What once required human intuition and experience can now be supported by algorithms. Technology that until recently seemed out of reach is now becoming a tool available at your fingertips. This massive leap in capabilities has suddenly allowed us to create applications straight out of science fiction movies! The market abhors a vacuum – with new possibilities unlocked, more and more companies are considering leveraging the potential of AI. If you’re reading this article, we guess you are too. So how should your company prepare for AI implementation?

“My neighbor has AI! I must have it too!”
First tip: the most important part of AI implementation is setting goals. Artificial intelligence should not be implemented just because “it’s popular and we absolutely must have it too.” If you don’t establish what you want to achieve with it, your actions will be chaotic and ineffective – you’ll waste time and resources. On the other hand, effective use can help you gain a competitive advantage and/or reduce operating costs.

So where should you use AI to ensure the technology brings profit?

Identify areas for implementation

Start by identifying the areas that would benefit from AI capabilities. This is probably the most important step. What’s the simplest way to find effective applications of AI? Use it to solve existing problems. There are likely areas in your company where AI could significantly reduce workload. Ask yourself:

• Do you regularly deal with time-consuming, monotonous processes?
• Are you struggling with a large number of documents?
• Do you feel something consistently takes a lot of time, but you don’t yet know how to fix it?

Answering “yes” to any of these questions is a signal that AI could have real value for your company.

A slightly more complex use of AI is in product development. The broad scope of AI capabilities means it can be used to:

• create an AI chatbot to support customers or employees 24/7,
• automatically generate graphics and texts for different platforms,
• derive business insights from historical data.

And that’s only a fraction of its possibilities. Unfortunately, organizations have different processes and face different challenges, so it’s impossible to universally point to where AI will fit for every company. Defining the scope of implementation should be handled by, for example, a manager with long tenure in the company who has deep knowledge of how the enterprise operates, along with people who understand AI’s capabilities. This allows you to identify, first, what is worth optimizing, and second, whether and how it can be improved.

Take care of data quality

Data is the foundation of artificial intelligence. The more complete, consistent, and up-to-date it is, the better AI will process it. If data is of poor quality, no matter what we do, we won’t be able to implement AI at a satisfying level.

To illustrate with examples:

• Implementing an assistant on your website? Ask yourself:
  • Does the site contain all the information needed to answer customers’ most frequent questions?
  • If not, are they documented somewhere in internal resources?
  • If the information is on the site, is it all up to date?
• Want to create a tool to simplify project management? Ask yourself:
  • Do existing ticket descriptions provide full task context, or just brief references to live meetings?
  • Do projects have complete documentation, or is part of the knowledge only in team members’ heads?

It’s worth addressing these steps before attempting AI implementation. Documenting the above will be useful even without artificial intelligence!

Start with a pilot program

A pilot program should not introduce drastic changes. The first implementation should be small.

If the implementation is successful, the saved budget can be allocated to enhancing the pilot program or testing improvements in other company areas. If, however, the implementation does not bring the expected results, the company will lose fewer resources, and savings can always be redirected to experiments elsewhere.

The key is defining a good MVP – what must be included in the pilot program? Which elements could bring the greatest return compared to the effort put in? What should be noted for future implementation but not necessarily in the first phase?

The tools implemented should be optional to use. It’s worth showing employees that AI improves processes, but you can’t force drastic changes on them. Forcing the use of a potentially imperfect tool would quickly lead to dissatisfaction. The most valuable element at this stage will be feedback from experienced people who know the company’s operations best.

Define what you want to achieve and check if it worked

When implementing the pilot program, set clear goals.

For example, let’s assume you want to implement AI support for a time-consuming process. You calculate that by dedicating X budget to AI, speeding up the process by 30% will pay back within a year. Thus, a clear goal could be “speeding up the process by an average of 30%.” Thanks to this, you can easily determine whether the program meets its objectives.

Beyond quantitative data – e.g., how much faster the process became or how much we saved – it’s worth collecting qualitative data, such as employees’ thoughts on working with the system. It may turn out that although the process didn’t speed up, it became much simpler – resulting in far fewer mistakes or more satisfied employees.

It could also turn out that even though the goals were met, employees are very dissatisfied with the new tool’s usability. Collecting feedback systematically will help determine whether broader AI implementation is justified.

Continuously build AI knowledge in the organization

For the entire process, having AI knowledge is essential. An expert can much more quickly identify potential problems and opportunities for good AI use, as well as accurately estimate which project has a chance of success. Without this knowledge, you’ll spend a lot of time and money on trial and error.

However, in the long term, you shouldn’t rely solely on external consultations. Internal AI knowledge is essential. Unfortunately, there’s no simple way to develop it – because how do you develop an entirely new area of expertise across the organization? The first step in education is always the hardest.

We personally recommend starting with consultations with a specialist (or a team of specialists from an experienced software house – feel free to contact us at hello@x-one.pl), who will be able to answer all important questions and set a good development direction. Allocating funds to gain expertise will bring proportionally huge benefits to an organization starting from scratch!

In the long term, training sessions and workshops will raise knowledge levels across the organization. It’s also valuable for a motivated person to pave the way in acquiring AI knowledge and take on the role of pioneer. Thanks to this, others will feel much more confident knowing they have someone to turn to for support. This will also enable efficient idea exchange with people in different roles, who may have diverse perspectives.

AI implementation is a process that requires a thoughtful strategy, openness to change, and team engagement. Proper preparation, gradual testing of solutions, and employee education will help avoid chaos and maximize the potential of artificial intelligence.

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