AI in Car Dealer Groups and Automotive Transport: Hype or Hope?

We’re deep into the AI hype era. Ironically, much of the content on AI is now being created or at least drafted using AI tools like ChatGPT.

But this article isn’t just another SEO-driven click-bait piece. At Jigcar, we want to share our thoughts on where car dealer groups can actually leverage AI in a meaningful and impactful way—beyond the hype.

We believe that dealer groups can unlock massive efficiencies by implementing the right technology, but it’s important to avoid applying AI as a superficial fix to deeper operational issues. Simply plastering AI over outdated or disconnected systems won’t lead to transformative results.

In most dealer groups, operations run on a patchwork of manual processes, point solutions, and closed systems. Introducing AI into that mix without addressing the underlying inefficiencies is like trying to patch a leaky roof with duct tape. That’s why we believe it’s crucial to get the fundamentals right before layering on AI-powered enhancements.

Real AI Use Cases for Car Dealers

Let’s take a look at some practical examples where car dealer groups can genuinely benefit from AI today.

Take James Baggott’s AI Car Dealership project as an example. Even with a vision of integrating AI from the ground up, he primarily used it for simple tasks like generating prompts, re-writing ad copy, and brainstorming ideas. That’s probably where many dealer groups are right now—someone in the office experimenting with ChatGPT to handle basic content generation tasks.

However, there are already AI-powered products offering real value for dealers. Here are a few concrete examples:

1. Marketing Descriptions: Tools like ChatGPT for written descriptions and Eleven Labs for generating audio content.

2. Marketing Imagery: AI tools like DALL-E and Midjourney for conceptual images, or industry-specific tools like Phyron-AI for automotive imagery.

3. Automated Communications: Products like GardX Sales AI, Impel AI, and even Sandra AI (recently launched from Y Combinator) that manage inbound and outbound customer calls or messages with AI-driven automation. These tools aim to capture every customer opportunity with a high level of personalization.

Many of these tools still require human oversight after the first AI-generated draft to ensure accuracy and relevance, but the level of personalization these systems can deliver—through CRM integration and interaction history—shows the potential power of AI.

AI in Logistics and Transportation: The Reality

But what about AI in logistics and transportation? Is it already making a difference there?

In some ways, yes. AI has been part of the supply chain for a while, especially in areas like routing and load building. Systems like Descartes or Paragon have been optimising delivery routes for years, but these technologies weren’t designed specifically for moving vehicles. Fleet management has also seen AI-powered advancements, especially in compliance monitoring. 

For example, Samsara and Motive use AI-enhanced dashcams to track driver performance and ensure safety.

Another emerging use of AI in logistics is damage inspection. Solutions from Tractable, Ravin AI, and Pave AI leverage AI to quickly assess vehicle damage and generate repair cost estimates.

The Challenges of Applying AI in Automotive Logistics

However, the logistics space—especially in automotive—is plagued with data challenges that prevent sophisticated AI from delivering on its full potential. In car dealer networks, legacy systems, closed data architectures, fragmented partners, and outdated processes make it hard to unlock the efficiencies AI promises.

Dealer groups face additional hurdles like stretched resources, tight margins, and distributed networks. The dream of AI-fueled efficiencies can feel out of reach. But when AI is applied to the right problem, it can be truly transformational. 

The key to unlocking AI’s potential lies in data. AI thrives on large volumes of structured data, identifying patterns, and delivering actionable insights. Without good data, AI’s capabilities are limited.

Turning AI Hype into Hope: Nailing the Basics

At Jigcar, we believe there’s a stepped approach for dealer groups to truly benefit from AI:

1. Digitise manual processes

2. Streamline workflows and improve efficiency

3. Create structured data for analysis

4. Leverage insights generated from that data

5. Optimise operations based on these insights

6. Forecast and predict future outcomes based on historical data

7. Apply AI to specific workflows, such as pricing engines, stock location optimization, transport load building, and route planning.

By following this approach, dealer groups can lay a solid foundation for AI to genuinely enhance logistics and operational efficiency.

The Long-Term Vision: Domain-Specific AI for Automotive

Our vision is for dealer groups to implement domain-specific AI solutions that are built on a foundation of well-structured, data-rich workflows. Nailing the basics first is critical for overcoming resource constraints, gaining operational control, and unlocking the full potential of AI within a logistics strategy.

Chatbots and avatars may grab the headlines, but the real long-term value for car dealer groups and those in the automotive supply chain comes from a clear strategy. That strategy should focus on addressing the fundamentals first, allowing AI to move from hype to hope, and ultimately delivering real, tangible benefits.