Artificial intelligence is rapidly moving from experimentation into core infrastructure, but adoption continues to outpace understanding.
Artificial intelligence is rapidly moving from experimentation into core infrastructure, but adoption continues to outpace understanding. According to a recent, enterprise AI use has grown 50% in the last year, but the percentage of those using AI in their daily workflow has remained unchanged.
This gap matters because AI is no longer confined to consumer applications or standalone software tools. It is increasingly embedded in the physical and operational systems that underpin the global economy. From data centers purpose-built for AI workloads, to industrial environments under pressure to modernize and decarbonize, to supply chains navigating constant disruption, organizations are being asked to make faster, more confident decisions. The next phase of AI is less about access to intelligence and more about how effectively that intelligence is translated into action.The rapid growth of AI has pushed companies to rethink the role and design of traditional data centers. Instead of general-purpose facilities, many organizations are now building what are often called “AI factories,” data centers designed specifically to support large-scale AI workloads. These facilities operate very differently from their predecessors, particularly around energy and cooling. “AI factories are densely packed with GPUs and specialized chips that generate significant heat, requiring advanced cooling systems that further increase energy demand,” said John Maculley, Global High Tech Industry Consultant at Dassault Systèmes. “A single query in an AI search engine requires up tomore energy than a traditional Google search, and AI workloads are expected to drive data center energy demand up by as much as, roughly one-third of U.S. adults are already using generative AI. As more of the population adopts this technology, this energy problem will only further compound.AI may also help address the challenges it creates. Maculley pointed to AI-driven virtual twin technology, scientifically accurate digital replicas of physical systems, as a critical tool for managing power consumption and improving efficiency. By creating a virtual twin of an AI facility, organizations can simulate real-world conditions and test scenarios before making changes in production environments. "When it comes to power consumption, virtual twins provide a comprehensive view of an AI factory’s energy behavior,” Maculley explained. “With that insight, operators can actively manage energy, cooling, and capacity in real time, anticipate demand spikes, prevent downtime, and significantly reduce operational costs.” As companies such as Microsoft, NVIDIA, and Meta continue expanding their AI data center footprints, virtual twins are becoming essential for long-term planning, helping operators balance performance, sustainability, and costs as AI infrastructure scales.The U.S. industrial sector is facing a convergence of pressures. Decarbonization goals, electrification initiatives, grid modernization, and the push to expand domestic manufacturing are accelerating simultaneously. “These demands are no longer aspirational goals but near-term requirements, colliding with entrenched challenges such as aging infrastructure and persistent labor and skills gaps,” said Andre Marino, SVP of Industrial Automation at Schneider Electric. While many industrial organizations have begun digital transformation efforts, execution often lags ambition. Talent shortages and skills gaps continue to slow progress, creating a disconnect between technological potential and reality. Marino sees AI as a key enabler in closing that gap. AI-driven automation and analytics allow industrial organizations to move beyond manual processes and fragmented systems, transforming operational data into real-time, actionable insight. Rather than reacting to issues after they occur, AI can analyze performance continuously, predict outcomes, and guide operational optimization as conditions change. The industrial water sector illustrates this shift. Rising populations, urbanization, and climate change are increasing strain on water systems, making it harder to deliver reliable supply. By analyzing historical usage patterns, asset performance, and external factors such as weather or production cycles, AI can help operators anticipate demand, identify inefficiencies, and make smarter decisions about system design and operation. This enables a proactive approach that helps utilities stay ahead of problems rather than reacting to failures. “Urgency is the defining factor,” Marino said. The technologies needed to improve efficiency, productivity, and sustainability already exist, but adoption has not matched the scale of the challenge. Open, vendor-agnostic software platforms are critical to unlocking AI’s value at the operational and asset level. By embedding AI into core workflows as part of an integrated digital and automation strategy, organizations can begin to bridge the gap between innovation and execution. “Those that align technology deployment with operational strategy and workforce development will be better positioned to translate innovation into measurable gains. Those that delay risk falling behind in a rapidly evolving landscape,” Marino added.Supply chains continue to face mounting pressure from shifting consumer demand, inflation, and geopolitical uncertainty. These forces ultimately impact business operations, making visibility and the ability to respond quickly essential. Anand Srinivasan, Chief Strategy Officer at o9 Solutions, has seen these dynamics across clients including AB InBev, Marks & Spencer, and New Balance. Many organizations, he noted, rely on traditional supply chain tools like spreadsheets that were designed for a more stable era. Today, disruption is constant, and the technologies supporting supply chain decisions must reflect that reality. “Scaling AI comes down to clean, intentional data and connected systems,” Srinivasan said. “Yet many supply chain leaders wrestle with messy, fragmented data. AI can’t think across silos if it can’t see the full picture. Without that picture, AI guesses. Knowledge is often trapped in spreadsheets, documents, or in employees’ minds. When information flows across internal, external, and supplier signals, decisions sharpen and teams stay engaged.” Before applying AI, Srinivasan advises organizations to clearly define business pain points and align the right data and tools to address them. “The challenge isn’t automation; it’s the absence of strategy,” he said. With the right foundation, AI can understand context and surface options that reflect real constraints and priorities. Once embedded, AI enables a shift from static planning to continuous decision-making. When demand changes, AI can rapidly evaluate scenarios, showing the impact of increasing production, repositioning inventory, or delaying orders before leaders act. The same intelligence extends into procurement and service operations, where AI agents manage routine exceptions and adjust plans as conditions evolve. Over time, this creates supply chains that not only respond faster but steadily improve decision quality.Across AI factories, industrial operations, and supply chains, the challenge is the same: turning intelligence into better decisions amid constant change. Organizations that move beyond isolated tools and embed AI directly into core workflows will gain an advantage. When data and systems are connected, AI becomes part of how work gets done, helping leaders make better decisions in real time across the value chain.. This is the same dataset used by the National Retail Federation, and available from Amazon Web Services, Bloomberg, and the London Stock Exchange Group for economic benchmarking.
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