Microsoft seeks to expand artificial intelligence use in oil and gas, electricity and mining by connecting industrial system data with the tools teams use. During a session hosted by the group at African Energy Week 2026 in Cape Town, its representatives presented integration of enterprise data and operational technologies as a way to improve decisions, maintenance and asset monitoring.
The discussion shifts the debate from model performance to integration into everyday activities. In resource industries, data already exist in equipment, management software, control centers and maintenance systems. They often remain spread across different environments, formats and teams. Bringing them together can improve understanding of an asset's performance, but requires reliable architecture, clear governance and appropriate safeguards.
Gargi Mishra, Microsoft's industry advisor for energy and resources, emphasized the possibility of connecting information generated by operations to detect problems earlier and inform decisions. Gareth Ireland, the company's senior AI specialist, stressed that introducing a model is not enough: it must integrate with systems and processes employees already use.
From demonstrations to recurring use
Initial AI tools are often tested in isolated use cases: summarizing documents, finding a procedure, preparing a memo or automating a repetitive task. These applications can improve individual productivity. However, their contribution to industrial results depends on their ability to fit into real processes, access the right data and help teams solve a defined problem.
In a power plant, mine or oil facility, needs may include identifying signals of an impending failure, planning maintenance operations or consolidating information from several pieces of equipment. To be useful, a recommendation must account for the technical context, historical records and safety procedures. A general-purpose model cannot, on its own, replace engineers' expertise or industrial control systems.
The difference between a pilot and large-scale deployment often lies in factors less visible than the algorithm: data quality and accessibility, system interoperability, responsibility for errors and employee training. Companies must also clarify who validates a recommendation and whether the tool only provides information or can trigger an action. In critical facilities, this distinction is fundamental.
Microsoft presented its Copilot ecosystem and Azure, Copilot Studio and Power Apps services as ways to connect information, create tailored applications and deploy AI agents for specific tasks. These offerings can form part of a company's digital architecture, but deployment does not itself guarantee operational improvement. Results will depend on use case selection, integration with existing tools and oversight by teams.
Potential gains depend on data quality
AI can help bring together operating data and administrative information to provide a more complete view of assets. A maintenance team could, for example, combine equipment alerts with intervention histories and inventory levels. A mining company could bring together production, transport and energy consumption data. In each case, the system's value depends on the consistency of the information used.
Incomplete or poorly labeled data can produce misleading results. Older and newer facilities do not always collect the same measurements; teams may use different names for the same equipment. Harmonization requires technical and organizational work that can be lengthy, particularly when several operators or service providers hold the data.
Companies will also need to evaluate tool performance on specific tasks. Criteria may include reduced search time, anticipation of certain failures, the quality of maintenance plans or the number of recommendations validated by specialists. Before-and-after measurement is essential to distinguish measurable gains from a mere impression of efficiency.
Cybersecurity and governance at the forefront
Integrating AI into industrial systems raises cybersecurity and access management questions. Operational data can reveal a facility's performance, vulnerabilities or routines. It is therefore necessary to define precisely what information can be used, by whom, where it is stored and how access is controlled.
Gareth Ireland emphasized cybersecurity and data governance, particularly when tools interact with sensitive commercial information or systems linked to critical infrastructure. These requirements must be defined before deployment: environment segmentation, identity controls, action logging, supplier reviews and incident continuity plans. Security cannot be added retrospectively to architecture already in operation.
Human supervision also remains decisive. An incorrect response can lead to a poor maintenance decision, a production shutdown or a safety risk. AI can help identify signals or organize information, but those responsible must understand the system's limits and be able to challenge its recommendations. Automated decisions affecting sensitive equipment require a level of control and validation appropriate to the risk.
An opportunity dependent on local capabilities
For African operators, expanding AI also raises a skills question. Companies need engineers able to understand both industrial processes and digital tools, data specialists and cybersecurity teams. Integrated solutions can accelerate some uses, but do not remove the need to develop internal expertise to operate, audit and adapt them.
The AEW session thus highlights a shift in the debate: the challenge is no longer simply to test AI, but to identify operations where it delivers measurable improvement without weakening safety or accountability. Those who successfully scale up will be those who address data, processes, cybersecurity and training together.
AI can help improve energy and mining asset performance, but replaces neither digital infrastructure nor teams' expertise. Its true value will be indicated by its ability to deliver more reliable decisions, better anticipated maintenance and verifiable operational gains.