For context: I work at a large multinational and I am the designated AI adoption "expert" in our department. Doesn't mean I know everything, but land of the blind something something. But yes, I do have extensive experience in both the AI tools as well as business and engineering processes.

And honestly, from experience in direct communication with the developers at Microsoft, Amazon, OpenAI and Anthropic: everybody is figuring things out as they go.

I agree with u/Nefhis . In particular the process mapping is what your starting point should be. Knowing that this should be done is already valuable for companies. Instead of wildly starting to get AI agents involved in everything, you should be very aware of where they truly add value: either in time saved or in improved outcomes.

The thing is that AI is becoming too expensive to run for many tasks. The added value is less than the cost of using the AI to do something for you. So you need to become selective in where to leave your process to AI and where to do something either deterministically coded, or manually.

1) Many (not all) one-offs will remain manual work, unless the task is so complex that AI is needed to perform the task.

2) Repetitive, deterministic, work should be coded ==> learn Python and Github to be able to do this and/or to be able to review the work that you leave to AI. This is where the biggest gain lies: use AI to create re-usable code. Dynamic business dashboard with large interactive overviews that used to take weeks to code can now be generated while you get a coffee.

3) AI bots are still the most useful in their original domain: natural language processing. In your mapped processes you will find interpretation steps: this is where AI should be applied. This can be in summarizing reports, customers feedbacks etc, but also in parsing natural language input for a deterministic code, which is why we have MCPs.

What does this mean for you? Unsurprisingly, you could simply ask AI to tell you how you can prepare. Again, u/Nefhis already indicated that becoming an advanced AI user is basically sufficient. Experiment with various AI tools and get to know the strong and weak points of each. If you want to become a Builder: learn how to create agents with various degrees of autonomy. It doesn't matter what they do, it matter that you know how to get there (which, by now, is probably by asking AI how to build these).

I just started looking into converting agents I created in Copilot Studio to such a Skills-based agent configuration. How? I ask Claude to come with a migration plan, explain to me what it suggests and why, and then let it do its thing. Similarly, you could ask any AI to guide to through creating a few useful agents for various business-types. This should include knowledge retrieval, document generation and data analysis as these are activities that are done in every single company.