What will it take to scale AI in Africa’s O&G sector?

This quiet choreography where machine precision meets human intuition captures what AI brings to Africa’s oil and gas sector. Across Nigeria, Angola, Ghana, and Côte d’Ivoire, AI is already at work, mapping complex reservoirs with greater accuracy, predicting maintenance failures before they halt production, and fine-tuning drilling and operations in real time.

However, amidst the transition to a more digitalized and automated future, we still face the question: can Africa’s energy ecosystem generate and sustain the intelligence it adopts?

AI adoption in Africa: Data as the new crude

AI is only as strong as the data it feeds on, and in Africa, that data is vast. Every seismic survey, well log, and production run now generates massive datasets. What has changed is the ability to derive actionable insight from these datasets in near real time, with algorithms amplifying human insight and uncovering signals once buried in noise.

In Angola, AI-driven seismic interpretation has reduced exploration time and helped identify plays previously hidden beneath complex formations. Meanwhile, in Nigeria, predictive-maintenance systems have reduced downtime by about 20% at several onshore facilities, while machine learning models flag equipment anomalies days before failure. Overall, AI and analytics could unlock USD 5.3–8.5 billion in value across Africa’s energy and hard-to-abate sectors by streamlining operations end to end. But data gravity still tilts away from the continent.

Africa has roughly 307 megawatts (MW) of data center capacity (less than 2% of the global total). More than half of this capacity sits in South Africa. But most digital workloads still execute in Europe, Asia, or the United States. Algorithms trained on African subsurface data are often optimized overseas and their insights sold back at a premium. It’s a familiar pattern: the continent supplies the raw material but imports the intelligence.

Without stronger local data infrastructure, reliable energy systems, and coherent digital policy, AI’s benefits will continue to flow unevenly. Africa holds 8% of the world’s natural gas and 12% of its oil reserves, yet captures only a fraction of the intellectual property derived from them.

Powering intelligence in a power-constrained world

AI systems, at their core, are powered by computation, and that computation requires electricity. In regions with constrained or unreliable grids, AI initiatives stall not because the models are weak but because power infrastructure is inadequate.

Nearly 600 million Africans still lack access to electricity, and even industrial users face routine disruptions. Across many markets, AI systems that depend on stable power and connectivity operate at the mercy of diesel generators and limited bandwidth. These conditions present a significant challenge for both data center developers and companies trying to adopt and integrate AI within their operations.

Just training a large-scale AI model can consume as much power as 100 average U.S. homes in a year. Continuous operation of the data center requires steady electricity and cooling, luxuries in markets where industries often self-generate. Unless certain inroads are made, Africa’s digital transformation will remain uneven and concentrated where grids are stable or hybrid systems exist.

However, positive developments are occurring. Governments and industrial operators are moving beyond pilots to full-scale hybrid power systems, which pair gas-fired generation with onsite solar and storage to keep critical computing resources online. At the same time, renewable power purchase agreements (PPAs) are helping to derisk electricity supply for AI-driven facilities and stabilize operations where grids are prone to failure.

In South Africa, for instance, Africa Data Centres has begun building a 12 MW solar farm to power its facilities. The project is an example of how hybrid energy designs can support digital operations in regions where grids are fragile, while at the same time contributing to sustainability and decarbonization goals. Progress is also being made in Egypt, where government-backed data centers are testing gas-renewable hybrid systems to sustain AI-driven industrial monitoring. Offshore projects in Angola are following a similar path by integrating renewables to stabilize digital operations.

But these success stories only represent pockets of resilience in a sea of fragility. Expanding digital adoption and scaling AI capabilities across Africa’s oil and gas sector will require strategic investments, so that computing follows dependable electrons. This is not a call to build stranded data centers, but to begin laying the foundation for an environment where AI can be leveraged without straining already fragile grids. AI can make power systems smarter, but it cannot generate the electricity it needs to think.

Prioritizing the human element

Reliable power is not the only resource needed to accelerate Africa’s digital revolution. There is also a human element to consider. AI is fundamentally reshaping the global workforce and shifting job responsibilities, and amidst the increase in automation, human intelligence has never been more important.

The Economist cleverly invokes the idea of “lost Einsteins”—innovators whose ideas never surface because the systems around them fail to nurture their potential. For Africa, that insight feels less like theory and more like reflection.

The continent has never lacked brilliance. From petroleum engineers in Port Harcourt to data scientists in Nairobi and field geologists in Luanda, Africa’s energy story has always been powered by its people. Today, Africa accounts for less than 1% of global AI researchers, and many of those migrate abroad in search of better opportunities for funding and job security.

Most of the continent’s oil and gas professionals are masters of geology and engineering but are not (yet) proficient in the digital technologies transforming their field. The next frontier is hybrid fluency: professionals who can think in both code and geology, who grasp the physics of a reservoir and the logic of an algorithm. These are the new energy translators, and they are in short supply.

Across Lagos, Nairobi, and Accra, a growing contingent of AI enthusiasts is beginning to fill the void. Many of these individuals are self-taught and learn primarily from open-source courses and community groups. Even against the backdrop of unreliable power, limited bandwidth, and scarce access to industrial data, they manage to collaborate globally, design contextually grounded solutions, and prove that Africa’s constraint is not intellect but infrastructure.

The Deep Learning Indaba, for example, is Africa’s largest machine learning community and convenes researchers and engineers to strengthen technical expertise and shape equitable AI policy. Its alumni now lead labs, startups, and university programs that anchor innovation locally. Microsoft’s AI Skilling Initiative in South Africa, led by Tiara Pathon, trained 1.2 million people by mid-2025 (surpassing its 2026 target). In October of the same year, Microsoft, the German Agency for International Co-operation (GIZ), and South Africa’s Department of Higher Education launched V-Digital, a platform that delivers AI and digital skills to 50 technical colleges, including offline access for rural learners.

Economics remains the limiting factor. Machinery, software, and high-skilled labor can be up to 40% more expensive in African markets, with three out of four firms suffering recurrent power outages. These conditions shape whether digital adoption can scale. Even so, modular, job-linked training programs, measured by capability, not attendance, are helping to translate “AI literacy” into operational competence.

Another challenge lies in the linguistic layer of intelligence. African languages are thinly represented in AI training data, creating an access barrier to many digital tools. This can lead to voice interfaces misinterpreting commands or document agents missing context in regulatory narratives. It’s not simply a matter of convenience; the lack of existing datasets in African languages poses risks to accuracy, safety, and compliance.

To put things in an oil and gas analogy, building AI capacity without strengthening human capacity is like refining oil without investing in engineers. Africa’s success will depend less on imported algorithms than on indigenous intellect, and certainly more on designing and governing systems that reflect local realities. Because AI, at its core, does not replace human intelligence; it amplifies it.

The continent’s greatest resource has never been oil. It has always been talent. And in the age of AI, that talent has never been more valuable or more urgent to protect.

The ownership imperative

There is no question AI adoption across Africa’s oil and gas sector is accelerating, but adoption is not transformation. Real transformation requires ownership of data, systems, and intellectual property. This raises a deeper question: Who truly owns Africa’s digital oilfields—the companies that produce the barrels or the ones that hold the code?

This question of ownership isn’t new. Over the past decade, the divestment of international oil companies (IOCs), particularly in Nigeria, has shifted upstream assets to local independents, transferring not only barrels but responsibility. Those transactions marked a turning point, placing operational control in African hands and deepening local value creation. Yet as this generation of operators masters physical assets, another frontier of ownership has emerged—one of data and digital capability.

Most AI technologies deployed across African oilfields still originate from global service providers. Their platforms process local data yet remain tethered to external systems. The advantages (e.g., global expertise, speed, and scale) are clear, but so are the risks, not least of which are dependence and data asymmetry.

There’s precedent for the current situation. In the early 2000s, mobile-telecom innovators invested directly in local infrastructure and capacity, enabling Africa’s digital leap and the eventual rise of fintech ecosystems like M-Pesa in Kenya. AI in energy can follow a similar path if stakeholders share not just technology, but also know-how and governance.

That means creating frameworks where data generated in Africa stays within African jurisdictions—where governments, companies, and universities coinvest in AI research hubs linked to industrial applications, while policies protect both privacy and innovation. Because true transformation will only begin when African operators move from consuming algorithms to cocreating them.

Industry leaders are already collaborating with African governments, academic institutions, and operators to engender digital competence and set the direction for change with coordinated efforts to build talent and innovation ecosystems. At the Global AI Summit for Africa in April 2025, a multi-stakeholder initiative committed USD 60 billion to strengthen AI capacity, infrastructure, and governance across the continent. Rwanda’s President Paul Kagame emphasized that Africa must be “an active player, not just a market, in the age of AI.”

But these initiatives need to be scaled, because the next wave of energy intelligence will not be decided by who adopts AI fastest, but by who controls its logic.

Opportunities and challenges that lie ahead

The ascent of AI in Africa’s oil and gas sector reveals both the continent’s progress and its precarity. It has made operations safer, more efficient, and increasingly data-driven, showing that Africa is not merely catching up but experimenting boldly with frontier technologies. Yet, at every turn, familiar vulnerabilities return, including power shortages, fragile infrastructure, uneven talent development, and fragmented governance.

AI is not a miracle cure for these structural realities, but a magnifying glass. It illuminates what works and exposes what doesn’t. The paradox is that even as AI strengthens Africa’s energy sector, it highlights the fragility of the systems beneath it.

Emerging markets, like Africa, can unlock outsized benefits from AI through predictive maintenance, advanced forecasting, and demand-side management, but only if energy and digital buildouts are coordinated and sequenced. Adoption will fail if complementary inputs are too scarce or too costly. Success requires disciplined, locally anchored execution.

And that, perhaps, is the opportunity. To sustain AI, Africa must confront the very weaknesses that have long constrained its growth. If AI becomes the catalyst that forces those issues to the surface, it will push the continent to invest in its foundations, driving not just technological adoption but true transformation.

In summary, Africa’s oil and gas industry now stands at an inflection point between adopting intelligence and owning it, between progress and self-reliance. Whether AI becomes a great equalizer or another frontier of inequality will depend on the region's ability to build a framework that powers both machines and minds.

The path forward is evident: invest in dependable power for digital operations; scale modular, job-ready training; build and govern African datasets; and structure vendor relationships for co-development, not one-way consumption.

If those elements hold, the continent won’t just keep the lights on for AI; it will use AI to keep the lights on for everyone.