Operating Like It’s $50 Oil: It is time to Industrialize Enterprise AI

The Energy industry has always been defined by its cycles, but today we are witnessing a convergence unlike any other. As global leaders gather for CERAWeek, the conversation is no longer just about the price of a barrel or the pace of the energy transition—it is about the rapid integration of artificial intelligence into an infrastructure that was never designed for it.

Seasoned Energy executives know that stability is an illusion. Geopolitical shifts and economic nationalism are fracturing supply chains, while the dual mandate to fund low-carbon initiatives and maintain legacy assets creates a permanent squeeze on working capital. To thrive, companies must adopt a "$50 barrel mindset": the relentless pursuit of lean, disciplined operations that treat every dollar of waste as a lost opportunity for future investment.

The challenge is that most digital transformation efforts haven’t moved the needle on the P&L. We’ve spent years building "digital toys"—pilot programs and isolated AI experiments—that fail to scale because they lack a fundamental understanding of the "ground truth" of business operations. To move from experimental AI to industrialized performance, we must master our operations at the process level but connected enterprise-wide.

The high cost of the "digital disconnect"

For decades, the industry has relied on a combination of siloed ERP systems, tribal knowledge, and an endless sea of spreadsheets. This fragmented landscape has created a "great disconnect." When a compressor goes down in the field, the financial impact isn't just the cost of the spare part; it’s the $5 million a day in lost production. Yet, in many organizations, the Maintenance, Procurement, and Finance departments speak different languages and operate on different data.

Traditional business intelligence (BI) and "gut-feel" management are no longer enough to bridge these gaps. AI is often touted as the solution, but AI without process context, reality on the ground, is like a high-performance engine without a roadmap. It might run fast, but it doesn't know where it’s going. Research from firms like McKinsey and Gartner suggests that the primary reason AI initiatives fail in the enterprise is the lack of clean, contextualized data that reflects how work actually flows across departments. In fact, Gartner predicts that by 2026, organizations will abandon 60% of AI projects that are not supported by "AI-ready" data management.

From manual wrangling to maritime optimization

The limits of human-manual optimization have been reached. People can only track so many spreadsheet tabs before the complexity of modern global operations becomes overwhelming. Consider the sheer scale of shipping bulk liquids at an oil & gas supermajor where they are managing the scheduling of hundreds of ships and thousands of different routes. What is at stake is spend measured in billions of dollars and yet the vessel planners are using manual data entry and "best guess" scheduling in spreadsheets.

By using the Celonis Process Intelligence Platform, organizations can create a digital twin of business operations. This isn't just a static map; it’s a living representation of every ship, every route, and every delay. When you feed this "process-ready" data into an AI optimization engine, the results are immediate. What used to take weeks of manual analysis can be optimized in seconds.

This is the essence of industrializing Enterprise AI: creating a system where the AI understands the business rules, the historical bottlenecks, and the real-time constraints of the Supply Chain. McKinsey notes that while 88% of companies are using AI, only 39% report an enterprise-wide EBIT impact. To bridge this gap, Enterprise AI must be transformed from a back-office chatbot into a core component of the operational value chain.

Eliminating mundane tasks, elevating experts

One of the most persistent myths of the AI era is that it will replace the human expert. In the Energy sector, the opposite is true. Industrializing Enterprise AI can elevate the expert by removing the time-consuming, mundane work of data collection and preparation so they can focus on optimizing operations and resolving strategic problems.

In a supply chain crisis, for example, you don't want your high-value people wasting hours hunting for critical information across disconnected databases; you want them to apply their expertise to high-level optimization and build long-term operational resilience.

Process intelligence is the essential context layer that enables people to make faster, more impactful decisions across the entire value chain.

Chasing the money to a positive future state

The path forward for Energy leaders is to "follow the money." Don't start with generalized back-office cost centers where the potential savings are incremental. Instead, target the operational use cases where process friction has the highest financial stakes: Plant Maintenance, Order-to-Cash, and end-to-end Supply Chain.

By industrializing Enterprise AI with process intelligence, you create an organization that is both resilient and agile. This is the Positive Future State: a business that maintains the lean efficiency of a $50 barrel environment even when prices are high, using that captured value to fund the innovations that will define the next fifty years of energy.

As we look toward the future discussed at CERAWeek, the winners will not be those with the largest digital budgets, but those who have the best "digital twin" of their operations—and the process intelligence to make it work.