The insurance landscape is complex for many reasons.

There are a multitude of products, riders and needs. Insurance is sold through four channels: company-tied agents (captive), multi-carrier brokers (independent), banks (bancassurance), and direct-to-consumer (direct). And that’s only the beginning.

Now add the fact that every state has its own insurance rules, that agents must prove their recommendations truly fit—and are in the customer’s best interest—and that reinsurance contracts can constrain what is sold and how it is priced, and it creates a dynamic, challenging environment begging for emerging tech tools to lighten the load.

So, why are AI pilots failing?

It’s not because the technology itself is faulty. They fail because most dabbling teams launch one-off experiments without the fluency, structure, or blueprint to ever reach scale.

For example an underwriting screener might work in one state or for one product, but needs constant fixing everywhere else. That pilot looked great in the lab, but falls apart once it is applied across different sales channels or new-business workflows.

People stop trusting it, and leaders start asking if the whole initiative was worth the hassle.

It’s no wonder nearly nine out of 10 pilots across industries never move into full production. The issue isn’t the latest tool. It’s the framework surrounding it.

Pilot Failure Isn’t the Issue. Pilot Planning Is.

Most organizations treat AI pilots like science projects. One function, one idea, one shot. Then silence. No clear rollout strategy. No alignment on metrics. And rarely a blueprint anyone in the business can defend.

Here’s the part few admit out loud: failure is the point.

A strong pilot should surface risk, expose misalignment, and include fluent operators shadowing outputs to catch errors early. That discipline prevents flawed workflows from scaling into costly mistakes.

The real danger comes when an AI pilot fails and no one learns. No clarity on what broke. No trusted metrics. No system to capture lessons. Just anecdotes and frustration, leaving the organization to wonder what happened.

AI Fluency Turns Failure into Feedback

With the right design, every workflow failure becomes structured feedback. Teams know when to adapt and when to stop because the success rubric is agreed upon from the start.

Well-structured pilots aren’t curiosities. They’re actionable, repeatable, and capable of scaling.

The best pilot architects don’t ask, “Did it work?” They answer the sharper question: “Is this worth scaling, and how do we know?”

Scale Starts with the Pilot

As with any framework, a pilot should begin with the end in mind: creating a workflow that consistently delivers measurable results. In late 2024, OpenAI introduced a scaling guide for enterprises; we’ve expanded it into five practical steps operators can apply to any pilot:

1. Discovery and Pilot → Identify high-potential use cases that address real operational or market problems with measurable business impact, not just “cool demos.”
2. Scalability Assessment → Score each pilot on impact, feasibility, and risk across new business, underwriting, and distribution channels.
3. Prioritization → Rank pilots by composite scores to eliminate low-value or high-risk efforts.
4. Scaling Roadmap → Launch with phased milestones, systems integration, change management, and built-in guardrails.
5. Continuous Evaluation → Track KPIs, reassess risks, retrain or retire as needed, and loop learnings back into discovery.

Pilots built this way don’t just show whether an idea can function; they prove whether it can hold up under the pressure of issue volumes, regulatory updates, and customer demand.

Three Ways to Doom a Pilot Before It Ever Launches

A recent MIT study found that 95% of generative pilots fail to deliver financial impact. The reasons are clear:

Success in a silo doesn’t scale.
A tool that works fine in a single e-App flow or one state form set may fall apart when rolled across multiple regions, channels, and products. Without stress-testing, performance slips and gaps in the data show up quickly.

Weak business cases get ignored.
If results aren’t tied to KPIs that matter at the executive level, such as how many apps actually turn into paid policies, how fast they were issued, how long customers stick around, and how much money we accidentally lose when handling claims, then leadership dismisses the AI pilot as a side project.

No fluency = no shared reality.
If managers don’t understand prompting and executives only hear buzzwords, they’re not aligned. That lack of shared language stalls momentum.

What a Scale-Ready Pilot Looks Like

A scale-ready pilot earns executive buy-in because it demonstrates both technical viability and operational readiness.

It starts with ROI tied directly to financial metrics. Entire workflows are mapped. Prompts are fine-tuned for consistency. Integration points are tested against policy administration, illustration, e-App, CRM, and compliance systems. Risk assessments and fallback plans are built in from Day 1.

Strong pilots also feature review loops to sharpen performance before scaling. Everyone knows when automation runs on its own and when human oversight is essential. Rollback plans exist for when something breaks.

That’s the distinction between an experiment and infrastructure. An experiment proves concept; infrastructure builds trust across the service center, the distribution organization, and the executive team.

It’s Not the Tech. It’s the Fluency.

Too many pilots fail because they’re treated as isolated, low-stakes tests. But innovation in this business isn’t a gadget you try once. It’s a capability embedded in how the business actually runs.

That requires upskilling across all levels. Executives set priorities. Managers define workflows. Employees in the field and in operations co-create adoption.

When teams share a common AI fluency and framework, scaling stops being a leap of faith and instead it becomes the logical next step.

From Pilot to Playbook

The best pilots don’t end. They produce playbooks. Playbooks that show how to repeat wins, align innovation with business goals, and scale when conditions are met.

And when that happens, you’re no longer running experiments. You’re building the foundation for how a business will operate: more resilient, more precise, and ready for scale.