I assist in managing one of the world’s most constrained supply chains, up close to the decade’s defining energy bottleneck
Across all sectors, companies are racing to prepare for an electrified, AI-powered future. Manufacturers are relocating production facilities back to domestic locations. Data center developers are securing land at breakneck speed. Transportation fleets are transitioning to electric, logistics hubs are undergoing modernization, and entire industries are shifting toward digital operations that demand far more power than legacy grid infrastructure was originally designed to handle.
Yet while focus remains on AI models and semiconductor chips, a more understated constraint is shaping the pace of business growth: the physical infrastructure required to power all these advancements. At this year’s World Economic Forum in Davos, global leaders discussed AI not merely as a software tool, but as a massive industrial expansion on par with the Industrial Revolution and the 1990s rise of the World Wide Web. AI is beginning to act as a physical force in the global economy, determining which regions can attract investment based on their ability to deliver reliable, abundant electricity.
This reality makes one fact unmistakeable: the AI transformation cannot proceed without new grid infrastructure. Power transformers have emerged as the defining bottleneck of the decade. These critical devices, which adjust voltage levels to move power safely and efficiently, are now among the most constrained assets globally: 92% of data center leaders cite grid constraints as a barrier, and 44% report utility wait times stretching beyond four years.
Demand has surged due to accelerating electrification, the integration of renewable energy sources, replacements for aging grid infrastructure, and the growth of hyperscale data centers driven by AI. As orders pile up, lead times are extending—driven not only by demand but by reduced access to essential materials. The consequences are tangible: delayed equipment can halt construction timelines, reshape capital budgets, and force companies to reassess site locations.
The limiting factor in corporate growth is not capital or talent—it is access to the equipment and energy infrastructure that enable modern operations.
Here’s what other leaders can learn from how [Energy] adjusted operations to strengthen one of the world’s most constrained supply chains.
Rethinking procurement in an era of structural scarcity
The critical issue here is timing. When equipment is ordered today determines whether projects stay on schedule. In many organizations, equipment procurement traditionally followed the finalization of core electrical and civil designs. This sequence no longer aligns with market realities. Distribution transformers typically have an 18+-month lead time, and large power transformers 30+ months, with timelines influenced by copper and electrical steel availability, component queues, and factory backlogs. Placing orders earlier—often in parallel with design development—results in fewer schedule disruptions, less price volatility, and a lower risk of projects spilling into new fiscal periods.
As timelines tighten, the next priority is how companies engage with the partners that make these projects possible. In tight markets, siloed or transactional supplier relationships are ineffective. My team is increasingly adopting a collaborative planning model with customers and suppliers: sharing forecasts, coordinating on capacity needs, and jointly mapping upstream constraints. Collaboration ensures suppliers scale alongside manufacturers, rather than reacting after bottlenecks emerge.
The next challenge is managing volatility. The instability affecting global supply chains—from policy changes and extreme weather to fluctuating commodity prices and surging electrification demand—means planning for a single future is a recipe for failure. We model multiple market scenarios and supply-chain conditions to adjust sourcing strategies early. Digital tools that enhance visibility into demand shifts and material constraints are no longer optional; they are essential.
How AI is helping meet customer demand
For all the focus on physical constraints, one of the most persistent hidden delays in large infrastructure projects occurs long before a transformer enters production. It happens in the documentation: the thousands of pages of technical, regulatory, and legal language that must be interpreted, validated, and aligned before a project can proceed.
To address this, Hitachi Energy developed ACE, an enterprise-grade AI platform that extracts and interprets complex content from customer requests for proposals and quotes (RFPs and RFQs), contracts, and technical documentation. Fully integrated into daily workflows, the tool was built with domain experts to ensure operational precision, helping teams progress from opportunity identification to execution faster and with greater confidence.
The impact has been significant. The time to capture technical requirements is reduced by more than half, legal review and redlining of commercial documents can be shortened by up to 90%, and bid accuracy improves as hidden customer needs are identified earlier. By creating a single, trusted knowledge base across proposals, contracts, and engineering inputs, the AI platform also reduces costly post-award errors—benefits that directly enhance the transformer production pipeline.
In a market where long-lead items dictate the pace and every month of delay carries real cost, the goal is to compress the phases we can control. Faster decisions, fewer ambiguities, and clearer handoffs help customers advance projects more quickly.
Lessons for every business facing constraints
Every industry now confronts some form of scarcity—skilled labor, components, energy, fabrication capacity, or regulatory throughput. Transformers are just one example, but the strategies we’ve learned apply broadly.
Diversification is not merely a risk-mitigation tactic; it is a growth strategy. Long-term supplier partnerships build resilience that transactional buying cannot. Data-driven forecasting enables proactive decisions rather than reactive ones. And using AI as a targeted operational tool can eliminate the invisible friction that slows companies down.