The Human Problem in AI

As a primer for this post, read Ian’s great post about the cost of intelligence and how value goes up the chain.

AI has a human problem. I don’t mean in the “I, Robot” sense, I mean in that humans mostly don’t know what to do with it. People will see that others have built chat bots and immediately go to do the same. This feels like a solution in search of a problem.

For some organizations there is no value add in owning the infrastructure. They end up overrunning on cost with a mess of infrastructure. Adding to this is the lack of foresight in how AI helps the problem they are solving. You should always, always, always start with the problem. What is it that is challenging for the user? Does AI solve it, or create a new problem?

If you’ve mapped the problem and AI provides some value add, first look to see if it has synergies with your organization. Are you handling lots of data anyway? Great, looks like a strong fit. Are you a business that doesn’t, but likes the opex savings? Plenty of companies will manage the AI in the equation for you, so you can focus on delivering your core competency.

Here, you need to make sure you are tracking what you are doing with AI. Without good visibility, there are going to be too many elements that can impact success. You should be able to see what inputs and outputs are at every step, and isolate if something breaks to where it broke. Ideally you have some metrics to do this that align with success at each step. Automated alerting would be great too.

You built an AI app, with visibility, that solves a real problem. Great! Now you need to make sure that it continues to perform as expected. Models will deprecate. Identify a path to validate new models that doesn’t impact your core solution, or that can easily be reverted. Remember those metrics from before? They should still be running in production. If they can’t, or it becomes too expensive, try another solution or metric.

And that’s it. If you liked this post, feel free to subscribe for more.