Every government decision has a location attached to it. Where should a city route a snowplow? Where would a new fire station serve the most residents? Which areas are most likely to experience flooding this spring?
Aaron Fusselman spends his days selling technology built around questions like these, and the technology he’s working with now looks very different from the mapping software many government agencies relied on a decade ago.
What “Agentic GIS” Actually Means
Traditional GIS software often required a trained analyst to pull together data, run a spatial query, and build a map before anyone could act on the information. A question that needed an answer that afternoon could end up taking days.
Agentic GIS platforms bring artificial intelligence into that process. A planner can ask a question in plain language and receive a useful answer without waiting for a specialist to run the analysis or write code.
Fusselman is the North American Sales Director for CARTO, a location intelligence platform built around this shift. CARTO connects to an organization’s existing cloud data warehouse, so agencies don’t have to migrate their data or build an entirely new infrastructure to start using the platform.
AI agents can then handle spatial analysis that once required specialized expertise, from optimizing routes to modeling risk across a geographic area.
CARTO has spent more than a decade working with organizations in telecom, retail, logistics, and financial services. The company has since brought that experience into the public sector, where many of the underlying questions are surprisingly similar.
A retailer deciding where to open a new store and a county deciding where to place a new health clinic are both trying to answer a location question. The stakes may be very different, but the underlying challenge is similar: use geographic information to make a better decision.
Where This Matters Most in the Public Sector
State and local agencies sit on enormous amounts of location data, but many don’t have the staff needed to make full use of it. A public works department might know where every water main in a city is located. Connecting that information with weather patterns, traffic volume, and aging infrastructure to determine which repairs should come first is a much more complicated task.
Aaron Fusselman has spent his career helping technology companies bring this kind of capability to agencies that often operate with lean technical teams and tight budgets.
Emergency management offices can use spatial analysis to model flood risk and plan evacuation routes. Transportation departments can use it to identify where road improvements could have the greatest impact. Public safety agencies can examine where incidents cluster over time to help determine how to deploy resources.
Utility departments can also use location intelligence to prioritize aging infrastructure before failures occur.
In each case, the technology isn’t deciding for the people running the department. It’s giving them a faster and clearer way to analyze the information they already have.
Selling a New Category to a Risk-Averse Buyer
Government buyers don’t adopt unfamiliar technology categories quickly, and for good reason. A tool that mishandles public infrastructure data or produces a flawed recommendation for an emergency planner can have serious consequences.
Fusselman’s approach to selling into this environment draws on the same discipline he’s used throughout his GovTech career. He starts by understanding an agency’s existing workflow and then demonstrates the value of a technology through a focused use case before discussing a broader rollout.
That patience is especially important with AI-driven technology. Agencies want to understand not only what a tool produces, but how it reached its conclusion. A recommendation that’s difficult to explain can be a tough sell to a government director who may eventually have to defend that decision publicly.
Part of Fusselman’s role, then, is translating a new technology into language that government buyers can understand and explain to their own leadership. That’s a very different challenge from simply demonstrating what the software can do.
The Adoption Curve Governments Are Actually On
Private-sector companies in retail and logistics began adopting location intelligence years ago, while many government agencies had fewer resources available for this type of technology.
That gap is starting to close as AI lowers the technical barrier to working with spatial data. A smaller agency doesn’t necessarily need a GIS specialist on staff to begin getting value from its information.
That shift could put capabilities once limited to large cities and well-funded state agencies within reach of mid-sized county governments. His industry profile reflects a career spent working across technology categories and helping organizations understand how new tools can fit into their existing operations.
Fusselman sees this as another step in a career focused on bringing enterprise technology to public-sector buyers. The products have changed over the years, from customer service platforms to AI front-desk tools and now agentic AI, but the core challenge remains the same: showing a skeptical, accountable buyer that a new capability is worth adopting.
What Comes Next
The agencies making the most progress with this technology tend to start with a specific problem, demonstrate that the solution works, and build from there. They don’t have to overhaul every workflow at once.
Fusselman expects government technology adoption of agentic AI to follow a similar path over the next several years, with adoption happening agency by agency and use case by use case.
For a sales leader who’s spent two decades helping technology companies earn the trust of public-sector buyers, that gradual process is familiar. Government agencies have to weigh the benefits of new technology against accountability, cost, security, and the practical realities of how their teams work.
Agentic AI may change how agencies use location intelligence, but convincing those agencies to adopt it still comes down to many of the same fundamentals Fusselman has worked with throughout his career: understand the buyer, solve a real problem, demonstrate the value, and give people a reason to trust the technology.
