A new global study, conducted by Censuswide and spanning 850 C-level and senior utility executives, highlights an energy sector operating under immense multi-front pressures driven by unprecedented load growth from data centres and electrification, and extreme weather events.

While global energy and utilities investment priorities were previously dominated by sustainability and decarbonisation goals, the research finds utilities have new priorities. They are turning to grid hardening, new energy sources and storage, and better asset lifecycle management, underpinned by industrial AI to optimise performance, improve resilience and deliver better outcomes for customers and regulators.

First, hardening existing grid reliability to build grid resilience against extreme weather events and cyber threats tops operational priorities, identified as the most critical operational challenges facing utility leaders today. This has led to energy and utilities organisations re-evaluating existing infrastructure – with 26% actively re-evaluating asset portfolios to prioritise investment in existing grid infrastructure.

Second, the majority consider AI to be essential for the energy transition. Industrial AI is emerging as the critical layer required to unite fragmented systems, predict equipment failures before they trigger grid downtime, and automate complex operational workflows. The survey found that 57% of utility leaders believe AI is critical for reducing operational costs, while nearly half (47%) say AI is critical for optimising asset performance and managing electrification-driven demand growth.

Third, asset lifecycle management is becoming the battleground for utility modernisation and bridging operational silos. The research found evidence that new technology adoption is hindered by foundational data challenges. Just under half of respondents (49%) said they are constrained by operational data silos that hinder real-time decision-making.  

Despite heavy investment, most utilities remain in the ‘developing’ stage of asset lifecycle management, with siloed systems and limited predictive capabilities – only 33% describe their asset lifecycle management capabilities as advanced, with 51% claiming they have asset management systems in place, but operate in silos, with limited predictive capabilities. This highlights utilities’ appetite for a modernised asset management approach being central to resilience, reliability and investment planning.

New storage, source and security in the grid of the future

As utilities struggle to balance capital investment between maintaining legacy infrastructure and expanding capacity, traditional grid management methods are reaching their limit. The research highlights a growing shift towards grid flexibility, including battery energy storage systems and distributed energy resource management systems to manage demand volatility and protect against prolonged outages.  

In the future, utility leaders predict advanced geothermal (22%) and small modular reactors (18%) will have the greatest long-term impact on securing continuous clean baseload power – while 46% say AI is critical for accelerating renewable and distributed energy integration.

The findings reinforce that digital transformation strategies in energy and utilities must prioritise measurable operational outcomes – specifically grid uptime, customer experience and regulatory compliance – over generic software adoption.  

Utilities cannot invest their way out of today’s challenges through new infrastructure alone. Addressing growing demand and affordability pressures requires a balanced strategy that gets more from existing assets while making smart investments in new capacity, grid modernisation and resiliency. Industrial AI provides the decision intelligence needed to optimise asset performance, maximise returns on capital and maintain reliable service in an increasingly complex operating environment.

Utilities cannot invest their way out of today’s challenges through new infrastructure alone. 
 

In wider industry, AI controls are still lacking

The IFS industrial index 2026 presented results of a survey of over 2,200 senior decision makers and front-line staff from across 14 countries in energy and utilities as well as manufacturing, aerospace and defence, construction and engineering, transportation and telecoms. The survey was carried out by Censuswide from 17 August to 3 September.  

It reported big gains from using AI in industry: a 93% improvement in first-time-right performance, a 90% increase in asset availability and an 88% improvement in on-time delivery schedule adherence.

The survey also found that 70% of organisations are claiming confidence in their safety and data policies.

However, critical controls are still missing:

• Only 11% of respondents have validated AI outputs for safety-, quality-, or mission-critical processes.
• Only 11% have established control mechanisms for AI use with sensitive or export-controlled data.
• Only 4% of respondents have implemented mandatory human oversight or approval requirements for AI-supported decisions.
• Only 20% have embedded industrial AI into supply chain management or maintenance repair and operations – the functions where operational failures carry the highest consequences.

In industrial operations, this gap between confidence and control is an operational liability.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of the Energy Institute.

• Further reading: ‘From blackout risk to batteries: can local networks help stabilise solar on our grids?’ With UK balcony solar now legalised, Graeme Hutchison, Managing Director of Grid Consultancy at Eclipse Power, explores the question of solar instability, arguing that demand – and intelligently designed grids – can help.
• ‘AI and energy: sovereignty, safety and trust’. Discover why ‘Sovereign AI’ in energy does not fit neatly into one box and is becoming a board-level question about control, resilience, capability and trust.