Last week, the Damage Prevention industry gathered in Colorado Springs for four days of discussion, collaboration, and debate at the 2026 Common Ground Alliance (CGA) Conference. Across sessions, panels, and conversations, a clear theme emerged: the industry is rapidly aligning around risk-based damage prevention.
Across 811 centers, utilities, and technology providers, there was a shared recognition that the traditional, volume-driven model for preventing damages is no longer sustainable. The shift toward prioritizing work based on consequence and probability is no longer theoretical; it is becoming the foundation for how the industry intends to operate.
That message was reinforced by Patrick Warren of the Office of Railroad, Pipeline and Hazardous Materials Investigations, who highlighted a core challenge facing the industry today: organizations are not lacking data, but many still lack the systems needed to turn incident data into meaningful prevention.
In many ways, this moment feels like a turning point. Concepts that have defined Urbint’s approach for years such as risk scoring, prioritization, and AI-driven decision-making are now being widely adopted and echoed across the market. Competitors are leaning heavily into AI messaging, 811 centers are beginning to experiment with upstream risk analysis, and utilities are investing in their own internal models. The direction is clear; the industry is moving toward risk.
What also became clear this year is that alignment on strategy does not equal maturity in execution.
AI was everywhere at the conference, often positioned as the unlock for scaling risk-based operations. Yet much of the conversation focused on capabilities rather than outcomes. There is still a meaningful gap between what is being marketed and what is being consistently delivered in the field. Many organizations are in early stages;, piloting models, building internal tools, or exploring how to integrate risk into existing workflows. The ambition is high, but the operationalization is still catching up.
At the same time, there is recognition that improving outcomes is not just about better models, but better inputs. Several discussions pointed to the impact of poor ticket quality and oversized or poorly defined scopes of work. Reprioritizing tickets based on actual excavation scope rather than treating every request equally has the potential to significantly reduce noise in the system and better align resources to true risk. It reinforces a fundamental idea that came up repeatedly throughout the conference: not all tickets are equal, and treating them as such introduces inefficiency and unnecessary exposure.
This gap becomes even more apparent when viewed through the lens of CGA’s “50 in 5” initiative to reduce damages by 50 percent in five years. It has become the industry’s defining goal, but for many it remains an aspiration rather than an achievable trajectory. The dominant question across sessions was not whether risk-based approaches are the answer, but how to make them work in practice.
That is where the contrast is most important while much of the industry is still working toward operationalizing risk, Urbint customers are already doing it. Risk models are embedded into their daily workflows, high-risk tickets are being identified and acted upon proactively, and interventions are driving measurable reductions in damage. In the context of “50 in 5,” this is not about future potential. It is about progress already underway, with customers already achieving or on track to achieve these targets, in large part due to these capabilities.
What will matter going forward is not who talks about risk, but who can operationalize it effectively and consistently drive outcomes. That means moving beyond scoring to influence behavior in the field, improve decision-making under real constraints, and prove impact at scale.
CGA 2026 underscored a shift Urbint has been driving for years: the future of damage prevention is risk-based. While the broader market is now catching up to that idea, Urbint has already moved beyond theory. We built our business on the belief that predictive risk intelligence could materially improve safety and operational outcomes, and our customers’ results show that it works. The next phase of the industry will not be defined by who talks about risk-based prevention, but by who can deliver measurable impact in the field.