We analysed AI search readiness in the energy sector. Here’s what the 10 best-prepared companies have in common.

We analysed 86 UK energy organisations to understand how prepared they are for AI-driven search. Looking at the ten highest-scoring companies, five trends stand out: they describe their services clearly, give important offers dedicated space, define who those offers are for, connect them to tangible outcomes, and provide evidence to support their claims.

What do our highest-scoring energy websites have in common?

We used our AI Performance Analyser to score 86 UK energy organisations across four areas: how accessible their website is to AI search tools, how often they appear in relevant AI answers, how they perform across the prompts we tested, and the strength of the credibility signals supporting them.

Each area receives a score that contributes to their overall AI Readiness score, marked out of 100. 

The ten highest-scoring organisations were:

1. GridBeyond: 73/100
2. Electron: 64/100
3. Pod (formerly Pod Point): 63/100
4. ClimatePartner: 63/100
5. Mer: 61/100
6. Sylvera: 60/100
7. Anesco: 60/100
8. Good Energy: 60/100
9. SMS: 58/100
10. RES (Renewable Energy Systems): 56/100

The sector average was just 36/100. So what are the companies nearer the top actually doing differently?

Looking across their websites, five patterns come up repeatedly:

1. They describe what they do in specific, recognisable language
2. They give important services enough space to explain them properly
3. They make it clear who each offer is for
4. They explain what the customer gets, not just what the service contains
5. They give AI search tools evidence to support what they claim

They describe what they do in specific, recognisable language

What we saw

The strongest sites don’t make you decode the proposition before you understand the service.

GridBeyond is a good example. Its Energy Management Optimisation page explains how the service works in practice. It describes how the platform benchmarks energy use against anonymised industry data, maps patterns in a customer’s own consumption, and uses machine learning to identify anomalies and trends. It then connects those insights to practical outcomes such as predictive maintenance, improved operational efficiency, lower energy costs, and wider carbon reduction. That gives AI search tools a clear picture of the service, the information it uses, and what a customer can expect to get from it.

Sylvera takes a similar approach with its Ratings product. The page explains what the product is and what it helps buyers do: use independent ratings to assess carbon credit quality, reduce risk, support fair pricing, and make better decisions. It then breaks that down further, showing how ratings assess areas such as carbon accounting, additionality, permanence, and co-benefits, while helping users understand pricing and interrogate project risks. That gives AI search tools clear information about both the product itself and the decisions it is designed to support.

Why it matters

Both pages make the buying logic explicit. They don’t just describe a capability – they show what the service assesses or changes, why that matters, and what decision or outcome it supports. That gives AI search tools a much stronger basis for deciding when the company is relevant to a specific question, which is ultimately what visibility in AI search depends on.

What marketers can do

Review your most important product and service pages and ask whether someone unfamiliar with your organisation could quickly understand what the offer does and how it works.

If the answer is no, AI search tools may have the same problem. Replace broad proposition language with specific information they can use to understand the service and recognise when it’s relevant.

Read more: How we measure what AI is saying about your organisation

They give important services enough space to explain them properly

What we saw

The top sites don’t try to squeeze the whole business into one broad capabilities page.

Anesco shows why dedicated service pages matter. Its Project Development page explains the service in clear stages, from identifying and securing land through feasibility studies, planning permission, and technical design. It backs that up with evidence too, including 106 ground-mount solar farms and 150MW of connected battery storage. That gives AI search tools a much fuller picture of what “project development” means, what Anesco actually does within it, and the experience behind the offer.

Battery storage is handled separately. Its Utility Scale Battery Storage page contains a block listing financial forecasting, system design, equipment procurement, construction, grid connection, asset management, maintenance, and reporting. 

Why it matters

Making those offers distinct gives AI search tools a deeper source of information for each service instead of asking it to unpack a broad “end-to-end” proposition.

Anesco shows the difference between making a page accessible and making it useful to AI search tools. Technical setup gets AI crawlers onto the page. Detailed service content helps give it enough information to understand the offer and match it to a relevant question.

What marketers can do

Look for important services that are only mentioned briefly on broader capability pages. If a service has its own audience, use case, or process, it may need its own page.

That gives you enough space to explain the service properly and gives AI search tools a clearer source to draw on when someone asks about it.

Read more: A framework for building service pages AI actually wants to recommend

They make it clear who each offer is for

What we saw

Another recurring pattern is audience specificity.

Pod’s fleet charging page makes the audience clear before you get into the detail: this is a solution for organisations transitioning fleets to electric vehicles, or, as the page succinctly puts it, “Home EV charging for your drivers.” The rest of the page stays anchored to that buyer’s needs, covering depot, workplace, home, and public charging, operating costs, fleet expense management, charging data, infrastructure capacity, and the practicalities of scaling an EV fleet.

Why it matters

That specificity gives AI search tools far more to match against a detailed question. If someone asks for a charging provider that can support a commercial fleet across depots and drivers’ homes, for example, the page contains explicit evidence that Pod addresses that scenario. It doesn’t have to infer the audience from a generic EV charging proposition.

That becomes increasingly important as AI searches become more specific to the buyer’s circumstances, rather than simple searches for a category of company.

What marketers can do

Be explicit about who each service is designed for. Name the types of organisations, assets, situations, or problems it is built around.

That makes it easier for AI search tools to connect your offer with a specific buyer question, rather than having to infer whether you’re relevant.

Read more: Will AI search stop people visiting my website?

They explain what the customer gets, not just what the service contains

What we saw

The better-prepared companies consistently connect capabilities with outcomes.

RES’s main services page leads with the business problems its services are intended to solve. It talks about minimising unnecessary downtime and maximising asset value. Its operations and maintenance offer then gets more specific, explaining that the service is designed to reduce lost production, unplanned downtime, component failures, and costs.

Why it matters

That gives AI search tools something different from a list of capabilities. They can connect the service to the reasons a customer would buy it. If a user asks how to reduce downtime or improve the commercial performance of a renewable asset, those outcomes create a clear link between the question and RES’s offer.

A feature tells AI search tools what a service contains. An outcome helps them understand why that service might belong in an answer.

What marketers can do

Don’t leave the benefit for the reader to work out. Explain what each important feature or capability actually helps the customer achieve.

That creates a clearer link between what you provide and why someone would choose it – useful information when AI search tools are comparing different providers.

Read more: Evidencing humanity: how B2B brands get found and chosen in AI search

They give AI  search tools evidence to support what they claim

What we saw

The final pattern is proof.

This is the one area where a good case study can be more useful than another service page, as it helps brands evidence why they should be chosen.

Good Energy’s case study for manufacturer bigHead, for example, doesn’t just say that commercial solar can reduce costs and carbon. It gives concrete proof of the result: a 49.05kW installation generating 70.16MWh a year, saving the customer £8,000 annually, cutting CO2 by 9.2 tonnes a year, and delivering a seven-year return on investment.

Why it matters

That turns a marketing claim into evidence an AI system can use. Instead of having to take “we help businesses save money with solar” at face value, it has a real customer, a defined project, and measurable results. Those details are particularly useful when an AI tool is trying to compare providers or explain why one might be relevant to a buyer.

Have you done this before? For whom? At what scale? What happened? 

Those are useful questions for a potential customer. They’re useful questions for AI search tools too.

What marketers can do

Support important claims with evidence wherever you can. Named customers, specific projects, measurable results, and clear examples all show that you’ve delivered what you say you can.

That gives AI search tools something concrete to use when deciding whether your organisation is a credible answer, rather than relying on the claim alone.

Key takeaways

• In our analysis of 86 UK energy organisations, the 10 highest-scoring companies shared a clear pattern: their websites make it easy to understand what they do, who they help, and why their offer matters.
• They describe their services in specific, recognisable language, rather than relying on broad brand claims or vague terminology.
• They give important products, services, and use cases dedicated pages with enough detail for AI search tools to understand how they work and when they are relevant.
• They make their audiences, problems, and outcomes explicit, helping AI search tools connect the company to more specific buyer questions.
• They support what they say with evidence such as case studies, named projects, customer examples, results, and performance data, giving AI search tools more substance to use when comparing providers.

Download the full report

None of these patterns amount to a secret formula for AI search. Technical SEO remains important, but our findings show why it isn’t enough on its own.

The more useful lesson from the top ten is that they make their businesses easier to understand. They use specific language, explain important services properly, define who they help, connect those services to outcomes, and give those claims something concrete to stand on.

That is a much better place to start than looking for an AI optimisation trick.

Download The state of AI visibility in the energy sector 2026 report.