Artificial intelligence has found its early commercial footing in the digital enterprise, improving how organisations manage information, automate workflows, and interact with customers. Yet much of the physical economy, from energy infrastructure and industrial machinery to utilities and transport networks, remains relatively untouched by these advances. For industries built around physical assets, the opportunity is substantial, but so are the operational demands.
Speaking to CXO Insight Middle East at Ai Everything Abu Dhabi 2026, Suliman Gaouda, who leads industrial AI across Asia Pacific, Japan, the Middle East, and Africa at IFS, described the physical economy as an “unclaimed category” in AI, where the scale of connected assets could eventually surpass demand from enterprise applications.
“When we look at partners in the region or the neoclouds, whether that’s G42 or any of the neoclouds abroad, many have made bold statements about future consumption commitments. We firmly believe that consumption is not going to come from enterprise AI. It’s going to come from industrial AI, where assets outnumber human beings and users,” he says.
Industrial environments involve vast networks of equipment operating across decades-long lifecycles, generating operational data that has historically been difficult to translate into immediate decisions.
“We’ve never seen adoption happen as quickly or as impressively as we’re seeing with AI in industrial use cases. How do you now connect an agent not only to your system but also to your machine? You start turning machine operations into a conversation,” explains Gaouda.
That conversation could involve equipment identifying changes in vibration, temperature, or energy consumption before a failure occurs. But the value extends beyond predictive maintenance.
“These are multibillion-dollar investments, and making sure CFOs depreciate them over the appropriate timeline is fundamentally important. We see that one in three assets has its depreciation timeline readjusted, which disrupts the business case,” he says. “Through industrial AI applications, we’re helping CFOs understand how to control, measure, and track that investment from the very beginning of the asset lifecycle.”
For IFS, industrial AI connects asset performance, operational intelligence, and financial outcomes across the systems supporting national infrastructure.
“Our systems serve the 70 percent of the workforce that doesn’t sit behind a desk. That 70 percent is the blue-collar workforce. That’s why I joke with customers and say that SAP and Oracle are the white-collar ERP systems, while we’re the blue-collar ERP,” he says.
The focus is on what happens at the operational level, where equipment conditions and performance data can reveal problems that conventional enterprise systems may not capture. “With SAP, Oracle, or any other ERP, you can see a decline in the productivity of an asset or business unit. But only IFS can give you the exact reason behind that decline. Is it a voltage spike? Is it humidity? Is it current spikes that are causing the asset to depreciate and, consequently, reducing productivity?” Gaouda explains.
However, extending AI into physical operations introduces greater responsibility, particularly when intelligent systems begin influencing critical infrastructure.
“We’re talking to nuclear companies about how to allow AI to assess the radiation levels of nuclear reactors. Getting it wrong in enterprise AI, and this is a bold statement, is annoying. Getting it wrong in industrial AI can cost lives. We simply cannot get it wrong,” he says.
Gaouda expects the boundaries of autonomy to evolve. “Maybe today, the conversation is about how AI assesses the radiation levels of a nuclear reactor. Two or three years from now, it could be about how agents manage that nuclear reactor. How do they manage the power output required by the smart grid?”
Human judgement, however, remains essential in establishing the quality of information on which these systems depend. “Humans will always remain at the very start of everything. Maybe AI takes over the ten steps that follow human input, but humans still control that input,” says Gaouda.
Across the Middle East, Gaouda sees growing momentum as organisations progress towards operational deployments, particularly in the UAE and Saudi Arabia. “My big, bold bet is that 2027 is going to be the era of physical AI and industrial AI. The conversations we’re having are beginning to turn into applied proofs of concept and move beyond the POC phase into practical use cases.”
Beyond the industrial applications, Gaouda also has a broader concern about AI’s future, particularly the environmental cost of supporting its growing computational demands.
“I love the analogy of E = I, energy equals intelligence. What scares me is that we continue to increase the world’s energy consumption, creating more carbon emissions and further deteriorating our planet,” he says.
What excites him, however, is the potential for advances in energy technology to unlock further progress in computing and scientific research. “The further we advance energy and energy quality, the further we advance computing power. That will allow us to make breakthroughs in scientific research, whether in cancer research, healthcare, or education, and help humanity make significant leaps into the future,” says Gaouda.
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