So, your team is using AI now. Great! Now what?
The AI frontier being the wild wild west that it is, how do we go from experimenting separately to learning together and moving forward with our best approaches as a team?
Or maybe you are getting pressure from your leadership team to start to “prove” you are picking up on AI and using it to get better (or you're waiting for that shoe to drop…).
I’m seeing teams start to measure progress and efficiency with AI over time to get a sense of what’s working. The tension is keeping those measurements light and not disruptive. We don’t want our jobs to become counting and comparing hours like beans!
My thoughts are that there are a few lenses you could evaluate AI adoption through.
Self-Assess: at the level of the person completing a project with AI – reflecting on specific projects right after they are done and how things went, as well as reflecting more generally on how their own skills and usage change at intervals over time.
Check the work: the level of peers who engage with the project but did not build it – getting a pulse on their reception, their feelings around speed and accuracy, and capturing their feedback.
Read the numbers: at the level of operations as a whole – tracking projects completed over time and average by person, proportion of the backlog still open, types of projects, impacts and outcomes, changes in demand, usage rates of the tool, and so on.
My background is in research, and so I’m thinking about AI with research in mind. But I think these approaches are general enough to be used in other roles, too, with some tweaks.
Which of these methods would you or your team find most valuable?
