The Automation Misconception: Start with Handoffs, Not AI

For the last two years, carriers have been told that their future depends on AI. AI copilots. AI-assisted FNOL. AI-driven underwriting. It’s an attractive storyline—and a convenient one. But when you sit with the people who actually run claims or underwriting operations, the reality is more grounded and far less glamorous: most cycle time isn’t wasted on judgment, it’s wasted on movement. The file isn’t slow because the adjuster is slow. It’s slow because the work keeps getting stuck between people, systems, and steps.

This is the core misconception behind most AI initiatives in insurance: carriers try to apply intelligence to an environment where the underlying workflow is unstable. They attempt to optimize expertise before they fix flow. Meanwhile, 70–80% of the delay in a claim or submission comes from handoffs, not from analysis or decision-making. AI can speed up thinking, but it cannot compensate for a process that isn’t moving.

Walk through any claims department and you see the pattern instantly. A file sits in the assignment queue waiting for someone to sort it. Then it waits for missing documentation. Then it sits for a vendor appointment. Then it waits for payment approval. Then it waits for closure verification. Adjusters often touch a file for a few hours across the entire lifecycle; the other 30 or 40 days are pure idle time. It’s not an intelligence problem. It’s a coordination problem.

This is why AI fails to deliver transformational outcomes when implemented too early. AI thrives in environments where work flows predictably, where inputs are consistent, and where handoffs don’t introduce noise. In a process with variable routing, unclear ownership, duplicated steps, or inconsistent data entry, AI becomes another layer built on top of the chaos. No model—no matter how sophisticated—can offset structural friction in the workflow underneath it.

The carriers that succeed with modernization start in a different place, and in a different sequence. First, they stabilize the workflow so that everyone is working from a single playbook. The goal isn’t optimization; it’s predictability. You remove rework loops, clarify who owns what, and define the minimum acceptable path for a file to move from start to finish. This alone often cuts measurable time from the process, because ambiguity—more than inefficiency—is the silent killer of operational performance.

Only after this foundation is laid does automation enter the picture, and its value is immediate. Automating handoffs—assignment, triage transitions, vendor dispatch, payment triggers—eliminates the largest pool of wasted time in the entire lifecycle. It converts “waiting” into “moving,” compressing cycle time without asking anyone to work faster. Automation creates the throughput that AI later amplifies.

Then, and only then, do you layer in AI. At this point, the system is stable enough for intelligence to matter. AI can meaningfully assess complexity, highlight severity patterns, identify coverage nuances, summarize interactions, and support decision-making because the workflow itself is no longer introducing chaos. AI becomes a multiplier, not a compensator. It operates in clean conditions.

The order of operations is everything. When carriers begin with AI, they inevitably run into stalled pilots, underwhelming results, and executives left wondering why the promised value didn’t materialize. But when they begin with flow—when they eliminate the friction between people, systems, and steps—AI becomes the final stage of an already-moving machine.

This is the modernization gap most carriers are unknowingly trapped in. They’re trying to build the third floor before they’ve poured the foundation. The real breakthrough comes from understanding that automation is not a precursor to AI—it is the prerequisite.

In insurance, speed doesn’t come from thinking faster.
It comes from moving smoother.

Solve the handoffs first.
Then automate.
Then apply intelligence.
That’s when everything accelerates.