Yet while attention concentrates on AI models and chips, a subtler constraint is shaping how fast businesses can grow: the physical infrastructure needed to power everything. At Davos this year, global leaders spoke about AI, not as a software tool, but as an immense industrial buildout on par with the industrial revolution, and the rise of the World Wide Web in the 1990s. AI is beginning to behave like a physical force in the global economy, dictating which regions can attract investment based on their ability to deliver reliable, abundant electricity. 

This reality makes one thing crystal clear: without new grid infrastructure, the AI transformation is not possible. Power transformers are critical devices that step voltage up or down to move power safely and efficiently are now among the most constrained assets on Earth: 92% of data center leaders cite grid constraints as the top factor slowing projects, and 44% report utility wait times stretching beyond four years.

Demand has surged due to accelerating electrification, renewable integration, aging grid replacements, and hyperscale data center growth caused by AI. As orders stack up, lead times are lengthening, driven not only by demand but by reduced access to essential materials. The consequences are real: delayed equipment can stall entire construction timelines, reshape capital budgets, and force companies to reconsider site locations.

The limiting factor in corporate growth isn’t capital or talent, its access to the equipment and energy infrastructure that make modern operations possible. 

Here’s what other leaders can learn from how Hitachi Energy shifted operations to improve one of the world’s most constrained supply chains. 

Rethinking procurement in an era of structural scarcity

The pivotal issue here is timing. When equipment is ordered now determines whether projects stay on schedule. In many organizations, equipment purchasing traditionally followed after core electrical and civil designs were firm. That sequence no longer aligns with market reality. Delivery times for transformers have increased. This is primarily due to the increased global demand and supply chain challenges. Hitachi Energy reacts by adjusting production strategies and investing in capacity expansion to better meet customer demand. Placing orders earlier, often in parallel with design development, results in fewer schedule resets, limited price volatility, and a lower risk of pushing projects across fiscal periods.

As timelines tighten, the next imperative is how companies work with the partners that make these projects possible. In tight markets, siloed or transactional supplier relationships simply do not work. My team is increasingly aligning with customers and suppliers in a collaborative planning model: sharing forecasts, coordinating on capacity requirements, and mapping upstream constraints together. Collaboration helps ensure suppliers scale alongside manufacturers, rather than react after bottlenecks appear.

The next challenge is managing volatility. The instability affecting global supply chains, like policy changes, extreme weather, fluctuating commodity prices, and surging electrification demand, means that planning for only one future is planning for failure. We model multiple market scenarios and supply-chain conditions so we can adjust sourcing strategies early. Digital tools that improve visibility into demand shifts and material constraints are no longer optional; they’re 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 happens long before a transformer enters production. It happens in the documents: the thousands of pages of technical, regulatory, and legal language that must be interpreted, validated, and aligned before a project can move forward.

To address this, Hitachi Energy developed an enterprise grade AI platform that extracts and interprets complex content from customer requests. Fully integrated into day-to-day workflows, the tool was built with domain experts for operational precision, helping teams move from opportunity to execution faster and with greater confidence.

The impact has been measurable. Cycle time to capture technical requirements drops by more than half, legal review and redlining of commercial documents can shorten dramatically, and bid accuracy improves as hidden customer needs surface earlier in the process. By creating a single, trusted knowledge base across proposals, contracts, and engineering inputs, the AI platform also reduces costly errors after award – benefits that directly improve the transformer production pipeline.

In a market where long-lead items set the tempo 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 sooner.

Lessons for every business facing constraints

Every industry now faces some form of scarcity – skilled labor, components, energy, fabrication capacity, or regulatory throughput. Transformers are only one example, but the strategies we’ve learned apply broadly.

Diversification isn’t a risk-mitigation exercise; it’s a growth strategy. Long-term supplier partnerships create resilience that transactional buying can’t. Data-driven forecasting enables proactive decisions rather than corrective ones. And using AI as a targeted operational tool can eliminate the invisible friction that slows companies down.

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