Your AI Product: How Far Will It Go?
In last week’s post, I introduced some initial thinking you’d want to do before tackling an AI project. In short, figure out the right capabilities based on where AI comes in.
Today, I’ll tackle the next step you want to get clarity on: What you want the AI tool to accomplish. This goes beyond defining the problem at which you time the tool. You also need to be clear on how far that tool will push it.
Coffee makers: An Example
I’ve got a fine yet simple espresso maker. I fill powder into the basket, attach it to the machine, turn a knob and wait for delicious espresso to pour out. It has a steam function, so if I want to make a cappucino, I can use that to froth up some milk.
My mom has a much fancier machine. She turns a dial to select from a number of beverage options, presses a button, and has the machine grind beans, heat and froth milk, and assemble them in just the right quantity and layering for a cappucino, latte, macchiato and dozens more.
Either produces a fine beverage, but I undoubtedly have to do more work than my mom. On the other hand, mine was an order of magnitude cheaper and requires no maintenance other than the occasional wipedown.
80/20
So with your AI tool and the problem you’re letting it loose on, you’ll also have to decide whether it requires some hand-cranking by the user or whether it should produce a perfect result autonomously at the press of a button. And just like with coffee makers, there’s an order-of-magnitude difference in cost and complexity:
Enough to just spit out a rough draft or some suggestions that the user then takes and runs with? Easy peasy.
The AI output needs to be near-perfect and only require user intervention in the rarest of cases? Quite a bit harder.
The user won’t even see the AI output and so it needs to be 100% reliable because it’ll feed into the next, unseen, part of the workflow, all to create a final result that needs to be absolutely correct? Now we’re talking absolute cutting edge of AI engineering.
As with many things in life, getting somethign that’s 80% there can often be achieved with 20% of the effort, and any incremental improvement on top of that will require much more effort.
Your challenge, then, is to find the optimal balance:
Conserve energy and resources and use the simplest approach possible, but
Deliver something complete and useable to your users
