Build, Buy, or Both? Five Questions for Insurers Putting AI to Work.

Choosing AI insurance software is not simply a build-versus-buy decision. Insurers make choices across models and providers; agents and applications; data and insurance context; workflow and orchestration; core execution; and governance, assurance, and monitoring. The right approach may differ at each layer. 

The challenge is to create a flexible, insurance-native foundation that connects an insurer’s context and expertise to AI, people, workflows, and core execution—without requiring teams to rebuild the integration and operational capabilities needed to put intelligence to work. 

Here are five questions insurance leaders should ask before deciding what to build, buy, and combine. 

#1 What Should We Own, Build, or Buy? 

An insurer’s differentiation lies in how it applies its expertise: product definitions, risk appetite, underwriting rules, workflows, service models, distribution strategy, and decision authority. These are the elements insurers should retain control over. 

That doesn’t mean rebuilding the foundation around them. Insurers can configure what makes their business unique while adopting an insurance-native foundation that connects that expertise to AI, workflows, and core execution. 

Agentic Product Configurator is one example. It helps turn an insurer’s product documentation and established configuration patterns into governed, deployment-ready configurations—giving teams more time to refine business intent and less time recreating requirements, XML, and validation. 

#2 Can Intelligence Work Within Real Insurance Workflows? 

Insurance workflow automation creates value when it carries relevant context into the next step of the work. That requires connecting trusted data and product, policy, and customer context with business rules, human review, and core systems across underwriting, policy, billing, claims, and payments. 

Duck Creek Clarity Data Foundation unifies insurance data in a governed environment, giving analytics and AI the context they need to support decisions. Within the Intelligent Core, that context connects to workflows, human judgment, and core execution across the insurance lifecycle. 

Duck Creek + Send makes that model tangible in underwriting, connecting submission data, risk appetite, AI, human judgment, and workflows from intake through quote, bind, and policy issuance. 

Read why underwriting transformation depends on connecting workflows and core systems → 

#3 How Will We Explain AI-Influenced Decisions? 

When an AI-influenced decision is questioned later, can the insurer reconstruct what happened? 

That requires more than a record of the output. Insurers should be able to trace the information used and its source; the model and version involved; the business rules and decision authority applied; any required human review; and the actions the agent took. 

Duck Creek’s trusted AI architecture combines explainability, decision traceability, and insurance-specific intelligence, so insurers can understand not only what an AI system recommended or did, but the context behind it. Its neuro-symbolic approach combines AI’s ability to interpret complex information with deterministic insurance rules, helping agents operate within defined authority while producing traceable, auditable decisions. 

Watch the Creekside conversation on trusted AI architecture → 

#4 How Will We Govern AI as It Changes? 

Governance and human oversight should be built into the operating architecture from the outset. Insurers need controls to define decision authority, determine when human review is required, monitor performance, and manage changes to models, rules, and use cases. 

These controls help insurers adapt as business conditions and priorities change, while keeping responsibility clear as AI takes on different roles in the operation. 

Duck Creek embeds governance in its own AI development process and in the operating architecture of its Agentic AI Platform. The platform enables insurers to configure decision authority, define where human oversight applies, and adjust controls as their use of AI evolves. 

Explore Duck Creek’s Agentic AI Platform → 

#5 Can We Scale What Works Without Creating Technical Debt? 

A proof of concept may solve an immediate problem, but scaling it takes more than a successful model. Insurers need to integrate it with existing systems, fit it into business workflows, and maintain it as the business changes. 

When each new use case requires custom connections and one-off processes, complexity grows and future changes become harder. A scalable foundation lets insurers reuse integrations and capabilities, making it easier to extend successful use cases and manage the ongoing cost of change. 

Duck Creek’s configuration-led, API-first foundation is designed to connect insurance context, AI, workflows, and core systems. That can help insurers launch the next use case without adding more complexity. Measure progress by the business outcomes those capabilities improve, then use what teams learn to refine them and extend them to new workflows over time. 

Buy the Foundation. Build the Advantage. 

Insurers need a governed, insurance-native foundation that connects trusted data, AI, workflows, people, and core execution. It should support the AI approach that fits their needs—Duck Creek capabilities, AI from partners, or models they bring themselves—while applying the business context, rules, and oversight that keep intelligence relevant to their operations. 

This flexibility gives insurers room to apply their distinctive expertise and expand from individual use cases to connected operating capabilities as models and priorities evolve.