As succinctly stated in this recent HBR headline, Algorithms Don’t Feel, People Do.

Also, algorithms don’t sense, read non-verbal cues, detect patterns in seemingly unrelated data, take risks, or form common bonds. They don’t feel, and they aren’t intelligent. And while their predictive capabilities are scary given enough data, they are not infallible, and when they do fail, they will fail in a big way. Let’s address these points one by one.

First of all, as noted by the author of the HBR article, algorithms don’t feel, and can’t predict how a person will respond to a message. Marshall McLuhan may have stated that the medium is the message, implying that the form of a medium embeds itself in the message and influences how a person will receive the message, but the reality is that, in today’s individualistic society, the message is what is interpreted by the recipient, and only someone with a shared understanding will be able to comprehend what that is and react accordingly. As a result, an algorithm can not negotiate.

Successful negotiation depends on a first party transmitting a message, agreeable to that first party, that the second party can accept, and, moreover, figuring out, of all of the possible messages that the second party might accept, which subset represent message that the second party are most likely to accept and which messages of the subset are the least distant from the desired message. An algorithm can compute which options are likely given certain assumptions, and which of these options are the least distance according to some metric, but cannot determine what assumptions to make. Only a person who can feel, and feel what the other party is feeling, can be the judge of what good assumptions are. And, secondly, algorithms cannot sense. They don’t feel, and they don’t have instinct — which requires real intelligence.

Thirdly, they can’t read non-verbal cues. Even if someone is stating that they may be agreeable to an offer, the reality is that they may have no intention of ever accepting the offer, and are only indicating the contrary to stall for more time. It’s often the case that such a person is not as good at masking their demeanor as they are at masking their words. It might be the case that their non-verbal cues give more away than they would like. Only a trained negotiator with years of experience and instinct can be the judge of this.

But even more importantly, they can’t detect patterns in unrelated data, as it’s typically the case they can only process specified data in specified ways. And a fixed data pool never tells the whole story. A fixed algorithm might not know that a fire today will impact resource availability in six months, that your main competitor is likely to go out of business do a massive loss in a patent infringement lawsuit, or that a new technology is going to make the current technology obsolete in 18 months, with prices and demand starting to plummet in six months. As a result, in each of these instances, the algorithm would buy (today) (at a much) higher (price) than it needs to.

Furthermore, algorithms don’t understand when to “trust your gut” and take a calculated risk such as betting the farm on a new technology or riding the spot-buy market when all signs point to locking in a price for three years. The reality is that real success often requires risk, and only a true pro will know when such a risk should be taken.

Finally, as algorithms are not intelligent, they don’t form common bonds with like-minded algorithms that would help them advance their company and their profession. Algorithms have their place, and properly used can take a great deal of tactical and low-value workload off of a Supply Management professional’s plate, but algorithms will never be smart enough to handle the strategic and high-value workloads without intelligent — human — supervision. Optimal is only optimal if all of the assumptions are valid and modelled. An expert will always be needed to define the assumptions, check the assumptions, verify the results, and tweak them according to an ever-changing Supply Management world.

In short, good technology can make you two, ten, and maybe even one hundred times more productive (depending on the metric), but it cannot replace you. So don’t be scared of new technology for your supply chain — embrace it. Given the ever-increasing demands being placed upon you, you will be glad that you did!