Internal knowledge and documentation
Finding answers in manuals, procedures and technical documents. I explore how AI could make information easier to access while preserving the ability to trace the original source.
Business and artificial intelligence
I am curious about AI and business.
I am interested in AI and gradually learning about its possibilities and limitations. With my background in technical environments and team leadership, I am particularly drawn to where it could be useful in the everyday running of companies.
I enjoy thinking about potential use cases, discovering connections and learning what using AI in practice involves. Here I share the topics and questions that come to mind along the way.
I look at AI through the lens of practical experience.
Engineering taught me to look for solutions. Leading people showed me that understanding who will work with a solution is just as important. With AI, I therefore think about the whole process: the task, available information, ways of working and responsibility for the outcome.
Potential use cases
Where I see room to explore
I see these areas as starting points for exploration. Each depends on the specific setting, the quality of the available information and the team's needs.
Information for decision-making
Preparing overviews, comparing options or summarising longer materials. I see value in giving people clear information that they can check and supplement.
Routine administration
First drafts of meeting notes, reports and recurring documents. I consider where AI could reduce manual preparation and leave people more time for work that calls for their experience.
The everyday work of technical teams
Sharing experience, understanding a brief or preparing for a meeting. I am trying to understand how AI could fit into everyday work in a way that is clear and useful for the team.
What I want to learn
How to tell whether using AI makes sense
Alongside the possibilities of AI, I am learning how its benefits can be tested. These steps make sense to me:
Start by defining the problem. What do we want to improve, for whom, and how is the work done today? Without that, it is difficult to assess the benefit of a new solution.
Choose a small, specific use case. Identify the information needed, the expected output and who will check it. Also consider data sensitivity and company rules.
Try it and compare. Track time including checks and corrections, the quality of the result and the experience of the people using it. Then decide whether to continue, adjust the approach or explore another path.
Let's exchange perspectives on AI.
I welcome different perspectives, interesting examples and experiences with AI in everyday work. If you share this interest, get in touch.
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