A manager sees time savings. An employee may wonder whether their skills are becoming less valuable or whether more work will now be expected for the same pay. A software manual won't answer those concerns.
Ethan Mollick discusses AI adoption as an interaction between leadership, experimentation and employee participation. People who know the work often spot best where AI helps and where the output needs fixing. Source: One Useful Thing.
Ask which work wears people down
Discuss activities that consume attention but add little value: repeated summaries, moving data or writing the same explanations. Don't assume the most visible task is the most demanding.
Choose one experiment together. Agree who tries it, what you measure and which data is appropriate. Discuss what the freed time is for: better customer work, learning or a calmer pace.
Make learning visible
Not every experiment has to succeed. Ask each person to show a good result and an error. Sharing mistakes improves the system when it isn't used to blame someone.
Record who updates instructions and whom to ask for help. If only one enthusiast can use AI, the team depends on them. A shared method needs to be teachable.
AILithe combines the systems and people perspectives. Tõnis-Denis focuses on processes and responsibility; Pille-Riin on brand, collaboration and culture. The aim is a way of working in which technology supports people's skills and values, and everyone understands why the change is happening.