they’re creating 6× the shareholder returns of laggards. The reason? Making bold choices about where to build, buy, or partner ... and rewiring the business, not just dabbling in pilots. Often cast as risk-averse, insurance shows the opposite here: when insurers center strategy with AI, the rewards are exponential. Leaders have created six times the shareholder returns of laggards over the past five years. My colleague Tanguy Catlin has spent years guiding insurance and financial-services clients through transformation. He and our insurance colleagues highlight that, to win, insurers can double down on four of the six rewired components: (1) Business-led roadmap: tie AI directly to value creation, not tech curiosity. (2) Operating model at scale: embed AI into how the business runs, not just in pilots. (3) Flexible AI stack: technology designed for speed, modularity, and distributed innovation. (4) Adoption & change management: because even the best AI fails without human adoption. Here’s what outcomes look like for insurers who get serious: domain-level transformation has already yielded a 10-20% lift in new agent success and sales conversion, 10-15% growth in premiums, 20-40% lower cost to onboard customers, and 3-5% improvement in claims accuracy. These aren’t incremental tweaks, they move core levers that impact the top and bottom line. Full article linked below and authored by Nick Milinkovich, Sid Kamath, Tanguy Catlin, and Violet Chung, with Pranav Jain and Ramzi Elias. https://lnkd.in/df2GXpuq

🌟 The ground just shifted beneath the world of risk! And most leaders missed it. Here is why...💫 Did you see this? Last week, Munich Re began insuring AI model errors for mortgage lenders. While this certainly demonstrates that AI is becoming a more prominent emerging risk in our lives, it also signals a seismic shift: the #AgenticFrontier is no longer a theoretical future—it has arrived. For years, we've talked about transformation. Yet Boston Consulting Group (BCG)'s data shows a stark reality: while 78% of P&C insurers are “dabbling” with AI in the claims process, only 4% have successfully scaled it. Imagine what this means across the insurance operations and the overall enterprise. The rest are caught in the “pilot trap,” a sinkhole for laggards. The gap between the talkers and the doers has become a chasm. The 4% are fundamentally redesigning their businesses around AI. This is no longer about whether you'll embrace #agenticAI. It's about how you'll lead the transformation. For corporate leaders, the mandate is clear. For founders, the 18-month enterprise sales cycle is now optional for those who can provide de-risked, insured solutions. Here is the playbook for those ready to move from ambition to action: 1️⃣ Stop the science projects. Pick one end-to-end process—claims, underwriting, finance, customer support—and commit to a complete, AI-driven redesign. The real ROI is in redesigning the unglamorous, high-impact back-end operations, not bolting AI onto broken workflows. 2️⃣ De-risk your transformation. AI error insurance is now a board-ready mandate. Use it to turn AI from a high-risk experiment into a scalable, enterprise-grade asset. 3️⃣ Reframe the protection gap as an innovation mandate. The same creativity used to insure algorithms must be turned toward insuring humanity against Nat Cat/ extreme weather risks and other systemic risks. This is the largest market opportunity of the next decade. The uninsurable world is a choice, not a necessity. The leaders of 2026 will be those who use the tools of the agentic frontier to rewrite the rules of risk. What is the most fundamental “gap” you see in your organization’s AI strategy right now? Please share... Is it the tech, the talent, or the trust? And enjoy this week's newsletter. 👏🏽 #CapacityGap #TrustbyDesign

Everyone wants AI. Very few insurers are prepared for what AI actually exposes. The conversation around AI in insurance has shifted. Five years ago, it was about innovation in theatre. Proofs of concept that never left the boardroom. Pilots that didn't scale. Now, it's different. AI is operational. Claims processing that used to take weeks now happens in hours. Underwriting decisions that required manual review are being triaged automatically. Fraud patterns that slipped through rule-based systems are getting flagged before payout. But here's the uncomfortable part, most insurers are discovering: AI doesn't hide data problems. It amplifies them. 𝘠𝘰𝘶 𝘤𝘢𝘯'𝘵 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘦 𝘶𝘯𝘥𝘦𝘳𝘸𝘳𝘪𝘵𝘪𝘯𝘨 𝘸𝘩𝘦𝘯 𝘱𝘰𝘭𝘪𝘤𝘺 𝘥𝘢𝘵𝘢 𝘭𝘪𝘷𝘦𝘴 𝘪𝘯 𝘴𝘦𝘷𝘦𝘯 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘸𝘪𝘵𝘩 𝘤𝘰𝘯𝘧𝘭𝘪𝘤𝘵𝘪𝘯𝘨 𝘧𝘪𝘦𝘭𝘥𝘴. 𝘠𝘰𝘶 𝘤𝘢𝘯'𝘵 𝘢𝘤𝘤𝘦𝘭𝘦𝘳𝘢𝘵𝘦 𝘤𝘭𝘢𝘪𝘮𝘴 𝘸𝘩𝘦𝘯 𝘩𝘢𝘭𝘧 𝘺𝘰𝘶𝘳 𝘩𝘪𝘴𝘵𝘰𝘳𝘪𝘤𝘢𝘭 𝘥𝘢𝘵𝘢 𝘪𝘴 𝘵𝘳𝘢𝘱𝘱𝘦𝘥 𝘪𝘯 𝘗𝘋𝘍𝘴 𝘢𝘯𝘥 𝘩𝘢𝘯𝘥𝘸𝘳𝘪𝘵𝘵𝘦𝘯 𝘯𝘰𝘵𝘦𝘴. 𝘠𝘰𝘶 𝘤𝘢𝘯'𝘵 𝘥𝘦𝘵𝘦𝘤𝘵 𝘧𝘳𝘢𝘶𝘥 𝘸𝘩𝘦𝘯 𝘺𝘰𝘶𝘳 𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘥𝘢𝘵𝘢 𝘪𝘴 𝘪𝘯𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦 𝘰𝘳 𝘣𝘪𝘢𝘴𝘦𝘥. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗯𝗮𝗿𝗿𝗶𝗲𝗿 𝘁𝗼 𝗔𝗜 𝗶𝗻 𝗶𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗶𝘀𝗻'𝘁 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. 𝗜𝘁'𝘀 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. The insurers making progress right now aren't the ones chasing generative AI headlines. They're the ones who spent the last three years cleaning up data architecture, building unified platforms, and establishing governance frameworks. They understood something critical: AI is only as smart as the data foundation underneath it. Here's what forward-thinking insurance leaders are prioritising in 2026: • Data quality before model complexity • Human augmentation over full automation • Explainability and governance as competitive advantages • Portfolio intelligence, not just process automation • Operating model redesign, not isolated IT experiments AI in insurance isn't a deployment problem anymore. It's an integration problem. The winners won't be the ones who launch the most pilots. They'll be the ones who embed AI into underwriting workflows, claims operations, and portfolio steering in a way that's scalable, auditable, and aligned with how the business actually runs. If your AI strategy still lives in a separate innovation lab, you're already behind. What's the biggest infrastructure gap blocking AI adoption in your organisation right now? #InsuranceLeadership #AIinInsurance #DataStrategy #InsurTech #FutureOfInsurance #DecisionIntelligence #AIGovernance

Part I of my AI in Insurance Series — AI Adoption & the Evolving Risk Landscape Across industries, AI has shifted from supporting isolated experiments to powering core business operations. In financial services, algorithmic trading, automated credit scoring, and fraud detection increasingly rely on AI outputs to make decisions worth billions. Healthcare organizations use AI in diagnostics and patient triage, where erroneous outputs may impact patient outcomes. Logistics companies leverage AI to forecast demand, optimize routes, and reduce costs, but overreliance can cascade into inventory overstock, delayed shipments, and contract disputes. In HR and marketing, AI models influence hiring, promotions, dynamic pricing, and customer recommendations, affecting legal and reputational risk. The operational benefits of AI are compelling, but come with unique exposures. Unlike traditional tech failures or human error, AI-related losses often originate from probabilistic outputs. For example, a financial institution’s AI credit model over-approves applicants due to subtle biases in training data, leading to portfolio losses. In healthcare, an AI triage system misclassifies patient risk because its training data was not representative of the full patient population, prompting regulatory review and litigation. A logistics firm’s AI-driven inventory forecasts lead to warehouse over capacity and shipments delays when its model overestimates demand. AI risk is operational, multi-stakeholder, and difficult to attribute to a single failure point. Complicating these risks is the intricate ecosystem in which AI operates. AI models rarely exist in isolation. They rely on foundation models provided by third parties, cloud platforms for deployment, APIs for integration, and external datasets for training. Failures or misalignment at any stage can produce cascading operational impacts affecting multiple insureds simultaneously. Insurers must assess not only an organization’s internal practices but also the maturity, concentration, and reliability of the third-party vendors supporting its AI operations. Governance and data integrity are critical. Organizations that fail to maintain version control, monitoring, human oversight, and proper documentation expose themselves to operational failure and regulatory sanctions. Regulatory frameworks now require transparency, explainability, and fairness in AI outputs, tying governance directly to potential liability. Poorly documented, poorly understood, or unmonitored models may trigger claims even if no tangible operational harm occurs. For insurers, these dynamics present significant implications. AI must be treated as an operational and governance risk, not just a tech risk. Losses may propagate across departments, contracts, and portfolios when multiple insureds rely on the same third-party platforms. Understanding these complexities is critical for underwriting responsibly and anticipating emerging claims. #Ai

A recent conversation made me reflect on how AI is shaping insurance underwriting and why this space is both exciting and complex. In underwriting, risk assessment isn’t just about numbers; it’s about finding the right balance. You’re weighing fairness, efficiency, and profitability—all while maintaining customer trust. This is where AI is transforming the game. AI does more than just automate; it personalizes. With tools like telematics and predictive analytics, insurers can now tailor policies based on individual data. But with this innovation comes a challenge: AI systems can unintentionally replicate biases found in historical data, which could lead to unfair premiums or coverage decisions. Transparency and fairness are critical. That’s why I see AI not as a replacement for human expertise but as a tool to amplify it. AI can handle repetitive tasks like data extraction and preliminary risk analysis, freeing underwriters to focus on the more complex, judgment-driven decisions. Thoughtfully integrating AI lets us deliver faster, fairer, and more personalized solutions, all while ensuring transparency and data security. What do you think is the biggest hurdle in adopting AI for underwriting? Would love to hear your thoughts. #ai #underwriting #insurance #propertycasualty #usa

Insurance is being pulled in three directions at once: customers want instant, transparent service, fraud is getting smarter, and regulation is tightening. I captured what we are hearing from insurance customers in a post in Insurance Edge: that the real bottleneck isn’t intent, it’s trusted data. Claims, FNOL notes, photos, repair estimates, emails, PDFs, medical records, adjuster narratives… Most of it is unstructured. And until that messy information becomes trusted, governed data, AI can’t reliably automate decisions, it can only assist in fragments. The next generation of insurance operations won’t be won by “more AI tools.” It’ll be won by the insurers who can: - detect risk earlier (without slowing down honest customers) - prove compliance continuously (with audit-ready traceability) - settle the simple claims fast and route the complex ones with confidence and, most importantly, - convert unstructured data into trusted inputs for end-to-end AI workflows That’s the agentic AI challenge in insurance, and it’s quickly becoming the challenge everywhere. (If you’re interested, my latest Insurance Edge byline explores this across fraud, compliance, dashboards, and claims trust. See in comments.) #Insurance #Claims #Fraud #Compliance #AI #Automation #DataGovernance #CustomerExperience Tungsten Automation

🇬🇧 𝗟𝗢𝗡𝗗𝗢𝗡 𝗜𝗦𝗡’𝗧 𝗘𝗫𝗣𝗘𝗥𝗜𝗠𝗘𝗡𝗧𝗜𝗡𝗚 𝗪𝗜𝗧𝗛 𝗔𝗜. 𝗜𝗡𝗦𝗨𝗥𝗘𝗥𝗦 𝗔𝗥𝗘 𝗥𝗘𝗧𝗛𝗜𝗡𝗞𝗜𝗡𝗚 𝗧𝗛𝗘 𝗣𝗥𝗢𝗖𝗘𝗦𝗦𝗘𝗦 𝗧𝗛𝗔𝗧 𝗥𝗨𝗡 𝗧𝗛𝗘𝗜𝗥 𝗕𝗨𝗦𝗜𝗡𝗘𝗦𝗦 𝗪𝗜𝗧𝗛 Appian. I just returned from a fantastic few days in London meeting with senior insurance executives, and one thing was crystal clear from the conversations in the room: 𝗧𝗵𝗲 𝗔𝗜 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗶𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗶𝘀 𝗳𝗶𝗻𝗮𝗹𝗹𝘆 𝗺𝗮𝘁𝘂𝗿𝗶𝗻𝗴. For the past couple of years, most discussions about AI across the industry centered around 𝗽𝗶𝗹𝗼𝘁𝘀, 𝗽𝗿𝗼𝗼𝗳𝘀 𝗼𝗳 𝗰𝗼𝗻𝗰𝗲𝗽𝘁, 𝗮𝗻𝗱 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗱𝗲𝗰𝗸𝘀. But what I heard this week was something very different. Leaders are no longer asking “𝘞𝘩𝘦𝘳𝘦 𝘤𝘢𝘯 𝘸𝘦 𝘶𝘴𝘦 𝘈𝘐?” They’re asking a much more consequential question: 𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗿𝗲𝗱𝗲𝘀𝗶𝗴𝗻 𝘁𝗵𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗿𝘂𝗻 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀? While some software companies have fancy messaging and statistics, insurance companies globally are partnering with Appian to leverage AI to drive 𝗿𝗲𝗮𝗹 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗧𝗢𝗗𝗔𝗬. Insurance has always been a 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗮𝘁 𝗶𝘁𝘀 𝗰𝗼𝗿𝗲 — underwriting, claims, broker servicing, fraud investigation, policy servicing, regulatory reporting. These processes form the 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗯𝗮𝗰𝗸𝗯𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲, and most were designed long before the idea of 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗼𝗿 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 existed. The mistake many organizations are making right now is trying to 𝗯𝗼𝗹𝘁 𝗔𝗜 𝗼𝗻𝘁𝗼 𝘁𝗵𝗲 𝗲𝗱𝗴𝗲𝘀 𝗼𝗳 𝘁𝗵𝗼𝘀𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀. A chatbot here. A copilot there. A clever document model somewhere else. It makes for great demos — but it rarely changes the 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻. 𝗔𝗜 𝘀𝗵𝗼𝘂𝗹𝗱 𝗻𝗼𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗰𝗼𝗿𝗲 𝗶𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀. 𝗜𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 𝗵𝗼𝘄 𝘁𝗵𝗲𝘆 𝗼𝗽𝗲𝗿𝗮𝘁𝗲. AI is inherently 𝗽𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘀𝘁𝗶𝗰, while insurance operations — particularly in 𝗵𝗶𝗴𝗵𝗹𝘆 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗲𝗱 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀 — must remain 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝘀𝘁𝗶𝗰, 𝗮𝘂𝗱𝗶𝘁𝗮𝗯𝗹𝗲, 𝗮𝗻𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗲𝗱. The companies that win this next decade will be the ones that learn how to 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲 𝘁𝗵𝗼𝘀𝗲 𝘁𝘄𝗼 𝘄𝗼𝗿𝗹𝗱𝘀 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿. This is where 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗱𝗲 𝗴𝗼𝘃𝗲𝗿𝗻𝗲𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 becomes powerful — extracting information from documents, validating it against systems of record, orchestrating decisions across teams and systems, and accelerating work while maintaining control. When that happens, 𝗔𝗜 𝘀𝘁𝗼𝗽𝘀 𝗯𝗲𝗶𝗻𝗴 𝗮 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗮𝗻 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆. Vicky Nisbet Sally Craxton Simon Harris Alex Boerescu Aneta Rutkowska