Welcome to another edition of the Car Dealership Guy Podcast Recap newsletter—the key lessons from top operators, founders, and execs shaping the future of auto retail.
Today's guest is Vishal Vadodaria, founder of AutoEngage.
We dive into the product obsession Vishal learned under Steve Jobs, why a major public dealer group bet early on his AI tech, and how Agentic AI is poised to reshape the future of the car business.
Product obsession learned under Steve Jobs beats flashy technology.
Working at Apple on Safari browser and Mac OS taught the critical lesson that great products solve real customer problems rather than showcasing impressive technology.
"It is all about building insanely great products. That's what I learned while I was at Apple. It was amazing to see how a tremendous amount of craftsmanship is needed to really turn a good idea into a great product."
Steve Jobs' customer-backward approach prevents the common trap of building technology first, then hunting for applications—a philosophy that drives AutoEngage's focus on genuine dealership retention challenges.
Why automotive customer relationships fascinated a former Apple engineer.
The intricate nature of automotive service relationships presented technical challenges far beyond typical customer support scenarios that intrigued Vishal at a 2019 Napa conference.
"Talking specifically about fixed ops. Different customers, there's different makes involved. They each have different inter-wheels. And again, the customer could have purchased a new vehicle versus a used vehicle. They might no longer own that vehicle."
These variables created an engineering puzzle requiring adaptive AI systems rather than simple response trees, complexity that made automotive more challenging than even Apple's support systems.
Outbound customer engagement requires fundamentally different skills.
Most dealership staff excel at helping customers who call in with questions, but proactively reaching out to inactive customers demands specialized relationship-building abilities.
"They also know that the skills needed for outbound is very, very different than somebody actually calling in."
When customers call dealerships, they've already decided to engage, but convincing someone who hasn't contacted you to book an appointment requires advanced persuasion and timing skills that are scarce in the talent pool.
Different dealer sizes require completely different AI strategies.
Small dealer groups prioritize immediate ROI measurement and effectiveness, while large operations focus primarily on reducing human dependency across locations.
"The bigger the dealer group, the more challenged they are when it comes to having talent at scale. And so for them, autonomy is really the number one factor."
Understanding your operation's specific constraints—whether talent availability, capital requirements, or management complexity—determines which AI approach will actually deliver results rather than create additional overhead.
Presented by:
1. CDK Global - CDK SimplePay is the only payment solution that's built into CDK solutions for unrivaled reliability and financial efficiency. To learn more or schedule a demo visit @ CDK.com/simplepay
2. OPENLANE - The world’s best online dealer marketplace for used cars, bringing you exclusive inventory, simple transactions, and better outcomes. Learn more at openlane.com
3. AutoEngage - LISA (Linguistic Intelligence Service Assistant) “proactively” engages service customers in 2-way conversations throughout their ownership lifecycle using the most advanced Natural Language Processing in the market and books the appointment directly in the scheduler with no human assistance to increase retention, drive revenue, reduce cost and maximize customer lifetime value. Learn more @ autoengage.ai
AI integration success depends on existing dealership systems.
The biggest implementation hurdle isn't the AI technology itself, but whether it can seamlessly connect with existing DMS and scheduling systems without creating workflow disruptions.
"We leverage the data that's already there in the DMS, but where the customers, like the large public groups, they all have some form of a CDP strategy."
Dealerships must honestly assess their data quality and system integration capabilities before choosing AI solutions, as clean data dramatically impacts ROI while fragmented systems can derail even the best technology.
AI proves value through three-tier attribution measurement system.
Concrete ROI tracking separates full attribution (AI completes entire conversation), partial attribution (human assistance required), and engagement attribution (customer books after AI contact).
"We've seen nine to 11 times ROI based on this incremental RO value."
This measurement framework demonstrates exactly which repair orders resulted from AI engagement versus business that would have arrived naturally, critical for proving incremental value.
Why automotive AI needs industry-specific intelligence.
Building effective automotive AI requires programming deep industry knowledge that generic language models lack, preventing dangerous customer interaction mistakes.
"There's so many things that just go into it. And if you don't do one thing right, it can all fall apart."
Unlike CRMs designed for human operation, the AI must understand automotive service constructs, pricing dynamics, competitive responses, and customer lifecycle stages independently.
Local customization maintains authentic dealership relationships.
Effective AI deployment requires balancing corporate consistency with location-specific operational differences, brand requirements, and customer demographics.
"If it's not fully customized at the store level, at the brand level, at that location level, it's not effective."
Ignoring local languages, market differences, and dealership-specific practices turns sophisticated AI into basic auto-reply functionality.
Current adoption splits between early movers and cautious observers.
The market shows clear bifurcation: large groups implementing at scale versus smaller operations experimenting carefully before committing resources.
"The bigger the dealer group, the more challenged they are when it comes to having talent at scale. And so for them, autonomy is really the number one factor."
Smaller groups focus more on immediate ROI measurement and effectiveness rather than full automation, creating different adoption patterns across dealer sizes.
The coming AI agent revolution: When machines stop listening to humans.
Future artificial intelligence will make independent decisions and challenge human instructions when logic suggests better alternatives—like children developing their own reasoning.
"If I were to tell you that AI doesn't have to listen to us anymore, can you imagine a world... where your AI agent says, just like your kid, ‘I'm not convinced about what you're saying.’"
This agentic AI will handle complex tasks like tire purchasing end-to-end: gathering customer data, researching options, checking inventory, negotiating pricing, and booking appointments autonomously.
