Drive Point Auto Group, a seven-rooftop Ohio dealer group representing nine brands, began weaving AI into its operations in April 2025. Owner Kyle Pisani now uses the technology across the business, and he has hard numbers: one aftersales tool, Drive Solutions, has generated up to $80,000 in missed net profit each month across all stores. Yet Pisani warns that AI still can't fully meet a dealership's needs - especially when a customer moves from an automated interaction to a human employee.
AI uncovered a billing practice that Pisani says he never would have caught on his own. The system flagged that the group was taking payment on repair orders before they were closed. "That is a question I wouldn't ask," Pisani said. The finding gave him a direct operational correction that improved process integrity.
Consolidating data for faster decisions
Drive Point uses AI to pull key performance indicators from its Dealer Management System, Customer Relations Management, and Digital Retailing platforms. The tools combine sales history, incentives, and inventory levels into a single report that goes to general managers. "Before, would we have looked at all those scenarios? Probably not," Pisani said. The AI suggests new angles for operational decisions, helping teams spot opportunities tied to inventory and incentive shifts.
For operations leaders looking to build the skills to interpret AI-driven KPIs, an AI Learning Path for Operations Managers offers structured training.
The breakdown in customer handoffs
Pisani is piloting Vinessa, a virtual assistant from Cox Automotive, which he calls a "massive distance" from earlier products. But the biggest friction, he says, comes when AI hands a customer over to a person. "I would rather see AI handle the whole thing or a human handle the whole thing," Pisani said. That transition gap is "extremely painful."
He sees promise in Cox Automotive's Full Path, an integrated customer data platform launched June 1. The tool gives a dealer visibility into every customer touchpoint, which Pisani says helps navigate the "maze" of data that dealerships collect.
Vendors must become more 'dealer-esque'
Pisani has dealt with "plenty" of vendors who promised capabilities that never materialized. He says AI companies need to understand the granular steps of car shopping - if a vehicle isn't available, what else fits the customer's needs? They must be upfront about what their products can actually do. "As dealers, we aren't technology people and we get a lot of information from vendors. There is a responsibility for them to be honest with us," he said.
The dynamic, he argues, has shifted. AI now has to adapt to dealer needs rather than forcing dealers to bend to a product. "Between what the customer desires and where we are today, I mean, there's some distance," Pisani said.
Why this matters for operations
For operations professionals inside dealerships, Pisani's experience surfaces a clear priority: AI can surface revenue leaks and process errors that manual oversight misses, but its value collapses if the handoff to human staff isn't designed with the same rigor. The $80,000 monthly lift from Drive Solutions and the repair-order catch both happened because AI worked in a closed loop. When a customer must switch from a bot to a person, the experience - and the potential profit - can fracture. Operations teams that evaluate AI tools should press vendors on how their systems manage that transition, and they should build internal protocols that make the handoff as smooth as possible. The use of AI to surface operational blind spots is a core focus of AI for Operations initiatives.
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