There is always that one person who knows how invoice approval really works and where the current template is. As long as Petra is around, everything works. 🙃
This is where many AI projects fail without anyone noticing
If a process only exists in Petra’s head, even the best AI models cannot speed it up. They need a process that can be described, checked and improved.
Petra is also a knowledge issue
Behind many stuck processes lies knowledge that is written down nowhere. For AI models, this is doubly difficult: they can only work with knowledge that exists somewhere, in documents, instructions or a knowledge base. What only exists in Petra’s head, they cannot find.
Knowledge management is therefore not a side issue, but a prerequisite for AI models to really help in everyday work. I wrote more about why context and expertise make the difference in “AI in organizations: context makes the difference”. And along the way, your company becomes less dependent on whether Petra is on holiday.
Make it visible first, then improve it
Once the direction is clear (that was part 2), the next question follows: which processes cost us the most time, nerves and follow-up questions today? Once these processes are visible and simplified, it often becomes very clear where AI models really help and where they don’t.
4 questions to get started
- Which process regularly causes frustration in your company?
- What happens if your key person is out tomorrow?
- For which task do you think: “This should be faster”?
- Where is your most important knowledge written down today, and would anyone find it?
The answers are a very good starting point for a process analysis. That is exactly where I support companies: making processes visible, simplifying them and then deciding where AI models make sense.
The series: What comes before the AI tool
- Part 1 · Kick-off: Why the tool is the easiest part
- Part 2 · Strategy & organization: Where should AI models take us as a company?
- Part 3 · Processes: Which processes should actually get better? (this post)
- Part 4 · Rules & compliance: What is the team allowed to do with AI tools, and what not?
- Part 5 · Information security: What happens to our data along the way?
A question for you
Which process is at the top of your list?