AI is already inside the business. Most organisations are experimenting with tools, running pilots or asking teams to explore what AI can do. But activity is not the same as value. In the first article of this eight-part series on preparing for the AI shift, Patrick Walsh, our Chief Commercial Officer, looks at why AI adoption is not AI value creation, and how leaders can assess whether AI is genuinely changing how the business works.
AI is everywhere. Almost every organisation is doing something with it. Employees are experimenting with ChatGPT, Claude, Copilot, Gemini, and other tools. Vendors are adding AI into every product. Leadership teams are talking about transformation. Boards are asking for updates. Teams are running pilots.
On the surface, it looks like progress. But look a little closer and a different picture often appears. There is activity. There is experimentation. There is enthusiasm. But there is not always measurable business value.
That distinction matters.
A company can be using AI and still not be meaningfully benefiting from it.
This is one of the biggest challenges leaders face right now. The barrier to AI adoption has collapsed. Anyone can open a tool and start experimenting. But the barrier to AI value creation is still real. It requires clarity, capability, ownership, governance, and disciplined execution.
The question is no longer:
“Are we using AI?”
Most organisations can answer yes.
The better question is:
“Where is AI changing how the business works?”
That is the difference between AI activity and AI value creation.
AI activity can look impressive.
A company may have rolled out Copilot. Teams may be experimenting with prompts. A few departments may be testing use cases. Someone may have built an internal chatbot. Leaders may be discussing AI in every strategy meeting.
None of this is bad. In fact, experimentation is often the right starting point.
The problem comes when activity is mistaken for progress. Leaders need to be careful not to confuse visible motion with measurable movement.
– A tool rollout is not value creation.
– A pilot is not a transformation.
– A list of use cases is not a strategy.
– A group of enthusiastic employees is not an organisational capability.
Real value appears when AI changes something that matters to the business: cost, speed, quality, risk, customer experience, revenue, capacity, or decision-making.
If AI is not changing one of those things, it may be interesting, but it is not yet strategic.
One of the simplest ways to assess AI maturity is to listen to how people talk about it.
Early-stage organisations often talk about tools:
“We are using ChatGPT.”
“We have Copilot licenses.”
“We are testing a few AI vendors.”
“We have some pilots underway.”
More mature organisations talk differently:
“These are the workflows we are improving.”
“This is the value we are targeting.”
“This is the metric we expect to move.”
That shift in language tells you something important. The first group is talking about AI as technology. The second group is talking about AI as an operating discipline.
The second group is much closer to value.
If you are a leader trying to understand whether your organisation is making real progress with AI, avoid starting with the broad question:
“Are we using AI?”
Instead, ask sharper questions:
– Where is AI creating measurable value today?
– Which workflows have changed because of AI?
– Which business metrics are we trying to improve?
– What have we stopped because it did not work?
– How are we building capability across the organisation?
These questions change the conversation.
They move the organisation away from “what are we trying?” and toward “what is working?”
That matters because AI creates value only when it becomes connected to business outcomes.
AI value can show up in different ways. It may:
– reduce cost by automating manual work
– increase speed by helping teams analyse information faster
– improve quality by reducing errors
– improve customer experience by making support more responsive
– improve revenue by helping sales teams identify better opportunities
– reduce risk by improving monitoring, compliance, or decision support
– increase capacity by freeing employees from repetitive tasks.
Not every AI use case needs to be transformational. Some of the best early wins are practical and specific. But the value needs to be named. If the value is vague, the initiative will be hard to prioritise, hard to fund, and hard to scale.
A useful test is this:
Can the team explain what will improve if the AI initiative works?
If not, the use case is not ready.
Experimentation is important. Organisations need space to learn, test, and build confidence. But experimentation should not become the end state.
The goal is to move from individual experimentation to coordinated execution.
That means:
– identifying priority use cases
– connecting those use cases to measurable outcomes
– assigning owners
– putting guardrails in place
– training the people involved
– measuring impact
– scaling what works
– stopping what does not.
This is where many organisations struggle.
– They have ideas, but not prioritisation.
– They have pilots, but not ownership.
– They have tools, but not capability.
– They have enthusiasm, but not operating rhythm.
AI value creation requires all of these to come together.
Leaders do not need to slow down AI adoption. But they do need to sharpen how they evaluate it. The aim is not to stop experimentation. The aim is to make experimentation more purposeful.
A practical next step is to review your current AI activity and group it into three categories:
1. Experimentation
People are trying tools individually. Useful learning may be happening, but value is anecdotal.
2. Emerging value
There are specific use cases with owners, metrics, and early evidence of impact.
3. Scaled impact
AI is embedded into workflows, measured, governed, and improving business outcomes.
Most organisations will find they have more in the first category than they expected. That is not a failure. It is a starting point. The key is to know where you are.
AI adoption is easy to see. AI value creation is harder to prove. The organisations that win with AI will not be the ones with the most tools, the most pilots, or the loudest ambition. They will be the ones that can answer a simple question with confidence:
“Where is AI making the business better?”
That is where real progress begins.
At Decoded, this is where we focus: helping organisations move from awareness and experimentation into capability, alignment, and action.
Turn AI activity into an AI adoption strategy that delivers business value. Take a look at our AI courses.
If you’d like to learn more about Decoded and how we can help transform your
organisation, we’d love to hear from you.
