Microsoft is pushing to accelerate the adoption of AI across Africa’s oil, gas, utilities, and mining sectors by bridging the gap between enterprise data and industrial hardware systems.
This comes as enterprises across the continent are rapidly digitising their operations to scale productivity.
According to data from the African Energy Chamber’s 2026 Outlook, Africa's oil and gas production is projected to hit 11.4 million barrels of oil equivalent per day in 2026, backed by $41 billion in upstream capital expenditure driven by expenditure in offshore prospects, including Angola, Mozambique and Nigeria.
Within this high-stakes landscape, AI deployment has transitioned from an experimental tool into a critical mechanism to secure and optimise massive capital investments.
During a Microsoft-hosted session at African Energy Week 2026 on Monday 12 October, Gargi Mishra, frontier industry advisor for energy and resources at Microsoft, explained how companies can move AI from individual productivity applications into operational workflows.
“The opportunity for the energy sector is to connect the data companies already generate across their operations with AI capabilities that can help them make better decisions," said Mishra.
“When information from industrial systems and business applications can be brought together, teams have greater visibility into what is happening across their assets and can use that information to identify problems earlier and improve how they manage operations.”
For industrial operators, the primary push is predictive maintenance and asset optimisation.
By linking operational technology (OT) systems - such as equipment sensors and control systems -with enterprise software platforms, companies can spot operational patterns, anticipate machinery failures, and plan repairs before unscheduled downtime occurs.
To scale these tools, Microsoft presented its suite of technologies, including Azure, Copilot Studio, and Power Apps, designed to let companies build customised applications and deploy specialised AI agents into existing workflows.
Gareth Ireland, senior AI specialist for Microsoft, explained, “The question is not simply how to introduce an AI model, but how to integrate it into the systems and processes people already use. In an industrial environment, that means connecting the right data, applications and workflows so that AI can support employees in their daily work and contribute to operational objectives.”
For oil and gas companies, mining operators and utilities, scaling these applications requires reliable data infrastructure and clear controls over how information is accessed and used.
Ireland highlighted cyber security and data governance as essential considerations, particularly when AI tools interact with sensitive business information or systems supporting critical infrastructure.
While AI can help employees process information and support decisions, its results require appropriate oversight, especially in operational environments where inaccurate information could have commercial or safety implications, noted the firm.
For the energy and resources industry, the challenge will be to combine AI capabilities with reliable data, existing industrial infrastructure and organisational expertise.
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