An underground utility is damaged every 2 minutes and 40 seconds in North America. Since those are just the incidents reported to Common Ground Alliance’s DIRT system, the number is higher.
At the same time, more than 100 million excavations happen every year — roughly three new jobs begin every second, including nights, weekends, and holidays.
While excavations continue to scale, resources do not.
No utility has unlimited locators. No organization has endless screening staff. No damage prevention team has enough time to personally evaluate every ticket with the same level of scrutiny.
That’s what makes Common Ground Alliance’s 50-in-5, reducing damages by 50% in 5 years, a significant challenge — without AI.
AI can help prioritize the riskiest tickets, so the right action is taken by the right people at the right time to avoid damage.
Despite the promise and enthusiasm for AI, 95% of AI pilots fail to deliver meaningful results, according to an MIT study.
In most industries, a failure rate that high would kill the category. Instead, AI investment keeps increasing, fueled by the belief that the technology will be the answer. When the results don’t match expectations, the AI models get blamed.
But AI projects often fail because organizations treat AI as software deployment rather than an operating model change.
A team builds a prediction model. A dashboard gets launched. Some automation gets introduced. Then everyone waits for the transformation.
But prediction alone does not reduce damages.
Outcomes only improve when predictions change decisions, decisions change actions, and those actions are continuously measured and refined.
That operating loop is where many organizations struggle.
Successful AI implementations tend to follow the same blueprint:
Historically, many damage prevention workflows were designed around consistency: every ticket processed similarly, every request moving through largely standardized procedures.
But scale has changed the economics of that model.
When incidents occur at roughly a one-in-a-thousand rate, the question becomes less about processing every ticket identically and more about identifying the small percentage of excavations most likely to create serious consequences.
That’s where risk-based operating models become transformative.
Instead of spreading scarce resources evenly across all activity, organizations can direct the highest levels of intervention toward the excavations with the greatest likelihood — or impact — of damage.
That intervention may include:
At the same time, lower-risk activities can move through increasingly automated workflows with confidence.
The industry is beginning to see measurable evidence that this approach works.
Organizations deploying AI-driven screening, risk scoring, and workflow automation are already reporting significant reductions in damage rates, improved operational efficiency, and faster response allocation.
In some implementations, AI-assisted workflows have reduced incident likelihood dramatically for the tickets touched by those systems while simultaneously lowering operational costs.
The challenge behind the CGA’s 50-in-5 initiative is real. Reducing damages at industry scale is extraordinarily difficult. But they’re also precisely the conditions under which AI delivers the most value — ranking millions of excavation tickets by risk, surfacing the 8% that account for half of all incidents, and directing the limited human attention that exists toward the places it matters most.
The technology can do this. It’s doing it today, in production, at organizations that got the organizational side right.
The question for every executive considering an AI initiative — in damage prevention or anywhere else — isn’t whether the technology works. It’s whether your organization is ready to operate it. That means aligned leadership, a sharp objective, disciplined iteration, and genuine accountability for outcomes.
To learn more about launching AI that works, tune in to the entire CGA presentation today.