I saw a post from Stephen Bartlett recently about AI adoption, and it really put things in perspective for me. For businesses, the gap between casual use and production AI automation is still much wider than the public conversation suggests.
The adoption curve is still very early
Each dot represents 3.2 million people. 2,500 dots for 8.1 billion humans. The graphic shows where people sit based on the most advanced AI interaction they have had.
Most of the world is still grey: never used AI. 84%. A smaller group is green: people using free tools. 16%. A smaller group again is yellow: people paying for AI and using it with intent. 0.3%.
Why the red square matters for organisations
Then there is the tiny red square: the people using AI to build, automate, code, and execute. 0.04%. That red square is the bit that stood out to me.
I am one of those rare people who make up the red dot. I pay for two or more subscriptions at any one time and have coded apps using AI. I spend at least one to two hours every day keeping up with the latest news.
For organisations, that red square is not about novelty. It is the difference between asking a model questions and redesigning process and workflow so AI can help move real work through the business.
Feeling behind can be misleading
I will let you in on a secret. I keep worrying that I am behind because the feed moves so fast. New tools, new models, new promises, every day. It is easy to think everyone is ahead of you. But the truth is, most people and most businesses are still nowhere near this.
How to move into the top tier
So how do you put yourself in the top 1%? Pay for one AI tool. Use it every day for a month. Read one book about it or watch one long explainer video. That is enough to put you in the top 1% of AI knowledge on planet earth. For a company, the next step is a practical AI implementation roadmap tied to workflow, data, governance and measurable operating value.
Where do you think you sit on this chart right now?
What advanced adoption looks like inside a business
For a business, advanced AI adoption is not measured by how many staff have tried a chat interface. It is measured by whether AI has been connected to repeatable work with a clear owner, an approved data source, a review point, and a baseline that shows whether the workflow improved.
A useful maturity path usually starts with individual capability, then moves into team patterns, then into governed workflow implementation. Staff learn the tools. Teams agree which tasks are appropriate. Leaders decide which use cases deserve investment, which data can be used, and which controls are required before AI affects customers, staff decisions, finance, or operations.
The practical top-tier habits
- Use AI daily on real work, not only demonstrations or novelty prompts.
- Build a small library of approved prompts, examples, and review rules for common workflows.
- Compare AI-assisted work with the old process using time, quality, rework, and adoption measures.
- Keep sensitive data, customer information, and regulated decisions inside a clear governance model.
- Turn successful personal experiments into team workflows only when ownership and support are clear.
Where ExIQ would start with a team
A practical first step is an AI adoption sprint around one role group or workflow. The goal is to identify the repeated information work that drains time, test AI assistance with real examples, document the review rules, and decide which ideas are suitable for production implementation.
That keeps adoption grounded. The business gets faster learning, but it also gets an early view of risks, data gaps, integration needs, and the support model required before AI spreads across the organisation.
The adoption measure that matters most
The strongest adoption measure is not how many accounts have been provisioned. It is how many repeatable workflows now have an approved AI pattern, a baseline, a reviewer, a source rule, and evidence that staff keep using the new approach after the novelty has faded.
That is the difference between broad tool access and genuine capability. A top-tier organisation turns useful individual experiments into governed team practices, then into workflow improvements that can be supported, monitored and improved over time.