AI confronts hidden industrial workforce productivity gap in Middle East

• The productivity challenge in industrial operations has never been about workforce capacity—it has been about the inability to continuously align effort with execution at scale.
• AI introduces an intelligent layer of visibility that captures how time, movement, and behaviour translate into output.

Consider what happens when a large construction site in the Middle East begins to mobilise. Thousands of workers pass through entry gates, supervisors scan rosters, and operations appear to be running at full capacity.

By mid-morning, however, subtle inefficiencies begin to surface—workers waiting for permits, teams misaligned with tasks, delayed shift starts that go unrecorded. On paper, productivity is intact. In reality, it is already slipping.

This gap between recorded activity and actual output is one of the least visible challenges in industrial operations today. And it is precisely where AI is beginning to play a defining role.

The Middle East’s industrial expansion has been defined by scale—megaprojects under NEOM like The Line and Oxagon, accelerated infrastructure development under Saudi Vision 2030, and rapidly growing logistics and manufacturing ecosystems. Yet beneath this visible progress lies a quieter, more complex issue – a structural productivity gap that traditional systems have been unable to quantify or correct.

Across the region, AI-powered systems are transforming workforce monitoring from static attendance tracking into continuous operational intelligence—capturing not just who is present, but how work unfolds in real time.

Limits of traditional workforce oversight

Industrial workforce management has over the time relied on periodic supervision such as attendance logs, manual reporting, and physical site inspections. These methods were sufficient in less complex environments, but they are increasingly misaligned with the scale and diversity of modern Middle Eastern operations.

A single site today may involve multiple contractors, thousands of workers, and overlapping shifts across vast physical areas. In such conditions, supervision becomes intermittent, and productivity becomes an inferred metric rather than a measured one.

The result here, is a persistent disconnect in the ecosystem. Workers may be present but not effectively deployed. Teams may be assigned but not synchronised. Delays occur not as isolated incidents, but as recurring patterns that remain largely invisible within traditional systems.

McKinsey & Company has highlighted how large-scale industrial projects routinely experience significant productivity losses due to fragmented workflows, poor visibility, and inconsistent execution on the ground. Most of the projects than run over their budget by 70 per cent and over schedule by 60 per cent.

This ascertains the suggestion by the International Labour Organisation that a sustainable productivity environment through integrated multilevel interventions is important across different sectors to address these issues.

Moving towards workforce intelligence

The shift underway with AI-based workforce productivity monitoring is subtle but fundamental – from tracking workforce presence to understanding workforce behaviour.

AI-enabled modules specialising in industrial productivity monitoring introduce a layer of continuous intelligence that connects identity, location, and activity. Contactless face recognition ensures accurate attendance while eliminating proxy check-ins and manual errors. More importantly, it establishes a verified digital baseline from which workforce movement and deployment can be analyzed.

From there, the intelligent systems reconcile gate-level attendance with on-ground presence, ensuring that workers are not only on-site but operating within their assigned zones. This capability addresses one of the most overlooked inefficiencies in industrial operations—the assumption that headcount reflects productivity.

By aligning roster data with real-time activity, organisations can detect misallocation, close coverage gaps, and deploy the right skills where they are actually needed.

Using AI to engineer discipline at scale

Productivity is not only a function of workforce size; it is a function of consistency. In high-density industrial environments like Middle East, even minor deviations in shift discipline like recurring late arrivals, not adhering to SOPs, extended breaks and early exits among workforces can accumulate into significant output loss.

Through continuous video analytics, patterns of shift adherence can be observed and benchmarked across teams, contractors, and operational zones using KPIs such as schedule adherence (planned vs actual shift start time), effective working time and relate them to labour utilisation rate.

In a Dairy and Beverage Facility in UAE, operational managers were facing constant challenges of workforce hygiene maintenance. Despite strict protocols, variations in adherence such as missed sanitisation steps, improper PPE usage, and inconsistent zone discipline—were impacting both product quality and audit readiness.

To match the required levels of compliance, the unit deployed AI monitoring. This led to 30 per cent improvement in workforce discipline with an achievement of more than 95 per cent hygiene compliance accuracy. More importantly, hygiene was no longer dependent on manual enforcement—it became a measurable, trackable operational KPI, embedded directly into daily workflows.

This transition marks a critical evolution: discipline is no longer managed through policy alone, but through data-driven operational design.

Uncovering the hidden cost of idle time

Beyond discipline, a substantial portion of productivity loss originates from idle time—moments when workers are present but unable to proceed due to external constraints.

These constraints often stem from systemic inefficiencies like delays in material availability, bottlenecks in approvals, or gaps in coordination between teams. Individually, they may seem insignificant. Collectively, they represent one of the largest drains on productivity.

AI systems can detect these patterns by analysing workforce movement, inactivity, and workflow disruptions. Repeated waiting periods, unnecessary movement across zones, and clustering of inactivity signals can all indicate deeper operational issues.

By surfacing these insights, organisations can move beyond reactive problem-solving toward proactive optimisation—addressing not just worker behaviour, but the structural inefficiencies that shape it.

Accountability in a multi-contractor ecosystem

The Middle East’s industrial landscape is heavily reliant on multi-contractor models, where different vendors operate simultaneously within the same site. This creates inherent challenges in maintaining consistent standards of productivity and accountability.

AI introduces a unifying framework by enabling contractor-level benchmarking based on consistent, objective metrics. Output per man-hour, adherence to schedules, and workforce utilization can be measured across all contractors, regardless of size or scope.

This level of transparency has implications beyond productivity. It strengthens payroll accuracy, supports compliance with wage protection systems, and reduces disputes by providing verifiable records of workforce activity.

A structural shift in how productivity is governed in 2026

What is emerging is not simply a technological enhancement across the industrial sites in Middle East, but a redefinition of industrial productivity itself.

The productivity challenge in industrial operations has never been about workforce capacity—it has been about the inability to continuously align effort with execution at scale. AI introduces an intelligent layer of visibility that captures how time, movement, and behaviour translate into output.

This perspective reflects a broader transition across the industry, where, productivity is no longer assessed retrospectively through reports and audits. It is increasingly governed in real time, through continuous data and adaptive decision-making.

• Gary Ng is the CEO and Co-Founder of viAct, one of Asia’s top Sustainability-focused AI company that provides “Scenario-based Vision Intelligence” solutions for risk prone workplaces.

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