Across dealership finance, AI has moved past the 'interesting idea' phase and into the operational reality. It's showing up in the tools your team already uses, in the workflows vendors expect you to maintain, and in the questions leadership asks when cash gets tight.
What makes 2026 feel different is the timing: AI is accelerating at the same moment many dealerships are navigating a changing of the guard in finance, with seasoned leaders stepping back and newer team members stepping in.
Most teams talk about AI like a software rollout. In dealerships, it behaves more like a stress test, because the environment is high volume, high variation, and deadline-driven. When a key step lives in someone’s memory, AI can’t reinforce it. It scales whatever is already there, which is why consistency matters so much in a multi-rooftop environment.
That’s why the first question is not “Which AI tool should we buy?” The better question is “Which parts of our process need to be true, every time, even when the team changes?” Start there, and AI adoption becomes less about chasing tools and more about keeping control as roles evolve.
The adoption curve is already steep. McKinsey’s 2024 Global Survey on AI found that 65% of respondents say their organizations are regularly using generative AI. In McKinsey’s 2025 edition, that figure rises to 71%. That pace is reshaping expectations across vendors, systems, and leadership teams, even when the day-to-day still revolves around close.
Dealership finance is not adopting AI during a quiet quarter. You’re adopting it in the middle of month-end close, multi-rooftop approvals, parts and service exceptions, and vendor payment pressure. That reality is not a reason to avoid AI. It’s a reason to adopt it with guardrails that keep outputs explainable and decisions defensible.
The second force shaping adoption is people. Dealership finance teams are being asked to do more with the same headcount, and hiring is not getting easier.
The AICPA reports accounting graduates who earned either a bachelor’s or master’s degree in accounting fell to 55,152 in the 2023 to 2024 academic year, a 6.6% drop compared to the previous year. That shows up as slower backfills, more cross-training, and fewer “buffers” when exceptions stack up.
Expectations are shifting, too. Newer finance professionals want systems that explain themselves, with clear ownership and clear history. Veteran leaders often carry process context in their heads because that’s what kept the business moving. AI can bridge that gap, but only when it’s paired with repeatable controls.
When controls are unclear, nuance doesn’t always translate cleanly into a system. That’s when the team spends time reconciling what the system thinks happened versus what actually happened. Once teams have had to clean up a few mystery exceptions, they stop caring about “AI features” and start caring about trust.
On paper, AI adoption sounds like a productivity story. Inside a dealership, it feels like a control story.
It shows up when a parts invoice comes in with three pages of line items, freight, and a surprise surcharge. It shows up when two rooftops code the same vendor differently, so matching works in one store and fails in the other. It shows up when approvals drift back into email because it feels faster. Suddenly it gets harder to answer a simple question quickly: where is this invoice, and what is holding it up?
It also shows up in risk. The FBI’s 2024 Internet Crime Report combines information from 859,532 complaints and details reported losses of $16.6 billion, a 33% increase from 2023.
Separately, AFP reports 79% of organizations were victims of attempted or actual payments fraud in 2024, with business email compromise cited as the top avenue for attempts by 63% of respondents.
Dealership teams already manage enough urgency. The goal is to add friction only where it protects the business, and remove it everywhere else. That’s why explainability is not optional. It’s the hinge that makes adoption stick.
Here’s where AI adoption gets tricky in dealerships: speed only helps when the output is easy to explain and defend.
When a controller is asked why a charge hit a certain GL, why a location got assigned, or why a payment went out, “the AI said so” doesn’t hold up. It also doesn’t hold up when a vendor disputes a short pay or an audit asks for support.
An explainable process doesn’t have to be fancy. It has to be clear:
Once explainability is in place, adoption stops feeling like a leap of faith. It becomes a process decision.
When AI projects stall, it’s rarely because the technology can’t read an invoice. It’s because the team stops trusting the outputs, and familiar workarounds come back.
A spreadsheet appears “just for tracking.” Invoices get forwarded again “just in case.” Approvals happen in inboxes because it feels quicker. A senior team member becomes the exception handler for everything, and now the process depends on one person again.
Those workarounds are not stubbornness. They’re a rational response to uncertainty, especially during close. The goal is to make the reliable path the easiest path, across rooftops and across roles.
Before calling this an AI problem, pressure test the process. These three questions usually reveal where trust breaks first:
When one of these is shaky, you’ve found your first guardrail. That’s a practical starting point, not a setback.
Responding to this moment doesn’t require perfect data science. It requires a few practical moves that make outputs more reliable and handoffs more consistent. Think of these as control upgrades that make AI easier to trust.
Move 1: Make capture trustworthy before you make it fast
For most dealerships, the best starting point is invoice intake and capture. Small misses here multiply into big cleanup later.
A misread vendor name creates duplicate vendors. A missed PO number triggers matching exceptions. A wrong location creates approval confusion and reporting noise. These misses don’t just slow AP. They pull controllers, managers, and branch teams into exception mode.
A stronger approach pairs AI extraction with verification on the uncertain fields before the invoice moves forward. In dealerships, the risky fields are usually predictable: PO, location, totals, freight and surcharge lines, and vendor identifiers that drift over time. When those are right, matching rules and approvals have a chance to work as intended.
Move 2: Put clear checkpoints where money or master data moves
Dealership finance doesn’t need AI to make the final call on vendor bank changes, policy overrides, or payment release. It needs AI to surface what looks unusual and make the decision point obvious.
Keep human checkpoints at the moments that change money movement or vendor truth. That includes vendor setup and maintenance, bank and remit to updates, approvals above thresholds, and exceptions that override matching rules.
When the system captures who changed what, who verified it, and when it happened, adoption gets easier. In a multi-rooftop environment, that visibility prevents one store’s workaround from quietly becoming everyone’s problem during close.
Move 3: Build role evolution as the adoption plan
AI changes the job, and that shift becomes a win when you name it explicitly and train for it deliberately.
In dealership AP, AI should pull the team away from rekeying and chasing, and toward exception management, policy enforcement, vendor compliance, and visibility. Those responsibilities are easier to transfer during turnover because they can be taught and measured.
Role evolution also supports the generational shift. Veteran leaders get to codify the instincts they built over years. Newer team members get a system that shows them the “why,” not just the “what.” That’s how you keep process discipline without relying on tribal knowledge.
In month one, stabilize intake for one high-volume invoice type (often parts) and make exceptions visible with clear owners. In months two and three, align approvals and thresholds across rooftops, then tighten controls around vendor changes and payment release. That’s when AI starts feeling like capacity, because the process is predictable enough to trust.
AI adoption isn’t a separate initiative from AP automation. The best results come when AI is embedded in a connected invoice-to-pay process, so capture feeds workflow, workflow feeds approvals, and approvals feed payments with full visibility in between.
That’s where tools like smarter capture technology, workflow automation, and integrated payments matter. Smarter capture turns messy invoices into usable data, and verification steps keep uncertain fields from becoming downstream exceptions. Workflow automation keeps approvals consistent across rooftops and departments, and it makes exception routing visible instead of informal.
Payment controls and payment status visibility reduce follow up noise and make it easier to spot irregularities before money moves. When those pieces work together, AI reduces dependence on tribal knowledge. It also keeps the process consistent through role changes, which is the point of this whole moment.
Dealerships don’t have the luxury of chasing every AI trend. The teams that get real value will be the ones that focus on control and repeatability, especially as roles evolve and responsibilities shift across rooftops.
That starts with a process you can trust end to end: capture that’s accurate enough to drive matching, approvals that stay in the workflow (not scattered across inboxes), and checkpoints that protect vendor truth and payment release. When those pieces are in place, AI stops feeling like a leap of faith. It becomes a disciplined way to reduce rework, tighten visibility, and keep the operation steady through change.
If you want to go one level deeper on the “why” behind finance hesitation and how to move forward without sacrificing trust, check out Why Finance Teams Are Hesitant About AI and What They’re Missing. It breaks down what’s really behind finance hesitation, from explainability and audit trails to data quality, integration, and ROI questions. It also lays out practical guardrails: defined use cases, auditable outputs, cross-functional governance, and a cleaner data foundation.