AI Redefining Vehicle Inspections – Remarketing

The clipboard-and-pen days of auto inspections are beginning to look as quaint as Texas Instruments calculators and antenna TVs.

Artificial intelligence is rapidly transforming how vehicle remarketers and fleet operations inspect and rate vehicles.

Experts on the 2025 Conference of Automotive Remarketing in March shared insights on how auctions, dealers, consignors, and rental fleets can profit from faster, more accurate real-time condition reports.

“Once you take a have a look at what this industry has been through over the past five years, especially on the technology side, much change is being driven by the pandemic, forcing sales to go off or online, out of the physical auctions, and in between,” said Dan Kennedy of DAK Consulting, moderator of the session, “Way forward for AI in Vehicle Inspections.” 

Since 2022, AI has emerged to remodel vehicle inspections, resulting in constantly evolving condition reports because it gorges on increasingly data.

Joining Kennedy were panelists John Coles, vp of information science and analytics at ACV Auctions, Dustin Cruz, vp of operations of OPENLANE, Raj Pofale, founder and CEO of Claim Genius, and Kevin Kostuk, co-founder and director of Ariaal.

AI Can Augment the Work of Human Inspectors

Relatively than replace human labor, AI to date has proven more of a helpful tool for human inspectors.

“The best way we give it some thought is it gives our inspectors superpowers,” Cruz said. “It’s making them higher and more consistent at their jobs, allowing them and the method to be more efficient. They’ll do more inspections.”

By enabling vehicle inspectors to work faster with more precision, AI frees them to focus more on complex tasks that require human judgment.

In a single example, Coles cited how AI tools can boost productivity and ease on-the-job hassles.

“By putting tools in our inspectors’ hands, they are not kneeling on blacktop in Texas in the summertime to see what’s happening underneath a automobile while assessing that vehicle. After they use tools reminiscent of virtual lifts and mirror arrays to evaluate the undercarriage and convert that into insights without crawling to take pictures, it’s an amazing efficiency and quality-of-life improvement for our teammates.”

Pofale underscored how AI can provide unbiased decisions when assessing vehicles. “I can say confidently that each one of us within the room would assess a vehicle in another way, because our understanding of the assessment varies.” 

AI-driven standards also make it possible to scale inspection procedures and processes across a complete auction operation or wholesale used vehicle markets, he said.

Kostuk sees AI as a tool to scale back relieve employees of the stresses of tedious and repetitive tasks.

“Persons are busy. They’re running hard. Anything we will do to scale back the cognitive load and get them to do add value will probably be appreciated and help them live a greater life and revel in their jobs more.”

Challenges in Data Quality and Model Training

Despite AI’s benefits, data quality challenges still have to be resolved, panelists said. Barriers to training accurate AI models include poor image quality, inconsistent lighting, and mislabeling. The goal of AI is to make sure consistent performance across vehicle types and geographic regions.

Coles said older datasets often lack proper resolution or metadata, making them less effective for training AI systems. “That foundation of high-quality, consistent, and longitudinal data takes time to generate and to annotate, and you will need to continually refine it versus assuming you’ve got it.”

AI must consistently be learning, Cruz added. “When you consider all of the locations across the country where we inspect cars, a 2020 Chevy Silverado will look different within the northeast within the snow than the way it looks in California. AI must learn and understand all that.”

Nevertheless, Cruz and Cole advised that while imperfect, an AI with a solid database is close at hand.

If images are unclear or poorly taken, even probably the most advanced AI will produce flawed assessments, said Pofale, whose company includes app-based guidance to assist users capture the perfect photos immediately. The resolution of the photographs, the positioning, the sunshine conditions — all the things must be right. In such a case, you’ll get probably greater than 90% of the accuracy.”

Kostuk identified a more systemic challenge — access to third-party data. “It is a large opportunity and barrier as well, not only the APIs and technical access but alignment of business models to access what has been historically very tightly held data within the industry. If we will discover a solution to join those data sources with inspection data on the bottom, it advantages everyone.”

Mobile vs. Fixed Inspection Platforms

Vehicle inspections have to accommodate quite a lot of platforms, from fixed-location kiosks to mobile apps, said panelists, whose business use differing kinds. Each has its use cases and technical requirements, especially when coping with user-generated content.

Coles said ACV Auctions uses apps for consumers, dealers, employees, and glued sites.

“The important thing elements are guiding the person and the operational component at every level through the identical set of steps after which providing rapid feedback,” Coles said.

Pofale identified that while inspectors are trained to take skilled photos, consumers don’t accomplish that much. “We were working with one among the rental automobile firms, and on one automobile claim, [the renter] had 500 images. God knows what he did. But that brought down our algorithm. It depends upon the top user and the way we’re training and educating them.”

Kostuk added that his company has adapted its algorithms for various use cases. Consumer devices, for instance, need power-efficient, edge-based models, while fixed systems can process within the cloud.

“Pondering clearly concerning the use case is critical. What is the marketplace? Who’s the client, and who’s the inspector? Should you consider all of the mixtures of those three questions, it will possibly be a highly fragmented set of use cases, and a special value proposition for every of those different hardware requirements.”

Real-Time Processing and Edge AI

One promising trend is the move toward edge AI, where image processing occurs directly on mobile devices as a substitute of on cloud servers. This reduces lag and improves inspection efficiency.

Such models can deliver results straight away as inspectors walk across the automobile and the system detects and documents damages, Pofale said.

Such advancements rely upon improved microchips and shorter algorithms that may streamline the method.

Latest Developments in AI Technology

Panelists offered a roundtable of forecasts for specific AI improvements in vehicle inspections:

• Kostuk: “If we will look farther from the present large language models, which must do with humans interrogating an AI, we’re beginning to see a pivot towards AI with the ability to interrogate a human, or AI with the ability to interrogate a vehicle via a human. That’s where it gets exciting after we have a look at a number of the future agentic models for AI. A whole lot of our R&D on shouldn’t be on the front-end technologies.”
• Pofale: “Technologically, all the things is getting more advanced. After we began six years ago, we had to coach our algorithms with tens of millions of images, which needed to be labeled and curated. It took at the least 1 / 4 to coach, but now with open frameworks, we will train and customize our models to each customer. It takes just a few thousand images inside three to 4 weeks to tweak the algorithms to deploy for that customer.”
• Cruz: “When we expect AI, we expect of exterior damage. But there are such a lot of other areas of the vehicle that AI can [evaluate], and that’s where we’ll see loads of growth in the course of the next six to 12 months. Whether it is the undercarriage, interior, mechanical, or audio sounds — all those [capabilities] are coming.”

Buyer and Seller Advantages

AI brings transparency and standards to condition reports, making them more trustworthy for all stakeholders, Pofale said. “When humans did them, buyers needed to consider regardless of the inspector was saying. Now all the things is visible and open because the outcomes can be found immediately. It’s giving more transparency to the buyers.”

Cruz added, “Vehicles turn into more consistent, which just helps with pricing and floors and higher reconditioning. You’ll be able to be smarter whenever you’re buying vehicles.”

AI can be helping reduce post-sale arbitration, especially for rare but serious oversights, Kostuk said. “We’ve caught things like bullet holes in headliners — stuff inspectors missed. That’s where back-end AI systems with their adaptive learning technologies have helped catch arbitrations before they turn into an issue.”

Coles raised concerns about information overload. “If damage is documented in image 500, what does that mean to the client?” He said future condition reports must summarize findings clearly to stop disputes.

Cruz agreed, calling for “shared responsibility” and user-friendly interfaces that make critical information easily visible to sellers and buyers.

AI Realities and Precautions

Panelists offered the next tips on handling obstacles to AI-driven condition reports:

• Many potential clients have been burned by vendors overselling AI’s capabilities. 
• Disillusionment cycles are common in tech. Overhyping AI results in unrealistic expectations, which, when unmet, generate skepticism across the board.
• Practical barriers can delay fitting AI into existing workflows without disrupting operations.
• Being honest about what AI can and can’t do is important. Overselling sets everyone up for failure.

Most Inspectors Will Keep Their Jobs

All panelists agreed that irrespective of how much AI advances, it is going to never replace vehicle inspectors.

“I am going back to automation and the push within the Eighties and 90s to introduce a burger flipping machine at McDonald’s to interchange everyone,” Coles said. “It didn’t work. It was too slow and couldn’t figure it out. If I take into consideration where AI is being introduced, it’s intentional and effective, like introducing the PC into the office environment. I exploit a PC, however it has yet to interchange me. AI is like introducing a pc, not a robot, to do a job.” 

Pofale suggested some easy inspections may turn into fully automated, but humans will remain essential, especially when handling complex or high-stakes evaluations.

“The variability of the damages and the claims are a lot that you just would want humans to consistently and consistently train these models. Humans cannot go away 100% and AI cannot take 100%. It’s only good for repetitive sorts of tasks.”

AI won’t just change vehicle inspections — it is going to redefine them.

Coles summed up that AI is about providing latest data sources and tools that enable faster, more reliable buyer-seller partnerships.