What We Mean by
Applied AI
Applied AI refers to intelligence designed for real operational environments, where safety, reliability, and performance are non-negotiable.
It involves systems that:
Work directly with live operational and engineering data
Incorporate physics, process understanding, and constraints
Provide explanations alongside predictions
Support rather than Replace human decision-making
Integrate naturally into existing workflows
DESIGN PRINCIPLES
One of the most consistent barriers to AI adoption in energy is not technology, but organisational readiness.
Applied AI for Energy treats adoption and capability-building as foundational design principles
Building Skills and Confidence
Supporting operators, engineers, and leaders in understanding:
What AI is doing
Why it is making specific recommendations
Where human judgment remains essential
Embedding into Real Workflows
Designing AI systems that fit into:
Existing operational processes
Established engineering practices
Day-to-day decision cycles
Trust Through Transparency
Ensuring systems can:
Explain outcomes and assumptions
Surface uncertainty
Support auditability and accountability
Managing Change Deliberately
Recognising that sustained impact requires:
New operating models
Cross-functional collaboration
Cultural as well as technical change
Applied AI for Energy is as much about how organisations adopt AI as it is about the AI itself.
FOUNDATIONS
Building Shared Foundations for Adoption
Moving AI into core energy operations cannot be solved by individual organisations in isolation.
Applied AI for Energy aims to contribute to shared, practitioner-curated industry foundations that support adoption at scale.
Shared Benchmarks
Creating common reference points to help organisations:
Evaluate AI performance in real operational contexts
Compare approaches across similar asset classes
Move beyond isolated pilot metrics toward meaningful operational outcomes
Open, Practical Frameworks
Developing usable frameworks that address:
Where and how AI should be embedded into operational workflows
How data, physics, and models interact in production environments
What good looks like for explainability, governance, and trust
Playbooks for Adoption
Documenting and sharing lessons learned around:
Organisational readiness and change management
Upskilling operators, engineers, and leadership teams
Integrating AI into existing operating models
Common failure modes — and how to avoid them
The intent is to reduce friction, repetition, and risk across the industry.
Over time, these shared assets can help organisations move faster, with greater confidence.