Debating Relevance With A Robot

 

In the Artificial Intelligence (AI) crowd’s attempts at portraying it as performing similarly to the ways humans manifest intelligence, the ability to discover meaning is probably going to be the last aspect acquired by AI. But there’s a parallel in current PM initiatives, having to do with which information streams are essential, which are nice to have, and which are completely useless. In this respect, let’s compare and contrast the computer’s quest to discover meaning with the PM’s efforts to get a straight answer to questions like “This risk register helps me … exactly how?”

Hatfield’s Incontrovertible Rule of Management #22 reads:

All useful management information has the following three characteristics:

• • It is accurate,
  • It is timely,
  • And it is relevant.

The first two bullets can be objectively measured, but not the third. But the absence of a readily articulatable litmus test for which PM-oriented information streams are relevant and which are not has created a loophole in the PM codex large enough to drive a $28 Billion[i] (USD) clown car through. While not a complete test in and of itself, I think one excellent question which can help ascertain which is which would be: “Is this information actionable?” In other words, does the subject information stream provide the basis for making a decision about a particular strategy, its timing or implementation, or help with the selection of an optimal technical approach from among several options?

Let’s pivot to AI for a moment. Most people have seen some form of AI-generated art. Some of it is compelling, some of it is just creepy. What I believe is critical to keep in mind when it comes to AI-generated art is that no computer knows what color is, at least not the way humans do. When a computer generates a graphical image, it’s not selecting a color off of a pallet. It’s assigning the binary code sequence of what we humans perceive as color, and places it in a pixel that occupies a specific coordinate in an X-Y array. Depending on the extent that the program it’s running allows for guided (or even random) variance in color selection and placement, the results can be derivatives of already existing works, completely random, or anywhere in-between. If the outcome is attractive, it’s considered an AI success. If not, well, it’s usually relegated to the let’s-not-do-that-again bin, and a new iteration is initiated.

Meanwhile, Back In The Project Management World…

There are lots of analysts laying claim to the term “data scientist,” and many of them are swimming around in the PM world. But the litmus test for which information streams are relevant in that PM world and which are not has to be very different from the test for which AI-generated pieces of art are attractive, and which are not. Art is subjective – the relevance of PM information streams, well, (ahem) it is also subjective, but it shouldn’t be. The act of collecting data, using some methodology to convert it into usable information, and presenting that information in a way that decision-makers can make use of it isn’t usually cheap, or easy, at least not for the timely, accurate, and relevant stuff. The Director of a start-up PMO who spends her money and time developing a robust Quality Assurance capability, complete with fishbone diagrams and five-whys questionnaires, will be almost certainly out-performed by the one who uses a limited budget to create an Earned Value-based cost and schedule performance measurement system that can be actually implemented across the portfolio. Placement of the competing PM-oriented information streams within the scale between the extremes of completely superfluous and essential-for-PMO-success may be the most important strategy decision of PMOs everywhere.

Which all leads us back to the role of AI within PM. Can Artificial Intelligence aid in identifying which PM-oriented Management Information Systems (MISs) are relevant, and which are not? Well, yes, but only in the same sense that they can create works of art. Since AI can only “learn” through trial-and-error, there’s really no way for it to ascertain which information streams produce irrelevant output, much like it really has no way to “know” when its artwork is just plain creepy, unless a human sets it straight, and modifies the parameters of the simulation(s). Sure, AI could utilize Bayes’ Theorem to try and determine the strength of the connection between projects that have set up, say, a robust Communications Management protocol, and the subsequent successes of those projects, but assigning the values for the prior probability and the marginal probability would almost certainly be wild estimates.

In short, I’m fairly confident that the debate over which PM information streams are relevant and which are superfluous will not be settled any time soon. And AI can’t help here, either.

[i] According to Allied Market Research, this is the size of the risk management (no initial caps) industry forecast for 2027. Retrieved from https://www.alliedmarketresearch.com/risk-management-software-market on September 16, 2023, 20:17 MDT.