This article draws insights from a recent webinar hosted by IOSH and viso.ai, featuring:
- Duncan Spencer (IOSH’s Head of Advice and Practice)
- Nico Klingler (co-founder and co-CEO, viso.ai)
- Jeremy Michaels (Strategic Content Writer, viso.ai)
- Moderated by Kellie Williams from Redactive Media
The workplace safety landscape is transforming
While traditional safety processes have served well, they often fall short when teams are under pressure, sites are complex, and incidents can escalate in moments. The emergence of AI Vision technology is changing this paradigm, offering health and safety professionals powerful new tools to move from re-active to predictive safety management.
As Duncan Spencer, IOSH’s Head of Advice and Practice explained:
“AI is being used in project management; accident prevention, reporting and analysis; emergency response and management. No doubt that AI in all of its facets has a future in the management of OSH.”
The hidden costs of re-active workplace safety
Most organizations have robust policies and procedures in place, yet the execution remains inconsistent. Manual checks are patchy by nature, CCTV systems typically capture incidents after they have occurred, and under-reporting of near-misses continues to be the norm, rather than the exception.
This re-active approach carries hidden costs that extend far beyond compliance obligations. When safety systems break down, organizations face not just immediate injury costs, but reputation damage, legal exposure, operational disruption, and profound human impact. The scale of hidden risk is enormous: workplace injuries cost trillions globally each year. Even when processes look good on paper, they struggle to scale effectively across multiple sites, shifts, and operational contexts.
The challenge facing health and safety professionals is clear: how do we identify and address risks before they manifest into incidents? The answer lies in leveraging technology that can spot degradation before failure occurs, whether in behavior, equipment use, or site workflow.
Understanding AI Vision in workplace safety
AI Vision represents a significant leap forward from traditional monitoring systems. At its core, it is computer vision powered by artificial intelligence that monitors and analyzes live video feeds to detect specific safety risks in real time.
Think of it as giving your existing cameras the ability to ‘understand’ what they’re seeing – whether that’s someone not wearing required PPE, entering restricted zones, or working dangerously close to mobile equipment. But what makes AI Vision particularly powerful for health and safety applications is its consistency and reliability.
Unlike human oversight, it never gets tired, distracted, or overwhelmed. It operates 24/7, maintaining the same level of vigilance throughout every shift. The technology goes beyond simple motion detection – it understands intention and context, enabling sophisticated risk assessment and intervention capabilities.
Duncan Spencer from IOSH summarised by outlining:
“AI can capture real time information and flag problems or near misses as they are happening. This can reduce reaction time and it can result in savings in business costs.”
Modern AI Vision systems are trained on real-world risk events across industries, creating models that can identify not just what’s happening, but crucially predict what might happen next.
This predictive capability transforms safety management from a re-active discipline to a pro-active science.
Real-world applications driving results
The practical applications of AI Vision in workplace safety are extensive and impactful. These systems don’t just prevent incidents – they build comprehensive risk maps over time, revealing patterns and hotspots that inform broader safety strategy.
As highlighted by Nico Klingler, co-founder of viso.ai, in the webinar:
“Our models are trained on real-world risk events, across industries. They’re not just detecting motion: they’re understanding intention and context as well.”
Vehicle-person proximity detection has proven particularly valuable in manufacturing and logistics environments, where mobile equipment presents ongoing risks. Another highly prevalent use case is PPE compliance monitoring at entry points, which eliminates the inconsistency of manual checks while creating accountability loops. And a further example – dynamic hazard zone monitoring – addresses one of construction’s biggest challenges: constantly changing site conditions.
When workers see the data, they tend to engage more actively with safety protocols. It is essential to emphasise that this isn’t about punishment – it’s about empowerment through visibility.
AI Vision can adapt to new configurations in real time, triggering alerts the moment someone enters an active hazard area. This flexibility is crucial in environments where traditional static safety measures fall short. Emergency preparedness benefits significantly from continuous monitoring of walkways and exit routes. Many organizations discover violations they didn’t know existed, from blocked corridors to improperly stored materials that could impede evacuation.
Perhaps most importantly, AI Vision excels at identifying unsafe behaviors – climbing on machinery, improper lifting techniques, or shortcuts that bypass established safety procedures. Rather than simply flagging violations, forward-thinking organizations use these insights to redesign workflows, reposition signage, and modify access points.
AI Vision becomes not just an alert system, but a tool for intelligent workplace design.
Evidence-based safety management
The IOSH Principles for good occupational safety and health emphasize the need to evidence risk and use it to build knowledge and information. This leads to becoming a learning organization that proactively seeks improvement.
AI Vision technology directly enables these principles by providing unprecedented visibility into workplace risks and safety performance. Data-driven insights allow safety professionals to move beyond intuition and anecdotal evidence.
When one site shows three times the risk events of another despite identical policies, the technology reveals cultural and operational differences that require attention. Heat maps showing when and where incidents occur most frequently enable targeted interventions rather than broad-brush approaches.
This evidence-based approach transforms safety conversations at every level. Leadership discussions focus on specific, measurable risks rather than general concerns. Frontline coaching becomes targeted and relevant. Resource allocation decisions are backed by hard data rather than assumptions.
Addressing privacy and trust concerns
The implementation of AI Vision technology inevitably raises questions about privacy and workplace surveillance. These concerns are legitimate and must be addressed thoughtfully.
As Jeremy Michaels, Strategic Content Writer at viso.ai noted:
“It is important to take a ‘privacy-by-design’ approach. That means workforce dignity and consent are part of the technical architecture, not an afterthought.”
Edge AI processing means that video streams are analyzed locally rather than sent to cloud servers, addressing many data protection concerns. Object blurring technology ensures anonymization, while GDPR compliance and security certifications provide additional assurance.
Most importantly, successful deployments prioritize transparency and worker engagement. When teams understand what the system detects, what it ignores, and how data is handled, trust develops. Involving unions and worker councils in the implementation process builds confidence and support.
The goal is to position AI Vision as a digital safety colleague – one that never blinks, never gets distracted, and flags issues before they escalate. When workers see the technology as a safeguard rather than surveillance, adoption and effectiveness increase dramatically.
Implementation and scalability
Getting started with AI Vision doesn’t require infrastructure overhaul. Most deployments leverage existing CCTV installations, adding intelligence layers that transform passive recording into active safety management. Pilots can be operational within days rather than months, enabling rapid proof-of-value demonstration.
The scalability advantage is significant. What works for one facility can be replicated across global operations with minimal customization. This consistency ensures that safety standards and insights are shared across the entire organization, regardless of geography or local variations in procedures.
The future of predictive safety
The evolution toward predictive safety is the next frontier in workplace protection. Future AI Vision systems are moving beyond detecting violations to understanding scenes, context, and emerging risks. Rather than just identifying that something went wrong, these systems can explain why and predict what’s likely to go wrong next.
This predictive capability will enable dashboard alerts such as “This site is likely to have a risk spike tomorrow at 7.00am based on current patterns”. This foresight allows pro-active intervention rather than re-active response.
AI-powered workplace safety is here to stay
AI Vision offers health and safety professionals powerful new capabilities to protect workers and strengthen safety culture.
Duncan Spencer from IOSH aptly summarised this potential by saying:
“AI promises so much to modern safety management: it can have pro-active aspects, be data-informed, and scalable across different sites and types of operations.”
By providing consistent, data-driven insights into workplace risks, it enables the transition from reactive incident management to predictive risk prevention. The technology doesn’t replace safety teams – it enhances them with better tools, clearer visibility, and actionable intelligence.
For organizations serious about the path to achieving zero harm while maintaining operational excellence, AI Vision represents not just an opportunity, but an imperative. The question isn’t whether AI will transform workplace safety – it’s whether your organization will lead this transformation or follow, lagging behind.