CCC Q1 2026: AI Claims Revenue Crosses the 10% Threshold at $120M Run Rate

CCC Intelligent Solutions disclosed in its Q1 2026 results that AI-based solutions now generate approximately 10% of total revenue, a roughly $120 million annualized run rate. No other publicly traded claims-tech vendor has put an AI revenue figure on the table at that scale. The number matters less as a vendor milestone than as a marker of how much of the personal auto estimating chain now runs through one company's models.

Key Takeaways

• 10% of revenue from AI solutions, about $120 million annualized, growing at roughly 3.5 times the total company rate and contributing about one-third of year-over-year growth.
• $281.3 million of Q1 revenue, up 12% from $251.6 million and all organic, with adjusted EBITDA margin expanding about 300 basis points to 43%.
• 27 of the top 30 US auto insurers are CCC platform customers, and roughly 5% of total claims flow through Estimate-STP, with one national carrier at 20%.
• 98% software gross dollar retention and 107% net dollar retention, up from 106% for full-year 2025, on a platform processing around six billion transactions a day.
• Approximately 1 percentage point of revenue drag lands in the second half of 2026 from a planned legacy first-party casualty migration.

What the Quarter Discloses

Revenue of $281.3 million was up 12% from $251.6 million, all of it organic, against analyst expectations of $274.9 million. Adjusted EPS of $0.11 beat the $0.10 consensus, and the company moved from a $17.4 million GAAP net loss a year ago to $15.4 million of net income.

Metric	Q1 2026	Q1 2025	Change
Total Revenue	$281.3M	$251.6M	+12%
Adjusted EBITDA	$120.2M	$99.1M	+21%
Adjusted EBITDA Margin	43%	40%	+300 bps
GAAP Gross Margin	74%	74%	Flat
Adjusted Gross Margin	77%	74%	+300 bps
GAAP Net Income	$15.4M	($17.4M)	+$32.8M
Adjusted EPS (Diluted)	$0.11	$0.08	+38%
Free Cash Flow	$41.6M	$44.0M	-5%

Adjusted EBITDA grew 21% on 12% revenue growth, which is the operating leverage showing through: incremental AI revenue carries a higher margin than the legacy platform. Full-year guidance was raised to approximately 10% growth from a prior 8.5% to 9.5% range, with revenue of $1.155 billion to $1.163 billion and adjusted EBITDA of $484 million to $490 million at a 42% margin.

The AI figure behind the headline is the one worth isolating. AI solutions are growing at roughly 3.5 times the total company rate and supplied about one-third of the year-over-year increase, which implies AI revenue up roughly 42% against 12% for the whole. The wider emerging solutions category, which carries the EvolutionIQ casualty platform alongside APD diagnostics, reached 11% of Q1 revenue and grew 50%.

One definitional caveat travels with the 10%. CCC counts as AI revenue any solution using machine learning for prediction or automation, spanning computer vision estimating, natural language synthesis of medical records, and predictive triage. That is a broader category than generative AI, so the figure describes the full ML portfolio rather than a single new product line.

Where the Vendor Number Reaches the Loss Triangle

The distribution is what turns a vendor disclosure into an actuarial input. 27 of the top 30 US auto insurers run on the CCC platform, more than 6,500 collision repair facilities use its AI estimating tools, and the network spans over 125 insurers and 15,000 repair facilities at roughly six billion transactions a day.

Estimate-STP, the computer vision product that generates line-item estimates from customer photos, is at 40 insurer clients including seven of the top 10 by direct written premium. About 5% of total claims flow through it, though one large national carrier runs 20% of its volume that way, as our CCC Crash Course analysis noted alongside the repair-side complexity these models must keep learning.

That is where the reserving consequence sits. An AI-generated estimate lands within minutes of first notice rather than days, so the earliest evaluation on an AI-processed claim is materially more complete than the earliest evaluation on a manually adjusted one. Link ratios in the first development periods are being fitted across two populations whose reporting patterns differ by construction, and the mix between them is moving every quarter as penetration grows from 5%.

Severity selection has a parallel problem. AI estimates built from insurer-specific rules are more internally consistent than human estimates, which compresses estimate-to-estimate variability inside the AI cohort. A severity distribution fitted on blended data will show a variance that belongs to neither population.

The competitive shape reinforces the concentration. Verisk is larger, over $2.7 billion of revenue against CCC's $1.1 billion run rate at roughly 50% EBITDA margins, but its AI claims tools are modular add-ons beside ISO data and Xactimate rather than embedded in the daily transaction. Tractable competes on computer vision as a point solution. CLARA Analytics targets the adjuster decision layer. None of them sits where CCC sits, which is inside the workflow that produces the estimate.

One Model Set Behind Most of the Market

The margin structure explains why this concentration keeps building. Adjusted gross margins reached 77%, and a trained model that prices 100 estimates prices 10,000 at almost no incremental cost. A vendor at those margins can discount an initial deployment to win an enterprise account and expand on usage, which is consistent with the two-year testing period that preceded CCC's top-five insurer win and with the carrier-side expense savings that justify the spend.

Switching costs finish the job. Replacing CCC means migrating repair facility integrations, retraining data pipelines, and reconfiguring workflow automations at once, an 18 to 24 month exercise on industry estimates. Gross dollar retention of 98% and net dollar retention of 107% are what that looks like in the numbers.

The exposure this creates is correlation. When 27 of the top 30 carriers estimate through the same model family, a model error, a training data problem, or an outage does not hit one carrier's loss costs. It hits most of the personal auto market in the same direction at the same time, which is precisely the independence assumption an operational risk assessment usually gets to make. The worst version is a catastrophe surge, when the platform processes elevated volume for every client concurrently and any degradation propagates across all of them.

The gap between 82% AI adoption and 7% full-scale deployment means this is early. Penetration at 5% of claims leaves most of the book still adjusted the old way, and the concentration risk grows with the same curve the revenue does.