We can read this on a smartphone because of Thomas Edison. When the American inventor flipped the switch on his power station at Pearl Street in lower Manhattan, it sparked the beginning of the electrical era. At first, his system only powered a few blocks.

What turned that local curiosity into the backbone of modern life was not incremental lamp-by-lamp improvements. Rather, it was the systemic scale that created the national (and international) infrastructure we rely on. 

As we push through with the energy transition, today’s utilities confront a similar inflection point. Artificial intelligence (AI) may illuminate aspects of the business world, but only enterprise-wide grid intelligence can transform the system.

As we look to electricity to cut global greenhouse gas emissions, smart grids that encompass the entire energy ecosystem are the way forward.

Global electricity demand grew at 4.3% last year, a step change from the previous year’s 2.5% rate, according to the International Energy Agency (IEA). The biggest growth has come from data centers, the physical infrastructure behind AI.

Data centers’ electricity use could double by 2030, the agency says – just about the time generative AI’s economic dividend of $7 trillion, as estimated by Goldman Sachs, could start paying off. Over the intervening five years, we will see the Jevons Paradox at work firsthand – though by 2050 electrification could total 60% of all reductions needed to achieve net-zero emissions.

Economics lesson apart, utilities will need to make strategic decisions about grid modernization, and with them, asset allocation. We can’t delay – extreme weather events are already underscoring the limits of traditional infrastructure.

Connecting the system of systems

Comprehensive digital transformation lights the path forward. IEA forecasts show that digital technologies could save $1.8 trillion of grid investment globally through to 2050 by extending the lifetime of grids, while also integrating renewables and minimizing supply interruptions.

The utilities that dominate over the next decade will be those making strategic investments in comprehensive intelligent infrastructure to transform their entire operational model. Today’s grids rely on a system of systems: generation, transmission, distribution, markets and customers.

Knitting them into one coherent, adaptive organism – an intelligent or smart grid – requires an engineered stack built around internet of things (IoT) devices, an interoperable data fabric, AI-infused digital twins, probabilistic forecasting and platform services that monetize flexibility.

Such a layered stack is what unlocks true value for utilities. When data silos open up and industrial process information is shared up and down the value chain, every player across the ecosystem can access the same source of truth.

We see that time to insight drops, boosting proactive decision-making and enhancing operational efficiency – resulting in adaptive and resilient systems.

System-wide intelligence

Digital technologies are often a local play, flagging isolated issues. But when those gains are extended with large, enterprise-grade sensor networks, the resulting industrial intelligence enables system-level optimization by enhancing decision making.

The global renewable energy producer Innergex increased turbine availability from 93.5% to 99.5% using a scalable, tiered data architecture so teams could troubleshoot from the turbine floor as well as the corporate office.  Creating end-to-end lifecycle visibility and layering on AI-infused analytics was crucial to improving site reliability issues and tracking lost energy – issues that arose from a mixed set of software vendors and vintages across sites and functions.

Bringing that lost energy back to the grid means Innergex can now add new homes to the over 1 million households it supplies with clean energy.

Real-time adaptation 

At the other end, integrated systems deliver a bigger payoff: real resilience and higher renewable penetration, both essential to net-zero. By pairing automation with ecosystem-wide collaboration, generation can forecast and bid, loads can be redistributed, and retail platforms can signal customers to shift demand.

That coordination depends on disciplined change management – shared schemas, open APIs and cross-functional operating models. Consultancies show the difference: companies that treat digital twins and AI as platforms (not pilots) unlock innovation and better capital allocation; those that do not only realize marginal gains.

Split-second action

Autonomous response systems will soon add a competitive differential to utilities’ tech stacks. This agentic AI layer sits on top of existing data platforms. Its competitive advantages come from split-second, multivariate analysis and control as utilities combine edge inference for latency-sensitive decisions with centralized digital twins for scenario modelling and continuous learning.

This architecture enables both local resilience and participation in wider energy markets – a truly intelligent and autonomous grid.

Governance and security

To unlock these gains, utilities could learn from Edison, who had to navigate safety and standardization issues. Today’s equivalents are cybersecurity and governance. Scale requires guardrails such as explainability, fallback modes and CIS controls to  keep autonomous actions safe and auditable.

Likewise, human ingenuity remains central to maximize AI’s benefits, as the IEA notes. Just as engineers were instrumental in driving electrical adoption, utility teams are vital to overseeing AI systems today, making critical decisions and ensuring grid safety and reliability – as regulations like the EU AI Act already mandate.

A final constraint will be the previously noted AI compute paradox. Utilities can manage this with a stronger sustainability strategy: tie compute growth to renewable procurement, adopt carbon-aware scheduling, prioritize edge inference where possible and exploit heat-reuse as policy permits.

Connected grid intelligence for net-zero

For leadership, the decision is straightforward: Treat grid intelligence as capital infrastructure in service of net-zero. Start with a full-stack pilot, encompassing sensors, interoperability, a digital twin, edge inferencing and market integration.

Layer in CO2 measurement track MWh of curtailed renewables recovered, then embed explainability and CIS controls. 

Most importantly, scale those pilots into system-wide platform programs to avoid scattered gains and AI pilot purgatory. Only then will the new intelligent grid pay off through compounding: improved capacity, lower costs, new markets and superior resilience.

The compute paradox then becomes a design constraint, not a roadblock. Electricity pioneers like Edison taught us that infrastructure unlocks scale. Make grid intelligence available across the network, and cleaner, more reliable energy arrives faster.