Don't repeat mistakes already made by others: 5 most common problems when implementing AI

Interest in artificial intelligence has been growing sharply in recent years. Companies are investing in pilot projects, testing generative AI, Microsoft Copilot, and specialized tools for process automation. Expectations are high – higher productivity, faster decision-making, lower operating costs, or a better customer experience.

However, many organizations are finding that deploying the technology itself does not automatically lead to the expected results. AI is not an isolated tool that can simply be plugged into existing processes. Its success depends on a number of factors, ranging from data quality and process setup to management approach and adoption by employees.

Our experience shows that most problems emerge even before the actual model or technology is addressed. Companies repeat similar mistakes, which subsequently prolong implementation, reduce the expected benefits, or lead to AI remaining only in the pilot project phase.
Let's look at the five most common problems we encounter most frequently during AI implementation.

Artificial intelligence can excite an entire team within a few minutes. All it takes is a well-prepared demonstration, a few thought-out prompts, and suddenly the company sees a future full of automation, higher productivity, and faster work. However, this is precisely where the first problem often arises.

What works in a workshop or during a pilot demonstration can also work in routine operations, but the path to this goal is usually longer than it seems at first glance. A demo typically works with ideal inputs and pre-prepared scenarios. In a real environment, it is necessary to take into account data quality, security, integration with other systems, process specifics, and the work of end users. Many companies therefore mistake a successful demo for proof of immediate readiness for production deployment. In reality, however, technology is only one piece of the puzzle, and the success of an AI project is often determined by data, processes, security, and the people who work with the solution.

Therefore, before you embark on a large-scale deployment, verify not only the functionality of the solution itself, but also the readiness of your organization. It is precisely the ability to transfer the potential from a pilot demonstration into everyday operations that is one of the main success factors of AI projects.

Many companies expect artificial intelligence to bring better decision-making, faster information retrieval, or the automation of routine activities. Yet they often overlook one fundamental fact – AI only works with the data it has available. If information is scattered across emails, shared drives, several different systems, or exists in multiple versions at the same time, even the most advanced model cannot provide reliable results. Although AI can find, analyze, or summarize information, it cannot fix long-term data chaos on its own.

This is exactly why many projects hit a wall the moment they transition from a pilot demonstration to real operations. Suddenly, it becomes clear that there is no single source of truth, documents are not properly managed, or key data is incomplete and untrustworthy.

Before you start dealing with a specific AI tool, it pays to first focus on data quality and availability. The better the foundation you provide to the AI, the greater the benefit it can return to you.

As AI capabilities grow, so does the temptation to entrust it with more and more decisions. Modern tools can now suggest responses to customers, evaluate documents, recommend next steps in processes, or even independently perform certain actions. However, this is precisely where one needs to be cautious. While a recommendation can be checked relatively easily, an automatically executed decision can have an immediate impact on the customer, business relationships, or company operations. The greater the consequences of a given step, the more important it is to maintain human oversight.

Of course, this does not mean that artificial intelligence cannot help with decision-making. On the contrary. It often brings the greatest benefit when it prepares relevant background materials for a human, suggests solutions, or points out connections that might otherwise be missed. However, the final decision should correspond to its significance and risks.

When designing an AI solution, it therefore pays to define right at the beginning where AI will only recommend and where it can act independently. It is precisely a clear boundary between a recommendation and an action that helps prevent errors, increases user trust, and reduces the risks associated with automation.

What data can be entered into AI tools? Who approves new use cases? How should the accuracy of outputs be verified? And who bears responsibility if the AI makes a mistake? At this exact moment, the importance of AI governance becomes apparent. It is neither unnecessary bureaucracy nor a brake on innovation. On the contrary. Clearly defined rules help ensure that AI develops safely, consistently, and in line with the organization's goals.

Companies that underestimate this area often face the uncontrolled use of various tools, varying output quality, or security risks. Conversely, organizations with clearly set rules can implement new AI scenarios faster and with greater confidence.

Sooner or later, every company will have to answer a simple question: Who is actually responsible for AI?

Many companies perceive artificial intelligence as a way to speed up existing processes. This is undoubtedly true. The problem arises when an organization tries to automate a process that is already unnecessarily complex, confusing, or full of exceptions. Artificial intelligence by itself does not eliminate process deficiencies. If a complaint travels between several departments, information is copied between systems, and employees spend time tracking down documents, AI will only accelerate this chaos. The result is then not a better process, but a faster path to the same problems.

The most successful projects therefore do not begin with the selection of an AI tool, but with an analysis of the process itself. Companies first ask themselves which steps actually create value, what can be simplified, and what can be automated. Only then does artificial intelligence come into play.

AI can be an extraordinarily powerful accelerator of change. However, to deliver the expected results, it needs to stand on solid foundations. Otherwise, a simple rule applies: a bad process with AI remains a bad process. It just runs faster.

Before you dive into choosing a specific tool or a pilot project, try to honestly answer the following questions:

1. Do we have high-quality, up-to-date, and trustworthy data available?
2. Are we solving a real business problem, or are we just looking for a use for AI?
3. Do we have a clear definition of where AI will only recommend and where it can act independently?
4. Do we have rules, responsibilities, and a method for checking AI outputs in place?
5. Do we really have the processes we want to automate under control?

If you answered "no" to at least one of these questions, it does not necessarily mean you are not ready for AI. It merely means there are areas worth paying attention to before launching the project.
Resolving them is often what decides whether AI becomes a real benefit to the company, or just another interesting demo.