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AI 2.0 and the future of energy: From prediction to strategic intelligence

insight
July 09, 2025
9 min read

Authors

anurag

 
Anurag Sahay

Managing Director & Global Business Unit Head - AI and Data at Nagarro. He is strategically and technically leading the Generative AI segment, minimizing the risk and maximizing its impact for the clients.

 

Joteep_Mahato



Joteep Mahato 

Director and Global Head of the Energy & Utilities Line of Business at Nagarro. With over two decades of experience in digital transformation and engineering leadership, he drives strategic growth and innovation for global utility clients.

 

As the energy sector faces unprecedented disruption, AI is undergoing its own transformation. It is no longer restricted to a predictive engine or optimization toolkit; AI is evolving into the strategic operating system of tomorrow’s energy systems.

In this article, we explore how AI 2.0 is reshaping the energy transition: not just by improving processes, but by reframing the very questions we ask of our systems, data, and decisions.

Energy systems go nonlinear

For much of the last century, energy supply followed a linear, centralized model: demand dictated supply, and control was top-down. That model no longer fits the complexity of today’s energy landscape.

Today, volatility is already built into the system. Electrification, decentralized generation, and the unpredictability of the weather due to climate change have disrupted the old system. Households are now generating, storing, and trading electricity. Electric vehicles, heat pumps and smart appliances have turned consumers into active energy participants.

Renewables are inherently variable and require grids that manage bidirectional flows across millions of endpoints. At the same time, geopolitical instability and extreme weather conditions have made energy resilience a national priority. Ageing infrastructure, particularly in the US, is struggling to cope with increasing loads and expectations. Data centers, the backbone of artificial intelligence, have become wildcard load centers.



IEA warns that demand for electricity from AI, data centers, and cryptocurrencies could more than double by 2026, surpassing that of countries like Japan. McKinsey predicts that AI alone could consume 20% of the US's electricity by 2030. Can AI solve an energy challenge that is rapidly worsening as a result?
 

 


This complexity requires a new kind of intelligence, one that is not just reactive but inherently adaptive, not just digital but deeply strategic. AI must act as a system-wide layer that orchestrates distributed resources, anticipates disruption, and embeds resilience by design.

 

AI 1.0 to AI 2.0: From prediction to strategic reasoning

The first wave of AI in the energy sector AI 1.0 focused on prediction and optimization. It made processes smarter within existing boundaries. Applications included:

  • Predictive maintenance for grid assets
  • Renewable generation forecasting
  • DER orchestration for load balancing
  • Energy efficiency in buildings and factories


These tools brought clear benefits, including higher reliability, lower costs, and operational advantages. But they worked within a known system. AI 1.0 helped us do what we already knew, only faster and better.

 

We are now on our way to AI 2.0.

AI 2.0 surpasses optimization and ventures into the realm of strategic thinking. It doesn't just make predictions; it explores, asks questions, and creates new ideas. It helps leaders in the energy sector test the future, weigh trade-offs, and co-create intelligent systems that can adapt in real-time.

 

 

 

Emerging capabilities include:

Apple broadcast
Scenario simulations that consider climate risks, policy changes, and consumer behavior to support long-term grid planning
person with headphones-3
AI co-pilots that support planners and executives with contextualized recommendations for markets, operations and regulations.
Building-1
Autonomous agents that operate microgrids and virtual power plants, balancing supply and demand across thousands of decentralized assets.

 

 

 

“In the energy sector, AI 2.0 means creating systems that anticipate, adapt, and evolve in response to the world around them. It’s a shift from isolated efficiency to networked intelligence, where AI becomes a partner that helps shape the future together.”

Anurag Sahay

 

 

Designing for Intelligence: Towards smarter infrastructure

 

Tomorrow’s infrastructure must not only be more resilient, but also smarter, more adaptable, and more targeted. As the energy ecosystem becomes more distributed, dynamic and digital, we need to fundamentally rethink how we design, operate, and control the underpinning systems. This requires refactoring the fundamental design principles, from static technology to living, learning systems:

Adaptability over rigidity

In a world that changes by the hour, energy infrastructure must keep up. It needs to sense what’s happening, respond on its own, and evolve over time. Embedding intelligence at the edge, where the real action happens, is how we make that possible.

Adaptive energy infrastructure

Data as a living asset

Data isn’t just something we collect after the fact. It’s the lifeblood of modern systems. When designed right, it flows freely, connects everything, and gives us the context we need to make smarter, faster, more human decisions.

Data as a living asset

Thinking in systems

Every decision in energy touches something else, sometimes in ways we don’t expect. Electric vehicles don’t just change what people drive; they change when, where, and how we build the grid. The best designs see the bigger picture and plan for the ripple effects.

Thinking in systems

Learning through simulation

The future can’t be predicted, but it can be explored. AI gives us the power to model countless scenarios and ask, “what if?” before reality forces. This isn’t just about building certainty; it’s about preparedness for what lies ahead.

Learning through simulation

Learning by doing

What if infrastructure could get better on its own? With AI-powered feedback loops, it can. These systems improve over time, spotting patterns, solving problems faster, and making them more efficient with every cycle.

Learning by doing

Trust by design

People won’t follow what they don’t understand. Trust needs to be built into every decision, every model, and every outcome. This means making AI explainable, accountable, and always aligned with the public good.

Trust by design

Collaboration as default

No single team can build the future alone. The best solutions come when engineers, policymakers, designers, and communities build together, right from day one. It moves from collaboration to co-creation.

Collaboration as default

Sustainability at the core

We can’t afford to treat energy use as an afterthought. Every line of code, every design decision, every deployment must consider its environmental impact. Intelligence isn’t just about performance; it’s a core responsibility.

Sustainability at the core

 

 

Leadership cannot be bolted on; it must live in the system and shape it from within. To the extent that we embed intelligence into the infrastructure that drives our lives. Embedding values with the same depth. Fairness, transparency, and sustainability are not optional features; they are design principles that must guide the construction, management, and development of these systems.

Responsible AI is not defined by what is technically possible, but by what is ethically necessary. The question is not just what these systems can do, but what they should do, serve people, protect the planet and shape a future we can stand behind.

 

 

Strategic intelligence: A roadmap for energy leaders

Transforming energy systems into intelligent ecosystems requires leadership that treats AI not as a tool, but as a strategic force that influences long-term outcomes. For energy businesses that want to lead the change, four things are particularly important:

 

 

Redesign intelligence

Shift from traditional data science to systems science, an integrated approach that combines physical infrastructure, digital capabilities, and human understanding. AI must support dynamic strategies, long-term planning and policy adaptation, not just short-term optimization. Intelligence should be integrated into the system, not imposed on it.
AI
Measure what's important

Moving beyond ROI 0069s the only indicator of success. Develop metrics that reflect resilience, adaptability, energy equity and decarbonization effects. In an AI-driven world, what gets measured determines what gets built. KPIs must capture both business outcomes and societal value.
Business goals
Build trust through governance

Treat AI governance with the same rigor as financial or operational oversight. Prioritize transparency, explainability and ethical accountability at board level. Trust is not a by-product - it is a design requirement embedded from development to deployment.
Trust and governance
Encourage radical collaboration

Strategic intelligence thrives on connectivity. Success will depend on close partnerships between utilities, technology providers, regulators, research organizations, and communities. Shared governance and co-innovation must become the norm, not the exception.
Collaboration

This is not just about implementing smart technologies. It’s about aligning intelligence with purpose, across strategy, operations, and regulation. The leaders who embrace this moment will not just use AI; they will shape the future with it.

 

Also worth reading - AI readiness: Why Nuclear Energy for AI is gaining ground

 

 

 

The leadership shift

The most profound change in the energy transition is perhaps not technological, but cultural. Leadership in an AI-driven world requires a new mindset: adaptable, system-oriented, and characterized by humility. To master this moment, leaders in the energy sector must:

 

Embrace uncertainty
Trade static plans for dynamic, scenario-driven thinking.
Think across systems
Recognize second- and third-order effects across energy, technology, society, and policy.
Redefine success
Expand definitions of success to include transparency, resilience, explainability, and equity.
Lead with curiosity
Great leaders won’t just seek better answers. They’ll ask better questions, cultivate experimentation, and champion learning.

 

 

The best leaders don’t have the most sophisticated answers; they have the courage to rethink the questions.

To realize the full potential of AI 2.0, companies need to become Fluidic Enterprises: agile, intelligent and designed to constantly evolve. At Nagarro, we help leaders in the energy sector make this leap and embed strategic intelligence with lasting impact.

 

Don’t miss - AI Agents: The next frontier for enterprise decision-makers
 
 

Evolving into the "Fluidic Enterprise”

To unlock the possibilities of AI 2.0, energy companies need to become fluidic enterprises agile, intelligent, human-centered, and designed for continuous innovation. This development goes beyond the introduction of new technologies. It is about reshaping the way companies think, work, and adapt in real time.

Nagarro works with leading energy companies to embed intelligence at all levels, from infrastructure to operations to culture with strategic intent and sustainable outcomes at the forefront.

Know more about fluidic enterprise

Fluidic enterprise (2)



Intelligently shaping the future

 
AI 2.0 is not just smarter software; it’s a redefinition of possibilities. In an industry where climate resilience and global justice are at stake, we need not only faster tools, but also smarter systems and bolder leadership. The leaders who embrace this change, technologically and philosophically will quickly respond while also playing a key role in shaping the future with AI.

 

 


Appendix: Can AI solve the problem it is helping to cause?

Generative AI and LLM offer enormous potential but also increasing energy demand. The IEA estimates that the electricity consumption of AI, data centers and cryptocurrencies could double by 2026, surpassing Japan’s total annual consumption. However, if used responsibly, AI can become an efficiency factor:

  • In manufacturing: AI reduces energy consumption by up to 20% through real-time optimization
  • In buildings: AI-controlled HVAC and lighting systems reduce waste
  • In power grids: AI precisely balances supply and demand, minimizes power cuts and improves storage usage
  • At Google: DeepMind reduces data center energy consumption by up to 40 percent through AI-driven controls


The energy burden of AI is not a flaw. It’s a design challenge and one that we solve: intelligently, strategically, and sustainably.



Up next — Meta’s “Superintelligence” Lab: Inside the bold push toward AGI

 

Watch now: Full session from Global Energy Transition 2025

Don’t miss this session with Anurag Sahay as he explores how next-gen AI is reshaping the future of energy, moving beyond prediction to enable smarter grids, strategic intelligence, and more sustainable decision-making.

 

 

AI 2.0 - Rethinking Intelligence for the energy transition

 
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