Digitalization is no longer a modernization initiative. It is becoming the foundation for how energy companies compete in an AI-driven economy. The pressure is no longer just to modernize systems or automate isolated workflows. It is to build the data foundation required for better resilience, faster decisions and credible use of artificial intelligence across a sector defined by volatility.
Few industries operate with the same exposure to geopolitical shocks, commodity swings, and long investment cycles. In recent years, the sector has moved from the demand disruption of the pandemic to war-driven supply concerns, regional tensions and renewed pressure around energy security. Those cycles shape how companies invest. When prices fall, many organizations shift into preservation mode, reducing discretionary spending, and focusing on existing production. When prices rise, investment appetite returns, often around operational excellence and efficiency.
The challenge is that digital transformation cannot be treated as a project that starts only when market conditions are favorable. The industry needs digital capabilities most when conditions are uncertain. Dr. Thiago Ribeiro explained, “This is the power of digital transformation, to leverage the data that you have right now and be, on the average at least, successful in any part of the commodity cycle that is going on.” That point should resonate across the sector: the right time to strengthen resilience is before the next downturn arrives.
The challenge is becoming even more complex because today’s energy system looks very different from a decade ago. Many companies are diversifying into renewables; advanced nuclear, fusion energy and infrastructure tied to growing power demand. Data centers are adding another layer of complexity, both as major energy consumers and as part of the broader AI economy. At the same time, newcomers in emerging energy segments are often born digital, while traditional assets have evolved over decades with far less digital integration. The result is a two-speed market: newer energy technologies can embed digital capabilities from the outset, whereas established oil and gas operations must modernize legacy processes, analog workflows, and data fragmented across disconnected systems.
The result is not simply a technology gap; it is an operating model challenge. Energy value chains span reservoirs, drilling, subsea equipment, offshore production, transportation, refining, petrochemicals, and retail. Each area may have optimized locally over time, but today’s competitive advantage depends on seeing across the chain.
That end-to-end visibility is becoming essential as energy, chemicals, food, materials and infrastructure become more interconnected. Bottlenecks in one part of the system can affect another. That interconnectedness is visible in everyday supply chains. Food production depends on fertilizers, many of which are produced using ammonia and urea. Today, ammonia production remains heavily dependent on natural gas, creating a direct link between agriculture, chemicals and oil and gas markets. Disruptions, cost increases or supply constraints in one part of that chain can quickly ripple across the others, demonstrating how deeply interconnected modern industries have become. As these dependencies frow, operational decisions can influence emissions performance, supply reliability, capital planning and customer commitments far beyond a single facility or business unit. A company that only understands its own slice of data may improve a local process, but it will struggle to optimize outcomes across the broader ecosystem.
As John Nixon put it, “AI is like a human. It must be trained on good data, reliable data, well-orchestrated, traceable data in order for you to have insights and recommendations you can rely upon.” AI raises the stakes. The industry is excited about artificial intelligence because it can process enormous volumes of information, surface patterns and support faster decisions. But AI does not solve a weak data foundation. In fact, it exposes it. Models trained on incomplete, inconsistent or inaccessible data can produce outputs that are difficult to trust. For an industry where safety, reliability and regulatory compliance matter, that is a serious limitation.
The first priority, then, is not AI experimentation for its own sake. It is data strategy. Companies need to understand where their data resides, how it is governed, whether it is accessible and how it can be connected across legacy systems and operational domains. Many organizations still have information spread across databases, engineering files, operational systems and historical records that do not communicate easily. Without integration, AI initiatives can remain trapped in pilots or narrow use cases.
A stronger approach is to build a connected data fabric that brings together critical data points across the enterprise and supply chain. This does not mean every system must be replaced. It means companies need a practical architecture for making data accessible, contextualized and usable by people, applications and AI models. That foundation can support better root-cause analysis, predictive insights, performance optimization and more informed capital allocation.
One thing is becoming difficult to ignore: digitalization is becoming a prerequisite for competitiveness in energy. Volatility is not going away. Regulatory pressure will continue to evolve. Power demand will grow. New energy technologies will mature. AI will become more embedded in how companies plan, operate and compete. The organizations that move fastest will not necessarily be the ones with the most ambitious AI announcements, but those that treat data as a strategic asset and connect it to measurable business outcomes.
For energy leaders, the opportunity is to use today’s momentum to prepare for tomorrow’s uncertainty. Digitalization should help companies operate through the full commodity cycle, improve efficiency across complex value chains and make AI trustworthy enough for real industrial decisions. The companies that will lead the next decade won’t necessarily be those investing the most in AI. They’ll be the organizations that first build the digital foundation AI depends on.
