80% of AI projects fail. Here’s why and how you can be part of the other 20%

Research shows that 4 out of 5 AI development projects are not successful. That is more than twice as many as traditional IT projects!

In the past few weeks, we have investigated the most common reasons why these projects fail, and a clear pattern has emerged.

Below we discuss the themes and mistakes that play the biggest role in increasing the likelihood of failure.

1. Unrealistic expectations

What we often see is that companies expect AI to be a miracle cure that solves all their problems.

And not only that, they often expect it to be delivered for an astonishingly low budget and within an unrealistic timeframe.

This misconception stems from the marketing hype we live in, where agencies promote unrealistic figures and results to get clients excited. The downside is that most companies do not know what is actually possible and realistic.

The reality is that AI and automation are powerful, but certainly no magic.

2. No clear problem definition

One of the most common issues within AI and automation is that many people implement technology simply because they need to “keep up with the future.”

But in reality, you don’t need anything at all. Implementing technology is not an end in itself; it is a means to achieve something.

If you do not clearly know what that desired end result is (including what problem it solves), the chance is small that it will deliver real value.

When you want to use AI and automation, always start internally. Map out your problems and processes, and determine very concretely what the desired result of your solution is. Read more about our framework that helps you identify the actual opportunities within your organisation.

Investing must make sense, not be based on hype.

3. Lack of competence

What we surprisingly often see is that projects fail because the development team (internal or external) is unable to deliver effectively.

Although it is now easier to develop software, creating a well-functioning product that truly fits your company still requires craftsmanship and experience.

If these are lacking, you will see solutions that look fantastic in a demo but fail in practice, thus adding little value.

Work with a team that knows what it is doing, has the right background and experience, and can provide what you need, not just what they happen to be able to deliver.

4. Incorrect or poor data

While not every project depends on large amounts of data (most of our projects do not), many projects fail due to a lack of accurate, high-quality data.

For data-intensive solutions (such as predictions or calculations), you need to realistically assess the quality and depth of your current database.

If there is not enough data (something you can determine together with a developer), then break the project down into phases: first see what you can do with what is available, or build a module that gathers and structures data over the coming weeks or months, so you can then develop effectively.

5. Poor integration

AI solutions, automations, and other software products should not be standalone tools. At least not if you want to derive real value from them.

If a system does not integrate well with your technical infrastructure, processes, or workflows, no one will use it.

When developing a solution, always keep integration in mind, both technically (such as sharing or retrieving data from your CRM or ERP system) and practically in daily work.

If your team does not enjoy using it, they simply will not do so.

6. Focus on the short term

Another problem we often see, especially in more traditional organizations (such as semi-government agencies), is too much focus on the short term or the pilot phase.

Pilots mean little if you do not scale the successful ones.

If you start small with a pilot, always think about what happens next.

Plan how you can scale, expand, implement within the team, and make it sustainable.

Many companies have busy innovation teams but benefit little from technology because they never get beyond pilots.

7. No clear strategy or scope

This is one of the most common and detrimental problems: no clear strategy or scope for development.

A Scope of Work (SoW) is essentially a plan that clearly defines the problem, describes the solution (including the functionalities to be built), lists the deliverables, required tech stack, risks, investments, timeline, and more.

A good SoW forms the basis of every successful project, leading to faster development cycles, better results, and more satisfied customers.

It may seem like extra work, but do not skip this step. One wrong step during development can cost you dearly later. Read more about our framework for effective Scope of Works (SoW's).