Across the insurance industry, the promise of AI feels inevitable. The frustration is just as real: proof-of-concept pilots appear to work as intended, but they rarely answer the bigger question. Most carriers run pilots that look great during testing, only to hit a wall when it’s time to integrate them into business operations. Things get competitive, handovers get messy, and the frontline is left questioning the value. 

As Scott Field, Chief Product & Technology Officer & Co-Founder of OverseeAI often says, 

“An algorithm that works in a lab is just the starting line. What matters is whether it can stand up to the real complexities of insurance workflows, regulatory eyes, and the weight of a carrier’s reputation.”

We have seen claims teams run 94% accurate tests and still ask whether it saves them time or simply shifts the burden elsewhere. This skepticism is the real adoption gap.  Closing it requires more than just leveraging the latest technology. It requires governance, context and confidence.

Why measured progress beats rushed adoption

In insurance, what some call reluctance is really caution. And caution isn’t hesitation; it’s discipline. The kind of discipline that protects policyholders, keeps operations stable, and preserves trust with regulators. Carriers that succeed are not the ones rushing to deploy AI everywhere; they are focused on choosing the right starting points. 

The fastest path out of the POC loop is to focus on use cases that deliver visible value and are simple to test: Summarizing claims notes, Automating intake,Surfacing competitor insights. These are the kinds of projects that bring clarity to the organization and show immediate impact.  

When teams see AI helping them cut steps, improve accuracy or speed up decisions, trust builds naturally. With every successful use case in production, confidence grows and scaled adoption follows without friction.

Here’s how our CEO, Eugene Van Biert, frames it: 

“In insurance, being first to deploy isn’t necessarily the win. Being right is. Carriers that pick the right use cases and prove real value are the ones who create lasting advantage.”

P&C insurance: A different ballgame for AI

In insurance, AI cannot just be fast. It has to be right. Accuracy is non-negotiable, and so is transparency. The models driving business decisions need to perform as expected from a data science perspective, but that’s only half the story.

Business leaders and operations teams also need clarity. 

• How is AI influencing underwriting, claims, or portfolio strategy? 
• Which KPIs are being improved, and by how much? 
• Is the customer experience better or worse?

Without clear answers to these questions, adoption stalls. AI earns its place in insurance when it delivers accurate results and makes its impact transparent, so both IT and business leaders can see, measure, and trust the value it creates.

“Accuracy is table stakes. What matters is whether AI can show its work and prove its impact on the business in terms every leader understands.” – Scott Field, CPTO & Co-Founder

The traps that stall real progress

The first trap is believing that AI will work out of the box. In insurance, there is no “plug-and-play”. Models only succeed when they are grounded in context, governed properly, and aligned with the way underwriting, claims, and compliance teams actually work. 

The second trap is chasing perfection. If leaders expect AI to be flawless on day one, progress stops before it starts. The carriers that move forward don’t demand perfection. They select clear, contained use cases, measure results, and expand from there.  

Another common stall comes from point solutions and silos. A claims tool might work well in auto until a storm hits and property claims overwhelm it. In P&C, risk is too dynamic for single-focus AI to scale. And when IT, data science and frontline teams are disconnected, even strong models fail to fit into real workflows. 

But the biggest barrier is misalignment at the leadership level. For AI to move into production, business, IT, and operations leaders must agree on strategy, the metrics that matter, and the governance plan. Without that alignment, most POCs never leave the pilot stage.

The hidden goldmine in insurance data

Once leadership is aligned on strategy, metrics, and governance, the next challenge is putting AI to work on the right inputs. And in insurance, the most valuable inputs are rarely clean datasets.

Perfect data does not exist in this industry. What carriers do have is a goldmine of unstructured information: forms and filings, adjuster notes, emails, PDFs, and even call logs. These sources hold the context that drives decisions every day, yet they have been nearly impossible to use at scale.

AI has the power to change that when it is deployed with governance and transparency. Leaders need line of sight into what insights AI is surfacing, how those insights are being generated and how they connect back to business KPIs.

The carriers breaking out of the POC loop aren’t chasing perfect data. They are unlocking the data they already have, making it usable, and building trust by showing exactly how AI is improving decisions for underwriters, claims teams, and customers.

Why top carriers focus on value, not just metrics 

Accuracy in a test environment does not equal adoption. Carriers break out of the proof-of-concept loop when they connect AI directly to the outcomes that drive the business.

That means measuring value in terms executives and frontline teams recognize. Did claim settlement times shrink from weeks to days? Did underwriting hit profitability targets with greater confidence? Are adjusters handling more cases without rework? These are the metrics that matter, because they tie directly to both customer experience and financial performance.

Clarity is just as important as the results themselves. Leaders need to see not only that AI is improving KPIs but how. Which workflows changed? Where did time get saved? What was automated, and what was still left to human judgment? When results are transparent, adoption accelerates.

The playbook for moving beyond pilots 

Carriers move past pilots by starting small and proving value. The most effective playbook looks like this:

• Pick use cases that are simple to deploy and show quick results — claims intake, submission triage, summarizing adjuster notes.
• Define success upfront — clear KPIs and a governance plan so leaders know exactly how AI is creating value.
• Build trust through proof — once AI demonstrates results in production, frontline teams adopt it, executives see the impact, and IT is confident in expanding to more complex areas.

As Eugene Van Biert, our CEO, says, 

“The fastest way to scale is not to chase every use case at once. It is to prove value in production and let success create pull from the rest of the organization.”

The leadership blueprint for AI in insurance

Technology alone does not move AI into production. Leadership alignment does.

Every successful carrier makes sure business, IT, and operations leaders agree on three things:

• Strategy — Where AI will create the most impact.
• Metrics — The KPIs that will be used to measure success.
• Governance — How performance will be monitored and how risks will be managed.

When leaders are aligned, adoption accelerates. People across the organization know what to expect, how value will be measured, and where accountability sits. Without this alignment, most pilots never scale.

The end game: People, not just technology

Just like the internet or Bluetooth, AI is a technology, not a product. It enables solutions; it isn’t the solution itself. Adoption takes time. The early stages can be messy, but carriers that focus on steady, meaningful wins instead of chasing the flashiest use cases, will pull ahead.

The end game is empowering people to deliver greater value at the moments that matter most.