In 2026, IT’s role in insurance organizations will shift decisively – from supporting isolated AI deployments to owning the platforms, architectures, and governance that integrate AI across core operations. AI will no longer live in pilots or individual functions. It will operate as a shared capability embedded across underwriting, claims, servicing, and analytics, with IT serving as the connective tissue between AI systems and measurable business outcomes.
Insurance carriers, brokers, MGAs, and their IT leadership teams are increasingly recognizing that “doing AI” is no longer enough. The differentiator is whether AI is implemented with purpose – aligned to enterprise architecture, data strategy, security, and operating models – so it can scale reliably and safely across the business.
A cohesive AI strategy must be aligned with the company’s future-state vision – your target operating model (TOM). For IT teams, this means translating business ambition into concrete technical and operational decisions: reference architectures, platform capabilities, data flows, integration patterns, and governance models.
When defining or evolving an AI strategy, IT leaders should ensure their teams can answer critical questions such as:
What are your goals for utilizing AI? Are you looking to improve efficiency, effectiveness, and/or customer service?
Where in the enterprise value chain do manual handoffs, data fragmentation, or latency create friction?
Which AI capabilities must be centralized as shared platforms versus embedded in specific lines of business?
Is there a clearly defined enterprise roadmap for AI adoption, including scope, sequencing, and dependencies?
Are there corporate “silos” (data, systems, ownership) that may delay or prevent AI adoption and how can IT architecture help remove them?
How will change management, access control, and model lifecycle management be handled at scale?
What standards guide vendor selection, internal development, and integration with core systems?
While AI tooling will continue to evolve, successful enterprise AI programs remain grounded in familiar IT fundamentals: governance, data integrity, scalable infrastructure, security, compliance, and observability. Increasingly, this also includes AI “incubation” environments – controlled platforms where new AI capabilities can be tested, validated, and operationalized before becoming production standards.
Market cycles are where the consequences of IT and AI decisions become visible fastest. Insurance leaders are always asking a familiar question: When will the next soft market arrive, and how will it affect our cost structure and operating model? For IT organizations, this question is not abstract. Market cycles directly influence budget pressure, staffing levels, and technology investment priorities.
Insurance market cycles are driven by pricing dynamics, catastrophic losses, reinsurance costs, social inflation, and broader macroeconomic conditions – factors that directly shape capacity, margins, and the urgency of technology decisions. But regardless of market phase, IT architecture decisions determine how well an insurer can adapt when conditions change.
An AI strategy aligned to the future-state operating model provides a structural advantage. It allows IT to enable flexibility without introducing instability. When markets soften, organizations with strong AI foundations can adjust underwriting appetite, pricing, and workflows without rewriting systems or introducing manual workarounds.
Strong IT leadership plays a critical role here: building consensus around AI investments, ensuring platforms are reusable across lines of business, and preventing short-term market pressure from driving fragmented or tactical technology decisions.
AI cannot eliminate insurance cycles, but IT-led AI platforms can materially reduce their operational impact. The following capabilities are less about individual models and more about what IT must architect and sustain.
Precision Pricing and Underwriting: IT enables precision pricing by providing scalable data platforms that ingest telematics, IoT, geospatial, and historical claims data, along with governed pipelines for deploying and monitoring pricing models across underwriting systems.
Portfolio Optimization Infrastructure: AI-driven portfolio optimization depends on IT-managed analytics platforms that allow business teams to evaluate profitability by segment, simulate changes in appetite, and operationalize those insights through integrations with underwriting and policy systems.
Fraud Detection at Scale: Advanced fraud detection requires IT ownership of model orchestration, real-time data ingestion, and integration with claims platforms – ensuring insights surface early without disrupting claims operations.
Expense Efficiency Through Workflow Automation: IT reduces expense ratios by embedding AI into core workflows, eliminating manual handoffs between systems, and standardizing automation patterns across underwriting, claims, and policy servicing.
Dynamic Risk Selection and Feedback Loops: AI-enabled risk selection requires IT to support continuous feedback loops – connecting market signals, performance data, and model outputs so underwriting rules and AI behavior can adapt without extensive reengineering.
Customer and Distribution Enablement: Personalization, faster response times, and omnichannel engagement depend on IT platforms that support real-time decisioning, API-based integrations, and consistent data access across customer and agent touchpoints.
Why does this matter when building an AI strategy? In a soft market, IT’s ability to provide disciplined, data-driven platforms becomes essential. Without that discipline, competitive pressure can quickly lead to underpricing, operational strain, and technical debt.
For IT teams, AI strategy is no longer about tooling experimentation. It is about owning the operating foundation that allows AI to be deployed safely, scaled efficiently, and governed consistently.
Key responsibilities for IT leaders now include:
Owning the enterprise AI reference architecture
Designing data platforms that support both real-time and batch AI workloads
Defining integration patterns between AI systems and core insurance platforms
Embedding governance, security, and compliance into AI pipelines by design
Enabling business teams to move quickly without creating unmanaged risk
Together, these responsibilities position IT as the steward of AI at scale, ensuring innovation moves quickly while remaining secure, compliant, and aligned to business strategy. This shift allows insurers to operationalize AI with confidence rather than treating it as a series of disconnected initiatives.
The opportunity for IT is to shift from reactive support to proactive leadership – shaping how AI is embedded into the enterprise operating model.
By aligning AI strategy with architecture, governance, and platform design, IT teams enable insurers to scale intelligently, adapt to market cycles, and execute underwriting strategy with consistency and confidence. In doing so, IT becomes the fulcrum of successful AI transformation – turning innovation into a durable operational advantage.