Again, since we are in the situation where most claims of AI are just Applied Indirection to the lack of new technology being offered by the platform which is wrapping up old tech in a new UX with a little bit of RPA and, hopefully, better canned reporting and analytics, we are diving into the different levels of analytics to help you understand where AI might be and, more importantly, where it definitely isn’t. Because you don’t want to shell out six or seven figures (or more) for a “modern” solution that is actually only “modern” in the literary sense of the word (which defines the modernist period that started around 1900 and ended around 1965). And we’re not exaggerating here … some of the core statistical algorithms that form the foundation for a few of the bigger name analytic systems on the market date back to the 60s (and even 50s). (In other words, even the old grey beards who remember working on the last of the mainframes forty years ago wouldn’t have thought these techniques new back then.)

Yesterday we covered the first two levels of analytics. The next three are:

Level 3: Predictive

This is what most of the “advanced” analytic solutions on the market offer, predictive analytics, which, when you unwrap the messaging and peel off the fancy packaging, are simply statistical trend fitting and classic trend analysis algorithms that have existed in ERP for 20+ years and MRP for 30+ years. If the price is more-or-less going up according to a slight nonlinear curve, then the price is going to be predicted against the best non-linear curve the box-of-statistical-tricks can fit the data too. And so on. Again, not even a hint of AI here.

Level 4: Prescriptive

This is where AI in its weakest form MIGHT creep into the picture. The keyword here is MIGHT. You see, a prescriptive software application takes the results of a predictive analysis and makes recommendations on what you should do to improve the situation. However, there are two categories of recommendations here. The first category, which most of the applications are based on, is canned recommendations. For example, if the organization is currently spending over market price, prices are projected to go up, but demand still exceeds supply, the canned response will be an auction that invites the suppliers used in the past and highly rated alternative suppliers on the supplier network, as identified by community peers. No real intelligence, or even computation, there. The second category is dynamically computed recommendations, which may be based on a large set of rules or may actually use machine learning and dynamic computation and fall into assisted intelligence and actually make atypical recommendations when situations outside of the norm are detected due to unusual trend patterns or externally identified data (as per our example of web scraping in Part II).

Level 5: Permissive

A permissive system is a system that automatically executes a recommendation on your behalf but, contrary to manic marketing, is not autonomously intelligent. These systems are really just slick RPA (robotic process automation) systems that use a large rule base to drive workflows based upon whether or not recommendations are above a certain confidence interval, costs are within a certain bound, timelines are within reason, and so on (as configured by the vendor and the client on system implementation). More advanced systems will use analysis designed by experts to determine whether or not a certain recommendation can be automated, and then automate it with RPA if it can, and the most advanced — and these are extremely few and far between — will use Machine Learning that will record what a user does and then learn when a user is more than likely to take a certain response (based on past behavior) and when it can just begin to automate an action based on past behavior (and, in effect, define and modify it’s own automation rules). But the vast majority of systems still have no AI here whatsoever.

So, at the end of the day, while many vendors have sold their auto-classification, visibility, and prediction systems as AI — there was actually no AI under the hood and all the AI was applied indirection in the marketing organization. So, again, before buying such a system, be sure to apply a bit of logic and a sniff test. And if all you can smell is parfum de mouffette, you can be pretty sure there’s nothing there.