For decades, the energy industry has captured data faster than it can use it. Dan Jeavons reveals how Orbital is helping unlock untapped industrial data, redefining what intelligent operations looks like in the AI age. 

Executive Summary 

Dan Jeavons, President of Applied Computing, recently joined Geoffrey Cann on the Digital Innovations in Oil and Gas podcast to discuss how the energy sector is undergoing widespread AI transformation that is defining the future of energy intelligence.  

The conversation tackled one of the industry’s biggest challenges: 

Despite generating massive amounts of sensor data every second, the industry uses only about 8% of it to inform decisions.

In this wide-ranging appearance, Dan shares his journey from consulting and leading AI innovation at Shell to now heading Applied Computing, where he and his team are building Orbital, a foundation model purpose-built for energy. Orbital is contextual, explainable, and accurate by design. 

Listen to the full episode: Harnessing Energy’s Data Deluge 

1. The Energy Industry’s Data Dilemma  

Legacy systems and siloed solutions create what Dan calls “industrial gridlock”. Companies optimise small parts of operations independently but struggle to integrate insights across the entire organisation. Foundation models like Orbital were built on this very thesis, bringing a unified, system-wide intelligence to energy operations.  

Whether it’s production optimisation, reliability, or supply chain, the same pattern repeats. We solve one problem at a time and end up with hundreds of disconnected solutions.

2. Foundation Models Are the Breakthrough 

The arrival of foundation models offers a new path forward. Foundation models are defined as large AI systems capable of solving multiple problems without bespoke setups. As Dan explains, the energy sector needs models built specifically for its complex and high-risk environment. Generic, consumer-grade AI simply does not work in this context.  

A foundation model for energy must be contextual, explainable, and deliver zero hallucinations, because real-world assets depend on it.

Orbital is trained to understand the language and physics of energy operations, integrating time-series data, engineering diagrams, and operator logs from the start. 

3. From 8% to 100%: Unlocking Industrial Intelligence 

Energy companies collect trillions of data points across sensors, control systems, and equipment logs. Yet most of it remains unused. Orbital is designed to change that by understanding complex industrial datasets at scale.

We’re only using about 8% of available data today. Our mission is to unlock the other 92%, safely, with explainability built in, and at scale.

4. Built for the Field, Not the Cloud Alone

Cybersecurity and data control are paramount in energy. This is why Applied Computing’s foundation model was designed to operate on the edge or in secure cloud environments, giving organisations flexibility in how they manage and protect sensitive data.

We bring our model to the customer’s data, not the other way around.

5. A Vision Beyond the Fence Line 

Dan’s long-term vision extends far beyond single assets or companies. Foundation models like Orbital can one day connect intelligence across the entire energy value chain, enabling system-wide optimisation, from hydrocarbon allocation to refining to renewables. This vision serves as the company’s true North Star.  We bring our model to the customer’s data, not the other way around.

This isn’t about replacing the current system. It’s about running today’s energy system more efficiently and designing tomorrow’s low-carbon one

Dan also envisions immense potential for AI-generated design, where models could propose new, lower-carbon energy systems. 

Why the Energy Transformation Matters 

For energy leaders, this conversation underscores a turning point currently impacting the sector. The industry’s digital transformation is no longer about point solutions. It’s about building shared intelligence that understands the complexity and context of energy operations. 

Applied Computing’s approach shows how innovation grounded in domain expertise can lead to trustworthy, transformative AI for high-stakes environments.