For years, Kansas City’s road maintenance strategy relied on outdated windshield surveys and a rigid focus on the date of a road's last repair. With a pavement condition index of only 52 out of 100, the city estimated it would take three decades to rebuild its 6,000-lane-mile network. Utility Manager Garrett Ross noted that the reliance on static spreadsheets hindered the team’s ability to allocate funding effectively, leaving critical infrastructure to deteriorate.
Kansas City Overhauls Infrastructure to Slash $4 Billion Maintenance Debt
Faced with a $4 billion projected maintenance backlog and a crumbling road network, Kansas City shifted from manual, spreadsheet-based repairs to an AI-driven infrastructure model. By replacing legacy processes with real-time data, the city tripled its annual street resurfacing output and halved its long-term financial liability.

Partnering with OpenGov, the city transitioned to a cloud-based platform that integrates real-time scenario modeling and public-facing maps. This digital pivot allowed officials to prioritize repairs based on actual road conditions rather than arbitrary timelines. The impact was immediate: annual funding for street maintenance jumped from $20 million to $40 million, and the city successfully resurfaced over 1,500 lane miles in three years. Beyond the physical improvements, the automation of workflows saved staff more than 900 hours of manual data entry annually, creating a scalable blueprint for other municipalities struggling with aging infrastructure.



Comments (0)
No comments yet. Be the first!