AI is the buzzword, or, more precisely, the buzz acronym. Just about every enterprise vendor is claiming they have AI, even if all they have is RPA (and even if what they have is pushing the definition of RPA). However, whether your vendor has AI or not (and the answer is that they probably don’t, as most of the best vendors just have ML, possibly enabled by AR, but probably not), it is coming, and if you don’t adopt (at least) the (precursor) technology available today, your Sourcing and Procurement organization may be left in the dust.
And by now you are probably firmly bamboozled, so let’s set the record straight, starting at the bottom of the AI technology ladder.
At the bottom of the technology ladder we have RPA, short for robotic process automation, which is generally used to automate what would otherwise be very manual processes, usually by way of a rules-based workflow engine.
On the next rung we have ML, short for machine learning, which applies (usually improvements on, or variations of) open-source or standard algorithms that can extract a model from a set of inputs to produce the associated outputs with high probability. The better platforms use machine learning to tune, if not define, the rules used by the workflow engines embedded in the platforms.
Sometimes the mix of ML and RPA is so good that for certain, focussed, applications that the platforms almost seems intelligent, and this is often what passes for AI these days. But it’s not real artificial intelligence, it’s assisted intelligence as it helps you do a better job, but your intelligence is still required to identify the right recommendations and approve the right actions.
The next rung up is AR, automated reasoning, which can take a set of assumptions, encodings of logical rules and predictive models, and compute derivations that can surpass even a human expert most of the time for very well (and narrowly) defined applications or problems. It’s basically the modern equivalent of an expert system that can compute millions of inter-related logical inferences until new realizations are discovered.
The next rung up is the version of AI that exists today, augmented intelligence, which expertly integrates RPA, ML, and AR to produce applications that more-or-less mimic what an expert would do the majority (but not all of) the time. And that allows an organization to automate some low-value tasks that would otherwise require manual effort as they were generally identified as strategic, but not always worth the effort.
If it existed, the next rung would be the AI that is touted, true artificial intelligence, which does not exist today. (And that’s a good thing, because if there was true AI, would the C-Suite need you? Yes. But would they realize it? Probably not.)
But the final rung, and where everyone wants to get to, is cognitive. AI technology that is not only intelligent, and that can make great decisions unassisted every time, but make the decisions the best human buyer for every situation would make considering all hard and soft variables.
And that’s the technology ladder you are dealing with, and now you know that where you are is likely not where you want to be. But don’t fret, things are getting better. Stay tuned!