AI Will Transform Insurance Adjusting—Here's Why

"AI will benefit many professions, but insurance adjusters stand to gain the most."

When my co-founder Dave and I set out to create Benjee, we spent months immersing ourselves in the day-to-day realities of insurance adjusting. We heard a recurring theme: many adjusters were working 10–12 hours a day, six days a week. Some seemed to thrive under pressure, but others were clearly burning out—or worse, leaving the industry altogether. It became obvious that relentless claim volume and tight margins were pushing adjusters to their limits.

We dug into the causes. Three major factors stood out:

1. Claims Unpredictability – No two claims are the same, and each one can balloon in complexity.
2. Adjusting as a "Cost Center" – Budgets are always tight; staffing and technology can feel like afterthoughts.
3. Inadequate Toolsets – Traditional tech solutions just haven't met adjusters' real-world needs.

In this article, I'd like to focus on that last point: why available adjusting tools have lagged behind—and how AI is finally delivering technology that truly helps.

1. "Nothing Exists"

Ask an independent adjuster about their tech stack, and you'll often hear the same list:

• Microsoft Word
• Email
• A claims management system (often clunky)
• Maybe even a dictaphone

Some communication still happens over fax, and many insurance payouts are issued by physical check. Even in a world where software has transformed entire industries, adjusting seems to be lagging behind. Why? Because of unstructured data and edge cases.

2. The Challenge of Unstructured Data

In insurance adjusting, data typically takes two forms: unstructured and structured. Unstructured data—such as free-form text, images, or audio—is meant for human interpretation and doesn't follow a predefined format. Structured data, on the other hand, is neatly arranged according to fixed schemas or labeled fields, making it immediately readable by software. In essence, structured data is machine-friendly and tidy, while unstructured data is "messy" but more natural for human eyes and ears.

Insurance adjusters deal with unstructured information at every step: both the inputs they receive and the outputs they produce tend to be free-form. Think about it:

• Narrative statements from claimants, often detailed in phone calls or emails
• Photos of roof or water damage, sometimes annotated with hand-written notes
• Final reports written for insurers or for other stakeholders
• Comments and explanations left by examiners or colleagues in claim files

These elements aren't easily captured in spreadsheet rows or fillable fields; they're stories, images, and discussions meant for human interpretation. Traditional software, on the other hand, thrives on structured data—clearly labeled fields, checkboxes, or barcode scans. But an adjuster's workflow rarely comes in such neat and tidy formats. This mismatch between real-world, unstructured data and software that demands rigid structure is at the core of why adjuster-focused technology has historically lagged behind.

Unstructured Data:

Structured Data:

3. The Edge-Case Problem

Edge case: A problem or situation that occurs outside of normal expectations

Because every claim is different, it's nearly impossible to build rigid "if-this, then-that" technology. One claim might involve hail damage to a car; another might deal with water damage in a condo unit. The moment a new situation pops up, your software's carefully written rules can break. Adjusters often say, "No two claims are the same," and they're right—edge cases are an everyday reality in this line of work.

4. Enter Generative AI (GenAI)

These AI systems don't just crunch numbers or extract information from forms. They understand human language and learn from examples—even if the data is messy or unstructured. What does that mean for adjusters?

a. Adaptability

GenAI‘s aren't bound by strict, predefined rules. They can interpret diverse claim documents, spot key details in lengthy statements, and adapt to new claim types without the dreaded ‘edge-case meltdown.‘

b. Flexible Data Extraction

Because GenAI can process unstructured text, images, and voice transcriptions, they cut through the barriers created by traditional systems that demand standardized, fillable fields. Instead of forcing adjusters to conform to rigid templates, GenAI can pull out meaningful information directly from whatever format the data is in—be it typed, handwritten, or narrated.

c. Seamless Domain Adaptation

Customizing old-school software for each claim type or coverage line quickly becomes expensive and time-consuming, often requiring new code and endless rule-writing. In contrast, GenAI learn from real-life examples. They can be fine-tuned to an adjuster's specific workflow—whether dealing with auto accidents, property damage, or specialized coverage lines—without the hefty price tag of building an entirely new system from scratch. This built-in adaptability not only saves money but also ensures that the AI remains responsive as claim needs evolve.

Looking Ahead

The core responsibilities of insurance adjusting—investigating, documenting, and evaluating claims—aren't disappearing anytime soon. They still depend on human insight, experience, and sound judgment. But with AI and GenAI advancing rapidly, we're finally seeing technology that aligns with the way adjusters actually work. These tools can handle unstructured data, adapt to varied claim scenarios, and continuously refine their capabilities as they learn. There's no universal solution, but AI's evolution is already reducing administrative friction, improving accuracy, and helping adjusters keep pace with the industry's demands. As AI's role in claims continues to expand, we can expect a future where both adjusters and policyholders benefit from a process that's more efficient, informed, and consistent.