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Auto Transport Industry Needs Practical AI, Not Just 'Shiny' Tech

  //automotive-transportation.news-articles.net/co .. stry-needs-practical-ai-not-just-shiny-tech.html
  Published in Automotive and Transportation on Wednesday, January 7th 2026 at 8:20 GMT by Forbes

Beyond the Hype: Why Auto Transport Needs Useful AI, Not Just "Shiny" AI

The automotive transport industry, a critical component of the global supply chain, is increasingly eyeing Artificial Intelligence (AI) as a solution to persistent challenges like efficiency, cost reduction, and driver shortages. However, a recent article in Forbes (“Auto Transport Employees Need Useful AI, Not Flashy AI” by Kevin V. Hansen) argues that the industry is at risk of chasing “shiny” AI – impressive-sounding technologies that lack practical application – instead of focusing on implementing useful AI that genuinely improves the daily work lives of its employees and addresses core business needs. This isn't a dismissal of AI's potential, but a call for a pragmatic approach prioritizing tangible results over technological spectacle.

Hansen emphasizes that auto transport, encompassing everything from moving vehicles from factories to auction sites and delivering them to dealerships, is largely a “people business.” Success hinges on skilled dispatchers, drivers, and inspectors. Any AI implementation that alienates or de-skills these vital workers is ultimately counterproductive. The article points to a dangerous trend of companies investing in complex AI systems that promise automation, but ultimately create more work for existing staff through constant monitoring, error correction, and system maintenance.

The core argument isn't that AI can't help, but how it helps. The Forbes piece, and supporting documentation linked within, focuses on areas where AI can provide immediate, practical benefits without replacing the crucial human element. These fall largely into three categories: augmentation, optimization, and predictive maintenance.

Augmentation – Empowering Employees, Not Replacing Them: Hansen argues that AI’s most effective role is in augmenting human capabilities. This means providing tools that enhance employee efficiency, not automating their jobs entirely. Examples highlighted include AI-powered image recognition for vehicle condition reports. Currently, vehicle inspectors manually document damage, a time-consuming and potentially subjective process. AI could automatically identify and categorize damage from photos or videos, significantly speeding up inspections and improving accuracy. The linked article from AutoAlert (a company Hansen co-founded) illustrates this well, demonstrating how AI can analyze vehicle photos to detect pre-existing damage before transport, minimizing disputes and improving customer satisfaction. This doesn't eliminate the inspector; it frees them up to focus on more complex assessments and handling exceptions.

Similarly, AI-driven natural language processing (NLP) can streamline communication. Dispatchers often spend significant time sifting through emails, text messages, and phone calls. An NLP-powered system could automatically categorize and prioritize these communications, identify key information like delivery dates and locations, and even draft responses. Again, this doesn’t replace the dispatcher, but allows them to focus on critical problem-solving and building relationships with drivers.

Optimization – Refining Existing Processes: Beyond augmenting individual tasks, AI can optimize entire processes within the auto transport chain. Route optimization is a prime example. While GPS navigation is common, AI can go further by analyzing real-time traffic data, weather conditions, driver availability, and vehicle type to dynamically adjust routes and minimize delivery times. This goes beyond simple shortest-path algorithms; it incorporates a multitude of variables for truly efficient planning. The article suggests AI could also optimize load balancing, ensuring trucks are filled to capacity without exceeding weight limits or damaging vehicles.

Another area ripe for optimization is claims processing. The current process is often manual, involving extensive paperwork and investigation. AI can automate aspects of this process by analyzing claims data, identifying fraudulent claims, and speeding up legitimate payouts. AutoAlert's technology, as detailed on their website, explicitly focuses on this, leveraging data to identify risk factors and prevent losses associated with vehicle transport.

Predictive Maintenance – Preventing Problems Before They Happen: Hansen also points to the potential of predictive maintenance, particularly for the transport fleet itself. By analyzing data from vehicle sensors (telematics), AI can identify potential mechanical issues before they lead to breakdowns. This allows for proactive maintenance, reducing downtime, and extending the lifespan of vehicles. This is particularly crucial given the high mileage and demanding conditions faced by auto transport trucks. While not specifically detailed in the Forbes article, this application leverages the principles of Industrial IoT (IIoT) and is a common implementation of AI in logistics.

The article warns against the allure of "black box" AI solutions – systems that are difficult to understand and troubleshoot. Transparency and explainability are vital, particularly in a safety-critical industry like auto transport. Employees need to understand why the AI is making certain recommendations or decisions to trust and effectively utilize the technology.

In conclusion, the Forbes article argues for a pragmatic approach to AI adoption in auto transport. The focus should be on implementing useful AI that empowers employees, optimizes existing processes, and prevents problems before they occur. Instead of pursuing flashy automation projects, companies should prioritize solutions that deliver tangible benefits and improve the daily work lives of the people who keep the wheels of the industry turning. The key takeaway is that successful AI integration isn’t about replacing human expertise; it’s about amplifying it.