How AI-Powered Vision Systems Are Redefining Workplace Safety

As injuries persist despite strong safety programs, employers are turning to AI and computer vision to detect unseen risks, prevent incidents, and make safety a strategic advantage.

• By Chris Skipper
• Nov 20, 2025

Safety leaders face a persistent challenge: despite their best efforts and commitment to protecting workers, preventable injuries continue to occur. But it's not for lack of trying. The issue lies in the inherent limitations of traditional safety approaches, including competing priorities, resource constraints, and a critical shortage of leading indicator data. These factors make it incredibly difficult to stay ahead of risk.

In 2023, U.S. businesses absorbed over $176 billion in injury-related costs, including $53 billion in lost productivity and $36 billion in medical expenses (NSC). Unfortunately, more than 5,000 U.S. workers lost their lives on the job in 2023 alone (BLS). These aren't isolated failures; they result from small risks that go unseen and therefore unmitigated: cluttered walkways, risky forklift operating behaviors, ergonomic risk, and near-misses that drastically impact an organization's bottom line.

The business impact compounds quickly: employee downtime, interrupted shifts, turnover, rising insurance costs, and if the injury is severe, damage to the organization's reputation. Due to the manual nature of its collection, safety data is often biased, fragmented, delayed, and incomplete, making it hard to spot patterns, prioritize action, or prevent what's coming next.

Computer Vision (CV) is helping operators see risks in real time, intervene before injuries occur, and turn safety into a strategic operational advantage. Artificial Intelligence-driven (AI) safety isn't a promise for the future - it's here now, reshaping how operators prevent incidents, protect workers, and strengthen operations.

The Visibility Problem: What Traditional Safety Programs Miss

Even the strongest safety programs struggle with fundamental visibility gaps that make prevention difficult.

Industrial facilities span hundreds of thousands (if not millions) of square feet. Single supervisors are often responsible for dozens of workers across multiple zones. High-risk activities occur during off-shifts or weekends when visibility is most limited. A supervisor, no matter how vigilant, still can't be everywhere at once.

In most of today’s organizations, safety data is often fragmented, biased, and comes too late to prevent the next incident. True near misses are chronically under-reported, leaving safety teams without critical and timely intelligence about emerging risks. Manual audits and observations capture point in time data rather than patterns. By the time the data is collected and summarized, the information is more lagging than leading. Injury investigations, while critically important, occur after someone has already been hurt, and by then the best that we as safety professionals can do is prevent the next one.

Unfortunately identifying risks doesn't equal resolving them. Action items get lost between shifts, departments, and sites. While systematic ways to track interventions and measure effectiveness exist, they are often highly inefficient, requiring significant manual effort that competes with other operational demands. Untimely, biased, and incomplete leading indicator data makes developing a business case difficult at best, stalling action, and forcing us to wait for an injury to occur before implementing a control.

How Computer Vision Changes the Equation

CV AI uses existing security cameras to continuously monitor the workplace, applying already trained machine learning algorithms to detect unsafe behaviors, environmental hazards, and near-miss events in real-time. The technology analyzes video feeds to identify specific risk factors - from ergonomic issues to equipment proximity concerns - creating a continuous stream of leading indicator data that was previously unavailable.

These systems can work with existing camera infrastructure in many cases, though hardware requirements vary by solution and facility needs. They provide continuous monitoring across operations, detecting unsafe behaviors as they occur: ergonomic risk, missing PPE, forklift proximity issues, and other hazards. Perhaps most valuable, CV can identify near-misses that would be invisible to human observers. The close calls that never make it into incident reports but represent critical warning signs.

The real power lies in pattern recognition. By processing large volumes of visual data, AI systems can identify systemic patterns that would be impossible to detect manually, revealing correlations across shifts, locations, and equipment types. This analytical capability transforms safety data from lagging metrics into leading indicators that can help predict and prevent future incidents.

Different solutions offer varying approaches to supervisor notification and intervention workflows. The shift from post-incident investigation to pre-incident prevention represents a fundamental change in how safety teams operate. When evaluating solutions, safety leaders should understand how technology approaches worker privacy. Leading approaches focus on behavioral patterns and environmental conditions rather than individual tracking, analyzing general workplace safety trends rather than employee-specific performance metrics.

The Operational Impact: Safety as a Performance System

Early adopters are reporting significant reductions in high-risk behaviors within months of deployment. These behavioral changes translate directly into financial impact: reduced workers' compensation claims, lower medical costs, and decreased legal exposure. The indirect savings prove equally substantial: improved employee retention and company culture, retained productivity, reduced turnover, and the avoidance of costs that follow serious incidents.

The benefits also extend beyond injury metrics. Operational continuity improves as organizations experience fewer disruptions, investigations, and work stoppages. Real-time data provides strategic leverage with insurers. Visible commitment to safety builds workforce trust and improves retention - critical advantages in tight labor markets.

Safety data increasingly informs decisions far beyond the EHS department. Operations leaders use insights to optimize process design, adjust shift planning, and improve workflow efficiency. Safety observations augmented by AI prove to be more efficient and effective than purely manual observations, returning productive time to operators and enabling safety professionals to focus on solutions.

Implementation Considerations: What Safety Leaders Should Know

Successful implementation requires thoughtful planning. Understand deployment timelines and resource requirements. Assess your existing camera infrastructure. While many systems work with existing equipment, coverage gaps may require investment. IT security requirements deserve attention, as these systems may require hardware to be installed or access to your network.

Establish and communicate clear policies on data usage and worker privacy from the outset, defining explicit boundaries between safety monitoring and individual surveillance. Communicate the purpose and benefits openly with your workforce—transparency builds trust. Ensure compliance with GDPR, local regulations, and union agreements if applicable.

Secure executive sponsorship and cross-functional alignment before deployment. Train supervisors thoroughly interpreting insights and intervening effectively. Integrate AI outputs with existing safety protocols rather than creating parallel systems. Pilot programs can build organizational confidence before full-scale rollout.

Move beyond lagging indicators like Total Recordable Incident Rate (TRIR) and Days Away, Restricted, or Transferred (DART) rates as your primary measures of success. Track leading indicators, such as near-miss detection rates, intervention response times, supervisory engagement levels, and behavioral trends. Correlate safety improvements with operational and financial outcomes to demonstrate comprehensive return-on-investment.

The Strategic Shift

The technology exists. The business case is clear. Progressive safety leaders are asking different questions:

• How can I leverage AI to eliminate bias and guesswork in identifying risks before they become injuries?
• What if safety success was measured by safety engagement and mitigated risk instead of TRIR?
• How do we make foresight, not hindsight, the foundation of safety?

Computer vision AI is mature, proven, cost-effective, and accessible. The question is no longer whether AI will transform workplace safety and operations—it's whether organizations will lead or lag in adoption. Those who move decisively will build safer workplaces, stronger safety cultures, and more resilient operations. Those who wait will find themselves at an increasing disadvantage, competing not just on safety performance but on operational efficiency, workforce retention, and total cost of risk.

The technology is ready. Is your organization?

This article originally appeared in the November/December 2025 issue of Occupational Health & Safety.