Parking Powered by AI: Precision, Performance, and People First

‍AI has become a buzzword across nearly every industry, and parking is no exception. But the real question is: how can AI truly add value? 

It’s time to take a closer look a the specific challenges of embeding AI in parking operations and the varying levels of AI autonomy. Shane Nolan, Director of Product Management at gtechna, highlights the key opportunities and complexities parking operators navigate when integrating AI technology.

Key Takeaways:

• AI is revolutionizing parking management and enforcement, improving efficiency with tools like AI-enriched License Plate Recognition (AI LPR).
• AI can process large volumes of data and assist with more accurate ticketing, but it's still evolving from assisted and augmented roles toward full autonomy.
• Human oversight remains essential, especially in complex situations like obstructed plates, ensuring AI supports human decision-making rather than replacing it.
• Public perception challenges, particularly privacy and data security, must be addressed alongside legal compliance.

How is AI changing parking enforcement?

“It all began with handheld ticket issuance, which evolved as technology advanced. We introduced License Plate Recognition (LPR) cameras to scan vehicle plates for increased efficiency in parking enforcement. LPR technology can detect the plate, recognize the characters, and determine whether the parking fee for the vehicle is paid.

AI adds an extra layer of intelligence on top of that. Previously, the system would only check if the vehicle had paid or had a permit. But with AI, it's not just about payment status. It can also recognize violations like parking in front of a fire hydrant, even if parking is paid for that vehicle. Older systems couldn't make that distinction, but now, AI can integrate more data points, making the decision to issue a ticket smarter and more accurate.”

Is AI completely autonomous?

“We can talk about different levels of AI solutions—from assisted to augmented to fully autonomous. At the assisted level, AI helps users with suggestions or handles basic tasks, like processing simple data. As we move to the augmented stage, AI becomes more advanced, managing complex tasks and making decisions with human oversight.

Fully autonomous AI would handle everything independently, but we're not there yet—especially in the parking industry. We're working toward a fully autonomous system, but human input is still necessary. 

At its core, AI is about managing large volumes of data and helping officers make better decisions. AI surfaces the correct information at the right time, whether in the field or the back office. It's not about replacing people but empowering them to manage situations more efficiently.”

Do you see AI evolving beyond its current role?

“Absolutely. One potential future use case is for AI to recognize when a plate can't be scanned and automatically notify the operator. That's the direction we're heading.

The key is to stay realistic about what AI can and can't do. There's a lot of buzz around AI, but at the end of the day, it's about accuracy, fairness, and improving efficiency—not replacing human judgment. The fundamental objective is to help people make better decisions faster.

While full autonomy is still far off, AI is already helping us process more data and make smarter decisions. But human oversight will always be necessary. The goal isn't to automate everything for the sake of it—it's to enhance accuracy and efficiency. So, while "autonomy" might be the buzzword, the focus should be on how AI can help deliver better services.”

What are the potential challenges with implementing AI in parking management and enforcement?

“One concern is the public perception of AI, particularly around privacy and data collection. When we embed AI technology, there are always questions about how much data the system collects and how it uses it. For example, people might worry that the city knows where their car has been all month. While AI can handle that data, it's essential to clarify how it's protected and what's being done with it.

Cities need to manage this perception carefully. Even if the concerns aren't based in fact, the fear of "Big Brother" is real, and people will be wary of how much AI knows and does. This is why we must update policies to protect privacy and why transparency is essential.

Another barrier is cost. Implementing AI solutions—especially at scale—requires a significant investment, and for many municipalities, budget constraints are real. AI is still seen by some as a “nice-to-have” rather than an essential tool. So it’s about demonstrating the tangible benefits—how AI can improve operational efficiency, reduce costs over time, and change the parking experience for operators and the public. Once cities see the long-term value, it becomes easier to justify the upfront costs.

There’s also the complexity of integrating AI into existing systems. Many parking operations rely on legacy technologies, and integrating AI can seem daunting. That’s why we focus on creating AI solutions that are not just powerful but flexible so they can be layered on top of existing infrastructure without requiring a complete upgrade. Making the transition as seamless as possible is a top priority for us right now.”

How does gtechna address these challenges when developing AI products?

“Ideally, we aim to predict potential challenges and run controlled tests before launching a product. In many cases, we're able to do exactly that. We collaborate with communities to conduct thorough testing, ensuring our AI products comply with all relevant laws and regulations. Once the product is released, we monitor its performance in real-world conditions and adjust based on our learning.

But public perception isn't always as straightforward as legal compliance. Even when our products meet all regulatory requirements, concerns or misconceptions about how AI is used, particularly around privacy and data security, can still exist. This is where our reputation comes in—we rely on the trust we've built over time to help promote the benefits of AI-powered solutions. If we remain transparent in our processes and respond to community feedback, we can gradually shift public sentiment and reinforce that our products are designed with each community's best interests in mind.”

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Who is Shane Nolan? 

Shane is responsible for assisting gtechna to identify, define and deliver successful products that provide our customers with real world business value. He is currently focused on initiatives that will make it easier for our customers to deploy, configure and use our products with measurable results. Shane has worked with gtechna in the past and has rejoined our team with extensive expertise in parking management and technology solutions, leadership and customer experience. Successfully guiding our product development and market research, he has helped to enhance our product offering to include greater efficiencies, usability and successful results for our customers.

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