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Understanding the real value of an AI project in an SME: from hype to concrete numbers

•Martin Ramdane
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You run an SME and everyone seems to be talking about AI… but when you ask how much it really brings in and how to measure it, answers suddenly get very vague. Between marketing promises and the fear of wasting money, making a clear decision is hard.

In this article, we’ll leave the hype aside. The goal is not to build complex financial models, but to give you a simple framework to evaluate the value of any AI or automation project in your business. You’ll be able to answer very concrete questions:

  • Is this project worth doing now?
  • Where are the quickest wins?
  • How do we avoid chasing trends with no real impact?

We’ll first look at where AI really creates value in an SME, then how to measure that value with simple indicators. Finally, you’ll get a 5-step method to assess your next projects — no technical background required.

1. Where does AI really create value in an SME?

Before talking numbers, you need to understand where the gains come from. In an SME, AI doesn’t create value out of thin air. It amplifies what you already have: your processes, your people and your organisation.

You can group the value into four main categories:

  1. Time savings
    • Less copy-paste, re-typing and repetitive tasks.
    • Automatic preparation of documents, emails and summaries.
  2. Fewer errors
    • Fewer forgotten follow-ups.
    • Fewer mistakes in documents sent to clients, authorities or partners.
  3. Better decisions
    • Clearer view of your numbers (dashboards, alerts, simple forecasts).
    • Better prioritisation: which clients, files or projects to focus on.
  4. Human and organisational impact
    • Lower mental load and less stress from constant emergencies.
    • Ability to handle more activity without immediately hiring.

A good AI project is rarely spectacular at first. It makes the daily work a bit smoother, a bit more reliable, a bit calmer — every single day.

Concrete example

Imagine a 20-person consulting firm that spends hours every week writing client meeting notes.

  • Before AI: 1 hour per report, 15 reports per week → 15 hours weekly.
  • After deploying an AI assistant that drafts the first version: 20 minutes per report → 5 hours weekly.

Net gain: 10 hours per week, the equivalent of more than a quarter of a full-time role, without changing the org chart.

2. Turning those gains into simple numbers (without being a controller)

You don’t need a fancy spreadsheet to estimate the value of an AI project. Start with 3 basic indicators that are easy to track:

  1. Time saved per week
  2. Errors avoided per month
  3. Impact on revenue or cash flow (even a rough estimate)

2.1. Measuring time saved

For a given process, ask yourself three questions:

  • How many people are involved?
  • How much time does each person spend on this task every week?
  • What is a realistic target time after automation / AI?

Then, use a very simple calculation:

Time saved = (Time before – Time after) x Number of people

If you want, you can turn this into a rough financial value:

Monthly value ≈ Monthly time saved x average loaded hourly cost

The goal is not to be accurate to the last euro or pound, but to know if you’re talking about a few hundred or several thousand per month.

2.2. Measuring fewer errors

Errors have a cost that is often invisible:

  • Time spent fixing them.
  • Damage to your image with clients.
  • Late payments or penalties.

For each type of error affected by the project, estimate:

  • How many times does it happen per month?
  • What is the average cost (in time or money) of one error?

Even a rough estimate is useful. For example:

  • 5 incorrect invoices per month → 30 minutes to fix each one.
  • Average loaded hourly cost: €40.

Cost of errors = 5 x 0.5 h x €40 = €100 / month

If AI helps you cut those errors in half, you already save €50 / month, not counting the improved client relationship.

2.3. Measuring impact on revenue or cashflow

Some AI projects have a direct or indirect impact on revenue:

  • Better sales follow-up → more quotes chased up → more deals closed.
  • Automated client reminders → faster payments → fewer cashflow tensions.

Again, keep it simple:

  • Before: how many quotes or invoices were left “hanging” every month?
  • After: how many more are followed up or processed?
  • What is the average value per quote or invoice?

Even if it’s approximate, this helps you see whether the project is mainly about comfort or a serious business lever.

3. A simple visual framework to judge your project ideas

Instead of endless debates about “Is this a good idea?”, you can place each potential project on two axes:

  • Business impact (low to high)
  • Ease of implementation (hard to easy)

Here is a simple diagram:

Rendering diagram...

How to use it in practice?

  • For each idea, ask 2 or 3 people to rate it out of 10 for impact and simplicity.
  • Average the scores.
  • Prioritise ideas with impact ≥ 7 and simplicity ≥ 6.

Real value doesn’t come from the "perfect" project. It comes from small, well-chosen quick wins that build up over time.

4. A 5-step method to assess an AI project in your SME

Here is a concrete method you can apply in less than half a day for each new idea.

Step 1 – Define the problem in one sentence

Describe the problem without mentioning technology:

  • Bad example: “Deploy an AI chatbot on the website.”
  • Good example: “Reduce by 30% the time the team spends answering the same questions from clients.”

If you can’t write this sentence clearly, stop: the project isn’t ready yet.

Step 2 – Describe the current process

On a sheet of paper or whiteboard:

  1. List the main steps.
  2. Note who does what.
  3. Indicate the tools used (email, Excel, CRM, etc.).

The goal is to understand where time is wasted and where errors occur.

Step 3 – Estimate potential gains

For this process:

  • Total time per week?
  • Number of errors per month?
  • Any impact on revenue or cashflow?

Use the simple formulas above. Write down a range of gains (for example: between €500 and €1,500 per month).

Step 4 – Estimate effort and risk

Ask yourself four questions:

  1. Can we test on a limited scope (one type of client, one team, one product)?
  2. Do we already have the required data, and is it accessible?
  3. Is there any regulatory or reputational risk if the AI is wrong?
  4. Do we have an internal sponsor to own the project?

The more “yes” answers you get, the simpler and safer the project is to start.

Step 5 – Decide: go, later, or drop

Based on all of this, put the project into one of three buckets:

  • Go now:

    • Clear and significant expected gains.
    • Implementation possible in a few weeks.
    • Risks manageable.

  • Later:

    • Interesting, but depends on a prerequisite (data cleanup, tool change, process redesign).

  • Drop (for now):

    • Gains too small or too vague.
    • High risks (image, regulation, key clients).
    • No real internal sponsor.

The goal is not perfect accuracy. The goal is to professionalise the conversation about AI, even in a small business.

Practical section: your one-page decision framework

You can reuse the mini-framework below for every AI or automation idea.

1. One-page project sheet

Fill it in with your team:

  • Business problem (one sentence, no tech jargon)
  • Process involved (which department, main steps?)
  • Current total time (hours / week)
  • Main current errors (frequency, impact)
  • Business metrics affected (lead times, client satisfaction, cashflow, etc.)

2. Quick gain estimate

  1. Estimated time saved (range): … hours / week.
  2. Estimated errors avoided: … / month.
  3. Potential impact on:
    • Revenue: low / medium / high.
    • Cashflow: low / medium / high.
    • Team mental load: low / medium / high.

3. Decision checklist

Tick each item:

  • [ ] The problem is clearly stated without mentioning tools.
  • [ ] The current process is understood and documented.
  • [ ] The needed data already exists.
  • [ ] We can test on a limited scope.
  • [ ] Regulatory risk is low or manageable.
  • [ ] An internal owner is identified.

If at least 5 boxes are ticked and the estimated gains are meaningful, the project deserves a pilot test.

Conclusion

For an SME leader, the real question is not “Should we do AI?” but rather: “Where can AI create measurable value, right now, without putting the business at risk?”

Key takeaways:

  • The value of an AI project comes from time savings, fewer errors, better decisions and human impact.
  • You can estimate this value with a handful of simple indicators, no finance expertise required.
  • A visual impact/ease framework helps you prioritise the right ideas.
  • A 5-step method allows you to structure your decisions about AI projects, even in a small organisation.

If you run this exercise on 3 to 5 ideas, you’ll quickly see which ones deserve your energy — and which can wait.

If you’d like support with your digital transformation, Lyten Agency can help you identify and automate your key processes. Get in touch for a free initial audit.

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