Energy Transition. Built for Resilience. 

This blog has been co-written by Carol Johnston, VP, Energy, Utilities and Resources and Sophie Graham, Chief Sustainability Officer.

The energy transition is no longer just about sustainability, it’s about affordability, energy independence, and resilience. Two years on from Europe’s last gas crisis – then triggered by the Russian invasion of Ukraine, now by the Iran war, businesses across the globe are again faced with supply chain disruptions and growing energy prices. 

Now more than ever, reducing dependency on fossil fuels via AI-enhanced energy diversification and optimization is becoming a critical solution to energy security concerns. 

The big picture: where capital and policy are moving

Prior to the Iran war, the world was already experiencing stellar growth in renewables and electrification with a record 814 gigawatts of wind and solar generation capacity added in 2025, comprising 647 GW of solar and 167 GW of wind. And in the first half of last year, renewables overtook coal in the global electricity mix for the first time, passing 34% of global electricity generation (and including nuclear increases that share to over 43%). 

In April of this year, France quietly doubled its bet on the energy transition. Prime Minister Sébastien Lecornu announced that public support for electrifying industry, transport, and buildings will double to EUR 10 billion by 2030. In the same month, tanker traffic through the Strait of Hormuz, which usually transports about 20% of the world’s oil and liquified natural gas (LNG), remained at roughly 5% of the pre-war monthly average, reminding global executives just how exposed the world is to fossil fuel supply shocks. 

The consequences of the war are also visible in wholesale markets. Considering the period from January to April 2026, countries with higher shares of renewable generation, including Spain, Portugal, and the Nordics, had significantly lower wholesale electricity prices than countries like Italy or Ireland, which have a higher proportion of fossil fuel generation. For an industrial customer looking at a 10-20 year energy horizon, that translates directly into significantly lower price risk, and a more predictable cost base in the face of future fossil fuel supply volatility. 

Diversification is a win for energy security (and the planet) 

For most of the last decade, the energy transition was framed in environmental terms. Now the debate has changed. Diversifying away from fossil fuel reliance has become a strategic and commercial decision, because overdependence on volatile fossil fuel energy is now one of the largest unhedged risks on any industrial balance sheet. 

The 2022 gas crisis forced European governments and corporations to absorb a fiscal and competitiveness shock measured in hundreds of billions of euros. The UK Climate Change Committee makes the comparison explicit: the total cost of reaching net zero by 2050 is less than the impact of a single fossil fuel price shock of the 2022 kind. Cleaner can also be cheaper, faster, and more secure. 

As businesses face rising energy bills and new geopolitical headwinds in the wake of the Iran war, Boards are viewing diversified portfolios of renewables, storage, and electrification not as standalone sustainability initiatives, but as sensible allocations of capital to protect margins and enhance long-term business resilience. 

Building operational resilience, holistically 

Adding renewable generation alone – without the corresponding investments in energy infrastructure, storage, and grid control systems – will not enable a utility, or any industrial organization, to be more resilient to fossil fuel pricing and supply shocks. Building deep, organizational resilience requires a holistic view of both the entire asset portfolio and the full asset lifecycle. This is where Industrial AI comes in. 

Planning a strategy to evolve your portfolio to cleaner energy traditionally requires a lot of time, effort, and estimations – which only multiply as the complexity of the asset portfolio increases. IFS Copperleaf AI-powered Asset Investment Planning (AIP) solutions enable utilities and other asset-intensive businesses to model thousands of asset investment scenarios to balance operational performance with carbon, cost, and grid reliability and automatically select the optimum result for your business. How much storage do we need at each connection point, and when does it pay back? When do we retire the gas peaker, and what combination of wind, solar, storage replaces it? These are the questions AI-powered AIP can help answer. 

The future of industrial intelligence 

A wind farm without predictive maintenance, a battery without health monitoring, a substation without real-time condition data, an EV depot without smart charging – each of these scenarios will result in an asset not reaching its operational potential. 

In fact, as reported by World Economic Forum, badly maintained, defective, or outdated equipment and infrastructure is driving a staggering annual loss of global GDP of between $1 trillion and $3 trillion. This same poorly maintained equipment is also responsible for approximately 25% to 30% of global greenhouse gas emissions. Improved maintenance of these assets could significantly close this “maintenance and emissions gap” and go a long way to improving both the global climate and economic outlook. 

That is another area where Industrial AI can really make a difference. IFS.ai Asset Predictive Maintenance leverages AI to optimize and improve asset performance well before more costly reactive maintenance is required. Our agentic Planning, Scheduling and Optimization (PSO) engine dynamically optimizes maintenance jobs for field technicians on a continuous basis, even factoring in electric vehicle (EV) charging stations into route planning – resulting in an average reduction in travel distance of 37% across our customer base, plus a corresponding fuel and emissions reduction. IFS.ai Operational Intelligence (OI) enables anomaly detection and automated resolution or job scheduling in near real-time, helping minimize the risk of any costly unplanned downtime. By deploying these Industrial AI capabilities in combination, organizations can achieve levels of operational excellence that were previously unrealistic – benefiting both businesses and the planet. 

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