Microsoft Targets Wider AI Adoption In Africa’s Energy And Mining Sectors

Microsoft is seeking to expand the use of artificial intelligence in Africa’s oil and gas, power and mining sectors by connecting industrial systems with business data to improve decision-making and asset performance, a company adviser said on Monday.

Speaking at a Microsoft-hosted session at African Energy Week 2026, Gargi Mishra, the company’s frontier industry adviser for energy and resources, outlined how businesses could move beyond individual productivity tools and integrate AI into operational workflows.

Mishra said connecting data generated across operations with AI could help companies identify problems earlier and improve asset management.

“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,” she said.

For energy producers and utilities, these applications could help identify patterns, anticipate equipment failures and support maintenance planning. Integrating operational technology data with enterprise platforms could also provide a more complete picture of asset performance.

The session examined how companies could move beyond isolated AI pilot projects and deploy the technology more widely.

Microsoft presented its Copilot tools, alongside Azure, Copilot Studio and Power Apps, as platforms for connecting information, developing tailored applications and deploying AI agents for specific tasks.

Gareth Ireland, a senior AI specialist at Microsoft, said integrating AI into existing systems and processes was central to its adoption.

“The question is not simply how to introduce an AI model, but how to integrate it into the systems and processes people already use,” he said.

Ireland said industrial applications required connections between data, software and workflows so that AI could support employees and operational goals.

Wider deployment also requires reliable data infrastructure and clear controls over access to information, he said. Cybersecurity and data governance were particularly important when AI tools interacted with sensitive business information or systems supporting critical infrastructure.

The discussion also highlighted the need for oversight of AI-generated results, particularly in operational settings where inaccurate information could have commercial or safety consequences.

Effective adoption would depend on combining AI tools with reliable data, existing industrial infrastructure and employees’ expertise.