ADNOC Distribution's AI strategy boosts profitability, speeds service, and cuts emissions

ADNOC Distribution's AI Playbook: Profit Up, Friction Down

ADNOC Distribution is scaling AI across fuel and Non-Fuel Retail operations in the UAE, Saudi Arabia, and Egypt. The goal is simple: better customer experience and higher operational efficiency. This push supports national priorities under We the UAE 2031 and the country's wider digital transformation agenda.

As CEO Eng. Bader Al Lamki put it, AI sits at the core of the company's strategy. With more than 21 initiatives launched, several are already in production and delivering measurable gains.

What's live and working

• Customer demand analysis: Processes 220+ million transactions a year to pinpoint top products and peak shopping windows. Result: stronger on-shelf availability and leaner station operations.
• AI fleet routing: Uses traffic data to optimize routes from depots to stations, cutting travel time, improving delivery reliability, and reducing emissions. The fleet has been running on biofuels for two years to shrink its carbon footprint.
• Fuel demand forecasting: Fewer stockouts and tighter logistics planning, which lowers emergency redistribution costs.
• Smart Assortment: Tailors retail SKUs to each location. Less waste, higher sales, and improved profitability at selected outlets.
• Real-time monitoring: Faster response on maintenance and cleaning tasks, with up to a 40% reduction in response times and better service quality.

What's coming in 2025

• Fill & Go: Seamless fueling via license plate recognition or QR codes for faster throughput at the pump.
• Click & Collect: Order ADNOC Oasis products with personalized suggestions driven by data to lift conversion.
• AI self-checkout at scale: After a successful pilot, the system is rolling out more broadly. Transaction times are down by 60%+, increasing station capacity at peak hours.

Operational impact in numbers

• Lower emergency redistribution costs due to accurate demand forecasts.
• Fewer stockouts and tighter fuel inventory control per station.
• Higher retail margins from SKU localization and waste reduction.
• Faster maintenance resolution with real-time alerts and tracking.
• Reduced travel time and emissions across the distribution fleet.

Why this matters for operations teams

This is a playbook for turning data volume into practical gains: faster lines, leaner deliveries, fewer outages, and better basket mix. The common thread is clear ownership, closed-loop KPIs, and shipping systems that integrate with field operations, not just dashboards.

If your unit handles physical assets, retail flow, or logistics, these moves show where to focus: demand signals, routing, checkout, assortment, and uptime. Each directly ties to cost, throughput, and customer satisfaction.

A practical blueprint you can copy

• Start with demand truth: Consolidate POS, pump, and footfall data. Forecast at the station-SKU-hour level to plan labor, stock, and promotions.
• Fix last-mile friction: Optimize routing with live traffic. Measure "depot-to-nozzle" time and set targets per route.
• Shorten the checkout: Pilot self-checkout at high-traffic sites first. Track transaction time, abandonment, and uplift in peak-hour throughput.
• Localize assortments: Use store-level performance and demographics to adjust SKUs monthly. Remove slow movers fast.
• Instrument uptime: Real-time alerts plus SLA timers for cleaning and maintenance. Publish response-time leaderboards across sites.
• Close the loop: Tie every initiative to a financial metric: stockout rate, emergency logistics costs, gross margin per square meter, and average service time.

The bottom line

ADNOC Distribution's AI program is built around measurable outcomes and operational discipline. For operations leaders, the lesson is to automate where delays compound, monitor what drives cost, and ship tools that staff can use on day one.

If you're building similar capabilities, explore practical upskilling paths for operations teams focused on automation and applied AI. A good starting point is this resource on AI automation certification: AI Certification for AI Automation.