By Industrial sectors in Africa are currently undergoing a significant transition. The African Development Bank’s (AFDB) African Economic Outlook report for 2025, projected an economic growth to reach approximately 3.9% in 2025 from 3.3% in 2024, throughout Africa. Much of the anticipated expansion was expected to occur as a result of increased output in mining, oil and gas, construction, and large-scale manufacturing sectors. In addition, the AFDB estimated that Africa has roughly 30% of the world’s mineral reserves, and forecasts total investments in oil and gas production to reach $41 billion by 2026 across Africa. This reflects a rather significant change in terms of both the scale and complexity of industry overall.
But growth can be a liability without protection. Across African heavy industries, workers are at a greater risk of injury as they are daily exposed to some of the riskiest works like, interactions with equipment, lifting operations, and working in confined spaces associated with active industrial sites. The challenge that safety and EHS leaders face today is not if they should prioritize safety, but the bigger question is “how to operationalize safety systematically at scale”. This is why the AI-powered EHS solutions are becoming an integral part of leadership responsibilities rather than just an operational function.
Computer Vision and Real-Time Risk Visibility
At the core of modern AI-driven EHS systems is computer vision – the ability of machines to analyze live camera feeds and identify unsafe conditions in real-time, automatically, continuously, without any human fatigue or gaps in attention.
In fast-moving environments, hazards can change rapidly. For instance, a worker entering an operating machine zone, worker under suspended load, or a vehicle reversing without a spotter. These incidents often happen more quickly than a site supervisor can respond to, resulting in potential fatalities. The use of computer vision completely overcomes this situation. By integrating AI into existing CCTV systems, modern EHS platforms enable teams to monitor a large number of high-risk activities, from PPE non-compliances and danger zone intrusions to ergonomics risks and fall protection, and trigger instant, and contextualized alerts for the EHS teams to take appropriate actions even before risks turn into fatalities.
This change from reactive safety management to proactive identification of risks in African industrial sectors is crucial because the majority of serious incidents in Africa’s industrial sectors are “repeat-category events”. This has been upheld by the Department of Mineral Resources, South Africa, which recorded 1,841 occupational injuries in mining in 2024. Falls, transportation and machine-related incidents were the most attributed repeated accident types. These and similar other risks are precisely the scenarios where computer vision monitoring delivers measurable early intervention.
Beyond physical hazard detection, AI behavioural monitoring is also addressing risks that were previously invisible in EHS data. One such reference comes from Ghana, where a mining operator was experiencing a large number of workplace conflicts at their large site. Within three months of deploying AI-based monitoring system, the site witnessed a 62% reduction of aggressive behaviour and physical altercations, leading to declined shift-related disputes and improved workers’ morale, and an overall stable, cooperative, and productive work environment across all operational departments. This real-world case demonstrates how computer vision is redefining the EHS risk and why leaders who only use traditional methods to monitor safety exposure to physical hazards are only capturing part of the overall EHS risk view.
Leadership, Data, and Closing the Multi-Site Safety Gap
Technology in and of themselves do not contribute to improving operations unless the leaders rolling them out have proper organizational structure to accomplish an intended goal. Many organisations in Africa are seeing how important it is for them as leaders to use integrated EHS data as one of their most strategic advantages on a long-term basis, especially given that many of their operations span across remote, multi-site, and contractor-heavy environments.
Standardization presents a significant challenge for organizations to achieve. Whether it is a mining company that operates in multiple countries or an oil & gas contractor who manage rotating crews across dozens of well sites, these businesses cannot rely upon periodic audits and human observations to ensure that their safety performance is consistent across all locations.
AI-based EHS platforms provide a solution to this problem by giving users one common view of all leading safety indicators, such as, near-miss frequency, PPE compliance rates, risky behavioural patterns, and equipment anomaly information. By presenting this data on a single integrated dashboard, executives can identify areas where risks are accumulating prior to any incidents occur and can hold contractors and remote operations accountable to the same level of performance standards as their primary locations.
Another dimension where leadership-level EHS data is proving its value is “predictive maintenance”. The relevance of predictive maintenance in the African heavy industry is due to the magnitude of equipment-related incidents that are still the most prevalent accident types across both mining and construction industries throughout the continent. Consequently, through continued monitoring and detecting anomalous asset status using AI, allows companies to prosecute proficiently without having to monitor asset(s) as per their scheduled inspection and thus reduce the likelihood of discovering a mechanical flaw that may develop between inspections.
For EHS leaders with aged fleet maintenance or a significant volume of high utilised assets located in remote locations, the continuous intelligence provided by AI supports the transformation of maintenance from being a retrogressive cost centre to a pro-active functionality supporting the safety of workers.
“Emergency response readiness” is yet another area where leadership cannot afford to rely on traditional systems alone. For example, using an AI system that can monitor the environmental conditions or air quality in an underground environment, or can locate workers in real-time, are giving EHS leaders the situational awareness that manual systems simply cannot provide during an emergency. This has been strengthened by AI-enabled geofencing and LiDAR-based 3D spatial mapping, that cumulatively allow sites to enforce dynamic exclusion zones with centimetre-level precision and generate accurate environmental models of underground or high-risk zones.
Safety as the Foundation of Sustainable Industrial Growth
The growth of the African continent’s industrial sector is real and offers the continent’s workforce as well as economies many growth options. However, the increased size and speed of industrial growth also increase the amount of safety risk for workers. Leadership that continues to view EHS as merely a compliance requirement will continue to respond to incidents after they happen; while those that have embraced AI-powered EHS strategies throughout their operational processes and governance framework, will create a sustainable and evolving safety culture; making their organizations more resilient, productive, and attractive to the international investors and business partners that Africa’s growth ambitions require.
The evidence is already emerging across the continent. When computer vision identifies hazards that the best trained supervisors are unable to see, EHS data flow in real-time from a site in the DRC to a leadership team in Johannesburg, and when the pattern of near-misses can be identified and fixed before they result in fatalities; safety stops being a cost and becomes a competitive advantage. For industrial organizations in Africa, there is thus, a clear imperative that growth infrastructure must include safety infrastructure.
