The possibilities for our digital future within the energy industry are endless:
Considering the potential unlocked by an AI-driven digital transformation strategy, both immediate and long-term advantages become clear - ranging from faster data processing and improved information flow to reduced risk, enhanced communication, and greater automation. But the real challenge lies in moving beyond the hype to thoughtfully integrate these advanced technologies into daily operations in a way that aligns with industry needs. The key question is how AI will become embedded in routine business processes, supporting everything from workflow automation and data analysis to decision-making and customer engagement, ultimately reshaping how organizations operate and deliver value.
What matters most is not just the data you have but how you use that data. Data standards are an important part of our digital future, enabling companies to collaborate and co-innovate through system interfaces that integrate in the back end. Many progressive companies have put in place a solid data infrastructure and added select applications like a digital twin on top, making data more contextualised and accessible through simplified dashboards that make data easy to find, filter and apply.
However, data and dashboards are not enough of a springboard for AI to have the measurable value or ROI that companies expect. Instead, we need to start with a value-focused approach that zooms in on the specific use cases and services where AI can have the most influence through a digital operating model that builds on digital twin technology backed by physics-based and data-driven models. The successful implementation of an AI-infused digital strategy needs to be driven by desired business outcomes.
Effectively leveraging AI to move beyond "business as usual" in the energy sector requires deep familiarity with the industry's evolving landscape.
Once specific services within the potential high-impact areas are identified—typically those that are frequent and generate consistent, repeatable data patterns—they can serve as ideal candidates for AI-driven automation. These patterns enable AI and related technologies to support the development of value-focused applications that extract and process information, generate insights, and provide actionable recommendations. Many of these actions can be executed autonomously, ultimately contributing to greater energy efficiency.
Beyond the hype, it’s essential to keep people at the core of operations, supported by technology that can seamlessly handle diverse data types and sources. In this model, technology serves as an enabler—delivering the right amount of information to the right person at the right time. This enhances decision-making speed while reducing risk. Depending on the use case, the resulting benefits can scale significantly, from reduced emissions to earlier interventions in predictive maintenance scenarios.
Consider a methane emissions management workflow.
An emissions reduction team oversees 10 assets for a major exploration and production company, continuously monitoring each site’s carbon footprint. Their central tool: a cloud-based, dynamic digital twin that provides access to a configurable emissions management cockpit. This cockpit goes far beyond static dashboards—it highlights the highest energy consumers in real time, flags critical incidents requiring immediate attention, and suggests recommended actions based on live data streams enriched with historical and synthetic datasets.
At one facility, the cockpit detects that a main gas turbine is consuming significantly more energy than expected, pushing up the site’s overall emissions profile. The team initiates an investigation by querying the digital twin through an integrated AI-powered chat interface, retrieving targeted insights in seconds.
With the relevant data in hand, they virtually navigate to various system components—such as flare stacks and vents—to pinpoint the issue. Behind the scenes, complex data processing and integration are handled seamlessly, allowing the team to focus on decision-making. They consult a simulator view within the twin to compare real-time and modeled values, receiving prescriptive guidance on where and how to intervene. Armed with this clarity, the team can act quickly, supported by both actionable instructions and transparent rationale
Data becomes insight. Insight drives action. Action delivers outcomes. And outcomes evolve into prescriptive tasks—clear, traceable, and executable—enabling teams to move with confidence and transparency. This cycle supports a wide range of use cases, each aligned with business goals and value-driven outcomes. Forward-thinking energy companies are already embracing this shift. It’s not just better than business as usual—it’s the foundation for a smarter, more sustainable future.
