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I build AI architecture for decision support

How generative AI can structure complex issues and give commercial managers a better decision-making basis through AI deconstruction.

I build AI architecture for decision support

As a commercial manager, I have several times participated in strategy processes where the goal is to link ambitions to actual implementation. Such processes often involve many employees, external consultants and professional environments with different perspectives and agendas. The result can be extensive analyzes and presentations before you understand what the business needs to do differently.

The challenge is well known: the strategy is clearly formulated, but the link to operational action is far weaker. Strategy documents, KPI structures, sales processes, tender documents and regulatory requirements create large amounts of information that are demanding to analyze systematically.

I am therefore working on a method for using generative AI to structure and analyze such issues more effectively. The basic logic is simple:

Documents and data → AI deconstruction → strategic insight → decision support

AI analyzes text, structure and connections, identifies possible gaps and formulates hypotheses that give management a better starting point for assessments and decisions.

Through vibe-coding and tools such as GPT-4o, Claude, Gemini, Cursor and Streamlit, I have developed a technical foundation that makes it possible to build several types of decision-making tools on the same architecture.

From idea to tool
The same analysis model can be used in several areas within commercial management:

  • Strategy and implementation
  • Commercial excellence and sales
  • Tenders and RFP processes
  • Governance and compliance
  • Onboarding and capability development

A concrete example. is the solution "The Predictive Sales Coach", where salespeople train against a dynamic virtual customer based on DISC and concrete sales phases, while the system analyzes the dialogue and provides improvement points to increase the win-rate in real customer meetings.

The common denominator is the architecture behind and how Gen. AI can analyze complex issues, identify structural gaps and provide managers with a better decision-making basis.

👉 I work on developing and applying such solutions in practice. If you are curious about how this can be used in your own business, I would be happy to have a chat.

Relevant next steps

If you would like to discuss a related topic, feel free to get in touch.

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