AI agents are increasingly used in route optimization, with 35% of developers using them daily, but 56% of teams struggle because planned routes don't match real operating conditions like delivery windows, loading docks, and customer habits. The key to effective route optimization is accurately modeling real-world constraints, not just having powerful algorithms.
- 35% of developers, CTOs, and tech leads use AI agents daily for route planning, with an additional 23% using them occasionally.
- 56% of teams report a critical gap between planned routes and actual field conditions, making this the top difficulty ahead of performance issues.
- Real-world constraints matter most: delivery windows, loading docks, refrigerated vehicles, and customer habits must be represented in the model for routes to work in practice.
- 56% of teams build their own planning tools, though many supplement with off-the-shelf solutions or APIs, requiring significant ongoing maintenance.
- Cost and integration challenges persist, with 41% concerned about unexpected billing increases and 65% strongly bothered by tools without transparent pricing.
AI agents are making their way into the design of route planning tools, according to a survey by Kardinal.
In fact, 35% of the developers, CTOs and tech leads surveyed already use an AI agent daily.
Their main challenge, however, is that for 56% of them, planned routes fail to match real operating conditions, whether the tool is built in-house or not.
“Teams aren't short of algorithms: powerful solvers are available as open source. What makes the difference is the ability to represent what really happens in the field: delivery windows, loading docks, refrigerated vehicles, customer habits. As long as those constraints aren't in the model, the optimal route remains theoretical,” says Cédric Hervet, co-founder of Kardinal.
Key takeaways:
· One in three (35%) respondents use an AI agent daily for development, and 23% occasionally. Only 17% use none.
· Nearly two-thirds (65%) are strongly bothered by a tool without public pricing, and 26% refuse to even evaluate it. Their top concern remains a bill that could balloon (41%).
· The gap between planned routes and real-world conditions tops the list of difficulties (56%), ranking ahead of performance and compute time (47%) and constantly shifting business needs (43%). Nearly four in 10 (39%) even struggle to know whether a planned route is good. Tool cost (37%) and integration into the IT system (30%) come next.
· 56% of teams have built their own planning tool. Nearly one-third (31%) still rely on them, at least in part, as many as use off-the-shelf software or an API (31%). Nearly half of the teams that develop in-house also use another tool.
· The gap with field conditions affects teams that develop in-house (51%) as much as the others (50%).
· Among respondents who operate their own solution, 35% report regular maintenance and 18% heavy maintenance with dedicated resources. Only 12% say it requires almost no effort.
