It’s quite common today to hear employees say they already use AI regularly in their work, and in many cases, that’s true. Tools like ChatGPT and other AI assistants have quickly become part of everyday tasks. But occasional use is not the same as effective use. Many people experiment with AI, yet few are truly getting the most out of it in their daily work.
Over the past year, the way organizations view AI has also shifted. What started as something interesting to explore has become something practical. More companies now see AI as a tool to support employees, improve productivity, and simplify everyday tasks, often even tracking its impact through measurable results.
Because of this, the conversation is changing. It’s no longer just about “Why AI training is needed?”, but increasingly about “How to deliver AI training that works?”. Instead of asking whether to invest, organizations are now focused on how to introduce AI in a way that truly benefits their teams.
In many cases, general AI training is the natural place to begin. These sessions help employees understand the fundamentals of AI and give them space to explore what the technology can do. Just as importantly, they create a shared understanding across teams. When everyone starts from the same foundation and understanding, conversations around AI become productive and more focused. I have often seen how even a short introduction can shift the mindset to curiosity about AI capabilities.
At the same time, understanding the basics is only part of the journey. This is where said curiosity comes at play. At some point, employees begin to ask a more practical question: how does this apply to my work? That is where role-based AI training becomes especially valuable. The way AI supports a marketing team will look very different from how it supports sales, HR, or executives. When training connects directly to the tasks people already work with, AI becomes easier to integrate into everyday routines and starts to create real value.
Looking at how different organizations approach this transition, it becomes clear that while there is no single path, there are common patterns.
A good example is OpenText, an IT services company that develops AI solutions itself. Even with their existing expertise, they chose to start by strengthening the fundamentals across their teams.
- They began with an AI+ Prompt Engineer training, which focuses on building strong prompting skills and helping employees work with AI tools in a more structured and effective way.
- Participants learned how to design prompts that guide AI models toward more accurate and useful outputs, how to adjust prompt structure depending on the task, and how to troubleshoot and refine results to improve performance.
- The training also introduced more advanced topics such as working with image data and understanding how models interpret and generate visual content. This created a shared standard and ensured that everyone was approaching AI in a consistent way.
This approach is well reflected in how they describe their perspective on AI. As shared by Simona Dimova from Sales Operations, who also organized the initiative and took part in the training herself:
“In our team, we see artificial intelligence as a key driver of innovation and productivity. It is important for us that our employees feel confident using AI tools, both in their daily work and when supporting informed decision-making.”
Building on that foundation, they are now continuing with an AI+ Agent training. This is a more advanced, practical program focused on moving beyond individual prompts toward building reliable AI workflows. Participants learn how to structure context, chain tasks, and create their own AI agents using tools like Copilot and custom GPTs. The goal is not just to use AI, but to design systems that can be applied directly in daily work.
A different but equally interesting example is BULATSA, the Bulgarian air traffic authority. Instead of focusing primarily on how employees could use AI in their individual roles, they were working on projects that included AI integrations within systems. For BULATSA, the focus was not only on usage, but on how to structure and manage AI-driven initiatives.
- Their teams started with an AI+ Prompt Engineer training to build a solid foundation in working effectively with AI systems. From there, they moved into a Chief AI Officer training, focused on leading and structuring AI-driven initiatives at an organizational level.
- Given their work on AI integration projects, this was a natural progression—from understanding how AI works to managing how it is applied. The program focuses on building AI strategies, aligning initiatives with business goals, and ensuring responsible and effective use of AI, helping teams move toward a more structured and strategic approach.
This shift is best reflected in their own words. The participation of BULATSA was driven not simply by interest in using AI tools, but by the needed to better understand how AI-driven initiatives can be structured, managed, and governed at an organizational level. As shared by Margarita Staneva, Administrative Support Specialist, who organized the training and took part in it:
“The progression from building solid fundamentals through prompt-focused training to addressing strategic, leadership, and governance aspects reflected our needs, particularly in the context of AI integrations in complex environment. The training provided a coherent framework that supported a more structured and responsible approach to AI, aligning well with the challenges faced by an organization like ours.”
These examples highlight an important point. While the start may differ, the most effective approaches tend to follow a similar logic. Building a foundation first and then expanding into more specialized, role or context specific training.
Looking at the organizations that have had the most success with AI training, one thing becomes clear. The process rarely starts and ends with a single training. Instead, it develops step by step. A general introduction helps employees understand what is possible, while more targeted training helps them apply those possibilities in practice.
AI training is not just about learning a tool. It is about helping people feel prepared for a new way of working. In my experience, the organizations that see the most value are the ones that move past the question of “Why AI?” and focus on “How AI?” fits into their everyday work. When companies invest in structured learning pathways that combine both general and role-based training, they create the conditions for AI to become a natural part of how work gets done.