Almost every company has been through this. It builds a client portal, rolls out a CRM, or buys an AI tool, and six months later the question nobody likes to say out loud is sitting there: did that advance us, or did we just spend?
That question is what a maturity model is for. It does not concern itself with whether we are modern enough, because there is no good answer to that — there will always be a newer tool and a newer trend. It concerns itself with where your digital stands in the business, and it hands you a single test: does your digital make money, or cost money?
What a maturity model is, and what it is not
A maturity model is a simple thing: a scale that breaks development into a few recognizable stages. Each stage has its typical picture, its telltale signs, and a clear threshold where you cross into the next. The model does not grade you, it locates you — it tells you where you stand now and what the next sensible step is.
It gives three things at once. A shared language, so the team finally talks about the same thing. An honest diagnosis of where you actually stand rather than where you would like to believe you do. And a direction, meaning the next step. What it does not give: it is not a report card where the highest grade is the goal. That is the most common misreading, and we will return to it at the end.
The right question is not AI, it is money
Most digital maturity models are a technology ladder, with paper at the bottom and AI at the top. The trouble is that this measures the wrong thing. Adopting an AI tool does not mean you have advanced. If that tool changes neither what you sell nor what you earn, you have simply become more expensive.
Mana's model therefore does not measure the quantity of AI but asks one business question: does your digital make money, or cost money? As a company develops, three things flip at once. What digital means in the finances: first a cost, then revenue, finally the business itself. How hard it is to copy: from easily imitated convenience, to being embedded in the client's operation, to setting the terms of the category. And how the work gets done: by hand, then with systems, and finally largely by itself.
The question is never how much AI to adopt, but what will make your digital start earning money.
The five stages
Five stages describe that path. Importantly, these are business states, not technology levels. Moving up a stage means a change in what you sell, or in what you earn from it. AI and data are only what the steps require — the accelerant, not the destination.
I. Traditional
The company is a physical business, selling hours, units or deliveries. Any software is administration rather than something the company owns. Digital here is pure cost, and the company is easy to replace, because it competes on price and relationships.
II. Digital shopfront
There is a website, a portal, a CRM. It displays what already existed, but the business stayed the same: digital is a convenience extra nobody pays separately for. "We built it because a big client asked." A competitor can commission a portal too, so it never becomes a durable advantage either.
III. Digital product
Digital becomes value in its own right: separately priceable, and the client now chooses or stays because of it. The product learns from use, because the data flows back into development. This is where the first digital revenue appears — digital starts bringing money in rather than only taking it out. At this stage measurement (analytics, A/B testing) is not optional but a precondition.
IV. Digital company
Most of the revenue is already digital, and the company organizes itself around it. Through APIs you become embedded in the client's operation, which makes replacing you expensive and painful: you are not a supplier but part of how the client works. The physical service becomes fulfilment behind the system, and most of the revenue is recurring.
V. AI-first company
The same business, but running largely by itself: agents carry the daily execution, while people hold the direction, the decisions and the boundaries. Growth is no longer tied to headcount. This is not a target state for everyone. It pays off where the benefit of a self-running operation justifies the cost of stricter compliance. For many companies stage IV is the correct terminus.
The shopfront trap: where most companies stop
The whole model converges on a single point: the jump between stage II and stage III. At stage II the company has already spent on digital. There is a portal, a system, perhaps even an AI feature. Revenue has not moved, because digital only displays what already existed. This is the shopfront trap, and it is not a technology question — the technology often stays exactly the same at stage III.
An example of the difference. On the portal of a company servicing industrial refrigeration, the client can see the status of maintenance work. That is convenience, and nobody pays separately for it. The same refrigeration data as a subscribed alert that fires before the goods thaw is revenue, and the client stays because of it. The sensor and the data are identical. The only difference is that one shows up in the money and the other does not. Moving to stage III is not more technology but pushing the same technology towards the money.
The most expensive mistake: costly technology that never shows up in the money
Capability — how much data and AI sits in the system — and business outcome usually move together, but not always. The most expensive mistake is precisely where the two come apart: the company buys AI, builds a sophisticated system, and still sells the same thing for the same price.
The symptom is familiar: a beautiful client portal full of AI features that is nonetheless still a Digital shopfront, because the business did not change. Technologically strong, commercially irrelevant. So after every digital expenditure it is worth asking two questions. Did what you sell change? And did what you earn, or what you can operate on, change? If the answer to neither is yes, you bought expensive technology rather than progress.
The same path across three terrains
Development does not happen in the product alone. The model looks at three areas at once: the product you sell, the operation by which you work day to day, and customer acquisition and retention. All three travel the same five stages, and the same test applies to all three: digital first merely helps, then drives, and finally becomes the way that area earns money.
A company rarely sits at the same stage in all three areas. It may have a strong product while its customer acquisition is stuck at shopfront level. It is worth knowing that customer acquisition has its own maturity ladder tailored to marketing. The model discussed here measures the business development of the company as a whole rather than marketing channels. A different purpose calls for a different map.
How to read it
The model is worth something if you use it for decisions rather than self-flagellation. Do four things.
- Look honestly at where you stand. Do not ask what tools you have, ask whether your digital brings money in. If it merely puts data behind a login, you are at stage II, no matter how many AI features it contains.
- Look for the biggest stake, not the highest stage. The goal is not level V but the next sensible step — the one where digital tangibly starts earning. At most companies that is the move from II to III.
- Money test first, technology second. If your technology is strong but invisible in the revenue, do not buy more AI. Convert what you already have: turn the digital into a separately priceable, measured product.
- Decide where the right terminus is. Not every company needs to become an AI-first company, and for many the Digital company stage is the winning one. The model is a map of possibilities rather than a law, and it is your job to decide how far it is worth going.
Two things are worth knowing about the pace of the steps. Progress is accelerated by capability (organized data and AI), constrained by the mandatory foundations (from data protection to the EU AI Act, plus a scalable technical architecture), and slowed by the organization. Most stalled development is not caused by technology but by people not adopting what was built. Where that is the situation, it has to be dealt with before building the next system.
Where reality stands
Most of the Central European mid-market sits at stage I or II: either operating traditionally, or sitting in the shopfront trap because it built a portal and stopped there. That is not a disgrace but the starting point, and it is also where the largest and cheapest available gain is — not another AI pilot but a single stage towards the money.
Your problem is not that you are insufficiently AI-mature. Your problem is that your digital does not earn: it is convenience rather than product, cost rather than revenue.