Implementing AI in a company – three pitfalls that can derail a project

New technologies are talked about virtually everywhere. Artificial intelligence is constantly particularly buzzed about. More and more companies are also deciding to implement it. At the same time, there is a growing number of projects that end… in failure. Not because AI doesn’t work, but because it doesn’t work as it should. What is the reason for this and how to avoid the most common mistakes? In today’s article, I examine three pitfalls that can determine whether implementing AI in a company will bring real value.

Any system based on artificial intelligence, whether we’re talking about large-scale language models (LLM), predictive systems or image analysis – needs data. They are the fuel without which even the most advanced algorithm will not work.

In simple terms, the AI system consists of three layers:

One of the most common mistakes associated with implementing artificial intelligence in business is underestimating the problem of data quality. We often assume that “somehow it will work” – and later it turns out that the data is incomplete, inconsistent or completely useless.

Imagine a manufacturing plant that wants to implement AI in the company and use it to analyze PDF documents. The documentation is already in electronic form – it just needs to be structured and plugged into the system. In this case, data preparation is relatively simple and low-cost.

The situation is different when a company wants to analyze energy consumption and identify sources of losses. Here you need:

If the company does not yet have a measurement infrastructure, or the data is scattered and inconsistent – the cost of preparation increases many times over.

Implementing AI in a company is not just a matter of algorithms. It’s also a matter of being able to embed them into the technological environment. Artificial intelligence needs to exchange data with systems that are already running, and sometimes provide answers back – for example, to MES, ERP systems or BI tools.

If data is stored in several systems that artificial intelligence cannot access through a single API or a common database – it is difficult to talk about an effective implementation. In such cases, it is worth reaching for integration support solutions:

Without integration, AI can only analyze a fragment of reality. And then it will not effectively support business decisions.

AI in a company should work like a well-tested machine. Meanwhile, it is often treated like an experiment that somehow will succeed. Instead, it’s worth approaching the project like testing a hypothesis: it only makes sense when it can be tested.

Suppose a company wants to identify areas of excessive energy consumption, based on data from 100 sensors. Instead of connecting the whole system right away – it is better to start with 5-10 measurement points. This way we can:

At this stage it is easier to correct errors: change the data source, improve the model, improve integration. When the problem is discovered only after full implementation – it may be too late to make corrections without incurring large costs.

 One important (but often overlooked) aspect of implementing AI in a company is the choice of approach to data. We have two main scenarios:

In practice, the first option is usually safer and more scalable. Standardization enables faster deployment of subsequent projects, better quality control and more effective diagnostics.

For example – if the temperature in one machine is called “Temp_A” and in another “TMP_B”, the system may have difficulty in analyzing trends. Thanks to standardization – everything works in one scheme.

Implementing artificial intelligence in any business is not only a matter of technology, but above all organizational and strategic preparation. Too often AI projects fail because key steps have been skipped.

So how do you approach the topic to increase the chances of success?

The success of any new technology implementation depends not just on the algorithm itself, but on how we prepare the data, the environment and the entire implementation process. Thoughtful action at the start can avoid costly mistakes and build real business value.

Definitely – but with your head. Implementation of artificial intelligence will not solve all problems. It will not replace the competence of the team. But, if implemented well, it can support and automate business processes, save time, reduce waste, and above all – make suggestions where a human doesn’t have time to analyze every detail.

AI is not a black box. It’s a tool. And tools only work if we know how to use them.