Leading AI without a technical background takes frameworks. Many people feel rattled by AI, with so many tools and so many ideas. The leader’s job is to stay calm, help prioritise and review the ideas, and keep the work moving forward. The job sounds hard, and a framework makes it easier.
A framework is a set way to take in information, process it and set priorities. Here the information is the ideas. With a set way in place, every AI idea follows the same steps, so the volume of ideas stays manageable. One framework ranks every idea with two scores, effort and business impact, in three steps: a log, the two scores and a decision.
Ideas for using AI in the business reach the leader all the time. Whatever the idea, the first response is the same calm step of putting it in the log, and scoring follows. The log ends up holding every idea that has been raised, each with its two scores.
The effort score answers how hard a logged idea will be to do in practice, and the impact score answers how much the business gets from it. Side by side, the two answers give the leader a quick view of where the idea stands, and the decision follows from that view.
An idea with low effort and high business impact gets a yes, and so does one with mid-level effort, as long as the business impact is high. The work is modest and the business gets a lot from it. With both scores behind the idea, the decision comes quickly and the business does the work.
The opposite case is an idea with high effort and no business impact. It goes into the log too, so the person who raised it does not feel ignored. Logging acknowledges the idea without promising the work, and the idea still gets a decision, so the work keeps moving forward.
From the log, the idea joins the backlog, and the leader gets to it only if the business solves everything else first. That condition is hard to meet, so the idea waits.
