AI in the energy sector is most useful where it grounds answers in your own technical and regulatory documentation. The clearest wins are knowledge assistants over tariffs and policy, document and request triage, and structured sustainability reporting – not forecasting models that are already a specialist field.
The energy sector also has a particular reason to think carefully about AI's own energy footprint, an issue we have written about at length in our N3XTCODER series on AI and energy.
Knowledge assistants over tariffs and policy
Document and request triage
Field staff and operations support
The closest analogue we can point to is our work for the German Insurers Association (GDV): an AI Knowledge Assistant over tens of thousands of policy documents for 400+ member companies, handling conversational refinement and citing source material. The same retrieval-augmented generation (RAG) architecture transfers directly to energy: tariffs, contracts, regulatory and policy documentation. The smaller-scale version of the same architecture runs in production at a leading member network for 1,000+ HumHub members on a stack of n8n in Berlin, Qdrant in the EU and GPT-4 via Microsoft EU sovereignty, delivered in four short sprints.
Energy organisations should also read our N3XTCODER series on AI and energy: The Energy Impact of Artificial Intelligence, How AI and AI developers can help reduce the energy usage of AI, and How can society help reduce the energy usage of AI. The pieces cover real numbers – training run energy, the share of inference in total ML energy footprint, Jevons' paradox – and explore what governments, developers and users can actually do.
Customer service load down
staff time recovered from repetitive policy and tariff queries
Compliance documentation handled
structured drafting and summarisation for regulatory reporting
Time to first project
first version in four short sprints, the way the RAG chatbot for a leading member network was delivered
Honest about energy cost
we track and optimise AI's own energy footprint, not hide it
EU-hosted by default
n8n in Berlin, Qdrant in the EU, Azure OpenAI via Microsoft EU
1. Discovery workshop
2. Build a working prototype
3. Hand over and expand
We bring a decade of impact-tech experience and more than 160 AI projects since 2019. Through our free AI for Impact course, more than 100,000 people have learned how to use AI for the common good. We do not run inspiration days. We run scoping sessions and build engagements that ship, the way we have delivered AI for the organisations below:
AI itself uses significant energy – and the differentiator that matters is where you deploy inference. Training is mostly a one-off concern by the time you ship; inference is now the majority of the total ML footprint at major providers. The biggest lever is deploying inference to green energy hosts. Pick efficient stacks, smaller well-grounded models where they fit, and renewable-powered infrastructure. We have written about this at length – see How AI developers can reduce the energy usage of AI.
Forecasting AI is not magic. Energy demand forecasts already use sophisticated statistical models. AI may help in specific cases but is not a guaranteed improvement.
Customer-facing tools come last. Start with internal knowledge assistants. Move to customer-facing tools only when accuracy and grounding are proven.
Can AI handle complex regulatory documents?
Where should an energy company start?
What about data sovereignty?
Are these tools auditable?
How long to a first prototype?
What about AI's own energy footprint?
Tell us about your organisation and the document landscape you want to make navigable. We will reply with a proposal and a date.
Simon Stegemann
Co-Founder and CEO
