Parking operators must balance AI efficiency with human support, as consumers trust people more than technology when problems arise.

By Heidi Barber and James Paden

The promise of parking automation was straightforward: Remove friction, reduce staffing costs, and let technology do the heavy lifting. It has not worked out that simply.

At Parker Technology, we spend a lot of time examining data, having logged more than 13 million parking-support interactions across garages, lots, and campuses, and having fielded two original studies involving a total of 600 consumers across the U.S. and Canada in the past 18 months. The data keeps telling us that the gap between what operators assume customers experience and what those customers actually experience is wider than most people realize. That gap has direct consequences for revenue, loyalty, and reputation.

This article draws on both studies, our 2025 Consumer Data Report that we released last September and our 2026 artificial intelligence (AI) perception survey that we released in late April, to make the case for what good technology strategy actually looks like in a parking environment. The short version: It is not tech versus people. It never was.

The problem is bigger than operators think

Among consumers, 82% report feeling frustrated during the parking process, 52% say they frequently encounter problems, and 93% report issues with pricing or payment, according to our 2025 Consumer Data Report, which is titled “When Tech Falls Short: Why Human Help Still Matters in Parking.” These are not edge cases. Four out of five customers are dealing with some level of friction on any given visit.

The business consequence is direct: 62% of consumers say they have avoided returning to a facility after a single bad experience. That is potential recurring revenue walking out the door, permanently, because of one interaction that went wrong.

Operators sometimes assume that adding technology reduces these problems. In many cases, it increases them. We call this the Human-Technology Mismatch Curve. As facilities deploy more automation, including license plate recognition (LPR), contactless payment, and pay-by-plate systems, the complexity facing the average consumer increases rather than decreases. 

Only one in six consumers report feeling very comfortable with LPR, according to our 2025 Consumer Data Report. Ten percent to 15% have experienced misreads or access failures. When confusion hits, consumers do not shrug and walk away. They reach for help. Real, human help.

Here is how one customer at Indiana University put it: “I don’t think people understand the difference…seeing somebody there, with human-to-human contact. Trying to help you out, keep you calmed down.”

When things go wrong, people want people, fast

And they expect resolution fast. Fifty-eight percent expect their call to be answered in under 20 seconds. Seventy-five percent expect the issue fully resolved, not just addressed, within two minutes.

This is a critical consideration. Consumers are not simply calling to vent. They are in a lane, potentially holding up a line, managing a real time constraint. The moment they are stuck is almost always the moment that determines whether they come back. Meeting that moment well is not a customer-service nicety. It is revenue protection.

What consumers actually think about AI

The conversation about AI in parking tends toward two extremes. Either AI will replace human staff entirely within a few years, or it has no place in a customer-facing environment. The consumer data supports neither position.

Our 2026 survey, which is summarized in a report titled “When AI Answers: What Consumers Really Think,” found that approximately 70% of consumers are comfortable with technology in customer service broadly. They use it. They understand how it works. Their hesitation about AI is not a technology literacy problem.

The hesitation is about trust, and specifically about what happens when something goes wrong. Sixty-seven percent of consumers say they trust a live human most when it comes to resolving issues, according to the survey. This finding held consistent across multiple questions in the survey. It’s not a fluke. Consumers have internalized that a fast wrong answer is worse than a slightly slower correct one, and they have enough experience with AI systems that do not deliver to justify their skepticism.

Accuracy, not speed, was the top-ranked factor in customer service interactions. Fifty-nine percent said accuracy matters most. Speed matters but getting it wrong fast is not an improvement.

We also found a transparency expectation that cannot be ignored. Sixty-seven percent of consumers say companies should disclose when AI is responding to them. Thirty-two percent cite privacy and data use as their top concern with automated messaging. Operators who deploy AI without disclosure protocols are assuming a risk to customer trust that the underlying efficiency gains rarely justify.

Know what AI can and cannot do

Consumers are not categorically opposed to AI. They are situationally flexible. For transactional tasks, AI is genuinely useful. For problem resolution under pressure, AI is not ready to go it alone and may never need to be.

Where AI adds clear value in a parking context:

• Answering routine questions about hours, rates, and locations

• Routing and triaging incoming support requests

• Handling payment confirmations and receipt delivery

• Managing high call volume during off-peak hours

• Logging and categorizing interactions to surface operational patterns

Where human support remains essential:

• Troubleshooting equipment failures in real time

• Resolving issues for frustrated or time-pressed motorists

• Handling context-dependent exceptions, including lost tickets, validation disputes, and access failures

• De-escalation and reassurance in high-stress moments

• Anything that requires judgment about when a rule should be bent

The distinction matters because it reframes the question operators should be asking. The goal is not maximum automation. The goal is matching the right tool to the right task and ensuring that when a situation exceeds what AI can handle, the handoff to a human is immediate, seamless, and well within the consumer’s patience window.

There is a practical matter on the technology side worth noting here. Voice AI in a parking environment is not the same as voice AI in a call center. We are talking about open-air garages, lanes with noise from passing vehicles, older hardware, and inconsistent internet infrastructure. The overconfidence problem in current AI models, where the system proceeds with 100% certainty on a guess it made in a noisy environment, creates real failure modes that operators need to plan for, not assume away.

Operators who get this right will look different

The operators who retain customers during the next several years will not be the ones who automate the most. They will be the ones who pair automation with reliable human backup and who measure the right things.

At least 3% of parking transactions trigger a help interaction. That sounds small. But across any operation of scale, it represents thousands of moments per month where a customer’s impression of your facility is shaped entirely by the outcome of that interaction. Most operators have no systematic way to understand why those calls happen, what they cost, or what they could prevent.

Effective support infrastructure requires the following steps, which are less common than they should be:

• Visibility into why help calls happen, and not just that they did, so patterns can surface and operators can address root causes

• Consistent issue categorization across every interaction

• The ability to act during the call, including opening a gate, processing a payment, and resolving the issue without revenue leakage

• Multiple resolution paths for the most common payment failure modes

• Procedures that stay current, not documentation that was accurate 18 months ago

• Context at the moment of the call: who the parker is, what rules apply, and what the right next step is

None of these steps replace good technology. But none of the technology works without them.

Stop asking the wrong question

The industry conversation about AI tends to organize itself around a single question: How do we automate more? That is the wrong question.

The right question is, how do we support better?

Supporting better means using AI where it reduces friction and frees up human capacity. It means deploying human support where trust and resolution matter most. It means measuring outcomes, not just throughput. And it means understanding that the 62% of consumers who have already walked away from a facility after a bad experience are not coming back to give you a second chance.

Technology is not the future of parking. Technology plus people are.

HEIDI BARBER is vice president of marketing at Parker Technology. She can be reached at [email protected].

JAMES PADEN is chief product officer at Parker Technology. He can be reached at [email protected].