For energy companies, the value of technology increasingly comes down to what it can deliver. Preventing equipment failures, improving operational efficiency and reducing the resources consumed during production all carry financial implications. As artificial intelligence becomes more deeply embedded in the sector, its commercial relevance is being measured not simply by what it can do, but by the value it creates.
That is becoming an important measure of how AI is transforming the energy industry. Its influence reaches into decisions that determine how safely, efficiently and reliably energy is produced and delivered. Progress depends on whether those capabilities translate into tangible results.
An early warning on a drilling rig illustrates the point. Engineers gain time to investigate, adjust operations and prevent a disruption from spreading. The value lies in their ability to act on information they can trust. With these conversations at its core, ADIPEC’s 2026 edition will be taking place in Abu Dhabi from 2–5 November, with AI on top of the agenda.
According to organisers, AI & Digital Transformation attracted 1,296 technical paper submissions for this year’s SPE programme, more than any other technical category. The submissions give professionals an opportunity to examine the methods, findings and implementation experience behind AI applications. ADIPEC’s AI Zone also captures the energy sector’s growing focus on deploying advanced technologies to improve operational efficiency and deliver measurable value. Launched in 2024, the Zone grew by nearly 40% in 2025 and has expanded again for 2026, with AIQ as co-host.
That shift is already evident in the Middle East. In August 2026, ADNOC announced the deployment of an AI-enabled Real-Time Operations Center platform, developed with SLB, across more than 120 onshore and offshore drilling rigs. The company says the platform reduces engineering effort by 30–40%.
Bringing live drilling data into a single environment gives engineers a connected view of activity across the fleet. This supports risk identification and coordinated responses. It also places responsibility on operators to establish how recommendations are checked and who acts on them.
Saudi Aramco’s reporting shows the financial significance companies are attaching to these applications. Its 2025 annual report, published in 2026, recorded $2.6 billion in AI-driven “Technology Realised Value”. Aramco also says it had deployed over 200 AI solutions by late 2025. That figure is a company-reported measure. For professionals assessing AI investment, the useful detail sits within the applications: what they cost to implement, how their performance is measured and whether results hold under changing operating conditions.
Dennis Jol, CEO of AIQ, discusses the work that goes behind deployment: “Companies such as AIQ have been transforming data sets into actionable data that is being used to create real-world value in operations.”
Making data actionable requires an operational response. An alert needs an owner. An automated intervention needs defined limits. A model needs monitoring as equipment ages and conditions change. These are essential parts of making AI useful in an environment where decisions have physical consequences.
AI’s role also extends into the research behind industrial production. Jack Hidary, CEO of SandboxAQ, points to catalyst development, where the company uses AI to identify candidates for laboratory testing.
“With AQ-Cat we can narrow the scope down to the top ten candidates worth taking into a physical lab,” he says.
Catalysts enable chemical reactions central to petrochemical manufacturing. Identifying promising candidates helps researchers focus their investigations, with physical experiments establishing how the materials perform. The application places AI within the development process for technologies used in industrial operations.
AI is also shaping how energy producers substantiate the carbon footprint of what they sell. Dan Harple, Founder and CEO of Context Labs, points to carbon accounting for blue ammonia and energy value chains spanning several countries. “Each figure can be traced back to the measurements and method behind it, so a buyer or verifier can check it instead of taking it on trust,” he says. This gives AI a practical role in commercial accountability: helping connect a product’s stated carbon intensity to evidence that can withstand external scrutiny.
Understanding how such capabilities fit into an energy business requires access to the people developing, implementing and using them.
Through its AI Zone, AI, Digital & Technology conference and AI Leadership Roundtables, ADIPEC provides several settings for that exchange. Engineers can investigate how systems handle incomplete data. Procurement teams can examine integration requirements. Executives can discuss the investment, skills and governance needed to deploy applications across assets. The event’s value as a convenor (or rather a mediator) rests on making these connections useful. Demonstrations should help professionals understand a technology’s capabilities, requirements and limits. Discussions should equip them to make informed decisions within their own operations.
Those decisions include the electricity needed to support AI itself.
In its April 2026 report, Key Questions on Energy and AI, the International Energy Agency projects global data centre electricity consumption of around 950 terawatt-hours in 2030, accounting for approximately 3% of worldwide electricity demand. The forecast covers data centres as a whole, including those supporting AI.
For energy planners, this raises questions about grid connections, dependable generation, storage and cooling. Proposed facilities need credible arrangements for delivering and funding the infrastructure that will support them.
Amel Chadli, President, Gulf Cluster at Schneider Electric, identifies the responsibility: “We must also meet AI’s own electricity needs with efficient, resilient infrastructure.”
Workforce capability requires equal attention. The IEA’s 2026 research identifies digital skills as the largest barrier to AI adoption among surveyed energy companies. Fragmented data and cybersecurity concerns also constrain deployment.
These issues belong within ADIPEC’s technology discussions because they shape what professionals can achieve after the event. Access to developers, engineering expertise and operational experience can help teams understand the work required to make a system dependable.
Ultimately, AI’s impact on energy will be felt by the people responsible for keeping it flowing. An engineer needs information they can trust. A maintenance team needs time to act. A worker needs confidence in the systems supporting their safety. Technology earns its place when it serves those needs.
Those outcomes also matter to the wider economy. Safe, reliable and efficient energy operations support businesses, public services and the region’s ambitions for digitisation. AI is changing how that energy is produced and delivered, while the digital economy itself needs dependable power to grow. Scaling AI means planning for the electricity, grids and infrastructure behind it.
ADIPEC increasingly brings these two conversations together: how AI can improve energy operations, and how energy can sustain the digital economy. Their shared purpose comes back to where technology’s value begins, making everyday life better.
