AI Evolution Stages

1. Experiment & Discover

We use widely available generative AI like ChatGPT, Perplexity, Claude, Copilot, Gemini, and others. We experiment with multimodal applications, maybe images or GPTs too. In this stage we're still meeting basic needs — understanding what AI can do and discovering its limits. We look broadly at available tools, try many things, and often feel overwhelmed because it's hard to make sense of AI's possibilities. We often work alone here, trying to understand the first basics without pursuing a deeper strategy — acting instinctively and exploratively, looking for early wins.

2. Orientation & Focus

After trying various tools, we start combining them — e.g. Whisper (transcription), ChatGPT (text generation), Perplexity (research), DeepL Write (editing) — to turn, say, a recorded keynote into a book chapter. First, more complex applications emerge, already tailored to our personal or professional context. It becomes clear we need clarity about our own role and perspective to identify the use cases relevant to us. We recognize we no longer need to grasp everything at once, but should focus on what truly matters for our role, perspective, and business. This level also requires collaboration — we join networks, exchange with others, to reflect and support each other. Group exchange becomes increasingly valuable as it helps us move forward more efficiently and purposefully.

3. Integration & Automation

We start turning our insights into repeatable workflows, integrating them into our processes, aiming to automate further for consistent results. Focus sharpens here: many of the initial discoveries become irrelevant as we concentrate on what's actually relevant to our specific use cases. Instead of needing to understand everything, we learn that it's enough to know what possibilities and tools exist and who has mastered them. This leads to a clearer, more efficient approach to working with AI. We often focus alone here on optimizing and expanding our own processes — recognizing that AI isn't just an experiment, but a tool for systematization and scaling.

4. Specialization & Responsibility

Our expectations rise: we want data protection, our own AI environments, and specially trained LLMs to better reflect the context of our use cases and reduce hallucinations. We make important business decisions about our tech stack and standardizing our AI solutions. In this stage we develop a deeper understanding that our work runs in loops — we learn iteratively, adapt, take steps back, then move forward again. Ethical considerations also gain importance, as we want to use AI responsibly and humanely. Collaborating with networks becomes important again here, to reflect on our experiences and find the best solutions together — team work and connecting with like-minded people become key to our progress.

5. Innovation & Advancement

We start integrating AI into products and services, and new offerings that build heavily on AI may emerge. In this stage we focus on developing ourselves and our company further through AI. Our first AI-based products and services aim to strengthen our own competitiveness and to develop and establish AI-infused business models. Often it's existing customers or our close network who, out of trust, use these products & services. It's no longer just about using AI as a tool or feature, but about integrating it into the core offering of our own work or the company.

6. Competition & Differentiation

Focus increasingly shifts to the market and competitiveness. Our new products and services must now hold up against competitors' offerings. Areas that once felt like blue ocean increasingly become contested markets. As our customer base shifts to new target groups or segments, we encounter competition and need to reflect on their offerings. We must accept that our offering and processes need continuous development to hold up in this sometimes intense competition. Here it becomes crucial to involve our network and use others' expertise to expand our services and products with additional know-how and innovation — it's no longer just about standing individually, but asserting ourselves in the market through collective intelligence and collaboration.

7. Vision & Sustainability

At this stage it's about continually reflecting on and adjusting our personal and entrepreneurial vision. This stage asks us to look beyond short-term wins or efficiency gains, and to consider the long-term direction and the societal impact of our AI use. The sustainability of the decisions we make, and the question of how our entrepreneurial vision can hold up in a constantly changing world, move into focus. Here it becomes clear that AI is not just a tool for the present, but also the foundation for a sustainable future for our work and our company. It's about strategic growth and continuously checking and developing the connection between vision and market changes.