AI moves from pilot projects to smart utility deployments at Enlit Africa 2026

By Tsaone Segaetsho

Artificial intelligence is moving beyond experimentation in Africa’s electricity sector, with utilities and technology companies showcasing deployments of smart metering, predictive analytics, intelligent substations and real-time grid monitoring at Enlit Africa 2026 in Cape Town.

The conference highlighted how AI is increasingly being positioned as an operational tool for utilities and municipalities seeking to improve efficiency, strengthen revenue collection and manage increasingly complex electricity networks.

Sivi Moodley, chief executive officer, emphasised the importance of integrating AI and predictive analytics into energy management, arguing that the technology can help municipalities and private enterprises optimise energy consumption while securing revenue.

The focus reflects a broader shift in the utilities market, where large volumes of data from smart meters, network equipment and customers are creating opportunities for organisations to make faster and more informed operational decisions.

Smart meters become the eyes of the grid

Advanced metering infrastructure emerged as one of the most immediate applications of AI-driven utility technology.

Eskom has deployed more than two million smart meters, generating continuous streams of operational data. At Enlit Africa, utility executives highlighted the growing importance of turning that data into actionable intelligence as rooftop solar, electric vehicles and other distributed energy resources introduce greater complexity into electricity networks.

AI can help utilities identify consumption patterns, detect abnormalities and improve demand forecasting. For municipalities, the technology also offers potential benefits in identifying losses and improving revenue management.

Intelligent substations target manual processes

Huawei showcased its Intelligent Power Substation Solution for Sub-Saharan Africa, combining intelligent video, AI algorithms and secure wireless networks to automate monitoring, inspection, meter reading and analysis.

The deployment illustrates how AI is moving closer to physical electricity infrastructure rather than remaining confined to analytics platforms.

For African utilities managing geographically dispersed networks, automation could reduce the time required to inspect infrastructure and identify abnormal conditions while allowing technical teams to focus on higher-value interventions.

Predictive analytics and the revenue equation

Moodley's emphasis on predictive analytics also underlines an important commercial dimension of the AI transition.

Energy optimisation is not only about reducing electricity consumption. For municipalities and private-sector utilities, better visibility of consumption can help improve billing accuracy, identify potential losses and protect revenue.

Predictive systems can analyse historical and real-time data to anticipate demand, identify unusual consumption patterns and support preventative maintenance before equipment failures become costly outages.

This creates a direct link between digitalisation and financial performance: the more accurately an organisation understands how energy is being consumed, the greater its ability to manage costs and protect revenue.

Grid protection gets a digital upgrade

AI and digitalisation are also changing grid protection.

Modern protection systems can provide information across much larger geographical areas and in much shorter timeframes than traditional systems. Such visibility creates the possibility of wide-area protection, where an event in one part of the network can inform a protection decision elsewhere.

This becomes increasingly important as African electricity systems become more interconnected and incorporate larger amounts of distributed renewable generation.

AI creates a new electricity challenge

The growth of AI is simultaneously creating additional demand for electricity.

The expansion of data centres and AI infrastructure is expected to become an increasingly important new load for African power systems, creating pressure for reliable generation, transmission and distribution infrastructure.

This gives utilities a dual role in the AI economy: deploying artificial intelligence to make their own operations smarter while preparing networks to supply the additional electricity required by AI-driven businesses.

From technology showcase to business imperative

The significance of Enlit Africa 2026 was therefore less about demonstrating that AI can be used in utilities and more about showing how it can be connected to measurable operational and commercial outcomes.

Smart meters are generating continuous streams of data. Predictive analytics can turn that information into forecasts and warnings, while intelligent substations can automate monitoring and inspection.

For municipalities and private enterprises, the opportunity extends beyond operational efficiency. As Moodley highlighted, AI can help organisations optimise energy consumption while strengthening the systems used to secure revenue.

The next challenge is scaling these deployments across Africa's fragmented and often ageing electricity infrastructure.

Utilities will still need to address data quality, cybersecurity, skills, legacy systems and investment. But the direction emerging from Enlit Africa 2026 is clear: the future smart utility will depend not only on generating and distributing electricity, but on its ability to understand its data and act on it in real time.