Leading an AI deployment in an organization is no small feat for any CIO, but it’s a whole different animal when that organization is a city.
Case in point: Mark Wittenburg, CIO of the City of Raleigh. His IT team is spearheading an ambitious effort to apply AI in areas ranging from managing urban growth and addressing climate change to fixing potholes faster. Wittenburg, speaking at Gartner’s recent IT Symposium/Xpo event in Orlando, Fla., said the goal is to not only make city services smarter but also further establish Raleigh as part of a “smart region.”
“We have to be innovative,” he said, noting that expectations are high. The area’s constituents are looking for the same seamless technology experiences they get from private companies.
Indeed, innovation is part of the region’s DNA. Raleigh, the capital of North Carolina, anchors the brainy Research Triangle Park — the largest research park in the U.S. and home to corporate locations for Cisco, IBM, Dell, Lenovo, and other tech giants. Wittenburg said the city’s AI initiative is about improving services for its 500,000 residents, while drawing on the expertise of the region’s universities and colleges.
Partnerships with the local universities in Raleigh — North Carolina State University, Shaw University and Meredith College — include working closely with university students, Wittenburg said, sparking “some amazing ideas and how to solve some real-world challenges that we have.”
Breaking Down Silos to Streamline Workflows
The drive for IT innovation didn’t start with AI. Six years ago, before modern large language models and AI technologies emerged, the Raleigh IT team was already making an effort to work in a more innovative way. The IT team set out to examine siloed applications and processes across various city departments to see where those processes could be streamlined, Wittenburg said.
“How do we take these siloed workflows, like hiring somebody, and then what are the pieces those workflows touch along the way?” he said, as an example. “Then we start to streamline those by sharing data and sharing workflows.”
Testing AI Internally Before Public Rollout
The emergence of generative AI triggered another opportunity to improve the way things worked. The IT teams’ AI experts started using generative AI (GenAI) internally — Wittenburg didn’t want to immediately release it to the public should a major issue arise. “There’s a couple of good examples of where that’s gone bad,” he said.
By starting the AI journey internally, Raleigh’s IT team was able to begin improving its own processes first and create a more efficient troubleshooting workflow. Raleigh is using ServiceNow’s AI platform; Wittenburg said that as a result, 95% of help tickets are now auto-summarized and approved, and tickets are cleared 66% faster. By addressing tickets faster, the IT team is also able to clear out service backlogs quicker.
Enhancing City Services with AI
When considering how to use AI with public-facing processes, Wittenburg took a “crawl, walk, run” approach from ideation to production. That starts with asking a lot of questions about which types of AI technologies make the most sense for the city of Raleigh, what business value AI can deliver, and what the risks are should something go wrong, Wittenburg said.
The first application of a public-facing AI involved Raleigh’s water system.
“We digitized all the records, brought all that in, and then built a model using data analytics, AI, and generative AI to be able to start asking questions around our water infrastructure,” Wittenburg said, noting that the city’s paper records dated back to the 1700s.
The AI model could then be developed and used to identify and predict potential failures in the water infrastructure. In addition, the approach allowed for the creation of a closed model using Raleigh’s own water data with an internally built AI engine, which is a low-risk method for testing AI before deploying it widely, Wittenburg explained. City employees could also verify the AI’s predictions and improve the model’s forecasts over time.
Simplifying Government Complexity
Next, the IT team integrated ServiceNow’s AI platform with the city of Raleigh’s public chatbot to implement “agentic pieces to it,” Wittenburg said, referring to AI technology capable of autonomous decision making.
“It was amazing, because now it started to use the knowledge articles, but it also started to dig into those closed tickets to other workflows, and to start to put all that together. It’s just amazing watching it learn things that we didn’t know we were doing,” he said.
While learning internally has been critical, the ultimate goal of deploying AI is to bring value to the community, Wittenburg said. In that vein, Raleigh’s IT team plans to first use agentic AI to improve traditional city services, such as simplifying the process of identifying and fixing potholes.
Fixing a pothole seems straightforward enough, but it involves a number of steps, Wittenberg said, starting with identifying if it’s on a city, county or state street — three different agencies with separate phone numbers. People just want the pothole fixed, the problem solved.
“What if we could get all those AIs and those agents and all these services to hide the complexity of government from you all,” he said.
Vision Zero: Agentic AI for Safer Transportation
Another use case the Raleigh IT team is working on with AI is “Vision Zero,” using machine learning at traffic intersections to create models around vehicle and pedestrian interactions. The goal is to reach zero transportation-related fatalities in the city. For Vision Zero, Wittenburg’s team partnered with Nvidia and Esri, a GIS mapping software, to build an AI model that could determine if drivers were going the wrong way or if a car narrowly missed a pedestrian, for example.
The IT team is beginning to add GenAI capabilities to ask the AI model to suggest improvements at intersections based on “near misses.” The AI model has made suggestions such as adding a right turn light or implementing light change delays, Wittenburg said.
Guidance for CIOs
Ultimately, when deploying AI for the community, Wittenburg said it’s important for constituents to have a consistent experience and reduce complexity when they need to connect with a local agency.
His advice to other CIOs and IT professionals starting out on their IT journey is to identify the governance and “guard rails” for using AI, have a solid data foundation and “don’t let fear hold you back.”
