Stop rebuilding the wheel: why EV charging needs shared intelligence

Nearly 1,000 new EV chargers go live in the U.S. every week. Most fail to earn their keep.

Published on: August 1, 2025

Nearly 1,000 new EV chargers go live in the U.S. every week. Most fail to earn their keep.

Billions of dollars are being spent to blanket the country with charging infrastructure, yet too many stations underperform from the moment they open. Sites are built in the wrong places, priced poorly, or left without the data-driven planning needed to reach profitability.

Despite this, many charging networks still believe building internal analytics like siting engines, pricing models, and utilization forecasts will give them a competitive edge.

It rarely does.

The myth of in-house advantage

We often hear: “If we use a shared forecasting tool, how are we different from our competitors?”

The truth is that your edge comes from how well and how fast you execute, not from proprietary dashboards. Building internal models requires:

• Years of historical data that most operators do not have
• Dedicated teams of data scientists and engineers to maintain accuracy
• Millions in ongoing investment

Meanwhile, EV charging is becoming a market where cost and convenience drive consumer choice. The real risk is not losing differentiation. The real risk is deploying capital slowly or to underperforming sites.

Why investors care about third-party validation

Even networks with strong internal tools benefit from an independent view. Investors underwriting multi-million-dollar deployments expect rigor and transparency.

A credible third-party forecast can:

• Strengthen diligence by confirming internal assumptions
• De-risk capital allocation with objective, data-driven insights
• Build confidence that growth decisions are based on market reality rather than internal optimism

As one investor told us recently, “We do not need you to hand over the steering wheel. We need to know the dashboard is accurate.”

Shared tools do not erase differentiation

Shared intelligence platforms provide a foundation of high-quality data and analytics without dictating strategy. Networks can still pursue unique site and pricing strategies with:

• Better inputs for decision-making
• Unique insights beyond what data science and analytics can provide
• Faster speed to market
• Investor-ready credibility

The operators who are winning focus their internal resources on site negotiations, customer experience, and operational uptime. They use specialized tools to handle the complex, data-heavy work.

In a market trending toward commoditization, the real advantage is not who built the model. The real advantage is how quickly you can reach profitability with the data you trust most.