A GEO audit checks how well AI engines can find, understand, trust, work with and cite your business, and shows what to fix first. This guide explains what a GEO audit checks, how to run a basic one yourself, and where a professional audit earns its fee.

Ranking on ChatGPT means being the business it names when a buyer asks for a recommendation, not using ChatGPT to write your content. This guide covers how ChatGPT chooses what to mention, the seven things that make a business citable, and a five-minute test to see where you stand.

Generative engine optimisation (GEO) is how you make your business easy for AI engines to find, understand, trust and cite. This guide explains what GEO is, how AI engines choose who to name, and where to start.

SEO gets you onto the page. GEO gets you into the answer. This is the difference between GEO and SEO, and what being left out of AI answers now costs an established UK firm.

Most small firms have bought AI without knowing if it pays back. Here is where AI consulting earns its fee for a UK firm doing £2m to £20m, where it does not, and how to tell which one you are being sold.

Most consultants hide their price behind a contact form. Here is what AI consulting actually costs in the UK, and the one question worth asking before you sign.

Most firms choose on the wrong signals. Seven questions to ask on the first call, with the good and weak answers, to tell judgement from confidence.

Every founder asks how to use AI before asking where it would actually help. Run this 30-minute, eight-question audit yourself first. It tells you whether you need a consultant at all.

Most AI transformation plans end in a slide deck. A better first project saves hours on one workflow, scores the result in eight weeks, and earns permission for what comes next.

The SMBs getting the most value from AI in 2026 aren't the ones with the biggest tech stacks. They're the ones starting with the smallest, most focused projects.

95% of generative AI pilots fail to deliver measurable returns, not because the technology is wrong but because the working discipline is missing. CRO and experimentation practitioners have spent a decade building that discipline.

AI agents are already browsing websites on behalf of users, summarising what they find and deciding whether to surface you. If your site only speaks human, you are invisible to a growing share of your audience.

The real shift with AI isn't writing better prompts, it's designing the architecture around the model, breaking work into focused parts with clear interfaces so the system stays performant, legible, and maintainable. The long-term advantage isn't a bigger model, it's the engineering instinct to build rails around the right one for the task.

"What do we need to know next?" is a phrase that doesn't get the attention it deserves. This question can lead us through the maze of uncertainty to those "aha!" moments that are so important in the early stages of a product's life cycle.

Embracing digital experimentation is key to unlocking the power of what not to do. By identifying and testing the riskiest assumptions, you can save time, resources and avoid costly mistakes.

In today's competitive and face-paced environments, it's vital to work on the features that will bring the most value, keep costs low and use resource time as efficiently as possible. One way to do this is with Experiment Driven Delivery. 

AI tools will enable us to accomplish more in less time. Using AI in our daily tasks will become as natural as 'Googling' something. Here's an example of a tool I recently used to turn my 2D logo into a 3D one. From start to finish, the entire process took 5 minutes.

In today's fast-paced, ever-evolving world, creativity is more important than ever. But what if the very processes we rely on to solve problems are actually holding us back?

Explore how you can use experimentation to revamp your team workflows, boosting efficiency & effectiveness. Embrace this innovative mindset and transform your team's approach to work.

We often get lots of requests or ideas for testing, but how do you get the right mix in your experimentation programme? Think of it like cake!