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AI Strategy11 min read

Why 74% of Automation Projects Fail — and How to Be in the 26%

Gartner estimates that 74% of automation projects don't achieve their initial objectives. This failure rate is not inevitable — it's the symptom of five recurring mistakes that executives can avoid from the design phase.

In 2025, there is no longer any executive not convinced that automation and AI will transform their industry. The debate is no longer 'should we go for it?' but 'how do we avoid getting it wrong?' And that's a crucial question, because the majority of companies that try it don't get the expected results — not because the technology is immature, but because their approach is fundamentally flawed.

Mistake #1: Automating the wrong process

The first question to ask is not 'what can we automate?' but 'what deserves to be automated?' These are very different questions. A process is a good candidate for automation if it meets three criteria: it is repetitive and well-defined, it has sufficient volume for the scale benefit to be material, and it is not being redesigned.

The golden rule

Choose a single process for your first deployment. One that concentrates at least 15% of your organization's repetitive work volume. Succeed at it completely before moving to the next. One demonstrated success is worth a hundred abandoned pilots.

Mistake #2: Confusing automation with optimization

Automation allows doing the same thing faster and cheaper. Optimization changes what you do. These two objectives require radically different approaches, and confusing them is one of the most common causes of failure.

Mistake #3: Underestimating the data dimension

We estimate that 60 to 70% of an AI automation project's time is devoted to data preparation — extraction, cleaning, structuring, validation. Teams that plan a project 'in 3 months' without having assessed the state of their data typically discover that the data work alone takes 4 to 6 months.

Mistake #4: Ignoring organizational resistance

Automation transforms roles. It rarely eliminates jobs, but it profoundly changes what people do. Deployments that succeed invest as much in organizational change as in technology — often more.

How to be in the 26% who succeed

  1. A clearly identified executive sponsor who is personally accountable for project results and has authority to remove organizational obstacles.
  2. A deliberately narrow scope for the first deployment — one process, one team, one measurable objective.
  3. A preliminary data audit that honestly documents the state of available data before scoping the technical solution.
  4. A change management plan as detailed as the technical plan — with training, communication, and a human-AI coexistence period.
  5. A three-level measurement framework (efficiency, quality, business value) defined before deployment, not after.

Our first automation project failed. We automated the wrong process, without preparing the data, without bringing the teams on board. Our second project was a success. The difference? We spent twice as long preparing, and half the time 'delivering fast'.

— Digital Transformation Director, industrial group (1,200 employees)