Is AI over-hyped or transformational?

Artificial Intelligence (AI) has captured the world’s attention and investment. Global spending on AI technologies is projected to surpass 600 billion euros by 2030, according to a report by Strand Partners and AWS, while the number of generative models has multiplied exponentially in just two years. Critics draw parallels to past bubbles and point to lofty valuations unbacked by profit. Which leaves us with the question – is AI over-hyped or is it a transformation?   

We need to remember that AI existed long before the current “boom”. It stretches back nearly 70 years to the Dartmouth workshop in 1956, where a group of mathematicians and scientists coined the term "artificial intelligence” and lay the groundwork for the field. What we now call “modern AI” is simply the latest branch of a long-evolving family of information technologies. AI is the natural digital next step. 

The real question today is not whether AI will shape our lives, our work, and the systems that power our world. It already is. The question is whether we will guide this technological force toward accelerating energy-system decarbonization and delivering high social value, or allow it to evolve without meaningful purpose or fulfilling its potential.

From Consumers to Prosumers: The New Energy Landscape

One area where technology is already having a measurable impact is in the way we manage and consume electricity. For the first time since electricity began powering our homes in the 19th century, people are moving beyond the role of passive consumers. AI is accelerating the era of the prosumer, where households and businesses can make data-based decisions on when to produce, store, and actively manage their own energy. 

By turning vast streams of data into actionable insights, AI enables decisions that were once reserved for engineers to be made based on data by everyone. This democratization of energy is visible today in microgrids and virtual power plants (VPPs), where AI optimizes when to generate, buy, or sell electricity. Tools such as EcoStruxure Resource Advisor and EcoStruxure Microgrid Advisor automatically detect inefficiencies, identify anomalies, predict demand patterns, and optimize when to generate, store, or consume energy, turning distributed resources into intelligent, flexible assets that strengthen both efficiency and resilience. 

This shift reflects Schneider Electric’s broader role as an Energy Technology Partner, where electrification makes energy clean and digitalization makes it smart. And we are now able to measure this impact with real data. In a recent study on 600 educational buildings in Stockholm, our Sustainability Research Institute quantified the net effect of AI-powered HVAC optimization: the energy and carbon cost of the AI infrastructure itself is vastly outweighed by the savings it enables, by a ratio of 1:200. In practical terms, adding 1 tCO₂ to build and run the AI system unlocks a reduction of around 200 tCO₂ in operations. This is the power of making the invisible visible—allowing AI to anticipate demand, refine control strategies, and drive meaningful decarbonization across buildings, campuses, and eventually entire cities.

Real World Impact

Across the globe, digitalized factories are demonstrating how AI can dramatically reduce energy and resource consumption sometimes cutting energy use by more than half in just a few years. For example, in Hyderabad, our smart factory cut energy use by 59%, water consumption by 57%, and CO₂ emissions by 61% within four years How? With an advanced, cloud-based manufacturing system powered by IoT-enabled devices, leveraging real-time data and predictive analytics for smart decision-making processes. 

Another real-life example: by pairing a cloud-based MPC (Model Predictive Control) optimizer with rugged edge controllers, Schneider Electric transformed distributed energy resources into self-learning microgrids that retrain every few minutes on real weather, tariffs, and demand patterns. Across 97 live sites, this shared AI “brain” has enabled a 12-person team to cut external energy draw by 458 MWh and reduce emissions by an average of 109 tCO₂ per site per year—around a 28% improvement. Scaling this model to 1,000 locations would eliminate nearly 100,000 tCO₂ annually, accelerating entire value chains toward net-zero.  This is why the company has been highlighted by World Economic Forum MINDS—the emerging global benchmark for AI excellence—for its AI-driven microgrid innovation. 

AI is also having a huge impact on operational efficiency. At Schneider Electric, we process more than a million energy invoices per month, and around 50,000 (5%) trigger anomalies such as faulty meter readings or abnormal consumption. Historically, resolving those anomalies required 12–15 person-days and achieved about 90% accuracy. With Resource Advisor Plus, our new AI-native energy and sustainability engine that uses agentic AI, resolution happens in seconds or minutes with more than 99.9% accuracy, enabling clients to make timely, high-stakes decisions in volatile energy markets, where the difference between acting quickly or slowly can have major financial and sustainability implications.

System-level Resilience and Efficiency

At a system level, AI strengthens resilience. Predictive analytics and advanced distribution tools such as EcoStruxure ADMS provide real-time digital maps of the grid, allowing automatic reconfiguration during outages and reducing downtime from hours to minutes. In wildfire-prone regions, AI is being used to detect and prevent risks before they escalate. These capabilities are essential as global electricity demand rises and as AI itself drives higher consumption in data centers, projected to grow from 460 TWh in 2024 to over 1 000 TWh in 2030 and 1 300 TWh in 2035, according to the International Energy Agency. To meet this challenge, Schneider Electric and NVIDIA are co-developing AI-ready data center architectures that reduce cooling energy use by 20% , while addressing power densities of up to 1MW per rack.

And there are more examples where AI plays a transformative role. Buildings, which account for 40% of global CO₂ emissions, can reduce their footprint by up to 70% with digital and electric solutions that harness AI.  And in homes, intelligent devices such as Wiser Home learn from daily habits, weather forecasts, and tariff data to optimize energy use in ways that benefit both families and the grid. Across all these applications, AI is not only cutting waste but also building flexibility into the energy system, ensuring that clean power can be scaled reliably.

AI Amplifying Human Expertise

The most important point is this: AI does not replace human expertise, it amplifies it. It equips engineers, operators, and consumers with the insight to make faster, smarter, and more sustainable decisions. Across industries, companies are already redefining how people and intelligent systems work together. For example, Moderna has merged its Human Resources and Information Technology departments under one leadership role, led by Chief People and Digital Technology Officer Tracey Franklin, to build an AI-first organization where human capital strategy and digital innovation operate as one. 

By bringing workforce planning and AI deployment together, Moderna is treating intelligent systems as collaborators that enhance human capabilities rather than replace them. This evolution reflects a broader shift across industries, showing how digital intelligence complements human judgment and extends the reach of energy management, turning efficiency into a competitive advantage and resilience into a shared benefit.

The Path Forward

Like any technology innovation, AI should be considered both for its practical benefits and its potential, rather than its technical promise alone. The evidence is clear: from factories and data centers to homes and cities, AI is delivering measurable improvements in efficiency, resilience, and sustainability. But the true potential of AI will only be realized if we move beyond discussion and take decisive action.

Now is the time for businesses, policymakers, and individuals to embrace AI as a strategic tool in the energy transition. By doing so, we can create energy systems that are not only cleaner and more efficient, but also more flexible and resilient in the face of global challenges.