I decided to build – not just think about AI

The week on the master's course Generative AI for Business at BI has been particularly interesting and educational. What makes this relevant is not just the frameworks, but the practical work. We have worked in the Google ecosystem with Gemini, Google AI Studio and Workspace integrations, tested model selection, structured prompt architecture and evaluated output against concrete business cases. When you have to manage the context, parameters and data base yourself, you get a completely different understanding of precision and limitations.

At the same time, I have spent a lot of time testing and exploring AI tools in practice. Within writing and analysis, there are clear differences between solutions such as Claude, ChatGPT and Perplexity when it comes to understanding context and structured reasoning. On the development side, tools such as Cursor, Replit, Lovable and AI Studio have made it possible to go from idea to working prototype quickly, through AI-assisted coding, model testing and efficient workflow. Within knowledge structuring, NotebookLM and local LLM solutions have shown how crucial context and data quality are.

The point is not the tools themselves, but to understand what they actually can and cannot do, and how they can be linked to value creation.

The WEF points out in the Future of Jobs report that analytical thinking, creative problem solving and technological understanding are among the most important skills up to 2030, while companies report a significant skills gap. The data shows that the effect of generative AI is not linear, but divergent: the difference between adopters and laggards increases over time. In other words, the skills gap is escalating, not static.

The work this week has already resulted in further development of an earlier framework into a concrete application: The Predictive Sales Coach. A solution where salespeople can train against a dynamic, virtual customer based on DISC psychology and concrete sales phases, while the system analyzes the dialogue objectively and provides precise points for improvement. The main goal is to train for an increased win-rate through structured and targeted training before you meet the customer.

Before the course, I also launched my own website (link in the first comment field). Overall, I feel that I have taken a clear step forward, from analysis to actual construction. AI is fundamentally not about technology alone, but about management, prioritization and the ability to connect insight to execution.

If you are curious about the app solution, or want to discuss the practical application of AI in your commercial business, I would be happy to have a chat. The days are also used for meetings with exciting resource persons and professional environments within AI, technology and commercial development - which both provides perspective and new ideas for further projects.

The motivation is great - the inspiration is greater!