Building a Solid IT Strategy for Insurers

Ready for takeoff: Generative AI in insurance Boards at every insurance company are talking about gen AI. But the discussion has changed from POCs to now rapidly executing ideas for responsible, secure, scalable, and commercially successful gen AI. The direction of travel !! Some insurers are already using gen AI in the back office for tasks like knowledge management. But since insurance is all about probability & statistics, we expect to see it soon across the entire enterprise. The next wave of deployment will include areas like risk scenario modelling & enhancing cognitive processes (alongside AI and RPA) where human intervention was previously necessary. Customer-facing uses are being created and we expect insurers to use gen AI to understand customer preferences and drive personalized products and services. First things first  For a successful gen AI-led transformation, insurers need a well-planned and well-communicated  change roadmap made by a cross-functional team, from an enterprise-wide point of view. At this stage, leaders would be well-advised to develop an ecosystem of partnerships to share gen AI expertise, since there is serious competition for capable talent. Tackling data demands  Data is the greatest challenge to getting gen AI right, since all generative large language models rely on high quality data and excellent prompt engineering for their success. Insurers will need to make sure that the way they train their gen AI models is transparent, fair, and accountable. This means knowing where their data comes from, where it’s housed, how secure it is, and whether their planned uses are ethical and responsible under todays’ data laws. To train gen AI models effectively, they will have to put old customer data into today’s context and use synthetic data to overcome gaps in their data that could lead to bias, as well as look for potential unfair correlations with external data sets that could deliver poor outcomes. Keeping compliant   The data challenge is where regulators are focusing their attention. Already there are laws in some US states (Colorado & California), and in Europe, that require insurers to, e.g., backtest some gen AI-delivered outcomes. And then there are industry agnostic laws governing gen AI, that capture insurers too, e.g. use of external consumer data. Expect regulation to get tighter and more specific. The regulation requirements need not be considered adversarial. Instead, they should be prepared to answer on data lineage, audibility, and governance structures.  As insurers begin to implement gen AI across their business, it is important to focus on fair & transparent outcomes, build a strong data foundation, and partner with expert vendors to help them achieve their goals.  ... But it isn’t all challenge and competition, insurers should feel positive that Gen AI can help them to better deliver for and delight their customers. Ben Podbielski Ramesh Sethi Maria Kokiasmenos Genpact

𝗔𝗜 𝗶𝗻 𝗶𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗳𝗮𝗶𝗹𝗶𝗻𝗴 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. 𝗜𝘁'𝘀 𝗳𝗮𝗶𝗹𝗶𝗻𝗴 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗶𝗻𝘀𝘂𝗿𝗲𝗿𝘀 𝗮𝗿𝗲 𝗮𝘀𝗸𝗶𝗻𝗴. Last month, I sat with a Chief Risk Officer who had just shut down their third AI initiative in two years. Same story every time. • Model looked promising in testing. • Results were inconsistent in production. • Executive confidence evaporated. • Project quietly shelved. He looked at me and said, "𝘞𝘦 𝘬𝘦𝘦𝘱 𝘪𝘯𝘷𝘦𝘴𝘵𝘪𝘯𝘨 𝘪𝘯 𝘈𝘐. 𝘉𝘶𝘵 𝘸𝘦 𝘬𝘦𝘦𝘱 𝘨𝘦𝘵𝘵𝘪𝘯𝘨 𝘣𝘶𝘳𝘯𝘦𝘥. 𝘞𝘩𝘢𝘵 𝘢𝘳𝘦 𝘸𝘦 𝘮𝘪𝘴𝘴𝘪𝘯𝘨?" I asked him one question: "𝘞𝘩𝘦𝘯 𝘺𝘰𝘶 𝘣𝘶𝘪𝘭𝘵 𝘵𝘩𝘦𝘴𝘦 𝘮𝘰𝘥𝘦𝘭𝘴, 𝘥𝘪𝘥 𝘺𝘰𝘶 𝘴𝘵𝘢𝘳𝘵 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯 𝘺𝘰𝘶 𝘯𝘦𝘦𝘥𝘦𝘥 𝘵𝘰 𝘮𝘢𝘬𝘦 𝘰𝘳 𝘵𝘩𝘦 𝘥𝘢𝘵𝘢 𝘺𝘰𝘶 𝘩𝘢𝘥 𝘢𝘷𝘢𝘪𝘭𝘢𝘣𝘭𝘦?" Silence. That's the gap killing AI adoption in insurance right now. Most insurers are building AI solutions around data availability. They should be building them around decision necessity. Here's what that looks like in practice: 𝗪𝗿𝗼𝗻𝗴 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵: "𝘞𝘦 𝘩𝘢𝘷𝘦 𝘤𝘭𝘢𝘪𝘮𝘴 𝘥𝘢𝘵𝘢. 𝘓𝘦𝘵'𝘴 𝘣𝘶𝘪𝘭𝘥 𝘢𝘯 𝘈𝘐 𝘮𝘰𝘥𝘦𝘭 𝘵𝘰 𝘢𝘯𝘢𝘭𝘺𝘴𝘦 𝘪𝘵." 𝗥𝗶𝗴𝗵𝘁 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵: "𝘞𝘦 𝘯𝘦𝘦𝘥 𝘵𝘰 𝘳𝘦𝘥𝘶𝘤𝘦 𝘤𝘭𝘢𝘪𝘮 𝘭𝘦𝘢𝘬𝘢𝘨𝘦 𝘣𝘺 15% 𝘪𝘯 𝘵𝘩𝘦 𝘯𝘦𝘹𝘵 𝘲𝘶𝘢𝘳𝘵𝘦𝘳. 𝘞𝘩𝘢𝘵 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯𝘴 𝘸𝘰𝘶𝘭𝘥 𝘨𝘦𝘵 𝘶𝘴 𝘵𝘩𝘦𝘳𝘦? 𝘞𝘩𝘢𝘵 𝘥𝘢𝘵𝘢 𝘥𝘰 𝘵𝘩𝘰𝘴𝘦 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯𝘴 𝘳𝘦𝘲𝘶𝘪𝘳𝘦? 𝘞𝘩𝘦𝘳𝘦 𝘢𝘳𝘦 𝘵𝘩𝘦 𝘨𝘢𝘱𝘴?" The difference is everything. When you start with the decision: • You know what success looks like before you build. • You identify data gaps that matter, not just data you have. • You design for accountability from day one. • You connect AI outputs directly to business outcomes. • You build executive confidence through clarity, not complexity. When you start with the data: • You optimize for what's easy, not what's important. • You create insights no one knows how to act on. • You struggle to measure ROI because there was no decision tied to it. • You end up with impressive models that change nothing. This is why so many AI projects in insurance feel like science experiments instead of business tools. The technology works. The strategy doesn't. AI doesn't fail because models are wrong. It fails because the questions guiding those models were never clear enough. Before you build your next AI initiative, ask yourself: What decision are we trying to improve? Who owns that decision today? What would change if we got this right? If you can't answer those three questions with precision, you're not ready to build. You're ready to clarify. In Insurance, AI without decision clarity is just expensive noise. #AIinInsurance #InsuranceLeadership #DecisionIntelligence #InsurTech #RiskManagement

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

🚨 AI in Insurance: The Strategy-First Advantage When I speak with insurer clients, one thing is clear: AI isn’t a budget problem - it’s a strategy problem. There’s a misconception that successful AI implementation requires massive budgets and internal armies. In reality, many capabilities have been democratized. But doing it well? That’s the hard part. 💡 Where should insurers start? Not with tools. Not with trends. Start with your business strategy - then work backwards to identify where AI can drive real value. Sometimes, the best solution isn’t AI at all. 📊 What’s the ROI? One carrier implemented an underwriting guidance model and saw a $7M profit improvement. In other cases, we’ve seen returns that double the investment - or more. ⚡ Quick wins? Yes. Proven use cases from 10–15 years ago still deliver value today. Many carriers haven’t tapped into them yet, making them low-risk, high-reward opportunities. 🚧 Biggest barrier? It’s not the tech. It’s change management. Success requires early focus on metrics, workforce strategy, and integrating AI into the broader business transformation. 🎥 I shared more on this with AM Best TV at National Association of Mutual Insurance Companies (NAMIC)’s 130th Annual Convention. Watch the full interview: https://lnkd.in/g2iF4VJv Let’s stop chasing shiny tools - and start building smarter, more resilient businesses. #InsuranceInnovation #AI #NAMIC2025 #AMBestTV

🔥 Underwriting is entering a new era and it’s not just automated, it’s becoming truly intelligent. The insurers who embrace this shift aren’t just upgrading technology… They’re elevating trust, accuracy, and the customer experience. Here’s what’s changing and why it matters. 👇 🌫️ The Reality Today Underwriters bring deep expertise but the system around them often slows them down: • Endless PDFs, handwritten forms, medical reports • Manual data entry across disconnected systems • Legacy platforms that don’t integrate • Delays in risk evaluation and pricing This isn’t a people problem. It’s a technology and workflow problem. 🤖 What Intelligent Underwriting Really Means This evolution isn’t about replacing humans. It’s about empowering them. With AI-led workflows, underwriting becomes: ✨ Faster — OCR + NLP extract data instantly from documents ✨ Smarter — ML models highlight risks humans might miss ✨ Consistent — explainable decisions through XAI ✨ More strategic — underwriters focus on complex, high-value cases Technologies like Azure Cognitive Services, Google Document AI, AWS Textract, HuggingFace NLP, Snowflake, and Microsoft Dynamics 365 make this possible. AI handles the repetitive tasks. Humans bring the judgment, empathy, and nuance. 🎯 The Strategy Behind Successful Transformation Insurers who get this right don’t start with tools.They start with a vision:- 1️⃣ Unified data foundation — Snowflake, Databricks, MDM 2️⃣ Intelligent Document Processing (IDP) — UiPath, ABBYY, Hyperscience 3️⃣ Predictive underwriting models — Vertex AI, Azure ML, SageMaker 4️⃣ Explainable AI — Responsible AI frameworks 5️⃣ Human-in-the-loop decisions — smart routing + case escalation 6️⃣ Incremental rollouts — one product line at a time, measurable results This is how insurers modernize underwriting without losing its core principles. 🚀 The Impact Insurers adopting intelligent underwriting are seeing: • Accelerated quote-to-bind cycles • Lower operational and processing costs • Stronger fraud detection • Better segmentation and pricing accuracy • A more satisfied, empowered underwriting team It’s the perfect blend of technology, transparency, and trust. 🌟 The Bigger Picture Intelligent underwriting isn’t a trend ,It’s the foundation of the next decade of insurance. The future belongs to companies that integrate AI + data + human expertise responsibly to deliver faster, fairer, and more personalized coverage. #InsuranceInnovation #AIInInsurance #UnderwritingTransformation #IntelligentUnderwriting #InsurTech #MachineLearning #ArtificialIntelligence #DataScience #Automation #DigitalTransformation #InsuranceTechnology #AIStrategy #IDP #Snowflake #AzureAI #GoogleCloudAI #ResponsibleAI #FutureOfInsurance