Is It AI or Not? A Score Card With 4 Dimensions

Scaling AI Florian Douetteau

AI is certainly a hot topic that everyone claims to be doing (or working on doing). But how many businesses are actually executing? One of the reasons it’s a difficult question to answer is that everyone seems to have a different definition of what exactly AI is.

One of the more common and fairly widely accepted definitions is that AI means going beyond simple statistics to mimic human skills in perception, learning, interaction, and decision making. But even this definition leaves some room for interpretation. So going one step further, this matrix breaks down the different parts of that definition and how they might manifest themselves in data science projects at different levels:

score cardPerception + Learning + Interaction + Decision = ? | Sum the score for your application and check if it's AI!

In general, projects that remain at Level 1 in most areas are — for lack of a better term (it would be helpful if there were one!) — Immature AI, where those at Level 2 or Level 3 move toward AI in the true sense of the word. The point here is not to say that a project that does these things in a Level 1 capacity is useless. For businesses that start at zero, execution at Level 1 is a perfectly good first step.

But there is an everything-is-AI-trend these days that would lead some to believe that Level 1 is enough to be able to say “yes, we’re doing AI.” The risk here is that businesses stop there because they’re already “doing” AI.

So while Level 1 execution is a good first step, the trend toward setting sights and goals on this level of execution is a dangerous one — beware of getting to Immature AI and then stopping there.

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