Should insurance companies use AI to assess claims or maintain human adjusters for complex cases? | AI Insights | Lumman.ai

Somewhere along the way, we got addicted to the idea that machines are better at tough calls than people.

They’re faster. They don’t have biases. They don’t sleep, unionize, complain about raises, or ghost customers mid-claim. Why wouldn’t we stick an algorithm in the driver’s seat of insurance claims assessment and call it a day?

Because real life is rarely that tidy.

And in insurance—the business of people at their absolute worst moments—“tidy” is a fantasy.

The Efficiency Illusion

Let’s get the low-hanging fruit out of the way: yes, AI should process simple claims. Rear-ended fender benders, stolen luggage, burst pipes in January because someone forgot to wrap the outdoor faucet—let AI handle those. Hell, in many places, it already does.

And it’s good at it. Fast, accurate (most of the time), and indifferent to your tone.

But that clarity breaks down fast when you move beyond black-and-white scenarios.

Take a cracked windshield.

That seems like AI territory, right? Snap a photo, cross-reference the car model, check your zip code, the model says payout = $450.

But what if the crack came from a botched roof replacement weeks ago that subtly warped the frame? Or if the policyholder has filed three suspicious claims this quarter—how much of that is intent versus coincidence?

Hint: the complexity wasn’t visible on day one.

That’s the problem.

Complexity doesn’t declare itself upfront. It crawls in through the side window halfway through the claim—and if your system assumes it’s dealing with a routine case, it will mishandle it with breakneck speed.

The “Let AI Handle the Simple, Humans Take the Complex” Cop-Out

It sounds smart in a boardroom:

“We'll automate 90% of claims and reserve human adjusters for the 10% of high-touch cases.”

Except here’s the uncomfortable truth: that 10% is where trust lives—and dies.

And there's no neat way to route trust.

AI doesn't know what it doesn't know. And most current models still suck at signaling, “Hey, this is outside my lane. I need help.” They just keep going. Confident in their certainty, oblivious to nuance.

So that “simple” claim becomes a viral customer service nightmare once it turns out grandma was denied flood damage compensation because her house sat one inch outside the FEMA-designated flood zone.

Technically correct. Morally tone-deaf.

And deeply brand-damaging.

Data Is Not Wisdom

Here’s the trap a lot of insurers are falling into: conflating “data-driven” with “decision-smart.”

But a system that sees patterns isn’t the same as one that understands context.

During Hurricane Ian, AI models trained on years of prior storm damage failed spectacularly in certain zip codes. Why? The damage patterns were unprecedented—wind + flood combinations the algorithm hadn’t seen before.

The model flagged them as outliers, potential frauds.

Insurance companies had to scramble human adjusters to undo the damage.

So much for efficiency.

Humans Aren’t Perfect, But They’re Built for Complex Trade-offs

It’s tempting to romanticize human judgment. Big mistake. Humans get it wrong all the time. They overcompensate, bring biases, miss patterns obvious to machines.

But humans do something machines still struggle with: they weigh ambiguity. They sense emotional undertones. They pick up the phone and think, “Wait… something feels off,” and investigate further.

For now, that matters a lot.

Take a COVID-era mess: business interruption insurance.

Thousands of claims were filed by companies forced to close. Many policies didn’t clearly cover pandemics. But did they exclude them? Was it “direct physical loss?” Does virus infiltration count?

That wasn’t a data problem. That was a problem of legal ambiguity, emotional stakes, and shifting public expectations.

You can’t solve that with a regression model.

AI Doesn’t Just Get Things Wrong—It Gets Them Wrong Without Warning

And worse? When AI screws up, there’s no one to appeal to. No sense that you’ve been heard. You’re stuck shouting into a digital void.

That’s not customer service—it’s Kafka by chatbot.

People don’t mind being told “no” if they think their story was heard. But “computer says no” doesn’t fly when your house is on fire (literally or metaphorically).

Especially not in a business where trust is the product.

Stop Thinking AI Replaces Humans. Start Building Systems Where They Interact.

The winning strategy isn’t “AI handles the predictable, humans handle the messy.”

It’s building systems that are humble enough to escalate intelligently.

Like:

AI handles early triage, but watches for red flags instead of rigidly categorizing cases
AI shows adjusters historical case patterns, not verdicts
Systems default to human review when metadata shows inconsistencies—or emotion-heavy contexts (e.g. death, displacement, disputed liability)

Think less “black-box oracle,” more “copilot who knows when they’re lost.”

In medicine, we thought AI would replace doctors. It didn’t. It became their super-reader of signals. Mammograms, radiology scans, early warning on septic shock. AI surfaces findings; doctors validate them.

That’s the model insurers should steal.

Rethinking Complexity

Another trap: defining “complex” by category, not context.

A three-car collision on the highway? Automatically kicked to human review.

But a water damage claim from a leaky dishwasher? AI-only.

Except… what if the leak originated from a recalled part that was already flagged by the manufacturer, but wasn’t repaired due to pandemic backlogs?

Now it’s a lawsuit waiting to happen. But your model thought it was a dishwasher claim.

Complex isn’t a claim type. It’s a set of conditions and consequences.

You can’t manage that without adaptive systems—or better yet, human-machine feedback loops.

Maybe It’s Not About “Replacing” Anything

The real productivity gain doesn’t come from cutting headcount. It comes from freeing up humans to do what they’re uniquely good at:

Investigating moral gray areas
Communicating hard truths with empathy
Catching anomalies that aren’t statistically significant but contextually gigantic
Acting as narrative interpreters, not just form reviewers

Let AI take friction off the table. Let humans bring nuance back into it.

What Insurance Companies Keep Getting Wrong

They're building AI to replace adjusters.

They should be building AI to make adjusters superhuman.

Not to rubber-stamp what the machine says. Not to sit around waiting for the AI to screw up.

But to engage with AI’s inputs and outputs like an orchestra conductor—balancing pattern recognition with lived experience, precedent with human weirdness.

Because here’s the unavoidable reality: the more you automate the easy stuff, the more what’s left isn’t easy.

That’s when it gets social, emotional, ambiguous.

And that’s when your entire reputation hangs in the balance.

The Only Metric That Really Matters

In a digital claims world, you're not just judged by how fast you approved a rental car.

You’re judged by how well you show up when everything goes sideways.

Speed is measurable.

Trust is not.

But it’s the thing your customers notice first when their world collapses—and the thing they remember (loudly, publicly) when the system treats them like spreadsheet line items.

AI can reduce friction. Great. But it can’t substitute for fairness, empathy, or context.

Not yet.

So… What Should Insurance Leaders Actually Do?

Treat AI as augmentation, not automation. You’re adding intelligence to your humans, not subtracting them.
Don’t hardcode complexity assumptions. Complexity is dynamic, not fixed. Build escalation paths based on unknowns, not claim categories.
Judge AI on second-order impact. Don’t just measure efficiency. Measure rework, escalations, complaints, lost renewals, and PR blowback.
Invest in empathy as a differentiator. AI cuts costs. Human empathy builds moats.
Train your staff for investigation, not form-processing. Use AI to do the brute force so humans can do the brain work.

Final Thought

Everyone’s trying to “optimize.”

Few are asking: optimize for what?

Speed? Sure. Consistency? Absolutely. Cost? Obviously.

But in insurance, your real product isn’t payouts—it’s reassurance.

And that doesn’t scale on autopilot.

The future isn’t a machine replacing a claim adjuster.

It’s a machine whispering, “This one’s out of my league.” And a human saying, “I’ve got it from here.”