By Elijah Daniel
For decades, the design logic of industrial operations has remained largely unchanged. Information belongs in the control room. The operator sits behind a workstation with real-time data. The field worker goes out with a paper checklist and takes notes by hand. Somewhere between those two realities, a disconnect exists. The person seeing the digital picture is not the person standing in front of the actual equipment. And the person in front of the equipment is working from memory.
That disconnect is costing industry more than most organisations admit. For Africa, where experienced engineers are unevenly distributed and operational distances are vast, the cost of that information gap is structural. The African Development Bank (AfDB) projects Artificial Intelligence (AI) deployment could generate up to $1 trillion in additional gross domestic product (GDP) across the continent by 2035. The sectors driving that number are oil and gas, mining, energy, and manufacturing, the same industries where this disconnect plays out every day.
The data beneath the intelligence
Here is what most commentary on industrial AI misses: AI in isolation is not a competitive advantage. What matters is what you feed it. Every oil and gas facility, every FPSO, every mine holds years of irreplaceable operational history; process data, maintenance records, alarm histories, energy consumption patterns, operator actions. Most of it has never been connected to anything capable of learning from it.
This is the difference between artificial intelligence and what we call industrial intelligence. General AI goes to the internet for answers. Industrial intelligence is trained on your specific assets, your specific data, your specific history. That knowledge is yours. Nobody else has it. Once connected to AI, it becomes something a competitor cannot easily replicate. ADNOC reported $500 million in AI-driven value in 2023 alone. Norway’s Equinor generated $130 million in AI-related savings in 2025 alone. According to Rystad Energy, digitalisation and AI will create close to $500 billion in cumulative value for oil and gas companies between 2026 and 2030. These organisations are not winning because they bought better tools. They connected their institutional knowledge to intelligence before their competitors did.
From information overload to actionable insight
Walk into most industrial control rooms today and you will find operators drowning in data. Historians, workstations, process books, maintenance systems, information fragmented across too many sources. The result is not empowerment. It is overload.
The goal was never more data. The goal is the right insight at the right moment, with a clear action attached. There is a meaningful difference between telling an operator a compressor has failed and telling him it is likely to fail within 30 days, here is the most probable reason, here is what to do, and here is what happens if nothing is done. One is a notification. The other is intelligence.
Consider a field technician dispatched to fix a pump. In the old model, he carries a manual, radios back to base, and works from memory. In the model Schneider Electric and AVEVA have built, he queries a system trained on that specific asset’s history, describes the symptom, and receives a structured diagnosis before he touches a single valve. That capability exists today.
The holy grail of operations
A field worker with the same live information in his hands as the operator in the control room, real-time process data, equipment history, maintenance records, all in one place, wherever he stands. That is what we consider the holy grail of operations.
It is what Schneider Electric and AVEVA have oriented their connected worker strategy around. Industrial technology for too long has been designed around assets. The shift is designing it around the people who operate those assets. An engineer in Lagos, an operator on the Bonga FPSO, a technician in the field, all working from the same operational context in real time. Not because they are in the same room, but because the room has come to them.
The productivity gap is already open
The data confirms what operators across this region are beginning to feel. The PwC 2025 Global AI Jobs Barometer shows industries most exposed to AI have seen three times higher revenue per employee growth, 27% against just 9% in less exposed sectors. The Stanford AI Index reports 78% of organisations used AI in 2024, up from 55% the year before. The separation between early movers and those still deliberating is already here.
The fear that AI eliminates jobs is not supported by evidence. An OECD survey across seven countries found 83% of firms that adopted AI reported no change in staffing levels. AI does not replace the skilled worker. It removes the friction that prevents the skilled worker from performing at his best.
The future control room
The future control room is not a room. It is wherever the worker happens to be standing. The companies that outperform competitors in the coming decade will not have more assets or more people. They will have better industrial intelligence.
Our conviction at Schneider Electric and AVEVA is this: the most successful organisations ahead will not be those with the best technology. They will be those with the most empowered workforce. Industrial transformation succeeds when technology adapts to people. Not when people are forced to adapt to technology.
Daniel is Country Sales Director for Process Automation across Sub-Saharan Africa at Schneider Electric. He works at the intersection of industrial AI, workforce productivity, and digital operations.
