Energy companies operate in a demanding environment. With volatile markets and ageing infrastructure, there is rising pressure to increase efficiency and reduce emissions. In energy, operations are highly complex and asset-intensive. In this environment, scaling artificial intelligence (AI) and integrating it into the organisation’s workflow can be the real competitive differentiator.
The energy industry has already run several pilots. Many have proved to be successful. But the harder task is moving beyond such isolated wins. McKinsey’s recent AI survey found that nearly two-thirds of organisations have not yet begun scaling AI across the enterprise. An AI-powered pilot may improve performance in one area. Extending that success across the business is the tough part.
Beyond the Pilot: Why Scaling AI is Difficult
Data is usually the first obstacle, with many energy companies carrying significant data debt. Systems were built over decades for operational reliability, not analytical openness.
However, data is just one part of the story. Governance structures, organisational alignment, and cultural readiness matter just as much. AI struggles to scale when treated as a technology deployment rather than a change in how the business operates, especially in heavy-industry sectors like energy.
Operations span exploration, production, transportation, and refining. Different systems and teams must work together, yet insights rarely travel far beyond the group that generated them. Early success builds enthusiasm, but impact plateaus when organisations struggle to replicate isolated wins.
Recognising this pattern, companies that aim to make real progress are rethinking how their organisations work. They are investing as seriously in operating models as they are in technology. So, what changes when organisations get the conditions right?
What Enterprise-scale AI Looks Like in Energy
At its best, industrial AI operates in the background, embedded into how operations run. In upstream operations, AI-powered monitoring of distributed assets gives operators a level of real-time visibility that periodic manual processes simply cannot match. In midstream, it supports pipeline integrity, compression efficiency, and transportation optimisation across geographically distributed networks. In downstream refining and distribution, it enables smarter scheduling, yield optimisation, and more responsive supply chain decisions. Across all three, the pattern is similar: as teams respond faster, decisions improve, and problems are spotted earlier to be handled more effectively.
Predictive maintenance is one of the high-impact areas for improvement. Instead of relying on fixed schedules, operators can spot signs of failure early and intervene before problems escalate. Peer-reviewed research suggests AI-driven predictive maintenance can reduce downtime by up to 30%, delivering real financial returns through operational efficiency gains. In an industry where a single hour of downtime can cost hundreds of thousands of dollars, those numbers count.
Building the Conditions for Scale
Scaling AI successfully comes down to a few conditions that tend to get less attention than the technology itself.
The starting point for all technology transformations, including AI, is data readiness. Without trusted, interoperable data, even sophisticated AI can fall disappointingly short. AI delivers its greatest value when it can draw on reliable data from across the organisation.
The organisations moving ahead are creating closer alignment between business, operational, and technology teams so AI becomes part of how decisions are made. They build trust in AI outputs through transparency and demonstrated results. They also invest in AI fluency across every team that touches the value chain. Finally, they measure success through real, quantifiable business outcomes.
The Conversation Ahead
The next phase of AI in energy will be defined by how well it is executed across the organisation. Companies have crossed the stage where they need to prove value in isolated pilots. Embedding intelligence into how the business runs is what is going to count.
These are the conversations we are looking forward to at Future Oil & Gas 2026 in Aberdeen this June. We hope to see you there.
For a broader perspective on AI-led transformation in energy, explore our AI for Energy Evolution initiative.
About the author
Joseph Alenchery, Joseph Alenchery, SVP & Business Head- Energy Next, Infosys
Joseph heads Infosys’ Energy and Services (MEA) segment focused on a portfolio of Strategic clients across Europe, Middle East and India. Infosys’ Energy Practice is currently rated among the top two globally. He has been with Infosys since 2002 playing different leadership roles encompassing Portfolio P&L leadership, Corporate Strategy, Mergers & Acquisitions and Start-up Practice Unit Creation. ALM’s Consulting Magazine recognized Joseph as one of the most influential consultants globally in 2025. He actively contributes to the future of Energy through forums such as the IEA. He led Infosys’ delegation at The 2026 IEA Ministerial Meeting in Paris. Joseph has worked with several marquee clients in the Energy sector and is featured as an “Expert” in the Oil & Gas journal. He is part of the prestigious ONS (Offshore North Seas) Expert Panel for Net Zero Markets.
Prior to his current role, Joseph co-founded the Corporate Performance Management practice as an intrapreneurial effort, led a team of practitioners focused on best practices in Corporate Strategic Planning and worked as Business Advisor to the Chairman of the Board. In addition, Joseph was the group director focused on M&A for the Energy practice. Joseph has published several papers on the topic of corporate strategic planning and Energy Transition, and has guest lectured at business schools across Europe, US and India.
