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5 mistakes that quietly sink your AI and automation projects (and how to avoid them)

•Xavier Vincent
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You run a small or mid‑sized business. Between client emergencies and staffing issues, everyone keeps telling you that you “should do something with AI and automation”. Maybe you have already tested a tool or launched a small project… with mixed results: disappointing impact, sceptical teams, the feeling that you now spend more time than before.

Most of the time, the real problem is not the technology itself, but the way the project is started and managed. In this article, we walk through 5 common mistakes we see in SMEs, with concrete examples and, above all, simple ways to avoid them – no technical jargon.

You will walk away with a practical checklist to de‑risk your next AI and automation projects, whether you are a business owner, finance director, HR manager, sales leader or operations manager.

1. Starting a project “because we need AI”

“I’ve been told we have to use AI in the company.”

That sentence is usually a warning sign.

What typically happens

A business owner decides to “do AI” or “do automation”:

  • without a clearly defined business problem;
  • without success metrics (time saved, error reduction, service quality…);
  • by asking someone internally to “see what we could do” with a trendy tool.

The result:

  • a lot of time spent experimenting without a clear frame;
  • teams who don’t understand the goal;
  • a project that ends up abandoned, with the feeling that “AI is not for us”.

How to avoid this

Always start from a business question, never from a tool:

  • Where do we lose the most time today?
  • Where do we make the most manual errors?
  • Which tasks do our teams hate doing?

Then define a concrete objective, for example:

  • “Cut the time spent on the monthly sales report by 50%.”
  • “Answer 90% of simple customer requests within 2 hours.”

AI or automation then become means to an end, not the goal.

2. Picking an overly complex process as a first project

Many SMEs want to start with a highly visible topic (end‑to‑end customer service automation, full production planning, 18‑month cash‑flow forecasting, etc.). On paper it looks attractive. In real life, it is the best way to get stuck.

A concrete example

A manufacturing SME wants to “automate the entire order‑to‑cash chain”, from order intake to invoicing. Very quickly, they hit:

  • exceptions everywhere (“except for this customer”, “except for that product”);
  • multiple systems that barely talk to each other;
  • manual approvals that are actually critical.

The project drags on, costs go up, and nobody sees any clear benefit.

How to avoid this

For a first project, choose a process that is:

  • simple: few exceptions, clear rules;
  • frequent: used every week, ideally every day;
  • visibly impactful: teams will feel the difference quickly.

Good starting points include:

  • automated reminders for overdue invoices with basic personalisation;
  • intelligent routing and triage of incoming emails (sales, support, HR);
  • preparing meeting summaries from notes or recordings.

The goal is to get a quick win to build internal trust.

Rendering diagram...

This diagram illustrates a realistic path: start small, prove value, then expand.

3. Ignoring the people who actually do the work

An AI or automation project can look perfect “on paper” and still fail in real life… because it was not designed with the people who actually run the process day to day.

What happens without the teams

  • The business owner or a consultant designs the “ideal” flow in theory.
  • Assistants, accountants, sales reps or technicians discover the result once it’s finished.
  • They feel bypassed, sometimes threatened.
  • They quickly spot unplanned edge cases and keep doing things “the old way”.

The tool exists, but is not used.

How to avoid this

Involve frontline teams from the start, in a very pragmatic way:

  1. Map the current process together using a whiteboard or a simple visual tool.
  2. Ask:
    • “Where do you lose most of your time?”
    • “Where do you see the most errors or customer complaints?”
  3. Build a first prototype and test it with 2–3 pilot users.
  4. Adjust before rolling it out.

A good sign: teams start asking “When can we go live with this?”.

4. Underestimating the work on data

AI and automation rely on clean, structured data. In SMEs, information is often scattered: Excel files, emails, partially filled‑in CRMs, scanned paperwork…

What goes wrong if you skip this

  • AI systems use incomplete or outdated data.
  • Automations fail because of empty or poorly filled fields.
  • Generated reports are challenged and not trusted.

How to avoid this

Before thinking about “AI models” or “automation workflows”, ask a few basic questions:

  • Where do the required data live today? (tools, folders, people)
  • In what format? (Excel, PDF, emails…)
  • Who is responsible for updating them?

Then:

  1. Standardise just enough: same fields, same formats, simple input rules.
  2. Assign a clear owner for each main data domain (customers, invoices, products…).
  3. Add simple controls in your tools (mandatory fields, dropdown lists, basic validation).

This work may not look “innovative”, but it is what makes the difference between useful AI and disappointing AI.

5. Forgetting to measure (or only measuring the tech)

Many projects are labelled “successful” because “the tool works”, even though real business impact is limited. Others are killed too early because nobody took the time to properly measure their benefits.

Common bad practices

  • Only checking that the automation workflow runs without errors.
  • Measuring only technical indicators (number of automated tasks, chatbot response time…).
  • Not comparing with the situation before automation.

How to avoid this

From day one, define 3–5 simple business indicators, for example:

  • Average time spent on a task before vs. after.
  • Number of errors or customer complaints before vs. after.
  • Average processing time before vs. after.
  • Team satisfaction (a quick 1–5 rating).

Then:

  1. Measure the situation before automation (even roughly).
  2. Launch the project on a limited scope.
  3. Measure after 2–4 weeks of real use.
  4. Decide whether to:
    • scale the solution;
    • adjust it;
    • or stop it if it brings no value.

You don’t need perfect KPIs. You need enough evidence to make a confident decision.

Practical section: an anti‑mistake checklist for your AI and automation projects

Use this checklist before starting (or relaunching) a project.

1. Clarify the “why”

  • [ ] What concrete business problem are we solving?
  • [ ] How will we know the project is a success? (3 KPIs max)

2. Choose the right first process

  • [ ] Is the process simple, with clear rules?
  • [ ] Is it frequent (at least every week)?
  • [ ] Will teams feel a clear before/after difference?

3. Involve the right people

  • [ ] Who currently does this work day to day?
  • [ ] Have these people contributed to defining the need?
  • [ ] Have we identified a small pilot group for testing?

4. Prepare the data

  • [ ] Do we know where the required data live?
  • [ ] Have we clarified who is responsible for keeping them up to date?
  • [ ] Do we have a minimum level of standardisation (formats, mandatory fields)?

5. Measure and adapt

  • [ ] Do we have a “before project” snapshot (even approximate)?
  • [ ] Do we know when and how we will measure results?
  • [ ] Who is responsible for follow‑up and adjustments?

By applying this checklist, you drastically reduce the risk of pouring energy and budget into a technically “sophisticated” project that brings little or no value to your business.

Conclusion

To sum up, AI and automation projects rarely fail because of a “lack of technology”. They fail because they are poorly framed, too ambitious for a first attempt, disconnected from people on the ground, built on weak data or evaluated only on technical criteria.

Key ideas to remember:

  • Start from a concrete business problem, not from a tool.
  • Begin small, with a simple, frequent process.
  • Involve the people who do the work every day.
  • Take care of your data, even with a few basic actions.
  • Measure business outcomes, not just whether the tool runs.

With this mindset, AI and automation become reliable allies for your SME, supporting what really matters: saving time, reducing errors, serving your customers better and freeing up your teams for higher‑value work.

If you want guidance on your digital transformation, Lyten Agency can help you identify and automate your key processes. Get in touch for a free assessment.

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