Where claims are going and why your platform decision today decides if you lead or fall behind.
By: Branson Smith, CRO
H ere’s the question I get asked more than anything right now: what does claims administration look like in five to ten years? The short answer: faster by order of magnitude, leaner by design, and almost unrecognizable to anyone who’s been running operations on legacy platforms. The longer answer isn’t just where the tech is going; it’s how most organizations are already making decisions that will lock them out of it.
What the Next Decade Actually Looks Like
1 Speed: From Days to Seconds
Start with speed. Today, a mid-complexity claim: VSC, multi-line repair, independent shop, might take two to four days from first notice to authorization. Within three years, that drops to seconds. Not because of more headcount. Because of the full adjudication cycle; intake, coverage match, invoice review, fraud scoring, decision, communication, collapses into a continuous, AI-driven workflow that runs in near real time.
The driver isn’t smarter models alone. It’s the convergence of three shifts:
- AI that can read, reason, and act across unstructured inputs (calls, emails, repair photos, technician notes)
- Reliability data that’s finally deep enough to predict failure likelihood with statistical confidence
- Agentic systems that can plan and execute multi-step workflows without a human in the loop for each step.
Put that together and you get this:
- Claims operation where a dealer submits a repair authorization request
- The system has already pulled VIN history, matched failure patterns, priced the repair, checked the servicer, and returned a decision, before the service advisor finishes the call.
The destination for leading operations isn’t just faster claims, it’s zero-touch claims. The goal is a world where the vast majority of claims are adjudicated start to finish without a human ever touching them, and your claims staff exists to manage a small, curated pool of exceptions: the complex, the disputed, the genuinely ambiguous. That’s not a reduction in quality. That’s what quality looks like at scale.
2 Cost: From Labor-Heavy to Near-Zero
Then the cost drops. Fast. Labor-intensive tasks; manual intake, document indexing, line-item review; status calls disappear entirely. The humans who remain are doing the work that requires judgment: complex coverage disputes, fraud investigation, dealer relationship management, model oversight. Operating cost per claim drops. Significantly.
3 Consistency: From Variable to Absolute
Beyond cost, there’s something less obvious but more strategically important: consistency. AI doesn’t have a bad Tuesday. It doesn’t apply coverage rules more generously on Friday afternoon. It doesn’t vary by adjuster tenure or mood. When the system decides, it makes it the same way every time, with an audit trail showing exactly why. For regulated industries, that’s not a nice-to-have. It’s protection.
The Penalty for Sitting This Out
Here’s the uncomfortable truth: organizations that are still running claims on platforms without native AI capabilities in 2027 won’t just be inefficient. They’ll be competitively unviable in certain markets.
Dealers and OEM partners are already starting to factor cycle time and decision quality into their administrator relationships. A 4-day authorization turnaround against a competitor’s same-day response isn’t a minor disadvantage. It’s a contract renewal conversation.
Every quarter you delay embedding AI into your core claims workflow is a quarter your competitors are using to train models on outcomes data you’re not capturing. That gap compounds. It doesn’t wait.
The deeper problem with non-AI-embedded platforms isn’t just that they’re slow today. It’s that they can’t learn. A legacy rules engine processes claims the same way in year five as it did in year one. An AI-embedded platform gets better with every decision — tighter fraud detection, sharper pricing benchmarks, more accurate approval predictions. The performance delta between AI-native and AI-adjacent platforms will widen every single year.
There’s also a workforce dimension. The next generation of operations talent expects to work with intelligent tools. Organizations that can’t offer that will struggle to attract and retain the people who know how to run modern operations. That’s not a soft concern — it shows up in hiring timelines and training costs.
Build vs. Buy: The Most Expensive Mistake in the Industry Right Now
I need to address this directly because I’ve watched too many organizations spend 18 months and several million dollars learning it the hard way.
Building AI capabilities for claims is not a software development problem. It is data, domain expertise, and ongoing model operations problems. And that distinction matters enormously.
When an internal team builds a claims AI, they typically solve the first version reasonably well. They fine-tune a model on historical decisions, build some integrations, and get it into production. Six months later, the model starts drifting — the failure patterns it was trained on don’t match the current vehicle population; pricing data is stale, the fraud signatures it learned have evolved. Someone must retrain it. And the person who built it has moved to another project.
The ongoing cost of maintaining claims AI in production — data pipelines, model monitoring, retraining cycles, regulatory compliance updates, integration maintenance — is two to four times the cost of building it. Most internal teams aren’t staffed for that. Most internal teams don’t realize they need to be staffed for that until they’re already in it.
Building claims AI is not a software problem. It’s data, domain expertise, and ongoing operations problem. Most internal teams don’t realize the difference until they’re already in it.
A purpose-built platform changes that math entirely. The models are pre-trained on industry-specific data, real vehicle reliability patterns, regional parts and labor pricing, adjuster outcome histories across thousands of claims operations. Integrations exist. The compliance framework exists. The retraining infrastructure exists. You’re not building any of that. You’re configuring it to your business rules and turning it on.
More importantly, you benefit from every other customer on the platform. When the network of claims operations using a shared platform collectively generates new fraud patterns, new failure data, new pricing benchmarks — every customer benefits. A solo build never has that. Your model only knows what your data tells it.
The buy argument isn’t about whether your engineering team is capable. It’s about where their capability should be applied. Build your product. Build your dealer relationships. Build your underwriting edge. Don’t build the AI infrastructure for claims processing from scratch when someone has already spent a decade doing exactly that.
The organizations that look back on this decade clearly will see the same inflection point we’re at right now — the moment when claims went from a cost center managed with rules and headcount to an intelligent operation that gets faster, cheaper, and more accurate over time. The ones who moved early captured the compounding benefits. The ones who waited caught up eventually. At a much higher cost.
What We Are Building Toward
This isn’t a theoretical vision for us. The platform we’re building: Claims Intelligence – InsightsIQ, AutomationIQ, and WorkforceIQ — is the operational infrastructure for exactly the future described here. Predictive adjudication. Agentic workflows. Intelligent routing. All of it is trained on real claims data, embedded in the core workflow, and designed to compound in value over time.
If you’re thinking seriously about where your claims operation needs to be in three to five years — Let’s have a real conversation about what that transition looks like. We can show you what the trajectory looks like for operations similar to yours, and where the highest-leverage moves are right now.
The best time to start was two years ago. The second best time is before your next contract cycle.