Insurance 2026: Agentic AI, Composable Core, and Governance

From “Digitalizing” Insurance to Rebuilding the Operating Model

2026 is the year technology stops merely “digitizing” insurance and starts reshaping the operating model itself. The shift isn’t about adopting more tools—it’s that automation moves from supporting isolated tasks to executing end-to-end workflows with accountability, traceability, and control. Gartner has been advancing this view through “agentic AI” inside enterprise applications, with a very specific prediction: by 2026, up to 40% of enterprise applications will include task-specific AI agents, up from less than 5% in 2025.

Agentic AI: Connecting the Value Chain End-to-End

We’ve been living automation in insurance since the earliest wave of RPA initiatives. But this agentic layer takes automation to the next level—and it changes the day-to-day reality of insurers because it connects what has traditionally been fragmented: underwriting, document management, claims, customer service, fraud, and back-office operations. McKinsey emphasizes that real value comes when AI is industrialized as reusable, standardized assets—not as isolated “pilots”: shared components, pipelines, and decision services that can be reused across domains (for example, a document classification engine that supports both underwriting and claims).

In practice, 2026 looks less like “deploying a chatbot” and more like building a decision factory: automated intake, evidence extraction and verification, task orchestration, and human handoffs only where risk or regulation requires it.

The Core System Reality Check: Why “Composable” Becomes the Default

This runs into the industry’s biggest elephant in the room: the core. Celent, in its Previsory 2026 series, frames it with a useful idea: trends are not “islands”—it’s the maturity of AI across the value chain that determines what can be done now and what cannot. They highlight that system modernization is one of the areas where AI is accelerating configuration and development work, while in distribution the value depends more on what can be exposed safely to customers and agents.

The typical outcome in 2026 is a composable approach: the policy system remains the system of record, but key capabilities are externalized into services (pricing, eligibility, fraud, document orchestration) so AI can be introduced without “breaking the plane mid-flight.” This pattern also enables a realistic transition to API-first and event-driven architectures, which are required to integrate partners, channels, and external data without impossible latency constraints.

AI Governance and Security Become a First-Class Discipline

The shift to agentic AI and a composable core introduces a new problem: AI security and governance as a discipline of its own. Gartner is already treating this as a strategic trend through the concept of “AI security platforms” to centralize visibility, policies, monitoring, and protection against risks such as prompt injection, data leakage, or unintended agent actions.

In parallel, 2026 brings heavy regulatory pressure: the European Commission has led the way by setting August 2, 2026 as the date when the AI Act becomes broadly applicable, with specific exceptions and transition periods for certain high-risk use cases. For insurers, this translates into mandatory—if “unsexy”—practices: model inventories, risk assessments and documentation, data usage controls, operational (not academic) explainability, auditable logging, and internal training in AI literacy. Skipping this doesn’t just increase exposure to sanctions—it blocks the path from pilot to production.

And in LATAM, we won’t be far behind in inheriting this security trend, as the acceleration of risk forces action at both country and regional levels.

Customer Experience: Personalization That Builds Trust

In distribution and customer experience, 2026 isn’t only about personalization—it’s about personalization with trust. Celent describes this as “personalization that builds trust” and “managing the AI transition” in its 2026 messaging: faster acquisition and underwriting, but with careful attention to what is put in front of customers and intermediaries.

The key technological change is that personalization moves beyond static segmentation and becomes contextual and dynamic—yet bounded by compliance rules and reputational guardrails. A new requirement also becomes non-negotiable: omnichannel consistency. If a customer starts onboarding with a conversational assistant and finishes it with a human agent, both must see the same status, the same evidence, and the rationale behind decisions—or trusxt breaks

Pricing, Risk, and Claims: “Data + AI” With Economic Focus

In pricing, risk, and claims, the 2026 trend is “data plus AI,” with an economic focus: reducing the combined ratio and lowering cost per claim, not “innovating” for its own sake. Traditional predictive models are paired with GenAI to automate the work surrounding the model: reading reports, triage, correspondence, claim narratives, inconsistency detection, and case-file preparation for adjusters and handler.

Another structural trend is that insurers are forced to model fast-moving risks more effectively: climate, cyber, and supply chain exposure. This pushes organizations to integrate external sources near real time and strengthen simulation and stress-testing capabilities—closer to “risk engineering” than traditional actuarial approaches, especially in commercial lines.

The Macro Pressure: Cloud, Control, and Vendor Independence

Accenture, from its macro outlook for 2026, positions AI adoption as a cross-cutting driver of corporate decisions and expected growth—within a broader context of cost pressure, geoeconomic fragmentation, and technology sovereignty demands. Translated to insurance, this drives three moves: more cloud, but with selective hybrid architecture; more automation, but with control; and a rethinking of technology sourcing, where insurers try to avoid becoming captive to either a monolithic core vendor or a model provider.

This also points to a clear need: accelerators for enterprise AI adoption in regulated industries, paired with a strong bet on large-scale upskilling. And that matters because the 2026 bottleneck isn’t a “lack of ideas”—it’s execution capacity with the right talent, security, and governance.

The Bottom Line: Insurance Becomes a Regulated Software Company

If we had to summarize the dominant pattern of 2026, it would be this: insurance becomes a software company managing risk under strict regulation. Competitive advantage will no longer be defined by who “has AI,” but by who can operate it securely, integrated, and profitably—agents doing real work, cores that don’t slow change, accessible data with lineage, and a governance layer capable of sustaining auditability and trust. Everything else is expensive noise.

As insurance evolves, new opportunities emerge to redesign operating models with AI and trust. Visit Softtek Insurance to see how organizations are turning strategy into action.