AI Computer Vision for Workplace Safety: How Leading Brands Turn Cameras into a Safety Engine
Workplace safety has never been more visible — or more expensive to ignore.
Recent international estimates show that work-related accidents and diseases now cost the global economy a mid–single digit percentage of GDP every year, translating into trillions of dollars in lost output, healthcare and compensation. At the same time, global studies put annual work-related deaths in the millions and non-fatal work injuries in the hundreds of millions.
Zoom in to a single country and the story stays serious. In the United States alone, official statistics for 2023 report around 2.6 million non-fatal workplace injuries and illnesses in private industry, alongside thousands of fatalities. Even with gradual improvements, that is still an enormous human and financial burden.
The gap is clear: training, PPE and traditional audits are necessary, but they are not sufficient. Cameras already watch most high-risk areas, but they typically act as passive recorders.
AI computer vision workplace safety systems change that dynamic — turning video into a real-time safety sensor. For a platform like Objiq, this is where computer vision stops being “just another AI technology” and becomes a strategic EHS capability.
1. Why Traditional Safety Programs Still Miss Too Much
Most organisations already invest in safety:
- Written policies, onboarding and toolbox talks
- PPE standards and signage
- Incident investigations and monthly safety dashboards
- CCTV coverage of production lines, loading bays and corridors
Yet serious injuries, near misses and unsafe behaviours keep happening. Four structural problems come up again and again in recent EHS research and commentary:
- 1.Lagging indicators dominate
- 2. Near misses are dramatically under-reported
- 3. CCTV is mostly forensic
- 4. Supervisors have limited coverage
AI computer vision aims directly at these blind spots — watching continuously, flagging unsafe acts in real time, and generating structured data that EHS teams can use to change behaviour and redesign work.
2. What an AI Computer Vision Safety Stack Actually Looks Like
Modern workplace-safety computer vision platforms broadly follow the same pattern, whether deployed in manufacturing, logistics, energy or construction.
2.1 Data capture from existing cameras
Most solutions connect to the CCTV or IP camera infrastructure that companies already own. Video streams from lines, racks, dock doors, walkways and yards are securely routed to AI models running at the edge, in the cloud, or in a hybrid setup.
2.2 AI models trained for EHS risks
These models are optimised to detect safety-relevant patterns such as:
- PPE compliance (helmets, vests, gloves, goggles, harnesses)
- People entering danger zones, line-of-fire or exclusion areas
- Forklifts and industrial vehicles travelling too fast or too close to pedestrians
- Unsafe working at height, blocked fire exits or poor housekeeping
- High-risk ergonomic postures and repetitive strain patterns
Recent academic and industry work on computer vision for occupational safety shows that these models can operate reliably at real-time frame rates and integrate with existing safety frameworks.
2.3 Real-time events and alerts
When a risk is detected, the platform can:
- Trigger on-screen or audible alerts in control rooms
- Send notifications to supervisors, HSE managers or team leaders
- Log the event with metadata and short video clips for coaching and root-cause analysis
Instead of manual tally sheets, safety teams get streaming data on how often, where and when specific risk behaviours occur.
2.4 Analytics and continuous improvement
Over time, this event stream becomes a rich dataset:
- Heatmaps of where unsafe acts cluster
- Trends by shift, contractor, line or site
- Before-and-after views of new training or engineering controls
Analyst firms covering video analytics for EHS now treat “safety computer vision” as its own category, with buyers evaluating vendors on detection coverage, privacy, deployment speed and analytics depth.
3. Case Studies: How Leading Brands Use AI Safety Vision
The most convincing evidence comes from named companies that have gone beyond pilots and published their results. Here are several public examples relevant to Objiq’s positioning.
3.1 Swire Coca-Cola and Intenseye: Safer Beverage Operations
Swire Coca-Cola, one of the largest Coca-Cola bottlers in Asia-Pacific, has publicly shared how it uses Intenseye’s AI-powered safety platform across bottling and distribution operations. After integrating computer-vision monitoring with existing CCTV systems, Swire Coca-Cola reported:
- A double-digit percentage reduction in safety hazards in the first year of deployment.
- A 27% decrease in Lost Day Rate (LDR), meaning fewer severe incidents keeping people away from work.
The company has also highlighted how AI-identified forklift–pedestrian near misses at shift change led to new procedures and training, improving safety before serious accidents occurred.
Intenseye has separately discussed a global beverage manufacturer in Japan that achieved around a 90% reduction in unsafe acts and conditions within six months by using similar technology for real-time monitoring and coaching.
3.2 Marks & Spencer and Protex AI: 80% Fewer Incidents in 10 Weeks
UK retailer Marks & Spencer partnered with Irish startup Protex AI to monitor safety in its large distribution centre at Castle Donington. According to Marks & Spencer, after rolling out Protex AI’s computer-vision system on top of existing cameras, the site saw:
- An 80% reduction in workplace incidents in just 10 weeks.
- An increase in near-miss reporting, as AI-generated observations made it easier to spot and discuss risk behaviours.
The system automatically identifies unsafe acts on video — such as pedestrians in vehicle routes, poor use of walkways or blocked emergency paths — and feeds clips into the EHS team’s workflow. Instead of relying solely on manual observation and self-reporting, Marks & Spencer now has a continuous stream of objective safety insights.
3.3 Major UK Packaging Manufacturer and Protex AI: 62% Fewer Safety Events
In another Protex AI case study, a major UK packaging manufacturer used AI video analytics to understand how people and vehicles actually moved through its production site. Within the first month of deployment, the company reported:
- A 62% drop in safety events.
- A 92% reduction in area-control risks, after redesigning layouts and walkways based on data from the system.
The key point is not just incident reduction; it is the speed of insight. Within hours of going live, the safety team could see patterns they had never been able to quantify before, making it easier to justify layout changes and capital investments.
3.4 Forklift–Pedestrian Monitoring with viAct
Computer-vision provider viAct has documented how AI monitoring of forklifts and pedestrians in industrial environments can:
- Create virtual safety zones around vehicles and danger areas.
- Detect close calls between forklifts and workers in real time.
- Push alerts to operators and HSE teams so they can intervene before collisions occur
Given that forklifts are associated with tens of thousands of injuries and hundreds of deaths globally each year, focused computer-vision monitoring of vehicle–pedestrian interactions is one of the fastest ways to reduce high-severity risk.
4. Market Momentum: From Compliance Tool to Strategic Investment
AI computer vision for workplace safety is no longer a niche experiment. Several recent data points show how fast the space is maturing:
- Global workplace-safety software and services: Market research estimates that the overall workplace-safety market is approaching USD 20 billion in the mid-2020s, with forecasts of roughly USD 38–39 billion by 2030 at a double-digit CAGR.
- Safety computer-vision adoption: Recent reports on the safety computer-vision market note that close to a third of surveyed firms plan to increase investment in computer vision or launch pilots in the next year.
- AI-driven ergonomics and EHS analytics: Forecasts for AI ergonomics and safety analytics suggest a market size heading towards the high single-digit billions of dollars by the early 2030s, driven by musculoskeletal-risk reduction and centralised EHS analytics.
For Objiq and its ecosystem, this matters in two ways:
- Budget availability – Safety leaders increasingly have board-level backing to invest in AI as a cost-saving and risk-reduction tool, not just a compliance line item.
- Vendor expectations – Buyers now expect concrete case studies, measurable ROI and enterprise-grade privacy and governance, not just interesting prototypes
5. Business Case: How AI Safety Vision Pays for Itself
When you connect macro statistics, company-level case studies and internal cost models, the ROI story becomes compelling.
5.1 Direct cost reduction
- Fewer recordable incidents and lost-day cases cut medical costs, workers’ compensation, overtime and replacement labour.
- Reduced equipment damage and unplanned downtime protect capacity and on-time delivery.
- Avoided serious incidents lower the risk of regulatory penalties and litigation.
5.2 Productivity and culture
- Supervisors spend less time chasing basic PPE violations and more time on coaching and process improvement.
- Near-miss visibility increases, allowing teams to address systemic issues instead of isolated events.
- Workers perceive that the organisation is serious about safety, especially when AI is framed as protecting them rather than monitoring them for punishment.
5.3 Strategic benefits
- Large customers and investors increasingly ask detailed ESG and safety questions; being able to demonstrate AI-driven leading indicators is a differentiator.
- Video-analytics infrastructure can support adjacent use cases, such as asset utilisation, quality checks and process optimisation, creating additional value on the same data.
For many of the companies cited above, AI safety vision delivered double-digit percentage reductions in incidents within months, not years — often at site-level payback periods well inside typical capital-investment thresholds.
6. Implementation Roadmap: How to Start with AI Workplace Safety
A sensible deployment path for AI computer vision workplace safety typically looks like this:
Step 1: Baseline risk and data
- Review two to three years of incident logs, near-miss reports and root-cause analyses.
- Identify hotspots: forklift–pedestrian interaction, high-risk maintenance tasks, traffic near loading docks, high-strain manual handling.
Step 2: Choose one or two lighthouse use cases
Examples:
- Examples: Forklift and pedestrian separation in a busy warehouse zone
- PPE compliance in a fabrication, foundry or bottling area
- Slip, trip and fall risk around wet processes or cold rooms
Step 3: Connect cameras to an Objiq-style vision platform
- Start with existing CCTV or IP cameras covering the chosen zones.
- Configure models for person detection, PPE detection, zone breaches, vehicle tracking or posture assessment, depending on the use case.
- Run in shadow mode initially, generating analytics without triggering live interventions.
Step 4: Calibrate and integrate
- Compare AI-detected events with supervisor observations and incident logs.
- Tune thresholds to minimise false positives while still catching meaningful risk.
- Integrate with communication tools such as Vaarta so that alerts can be pushed to supervisors, control rooms or safety champions via chat or mobile channels.
Step 5: Operationalise workflows
- Define clear playbooks: who receives which alerts, what actions they take, and how events are closed.
- Use weekly or monthly safety-vision dashboards to prioritise engineering controls, training and layout changes.
- Combine AI-identified clips with toolbox talks to make abstract rules concrete.
Step 6: Scale and govern
- Roll out successful patterns across additional lines, buildings and sites.
- Standardise policies on video retention, anonymisation and AI ethics to maintain trust with workers and unions.
- Continuously review leading and lagging indicators to ensure that AI vision continues to drive real improvements.
7. Where Objiq Fits: From Single Use Case to Safety Intelligence Platform
For Objiq, AI computer vision workplace safety is not just one more feature — it is an opportunity to become the safety intelligence layer that sits across cameras, plants and regions.
- Model hub for EHS risks — Objiq can host a library of safety models: PPE detection, unsafe zone entry, forklift–pedestrian interaction, line-of-fire exposures, ergonomics and more.
- Unified safety event stream — All vision-derived events — from Swire-style forklift near misses to Marks & Spencer-style unsafe pedestrian behaviour — flow into a single, queryable safety timeline.
- Deep integration with the wider ecosystem
- Vaarta for real-time conversational alerts and safety broadcasts.
- EHS and QHSE systems for record-keeping and regulatory reporting.
- Analytics tools for cross-site benchmarking and investment planning.
- Scalability and governance by design
8. Conclusion: From Cameras as Witnesses to Cameras as Guardians
Workplace safety is at an inflection point. The numbers — millions of injuries, millions of deaths, trillions in lost GDP — make it clear that traditional, reactive approaches have hit their ceiling.
AI computer vision workplace safety systems are redefining what is possible:
- Companies like Swire Coca-Cola and Marks & Spencer are publishing double-digit percentage reductions in incidents and lost-day rates.
- Packaging manufacturers and food distributors are reshaping their layouts based on real movement data, not assumptions.
- New EHS analytics and ergonomics markets are emerging around the idea that visual data should be treated as a core safety asset.
For organisations that want to protect people, reduce risk and build a modern safety culture, the question is no longer whether to use computer vision, but where to start and who to partner with.
Objiq’s role is to turn every relevant pixel into a continuous safety signal — and to plug that signal into the broader Marktine ecosystem so that safety insights become action, not just another report.