How AI and LiDAR Turn Idle Assets into ROI

In high-risk industries such as construction, mining, oil & gas, and heavy manufacturing, every machine, vehicle, and piece of equipment represents a significant capital investment. As per the data from the equipment manufacturer, a 36-ton excavator operating 1,000 hours per year wastes roughly 400 gallons of fuel simply by idling. That’s fuel, maintenance costs, and potential safety risks piling up without any contribution to productivity.

Imagine a mining fleet of 50 haul trucks, each costing $250,000 annually to maintain. If even 25% of the fleet is idle, the company is essentially losing $3.125 million per year in underutilized capital, not counting productivity delays and opportunity costs.

Yet, many organizations still rely on manual tracking, periodic audits, and reactive maintenance schedules in 2025.

AI & LiDAR technologies are now enabling organizations to rethink asset utilization, transforming idle machinery into revenue-driving assets. Beyond cost savings, these tools also enhance safety, predictive maintenance, and operational efficiency, helping EHS leaders meet both productivity and compliance goals.

Let’s dive into the details.

Before exploring the transformative advanced options provided by AI & LiDAR-enhanced technologies, it’s important to understand why idle equipment is such a costly problem.

Idle machinery impacts operations in heavy industries in three major ways:

Understanding these costs sets the stage for why organizations are turning to AI and LiDAR for solutions.

AI-powered monitoring systems address the hidden costs of idling by turning data into actionable insights. For example, General Electric uses a digital twin solution to prevent downtime by diagnosing and forecasting the risks beforehand. Siemens also uses AI to detect any form of anomalies, predict the underlying failures, and plan maintenance accordingly.

By analyzing machine activity, predictive patterns, and operational needs, AI enables organizations to actively reduce downtime and increase equipment efficiency.

Vision AI-powered intelligent monitoring tracks machine activity in real time. If a piece of equipment is turned on but not performing productive work, AI can automatically suggest shutting it down after a short period, such as 15 seconds. This reduces unnecessary fuel consumption, wear, and operational risk.

For example, in a large construction site in Dubai, AI tracked multiple forklifts simultaneously, identifying those being powered on but not actively engaged in work. The system, upon any identification, immediately alerts operators or supervisors, allowing them to intervene.

Over time, the AI can adapt to site-specific patterns, learning which periods of inactivity are normal versus avoidable idling. This continuous learning ensures idle detection becomes more precise, helping organizations maintain smoother operations while lowering operational risks and resource waste.

By analyzing sensor data and historical maintenance records, AI can predict equipment failures before they happen. This proactive approach reduces downtime and keeps assets available for productive tasks, rather than being sidelined for unexpected repairs.

Say, in a mining environment, computer vision can monitor key indicators such as engine performance, hydraulic pressure, and vibration levels. If the system detects a pattern consistent with a developing fault, it can automatically generate a maintenance alert.

Maintenance teams can then inspect and address the issue during planned downtime rather than reacting to an unexpected breakdown. This ensures equipment remains productive, reduces idle periods, and contributes to a more organized and efficient workflow across the site.

AI optimizes asset allocation by evaluating availability, capacity, and proximity. Idle machines can be dynamically reassigned to tasks where they are most needed, ensuring high utilization and efficiency.

For instance, on a drilling site, multiple pieces of heavy machinery may be required for different tasks simultaneously. AI can monitor which machines are underutilized and assign them to areas where work is lagging, ensuring that all resources are efficiently deployed. In an Abu Dhabi-based oil & gas company, viAct AI helped improve annual productivity by 50% with automated stoppages based on incidents.

Supervisors receive real-time insights into which machines are nearing task completion and pre-plan subsequent assignments, enabling a seamless handover of resources. This intelligent allocation reduces idle time, improves productivity, and allows the operations team to focus on critical strategic decisions rather than micromanaging asset deployment.

These AI capabilities illustrate the “brain” behind smarter asset management, but for AI to function effectively, it needs precise information about the location and status of equipment on the ground—and that’s where LiDAR comes in.

LiDAR (Light Detection and Ranging) complements AI by providing accurate spatial awareness of equipment and work zones. Where AI predicts, LiDAR observes, allowing organizations to monitor every asset with unprecedented precision.

LiDAR generates high-resolution, three-dimensional maps of entire worksites, offering a comprehensive view of equipment positions, terrain features, and site layout. This visualization allows EHS and operations managers to understand spatial relationships between machines, structures, and personnel, which is critical for both planning and safety compliance.

On an expansive construction site, LiDAR can map the locations of cranes, excavators, and trucks in real time. Safety managers can see exactly which machines are operating where and which areas might become congested or unsafe.

The 3D mapping also supports strategic decisions, such as determining optimal paths for machine movement or staging materials to minimize downtime. Over time, this capability helps maintain efficient workflows and reduces the risk of idle equipment due to logistical delays.

LiDAR-equipped drones can fly over haul trucks and excavators, capturing precise location data. AI then cross-references this information with task schedules, identifying idle assets and suggesting reallocation. This ensures that no machine sits unused when there is work to be done, improving overall fleet management and helping managers make data-driven decisions about task assignments.

LiDAR allows operations teams to compare actual site conditions against design models or project plans. By detecting discrepancies early, managers can allocate machinery and personnel more efficiently, minimizing downtime caused by misalignment, incomplete site prep, or unexpected obstacles.

On large-scale sites like construction, logistics, or mining, LiDAR scans can reveal areas where earthworks are ahead of or behind schedule. This information helps supervisors redirect equipment to critical zones before delays accumulate.

Early identification of potential issues not only keeps projects on track but also ensures that assets are actively engaged in productive work rather than waiting idle.

Successfully utilising AI and LiDAR requires more than just installing sensors or deploying software—it demands a structured, systematic approach that integrates technology with operational decision-making. For EHS leaders and operations managers, this framework ensures that asset utilization is maximized, idle time is minimized, and safety and productivity goals are met.

By following a clear sequence—from data collection to continuous improvement—organizations can transform raw data into actionable insights and measurable ROI.

Below is a step-by-step strategic framework for implementing AI and LiDAR effectively:

• Deploy LiDAR sensors via CCTVs, drones, fixed poles, or ground vehicles to capture high-resolution spatial data of the worksite.

• Integrate AI-enabled telematics from machinery to collect operational metrics such as engine status, idle periods, fuel consumption, and equipment location.

• Centralize data streams into a unified platform, enabling seamless correlation between spatial and operational data.

Outcome: Organizations gain a comprehensive, real-time view of equipment utilization and site conditions.

• Track machine activity continuously to detect idle periods, underutilization, or deviations from planned tasks.

• Instant notifications are sent to operators or supervisors when equipment is idle beyond pre-set thresholds.

• Visual dashboards display asset locations and status, enabling teams to respond quickly to inefficiencies.

Outcome: Idle assets are identified and addressed immediately, reducing fuel waste, wear, and operational risk.

• Analyze historical operational data to uncover usage patterns, recurring maintenance issues, and bottlenecks in task allocation.

• Anticipate maintenance needs before failures occur, scheduling interventions proactively to avoid downtime.

• Optimize task allocation dynamically, reassigning idle assets to areas where they are most needed.

Outcome: Equipment remains productive, fleet utilization improves, and workflow efficiency is maximized.

• Regularly review AI and LiDAR insights to assess performance, identify emerging trends, and refine operational practices.

• Iteratively update thresholds and rules based on evolving site conditions, workload requirements, and historical data.

• Integrate learnings across sites to standardize best practices, enhance safety, and maximize ROI across operations.

Outcome: Organizations achieve a culture of continuous improvement, turning idle equipment into sustained operational value.

Idle assets are costly—not only in fuel and maintenance but also in safety risks and lost productivity. By using AI for predictive analytics and task optimization and LiDAR for precise spatial awareness, organizations can transform underutilized equipment into measurable ROI.

For EHS leaders, embracing these technologies is no longer optional. It is a strategic approach that enhances safety, boosts operational efficiency, and supports sustainability goals, ensuring assets deliver maximum value across every project.

AI and LiDAR together are the key to smarter, safer, and more profitable asset management.

Absolutely. AI evaluates multiple factors such as:

Assets flagged as high-priority are highlighted for immediate action, ensuring teams address the most impactful issues first.

Yes. LiDAR data can be combined with telematics and AI platforms to provide a unified view of:

• Asset location and movement.

• Idle periods and productivity levels.

• Site mapping for safety and operational planning.

Integration ensures that managers don’t need to overhaul existing systems but gain enhanced visibility and decision-making capabilities.

AI and LiDAR solutions such as viAct are highly scalable:

• LiDAR can monitor multiple sites using drones or fixed sensors.

• AI platforms can handle hundreds of machines simultaneously.

• Cloud-based architecture allows data aggregation from multiple sites for centralized insights.

Expert Tip: Start with a pilot on high-value assets, then expand gradually to cover all equipment across sites.

Detection is in real-time. While AI continuously monitors telemetry data and identifies idle activity, LiDAR confirms the location and status of each machine. Alerts can be configured to trigger within seconds or minutes of inactivity.

This allows operators to intervene immediately, reducing fuel waste, wear, and downtime.

Yes. It can be applied to a wide range of heavy machinery, including excavators, cranes, haul trucks, and generators. Platforms can adapt to different asset types by configuring models based on operational patterns, sensor inputs, and workflow requirements, making AI solutions flexible for varied industrial environments.

Not sure how to use AI and LiDAR for Asset Management?