In this exclusive The Parking Stack content, we examine how AI is transforming how organizations use data, shifting the focus from producing reports to delivering actionable recommendations. By identifying patterns, highlighting issues, and suggesting responses, AI can help parking and mobility professionals make faster, better-informed decisions. Reports will remain valuable, but increasingly become secondary to insight, action, and human judgment.
From Reports to Recommendations: How AI Is Changing Our Relationship with Data
by Nick Mazzenga, PE
Three years ago, during the same week my daughter was born, ChatGPT was released to the public.
As a parent, it is remarkable to watch a child develop. In just three years, my daughter has gone from learning how to speak to dancing to her favorite songs, swimming in a pool, and constantly surprising me with what she can do next. The change is so gradual that you do not always notice it day-to-day, but when you step back and compare where she is today versus where she started, the progress is staggering.
I find myself having the same reaction when I think about artificial intelligence.
In just a few short years, AI has moved from being an interesting technology experiment to something many of us use every day. We often talk about AI in terms of efficiency, automation, or productivity gains, but I believe the bigger shift is happening in how we interact with information itself.
For decades, our industry has relied on reports to make decisions. The purpose of a report is straightforward: collect data, organize it, summarize it, and present it in a format that helps people understand what happened so they can decide what to do next.
But what if that process is about to change?
On the wall of my office is a graphic that I have kept for years. It illustrates the journey from raw data to insight using LEGO bricks. In the first image, the LEGOs are scattered in a pile labeled “Data.” In the next, they are sorted by color. Then they are organized into visual charts and graphs. Finally, the last image is labeled “Explained with a Story,” where the LEGOs have been transformed into a complete house with trees and a yard.
The message is that the ultimate goal of data is not the data itself. The goal is to understand what the data is telling us and to ace accordingly.
What is interesting is that this graphic was created before the rise of generative AI. Looking at it today, I wonder if it is already outdated.
Historically, organizations invested significant time and effort moving from data to story. We built reports, dashboards, and visualizations so decision-makers could interpret information and determine what actions were needed. AI is beginning to collapse many of those steps.
Increasingly, we are no longer asking systems to simply produce reports. Instead, we are asking questions and receiving answers. Rather than reviewing pages of transactions, occupancy reports, exception logs, and revenue summaries, AI can identify patterns, summarize findings, and highlight what deserves our attention.
The next logical step is even more transformative.
What if the parking system did not wait for us to ask? What if it proactively told us what was important? What if, instead of generating another report, it simply delivered a recommendation: “This facility is experiencing an unusual increase in exits without payment. Investigate this location.” Or “Demand patterns suggest an opportunity to adjust staffing on weekends.”
In that future, reports do not disappear. They become secondary. The focus shifts from finding the story in the data to determining the appropriate response.
There are certainly risks. AI will not be perfect, and human judgment remains essential. Yet the greater risk may be failing to use these tools at all. Historically, many organizations lacked the resources to fully analyze the mountain of information available to them. Important signals were often buried in the noise.
AI gives us a better opportunity to uncover those signals and act on them.
Perhaps the future of parking technology is not better reports. Perhaps there are fewer reports and better decisions.
And just like watching a three-year-old grow and learn, it is exciting to imagine what AI might be capable of three years from now.
Nick Mazzenga, PE, is an Associate with Kimley-Horn & Associates, Inc. He can be reached at nick.mazzenga@kimley-horn.com.