AI For Tolling: Beyond AI – 5 Questions About How Agile Thinking Is Transforming Tolling

Artificial Intelligence is transforming industries, but technology alone is not enough to create meaningful change. Success comes from combining AI capabilities with Agile thinking, continuous learning, and human expertise.

In this article, Dina Schutt, Director of Product Engineering at Emovis, explores five key questions about how AI and Agile are changing the way engineering teams build, improve, and deliver smarter tolling solutions.

Discover why the real advantage of AI lies not only in what technology can do, but in how teams build and evolve it.

1. If AI Can Think, Why Does Tolling Still Need Agile Thinking?

Artificial Intelligence is transforming what technology can achieve, but AI alone does not create successful solutions.

Behind every intelligent system, there are teams, processes, and decisions that determine whether technology delivers real value.

In tolling, where operations involve millions of transactions and evolving requirements, organizations need more than advanced algorithms. They need a way of working that allows them to adapt, learn, and evolve. This is where Agile thinking turns AI capabilities into operational value.

Agile provides the framework, but people drive the adaptation. Human collaboration and brainstorming are what create the next big idea and help solve today’s challenges. AI can support this process, but it does not replace the human conversations that drives innovation.

2. How Can Agile Transform an AI Idea Into a Real-World Product?

An AI concept may start with a simple question: “Could this process become smarter?”

However, transforming that idea into a reliable operational solution requires experimentation, collaboration, and continuous improvement.

Agile thinking enables teams to test ideas quickly, learn from results, adapt based on feedback, and deliver improvements step by step. In tolling, where accuracy, reliability, and scalability are critical, this approach helps transform AI concepts into trusted operational capabilities.

Each step in that cycle—testing, learning, adapting, and delivering—only works because people actively discuss what they are seeing, rather than simply reacting to metrics. Data needs to be interpreted within the context of the environment, ecosystem, and business needs. Teams need to understand what those metrics are really saying, whether the results are positive or negative, and what story the data is telling them.

3. Are We Still Building Software the Same Way We Did Before AI?

For years, software development followed a predictable path: Design. Develop. Launch.

But AI-powered solutions require a different approach. Intelligent systems continuously learn, analyze information, and adapt to new situations.

Building software is no longer only about delivering a finished product. It is about creating systems that continuously improve, evolve, and respond to change.

AI didn’t create the need for collaboration—it accelerated it. The feedback loop is now much faster, which means engineering teams need to collaborate continuously, not only at key milestones. Products can no longer wait for the next release cycle. AI evolves too quickly for that. It challenges teams to think strategically, learn continuously, and stay closely connected so they can adapt with confidence.

4. Can AI Replace Human Expertise, or Can It Make Teams Stronger?

One of the biggest discussions around AI is whether technology will replace human roles. However, its greatest value comes from empowering people.

AI can help teams analyze information faster, identify patterns, automate repetitive activities, and support better decisions. But human expertise remains essential.

Engineers bring creativity, experience, critical thinking, and business understanding—elements that technology alone cannot replicate.

AI makes what humans can do stronger; it expands what we are capable of accomplishing. However, it does not replace the human-to-human experience. It cannot replace the discussion between two engineers who disagree on a solution, challenge each other’s ideas, and work together to find the best answer. AI can strengthen individuals, but team collaboration is what strengthens the solutions.

The smartest teams I’ve built didn’t focus solely on the caliber of the engineers. They were the teams where engineers trusted each other enough to challenge ideas openly, without fear of ridicule or repercussions. AI cannot change that. It can help us reach those moments of challenge faster, but the trust, collaboration, and shared problem-solving still come from people.

5. What Will the Future of Tolling Look Like When AI and Agile Work Together?

The next generation of tolling will require systems that are smarter, faster, and more adaptable.

AI and Agile together create the foundation for this transformation: AI brings intelligence, Agile provides the framework to adapt, and human expertise brings judgment.

At Emovis, innovation is not only about implementing new technologies. It is about creating intelligent mobility solutions that continuously evolve with clients, operators, and road users.

AI brings intelligence, Agile brings the framework, and humans bring collaboration. That collaboration is where judgment happens, and decisions are made on what step to take next.” AI can suggest an approach or support the work, and Agile helps teams test and adapt quickly, but engineers are still the ones working together to determine whether the outcome makes sense or requires adjustment. I want to continue building teams that stay sharp, connected, and ready to guide that adaptation in the right direction.

Artificial Intelligence is opening new possibilities for tolling, but transformation depends on more than technology.

By combining AI, Agile thinking, and human expertise, teams can build smarter, more adaptable solutions that continuously evolve. Because the future of mobility will be built not only with intelligent systems, but with intelligent ways of thinking.