The Disruption Confidence Cycle in Insurance Claims Adjusting

By Joel Comm | A Trusted Voice in a Noisy Tech World

AI isn’t coming to insurance claims adjusting. It’s already here.

I’ve watched every major technology revolution reshape entire industries since 1980. Each time, the same pattern emerges. First comes disruption. Then doubt. Then clarity, confidence, and momentum.

Today, insurance claims adjusting sits squarely in what I call the Disruption Confidence Cycle’s most dangerous phase: doubt.

But here’s what 45 years in tech has taught me. The organizations that thrive aren’t the ones that ignore disruption or panic about it. They’re the ones that recognize the pattern and use it to their advantage.

The Doubt Phase: Where Insurance Claims Adjusting Stands Today

Right now, claims adjusting executives are asking the wrong questions. The questions aren’t about opportunity. They’re about threat.

“Will AI replace our adjusters?” “How do we protect our jobs?” “What if we invest in the wrong technology?”

This is classic doubt phase behavior. It’s the same conversation retail executives were having in 1999 when e-commerce was emerging. They worried about cannibalization instead of expansion. Most waited too long. The survivors didn’t.

The doubt phase in insurance claims adjusting manifests in three specific ways:

Analysis Paralysis on AI Implementation: Companies commission study after study about AI insurance claims adjusting without making meaningful moves. Claims departments spend 18 months evaluating AI tools that could have been piloted in 18 days.

Overemphasis on Risk Management: Every AI conversation becomes a compliance discussion. Don’t misunderstand me. Compliance matters. But when it becomes the only lens through which you view innovation, you’ve already lost the competitive advantage.

Fragmented Technology Adoption: Claims departments implement AI piecemeal. A chatbot here. An image recognition tool there. No cohesive strategy connecting these dots into transformational change.

The doubt phase feels safe. It’s not.

Ask yourself this: in a room of 300 adjusters, how many have personally tested an AI tool for claims processing in the last 90 days?

Based on what I see across the industry, the honest answer is somewhere south of 10%. In a field where AI could meaningfully reduce processing time and increase accuracy, only a tiny fraction have hands-on experience with the technology that will define their future.

This isn’t ignorance. These are smart professionals who understand risk assessment better than most. But they’re applying traditional risk frameworks to exponential technology change. It doesn’t work.

What the Next Phases Look Like for Insurance Claims Adjusting

Having observed this pattern across industries for four decades, I can predict what’s coming next for insurance claims adjusting with reasonable confidence.

Disruption (already underway): A major insurance carrier will announce — or has already announced — a breakthrough AI claims system that processes routine claims in minutes instead of days. Customer satisfaction climbs. Competitors scramble to catch up.

This isn’t speculation. Major insurers are already testing systems that can analyze photos, review policy terms, calculate settlements, and initiate payments with minimal human intervention. The technology works. The question is who goes first.

Clarity and Confidence (2–4 years out): AI in claims becomes table stakes. Companies that haven’t invested will face a brutal choice: massive catch-up investments or market exit. The successful adjusters won’t be replaced by AI. They’ll be the ones who learned to work with it.

Remember when digital photography hit the market in the early 2000s?

Film processors who dismissed it as “inferior quality” were right, initially. But they missed the point. Digital wasn’t competing on quality. It was competing on convenience and speed. By the time quality caught up, most film processors were gone.

Momentum (3–5 years out): AI in claims becomes invisible. It’s simply how claims get processed. The adjusters who thrive will be the ones who understand how to validate AI decisions, handle edge cases, and provide the human judgment that algorithms can’t replicate.

The pattern is predictable because technology adoption follows human psychology, not just technical capability.

The Pattern I’ve Seen Across Every Tech Revolution

I’ve had a front-row seat to every major technology shift since the personal computer arrived. The pattern never changes — only the timeline compresses.

The PC era (1980s). Executives said computers belonged in the back office, not on every desk. The companies that put a PC in front of every knowledge worker built a decade-long lead.

The commercial internet (mid-1990s). Traditional businesses called it a fad. “Why would anyone shop online when they can go to a store?” By 2000, those who adapted thrived. Those who didn’t became case studies.

Online banking (late 1990s). Banks debated whether customers would ever trust the web with their money. The institutions that built early online experiences spent the next decade compounding that lead while skeptics played catch-up.

Social media (2007 onward). Companies dismissed Facebook and Twitter as “time-wasters.” Social media wasn’t about the platform. It was about direct customer engagement at scale. The early adopters built communities. The laggards bought expensive advertising.

Mobile (2008 onward). “People won’t do serious business on their phones,” executives said. But mobile wasn’t a smaller computer. It was a different behavior. Companies that got this built mobile-first experiences. Others retrofitted desktop sites and wondered why engagement lagged.

Cloud computing (2010s). Security concerns dominated every conversation. “We can’t trust our data to someone else’s servers.” Enterprises didn’t move to the cloud because it was secure. They moved because security at scale required cloud infrastructure.

Generative AI (2022 onward). Here we are again. Same pattern. Same resistance. Same opportunity for those who recognize it.

The Disruption Confidence Cycle explains why this pattern repeats. Organizations don’t resist technology because they don’t understand it. They resist it because understanding it requires admitting their current approach might be suboptimal.

Insurance claims adjusting is following this exact trajectory. The only variable is timing.

The Disruption Confidence Cycle™ Applied to Claims

Here are the five stages, in order, and what each one looks like inside a claims organization:

1. Disruption. Generative AI, computer vision, and large language models hit the insurance stack. Carriers begin processing FNOL through conversational AI. Photo-based damage estimation goes mainstream. The status quo — adjusters manually reviewing every claim — is no longer reliable as a competitive baseline.

2. Doubt. This is where most carriers sit today. Leaders question whether the change is real, whether their teams can absorb it, whether regulators will allow it, and whether they should wait for “more mature” tools. Anxiety dominates. Budgets get studied to death. Pilots stall before they start.

3. Clarity. The fog lifts. Leaders identify which claim types are AI-appropriate (routine auto, simple property, photo-verifiable damage) and which require deep human judgment (complex liability, large losses, fraud-suspect cases). The question stops being “Should we use AI?” and becomes “Where, specifically, does AI multiply our adjusters?”

4. Confidence. Adjusters use AI assistance as a regular tool. Cycle times drop. Accuracy holds or improves. Skepticism is replaced by demonstrated results. The team stops debating whether AI works and starts debating which use case to tackle next.

5. Momentum. Early wins compound. The carrier handles more volume with the same headcount, expands into claim types previously too costly to service profitably, and turns customer experience into a differentiator. AI becomes a multiplier on the team’s existing strengths — not a replacement for them.

Every carrier will travel this cycle. The only choice is how fast.

Questions Insurance Claims Leaders Should Be Asking This Week

The quality of your questions determines the quality of your outcomes. Most claims leaders are asking defensive questions. They need to start asking offensive ones.

Instead of “How do we prevent AI from disrupting our business?” ask “How do we use AI to disrupt our competitors?”

Instead of “What jobs will AI eliminate?” ask “What new capabilities will AI create for our best adjusters?”

Instead of “How do we maintain our current accuracy rates with AI?” ask “How do we achieve accuracy rates that are impossible without AI?”

Specific questions worth raising in your next leadership meeting:

Strategic Questions:

• Which of our current processes take the most time but add the least value?
• What would our customer experience look like if routine claims processed in 10 minutes instead of 10 days?
• How could AI help us handle 3x the claim volume with our current staff?

Operational Questions:

• What data do we collect that we’re not using for decision-making?
• Which adjusters are already experimenting with AI tools (officially or unofficially)?
• What would we need to change about our processes to integrate AI seamlessly?

Competitive Questions:

• What would happen to our market share if a competitor offered 24-hour claim resolution?
• How would our pricing model change if our operational costs dropped 40%?
• What new services could we offer if routine processing became automated?

The breakthrough for most leadership teams comes when they stop asking “How do we protect our existing business?” and start asking “What business do we want to be in five years from now?”

The best questions create discomfort. That’s how you know you’re asking the right ones.

How to Move from Doubt to Confidence

The Disruption Confidence Cycle teaches us that doubt is inevitable but temporary. The key is moving through it quickly, not avoiding it entirely.

Five strategies that help organizations transition from the doubt phase into clarity and confidence:

Start with Low-Risk, High-Learning Pilots: Don’t bet the company on your first AI claims initiative. Pick a specific claim type or geographic region. Set a 90-day timeline. Measure everything. Learn fast. Think parking-lot fender-benders before you tackle comprehensive damage assessment. Low complexity, high volume, minimal risk. Build confidence through experience, not theory.

Create AI Literacy Across Your Team: Your adjusters don’t need to become AI engineers, but they need to understand what AI can and cannot do. The fear of the unknown is always worse than the reality of the known.

Build Feedback Loops Between AI and Human Judgment: The best AI claims systems don’t replace human expertise. They amplify it. Design processes where AI handles routine analysis and humans focus on complex judgment calls.

Measure Leading Indicators, Not Just Lagging Ones: Don’t just track claims processing time and accuracy. Track how quickly your team adapts to new tools. Track how often AI recommendations align with adjuster decisions. Track customer satisfaction with AI-assisted claims.

Celebrate Early Wins Publicly: When AI helps resolve a complex claim faster or catches fraud that might have been missed, share those stories. Success builds momentum better than mandates.

The confidence phase isn’t about having all the answers. It’s about having enough experience to ask better questions.

The organizations that will dominate AI in claims adjusting aren’t the ones with the best technology. They’re the ones with the best learning systems.

Technology changes. Learning systems endure.

The Opportunity Hiding in Plain Sight

Here’s what most claims leaders miss: AI isn’t just about efficiency. It’s about capability expansion.

Think about the claims director who’s worried about AI reducing team size. Flip the question: What if AI allowed that same team to handle claims they currently refer to specialists?

The conversation changes immediately. Instead of job elimination, it’s service expansion. Instead of cost reduction, it’s revenue opportunity.

AI in claims doesn’t just process claims faster. It processes claims that were previously too complex, too time-consuming, or too expensive to handle profitably.

The confidence phase begins when you stop seeing AI as a threat to your current business and start seeing it as an enabler of your future business.

Think about it: What would your organization look like if every adjuster had the analytical capability of your best senior adjuster?

What if every claim received the attention and expertise you currently reserve for the most complex cases?

That’s not science fiction. That’s AI in insurance claims adjusting working as a force multiplier for human expertise.

Your Next Move

I’ve spent four decades watching industries transform. The pattern is clear. The timeline is predictable. The only variable is whether you’ll be a leader or a follower in your industry’s AI transformation.

The doubt phase feels safer than the confidence phase. But safety is an illusion when the ground is shifting beneath everyone’s feet.

Understanding the Disruption Confidence Cycle gives you a roadmap through the uncertainty. But understanding isn’t enough. Action is what separates the companies that thrive from the ones that survive.

Here’s my challenge for claims leaders this week: Before your next team meeting, identify one specific AI claims pilot you could launch in the next 30 days. Not a committee to study AI. Not a vendor evaluation process. An actual pilot with real claims, real data, and real learning.

If you’re planning an insurance conference, executive offsite, or claims leadership summit and you want your audience to leave with a real framework instead of more anxiety, I’m available to keynote.

Bring me in to walk your leaders through the Disruption Confidence Cycle and help them move from doubt to momentum.

The companies that emerge stronger from this transformation won’t be the ones that had the perfect strategy. They’ll be the ones that started learning soonest.

What’s the first AI experiment your team could run this week?