Artificial Intelligence (AI) has advanced, but value realization has not
Over the past decade, insurers have steadily built the foundations for more intelligent enterprises. Modernized core systems have improved data integrity, advanced analytics have strengthened risk selection and pricing, and more recently, generative AI has accelerated how information is processed, summarized, and communicated across underwriting, claims, and servicing workflows. While the ingredients to transform workflows appear to be in place, enterprise-level value realization remains uneven.
AI is delivering measurable efficiency gains, but translating those gains into sustained operational impact remains a challenge. Work still slows at critical points in the workflow. Teams generate insights and make operational decisions, but those decisions are not always executed with the same speed, consistency, or control. The constraint is no longer intelligence; it is execution.
Insurers are encountering a structural gap between insight generation and execution within live workflows that becomes more pronounced as processes grow more complex, data-intensive, and regulation-bound. This gap is emerging as the defining challenge in scaling AI across the insurance industry.
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The execution gap is structural and segment-specific
The execution gap is a structural mismatch between how insurance systems are built and how modern AI operates. Core systems (policy, claims, and billing) are designed for deterministic transactions, while AI operates on probabilistic decisioning. Bridging the two requires more than just integration; it requires orchestration.
This gap manifests differently across insurance segments:
- In Property and Casualty (P&C) insurance, execution complexity is driven by event-based variability such as loss events, catastrophe exposure validation, vendor coordination, and jurisdictional nuances. Workflows are coordination-heavy and parallel.
- In Life and Annuities (L&A) insurance, friction concentrates in long-cycle and documentation-heavy processes such as medical evidence collection, suitability validation, and beneficiary servicing. Workflows are evidence-heavy and compliance-bound.
In both cases, the constraint is the same: insight exists, but execution is not embedded into the workflow fabric.
Systems of Execution (SoE): connecting intelligence to action
What insurance lacks today is not another system of record or another AI model. It lacks SoE, which act as an orchestration layer connecting intelligence to action and enabling AI to operate within workflows. SoE provide the structure for AI to plan, decide, and act, while remaining aligned to governance, authority thresholds, and regulatory expectations.
Exhibit 1 highlights the shift from disconnected, insight-led workflows to orchestrated, execution-driven systems.
Exhibit 1: SoE’s transformation potential
SoE leads this transformation by embedding:
- Orchestration across systems and workflows
- Context that is decision-ready and not fragmented
- Controls that govern what can be done, when, and by whom
- Observability that tracks actions, decisions, and outcomes
A critical enabler here is the capability to interpret insurance workflows through a domain-aware lens, using an insurance ontology as a semantic layer to understand how risks, exposures, policies, claims events, and customers relate to one another. This structured semantic layer enables execution systems to operate consistently across underwriting, claims, and servicing, rather than treating each workflow as a disconnected process.
Why copilots plateau and execution systems do not
The first wave of AI adoption in insurance focused on copilots. They improved productivity with faster summaries, better documentation, and cleaner handoffs. But they did not change the workflows. The simple reason is that workflows do not move forward on content, but rather on actions such as evidence collection, data validation, systems updating, and decisions executed within authority. Without an execution layer, AI remains advisory rather than operational, improving outputs but not materially transforming workflow execution.
Claims: where execution orchestration becomes visible
Claims is one of the clearest areas where the value of SoE becomes tangible. Insurers have already applied AI to improve document summarization, triage support, and customer communication. Yet, the biggest bottleneck persists in how claims execution progresses across the workflow.
The constraint is not lack of insight, but fragmented execution. This is where SoE fundamentally changes the mechanics of claims operations by orchestrating fragmented activities into an event-driven, governed workflow. Instead of workflows waiting for manual intervention between steps, execution becomes parallelized, context-aware, and control-bound.
Exhibit 2: Before vs. after: SoE-led workflow execution (claims)
From tasks to workflows: how claims execution changes
SoE introduces a different model of automation, one that is graduated, governed, and workflow native. Execution is no longer binary, defined as manual versus automated. Instead, it progresses across stages:
- Assist: summarize documents and notes, extract facts, and draft customer updates
- Recommend: suggest severity routing, evidence requests, next steps, and settlement ranges with rationale
- Execute with approvals: orchestrate vendor scheduling, evidence requests, settlement package preparation, and payment initiation within defined authority thresholds
- Execute with audit-only: handle low-complexity and rule-bound claims with monitoring, sampling, and exception reporting
In parallel, Human-in-the-Loop (HITL) becomes a designed control layer:
- Mandatory for denials and coverage disputes
- Required for high-severity and litigation-prone claims
- Central to suspicious claims and high-value payment decisions
This ensures autonomy scales without compromising regulatory defensibility, payment control, or operational risk.
Achieving this requires treating execution architecture as a system, not a layer, one that provides a coherent backbone for claims workflows.
Exhibit 3 outlines the core SoE enablers, including orchestration, decisioning, controls, and continuous feedback mechanisms.
Exhibit 3: SoE architecture: enabling governed, end-to-end workflow execution
Value shows up where the execution tax is highest
The impact of SoE is not uniform across the value chain. It is highest where the execution tax, in terms of handoffs, delays, rework, and control overhead, is most pronounced.
Not all use cases deliver equal value from execution orchestration. Exhibit 4 identifies where SoE adoption creates the greatest impact by mapping execution complexity against regulatory and control requirements.
Exhibit 4: Prioritizing SoE adoption: mapping execution complexity against control intensity
Since claims workflows combine high operational friction, measurable outcomes, and clearly defined control structures, a credible value case for SoE emerges early:
- Gross value: reduced cycle time, lower handling effort, improved customer communication cadence, and reduced leakage through disciplined controls
- Cost-to-run: HITL oversight and sampling, governance reviews, monitoring and evaluation, integration maintenance, and third-party data costs
The most practical entry point is FNOL and intake orchestration because it is measurable, repeatable, and governance friendly before expanding into broader claims execution workflows.
As insurers move from AI experimentation to enterprise-scale value realization, the focus is shifting from models to execution. In our Viewpoint, Systems of Execution (SoE) in Insurance: Activating Agentic AI to Close the Execution Gap, we explore how carriers can build execution architecture to operationalize SoE while maintaining control, governance, and auditability at scale.
If you would like to discuss how these themes apply to your organization or explore potential implementation pathways, reach out to Rugved Sawant ([email protected]) and Sohit Kumra ([email protected]).