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How AI in car dealerships is reshaping back-office operations

Jul 16, 2025 · Authored by A. Michael Mader

The dealership back office has often flown under the radar: essential but rarely seen as a driver of innovation. For years, it’s been weighed down by manual tasks, outdated systems and paper-based workflows. But that’s starting to change. Emerging technologies like AI in car dealerships, along with machine learning and robotic process automation, are helping dealerships reimagine their back-office operations as strategic assets that boost efficiency, generate insight and support growth.

On this month’s episode of Up to Speed, Mike Mader, principal with Baker Tilly’s dealership advisory service team, discusses the ways in which AI can transform the back office with Senior Product Marketing Manager at Yooz, Alastair Moore.

Unlocking latent value

Modern financial automation tools are helping dealerships uncover the hidden potential of their back offices. By automating accounts payable (AP) and receivable processes, AI can extract, validate and post invoice data, match purchase orders, flag anomalies and even predict optimal payment times based on cash flow and vendor terms. These capabilities not only reduce errors and processing time but also provide real-time visibility into financial operations.

Beyond AP, AI is improving contract management, compliance, general ledger reconciliation and forecasting. These enhancements help dealerships move from reactive financial management to proactive, data-informed decision-making.

Efficiency and scalability

One of the most immediate benefits of AI in car dealerships is greater efficiency. Dealerships using AI-powered platforms report significant reductions in invoice processing times, from 10 to 12 minutes per invoice to as little as 15 seconds. These time savings are especially valuable for operations managing thousands of invoices each month.

For example, one dealership reduced invoice processing time by 50 percent, allowing it to reallocate staff to more strategic functions. Another saw a 90 to 95 percent drop in invoice entry time across 2,500 monthly invoices, freeing up substantial resources. These gains streamline operations and support growth without increasing administrative overhead.

Smarter systems, not just faster

The intelligence of these systems comes from their architecture. Symbolic AI and second-order inference engines enable context-aware decision-making, such as routing high-value invoices to specific approvers based on historical behavior. Semantic machine learning models read and learn from invoice content, not just layout, making them resilient to vendor-specific formatting.

This shared learning model improves with each user interaction while maintaining data privacy. Combined with robotic process automation (RPA), these technologies automate more than just data capture. They also execute tasks like posting entries directly into ERP systems, enabling seamless end-to-end automation.

Human oversight remains essential

Despite the speed and scale of automation, human oversight is still critical. Effective AI platforms are designed to augment, not replace, finance professionals. They automate repetitive, rules-based tasks while leaving exceptions, escalations and strategy to people. This balance enhances speed and accuracy without compromising control or accountability.

Integration is key

To maximize value, AI must integrate smoothly with existing systems — especially Dealer Management Systems (DMS) and Enterprise Resource Planning (ERP) platforms. Leading solutions support more than 250 systems, including Tekion, CDK, Reynolds and Reynolds and Dealertrack. Proper integration enables two-way data synchronization, intelligent invoice matching and a unified financial ecosystem.

A strategic imperative

Dealership leaders should rethink the back office as a strategic lever rather than a cost center. Poor cash flow visibility, missed payment discounts and vendor errors can chip away at profits. AI-driven automation reduces these risks, protects margins and supports scalability, particularly during mergers, acquisitions or multi-rooftop expansion.

Embracing the future responsibly

While healthy skepticism about AI is expected, the goal is to approach it with informed curiosity. Key questions include: Does the solution integrate with current systems? Can it deliver measurable outcomes? Is it built to scale responsibly?

Modern AI platforms are not black boxes. They are practical tools built to solve real problems with transparency and accountability. When implemented thoughtfully, AI in car dealerships can turn a traditional cost center into a source of agility and competitive advantage.