AI in energy: Navigating the promise and the paradox in the Middle East

Arvin Singh is Partner and Head of Digital Advisory for the Middle East and South‑East Asia at Wood, where he leads digital transformation programmes that enhance asset performance, reliability and operational efficiency. With more than two decades of international experience, Arvin specialises in digitalisation, AI and advanced analytics across the energy, petrochemicals, power, mining and minerals sectors.

Artificial intelligence is rapidly reshaping the energy sector in the Middle East, creating new opportunities to optimise assets, improve performance and support a more sustainable energy system. But while the promise is clear, the path to real value is less straightforward.

Most AI initiatives still struggle to scale beyond early pilots, highlighting a gap between ambition and execution. At the same time, the growing energy and data demands of AI are introducing new pressures on infrastructure and resources.

Success will depend on how effectively organisations bridge this gap, combining strong governance, high-quality data and deep domain expertise to turn AI from isolated innovation into reliable, operational impact.

What’s holding AI back from scaling beyond pilot stage?

Despite the investment and ambition, more than 75% of AI initiatives still fail to move beyond proof of concept. The challenge is no longer identifying opportunity, but scaling it in a way that is responsible, resilient and delivers measurable value.

There is increasing recognition that governance is not a constraint on innovation but its foundation. AI must be designed, validated and continuously assured before it is deployed in critical environments. This is particularly true in the Middle East, where the scale and complexity of energy systems demand both precision and reliability.

How do we turn AI into real operational value?

At Wood, this approach is embedded in how we deliver digital capabilities across the region. Our Digital and AI Hub acts as a strategic catalyst, bringing together domain expertise, advanced technologies and local insight to help clients realise value faster and more sustainably. We are not simply deploying AI; we are embedding digital integrity and assurance into every stage of its application, ensuring systems are safe, compliant and built to perform in high-consequence environments. For example, in asset-intensive operations, we apply AI-driven predictive maintenance solutions that analyse real-time equipment data to anticipate failures before they occur, reducing downtime, improving safety and extending asset life, while ensuring all models are governed, transparent and aligned with operational risk frameworks.

Equally, AI is not a replacement for human expertise. The real impact comes from combining advanced analytics with deep engineering knowledge, enabling better, faster and more informed decision-making across the asset lifecycle.

This broader lens is essential when assessing value. AI’s return on investment is often framed in terms of efficiency gains, but that tells only part of the story. AI itself has a growing energy footprint, with rising demand from data centres and digital infrastructure. At the same time, AI-enabled solutions can significantly reduce emissions, improve asset performance and unlock new efficiencies.

What role does data play in successful AI integration?

Underlying this is the rapid expansion of data. Across the energy sector, the volume of data being generated continues to grow exponentially. Yet more data does not automatically translate into better outcomes. Storing, processing and securing vast datasets is resource-intensive, and legacy systems add further complexity.

The shift now must be from collecting everything to curating what matters. High-quality, contextualised data is far more valuable than volume alone. By analysing historical project data, spanning hours, costs, scopes and outcomes, Wood is already building an evolving database. This enables clients to move beyond isolated project assessments and instead compare performance against genuine like-for-like references. Ultimately, structuring and integrating this data effectively into our delivery is critical to unlocking AI’s full potential, something we see firsthand through our work across complex energy systems in the region.

At the same time, industry is struggling to keep pace with the speed of AI’s growth. Power availability, grid connectivity and hardware efficiency are becoming real constraints. Without a more sustainable approach to how AI infrastructure is deployed and powered, its benefits risk being offset by its own resource intensity.

How will AI reshape the energy landscape in the Middle East?

For the Middle East, this is both a challenge and a defining opportunity. The region has the ambition, capital and scale to lead in the integration of AI and energy, but success will depend on how effectively these elements are aligned. AI must support the dual objective of maximising efficiency in existing systems while enabling the safe and sustainable expansion of energy supply.

This is where integrated, locally grounded capability becomes critical. Through our Digital and AI Hub, Wood is working alongside national energy companies to apply AI in ways that deliver tangible and valuable outcomes, from optimising asset performance to supporting large-scale infrastructure development.

AI in energy is no longer a question of if, but how. The promise is clear, but real progress will depend on disciplined execution, grounded in governance, transparency and collaboration. In a sector where the cost of failure is high, getting this balance right is not optional. It is essential.