Redefining Task Risk Assessment with Computer Vision

–  Before lifting a tool, operating a crane, or stepping into a confined space, one simple question comes to every worker’s mind: “Is this task safe?”

For decades, heavy industries have relied on traditional risk assessment methods for different tasks to answer that question. These typically involve checklists, manual inspections, or supervisor approvals. While they bring structure to safety practices, they often fail to capture the real-world complexities of dynamic sites.

This is concerning when we consider the scale of the issue. When OSHA analyzed the fatality report by the Bureau of Labor Statistics, it showed a worker death every 99 minutes with an average of 15 lives lost each day of the year.

This is where computer vision, a novel safety tool of AI, enables machines to interpret and understand visual information and offers a fresh perspective.

Instead of static forms, the smart vision system activated Task Risk Assessment enables dynamic, real-time, and evidence-based methods which can adapt as conditions change. It doesn’t just support compliance, but actively empowers frontline workers and EHS teams by ensuring that the question “Is this task safe?” can be answered with confidence and accuracy.

Task Risk Assessment (TRA) is a structured process used to evaluate the potential risks associated with a specific task before it begins. Unlike broad workplace audits, TRA focuses on the task level. It asks supervisors and workers to break down a job into steps, consider what could go wrong at each stage, and identify the preventive measures that need to be in place.

For example, welding at height on a construction project involves several risks: sparks and heat exposure, falls from elevation, inhalation of fumes, or equipment malfunctions. AI for task-specific risk management in this scenario ensures that PPE like helmets and gloves are worn, guard rails are secured, fume extractors are installed, and the site is restricted to essential personnel only.

This step-by-step review makes AI-powered risk investigation one of the most practical and task-oriented tools available to ensure workplace safety.

Although dynamic risk assessment for tasks with AI often overlaps with other safety processes, it is important to distinguish it from various on-site process safety analysis. Each plays a unique role in workplace safety.

The key distinction is that TRA is the closest to the point of action. It is immediate, task-focused, and critical in deciding whether a job can safely proceed.

The need for an AI-based risk assessment for tasks arises from the fact that most workplace accidents occur during the execution of specific tasks rather than as a result of broad, general hazards. While a factory floor may be compliant in terms of safety signage, fire exits, or lighting, an accident could still occur if a worker attempts to operate a machine without proper lockout/tagout (LOTO) procedures.

It provides a proactive layer of safety, bridging the gap between general hazard awareness and the specific conditions under which a task is performed. It also supports compliance with regulations such as OSHA or ISO 45001, which require task-level risk evaluations. More importantly, an automated risk analysis empowers workers by involving them directly in safety planning, creating a culture where responsibility for safety is shared across teams rather than placed solely on management.

Intelligent video analysis redefines the traditional process by interpreting live video feeds, continuously monitoring tasks, flagging unsafe conditions, and verifying compliance with safety measures. In doing so, it transforms TRA from a static, paperwork exercise into a dynamic, adaptive, and evidence-backed safety tool.

Here are 5 ways in which an effective transformation for task analysis occurs with AI.

In the traditional setting, the risk assessment for every task often stops at the checklist stage. Supervisors tick boxes for PPE compliance or site readiness, but conditions can change in seconds. A crane’s load might start swinging, a worker may forget to wear a harness, or an unplanned blind spot could put someone in harm’s way. Static assessments simply cannot adapt to these dynamic risks.

AI-powered safety vision enables continuous, real-time validation. AI cameras automatically check if workers are wearing the correct PPE (helmets, gloves, harnesses) before a task starts. They also monitor environmental risks such as overlapping swing zones or unguarded machinery. Instead of relying on one-time approval, the system adapts instantly to shifting conditions.

For example, in early 2025, a Germany-based manufacturing company integrated computer vision-based safety monitoring for its welding operations. Before each task began, the system verified PPE compliance and scanned for nearby flammable materials. On one occasion, it flagged a gas cylinder placed too close to the welding zone—something a checklist had overlooked.

This real-time intervention prevented what could have been a catastrophic fire hazard.

Human judgment is subjective. Two supervisors may rate the same task with different levels of risk depending on their experience or mindset. Fatigue, workload, or pressure to meet deadlines further increase the chances of overlooking crucial safety elements. This inconsistency makes traditional TRA vulnerable.

AI-based intelligent monitoring introduces objectivity and evidence in task risk analysis. Risks are flagged based on visual data, not personal interpretation. Every unsafe act or non-compliance is documented with video evidence, which can later be used for training or accountability. Over time, the system identifies patterns in unsafe behaviors, making it increasingly accurate and context-aware.

In a high-rise construction project in Singapore, workers were scheduled to install prefabricated steel beams on the north wing scaffolding platform at approximately 12 meters above ground level. The activity involved:

• Climbing scaffolding using an extension ladder.

• Positioning and securing beams using power tools.

• Temporary anchoring with a safety harness and lifeline system.

• Coordination with crane operators for material hoisting.

The vision intelligence enabled AI risk assessment for the task generated the automated report just before workers began the task.

The intervention prevented a potential fall accident and averted a possible dropped-object incident that could have endangered ground-level workers. By addressing environmental risks, it also eliminated the chance of a wind-induced beam swing accident.

Task resumed safely within an hour, with risk score reduced from 8.6 (Very High) to 2.1 (Low), ensuring compliance and worker safety.

Traditional task assessments for risk are conducted before work begins. Once the task starts, there’s little structured oversight unless supervisors physically monitor the worksite—which is often impractical for large or hazardous operations. This gap means that mid-task safety breaches, like removing PPE or unsafe tool handling, can go unnoticed until it’s too late.

Computer vision extends the process beyond the start line. It monitors tasks throughout execution, ensuring safety measures are upheld in real time. If a worker removes protective gear midway, enters a restricted zone, or operates equipment unsafely, the system immediately alerts supervisors and EHS teams for corrective action.

Permit to Work (PTW) systems are critical safeguards that ensure high-risk activities—such as hot work, confined space entry, or electrical isolation—are conducted under controlled conditions. This human-led approach introduces vulnerabilities: rushed inspections, missed hazards, or over-reliance on subjective judgment can all lead to permits being issued even when underlying risks remain unresolved.

In dynamic worksites where conditions can shift within minutes, the gap between manual approval and real-time safety status becomes a serious concern.

Digitally integrated PTW systems use automation and objectivity directly in the approval workflow. Instead of relying solely on human inspection, these permit management platforms integrated with vision AI validate safety conditions through continuous video and sensor data. The system ensures that permits are not generated until critical preconditions are visually confirmed.

This creates a fail-safe, data-driven approval process where human oversight is supported, not replaced, by automated verification. Beyond approvals, the system maintains continuous oversight—meaning that if conditions deteriorate after a permit has been issued, alerts can automatically suspend the task until corrective actions are taken.

Traditional TRA is reactive—it identifies risks before a task starts but rarely helps in forecasting future risks. This means organizations miss opportunities to redesign workflows or training programs based on recurring unsafe behaviors.

Visual AI systems add a predictive layer to intelligent task risk evaluation. By analyzing large volumes of safety data, it identifies trends and recurring problem areas. If certain zones consistently see barricade breaches or if a particular task repeatedly involves PPE non-compliance, the system highlights these risks proactively.

This helps organizations not only prevent incidents but also refine long-term safety strategies.

The question “Is this task safe?” has always defined the very essence of workplace safety. Traditionally, the answer came from checklists, supervisor judgment, and experience. While valuable, these methods offered only a moment-in-time snapshot—one that often failed to keep pace with the changing realities of industrial sites.

With vision-driven safety systems, the answer transforms. Instead of asking once before a task begins, EHS teams and workers can now receive that answer at every stage: before, during, and even after the task is complete.

In this way, the age-old question no longer lingers with uncertainty. Computer vision provides a clear, consistent, and data-driven answer:

Deployment is usually simpler than most teams expect. In many cases:

• Existing CCTV or IP cameras can be used, so you don’t have to replace your infrastructure.

• The software can be installed on the cloud (for flexibility) or on-premises (for sensitive data environments).

• For sites with poor connectivity, edge devices can be added to process data locally in real-time.

In short, deployment is more about smart integration rather than overhauling your current systems.

Not at all. Most systems like viAct is designed to be user-friendly. Training usually covers two layers:

• Workers: How to respond when they receive alerts (like flashing screens, SMS notifications, or alarm sounds).

• Supervisors/EHS Teams: Using the dashboard, analyzing risk reports, and integrating the insights into ongoing safety reviews.

Training sessions are usually less than a day, with refresher sessions scheduled quarterly or after a system update.

While human supervisors can be highly skilled, they face challenges like fatigue, distraction, or limited visibility. It eliminates these constraints like 24/7 monitoring without breaks, consistency in enforcing rules (no bias, no oversight) and high accuracy with well-trained models achieve 90%+ accuracy in detecting hazards.

Instead of replacing humans, it complements supervisors—flagging issues instantly so they can focus on critical decision-making.

Yes. Algorithms in viAct  can be tailored to recognize hazards unique to construction, oil & gas, mining, or manufacturing. For example, crane swing detection in construction or gas detector monitoring in offshore rigs can be specified based on the requirements within seconds.

Organizations see fewer near-misses, reduced downtime, and improved compliance. It is seen that AI-enabled safety monitoring can cut incident rates by 20–40%, which translates into significant savings on accident costs, insurance claims, and project delays.

Are your Task Risk Assessments still performed manually?