In parts 1 and 2 of my All About…for Project Managers series, we’ve explored novel relationships between time and work, in both a historical and a futuristic context. This article continues along those lines, but with a focus on the final point in the PM triad, people.
People are arguably the least-defined variable within our PM equations, yet unlike time and work calculations, we struggle with ways to accurately quantify the effectiveness of workers. For time and work, we know what we are dealing with, but for people, we have to add chaos and uncertainty into the mix. Our software is great for predicting outcomes of work (in duration, cost, risk, etc.), but less so for qualifying our most valued resource: people.
For example, we take on faith that if Bill is assigned a project task, he will try to complete that task to the best of his abilities in the time allotted, or by a specific date. As a project manager, we might not know much about Bill before we make this assignment, except that he appears in our resource pool with job title and email address. What other attributes do we need to consider about Bill? We may have his resume, his availability, and his previous work stats, but can those facts alone be used to predict and measure Bill’s efficacy?
Herein lies the rub. We may be able to look at peak (or weak) efficiency data, but we have little to go on for predicting if Bill will be more successful than Jill, who has a like-set of variables and constants. In fact, we can’t even accurately predict (or measure) what really happens with Bill during the course of a project, outside of using limited descriptors such as % allocated, hours worked, and % work done.
We therefore run the risk of assuming that since the project was a success (on time /on budget) that Bill’s participation was a success as well. After all, Bill did complete his tasks, and during the post-mortem, we find the limited metrics we have for Bill all to be positive. So, as well, right?
Sadly, this false assumption adds to PMO woes of any size – one woe being that, on average, most attempted projects fail, or are only partially successful.1 Even if these projects are efficiently run, they often fail to meet their charter. If we dig deeper into these project stories and scenarios, we may find clues that indicate to us that even though Bill finished his work on time, if we had instead tasked Jill with the work, the outcome would have been better. How could we have known? Better math and science may help…
Better Math for Better People
MPUG member, Oliver Gildersleeve Jr., suggests one idea (posted in his comment here) – range estimates within a critical chain recovery plan. He notes a unique method of predicting productivity, and then customizes a few columns in a Microsoft Project plan, based on using CCM (Critical Chain Method). Good, we are on the right track…
But let’s zoom out a bit – say 90,000 feet – to see how others are determining “if Jill can be more successful than Bill” within any given project (in short, how are others calculating effectiveness of workers using hard science and math).
Take Mitsubishi of Japan, now using a novel approach of just letting AI make the staffing decisions, based on big data. That’s right, Mitsubishi now has AI supervisors managing people – as they build some of the world’s most impressive 4th Wave machinery.
In this way, the job of the project manager (if there is one left standing) is greatly simplified when resourcing a project.
Incredible enough, there are other Japanese corporations toying with this idea, with one notable company taking it to next level. Japan’s Cyberdyne is the maker of Hybrid Assistive Limbs (the HAL 5), and that device is revolutionizing quadriplegic medicine with their exoskeleton tech. In this case, you have AI not only managing people, but helping them work harder / longer / better, as well. (If references to the movies Terminator 3: Rise of the Machines and 2001: A Space Odyssey escaped you, good, you’ll sleep better tonight).
This brings into focus another important variable when looking at people from a PM perspective: culture. Having an AI boss might work well in Japan, but I doubt there are many workers in Dogtown, Alabama that would be happy with this unique management shuffle – would you?
Better Data for a Newer Math
China is also working on predictive HR systems, again using AI, big data, and supercomputing to solve the people-productivity puzzle (as well as other conundrums far more nefarious than project management ones). Let’s allow the folks at Brookings worry about that, while we concentrate on the effectiveness of workers…
First, Chinese culture allows for the surveillance and data gathering on an extraordinary scale, and second, they are using 4th Wave tech to profile individuals down to their courteousness and trustworthiness indices, as well as dozens of other data points (many HR-related).
For the AI “project manager of the future,” this is a goldmine that can be used to schedule workers and predict outcomes, based on previously unheard of data points. For example, how fast a worker is walking across the factory floor, using gait recognition in real-time.
While to most participating in western democracy, this type of surveillance integration (full-time) may seem creepy; the idea of digitally pre-determining worker biometrics (even down to levels of their consciousness) is fascinating from a PM perspective, even if the practice seems invasive. Newer PM and HR systems are being based on big-data and machine learning world over (with some data derived organically from worker’s backgrounds collected outside of work). This class of innovation inevitably will be deployed within cultures that allow the practice. However, the “West” seems to be approaching the integration of 4th Wave tech within the workforce much differently…
Better People for Better Work
In Part 2 of this series, we examined IBM’s Project Debater, an AI system seen as a prototype decision- maker, designed to partner with a human manager when making those tough calls. Other companies are taking a different approach, one that injects AI directly into the manager’s brain…
Neuralink is an American neurotechnology company (founded by Elon Musk), that is secretively working on Brain-computer interfaces, cortical implants, neurorobotics, and Stent-electrode recording arrays. In short, this all means that Neuralink (as well as others) are planning on wirelessly connecting us to all aspects of the workplace – directly to work tasks, to other co-workers, and to a central AI-controlled command post. New 4th Wave devices are being developed to assist, augment, and yes, repair human cognitive and sensory-motor functions. In short, this more “democratic” approach of increasing productivity integrates new tech into our very being. We have already started down this path. As any casual observer of human behavior can see, there is now a 5th appendage in the hands of most all, the smartphone.
Regardless of whether 4th Wave industrial tech winds up being wirelessly connected to the inside or outside of our bodies, as project managers in 2019, we must start to think about 2030 and beyond. For those already working on 5- to 10-year plans, you must already be grappling with ways to predict the productivity of our future workforce. For example, how can we manage Bill and Jill using today’s software, when the Bill’s and Jill’s of the workforce are now connected to augmented reality devices – or just directly connected to a super computer, operating at hundreds of petaflops per second.
Let’s face it, we are on the cusp of a technological singularity (where the advent of artificial superintelligence will trigger runaway technological growth, resulting in unfathomable changes to human civilization…oh my! At this point, I find solace in the words of Carl Sagan: “Science is an attempt, largely successful, to understand the world, to get a grip on things, to get a hold of ourselves. To steer a safe course.”
Steering a Safe Course…
As shepherds of the workplace (my favorite descriptor for project managers), the coming decades will be taxing. We might be scheduling work for newly-settled workers on Mars, leading AI-augmented workforces in virtual spaces, and managing massive re-training efforts to support a disruptive re-tooling of the workplace. Unfortunately, after the tech-singularity hits, we may find ourselves with more resources than suitable work, reversing previous industrial norms, such as having too much work and not enough qualified humans. We may also be managing Plank time instead of Standard time, and dealing with computing systems that manage us, instead of the other way around. With all of this quantum change on the horizon, our traditional role of steering a safe course within the workplace is more important than ever. Best of luck!
1 See Mayday! Project Crash Investigation, Determining Why Projects Crash, MPUG.org, Sept. 2016.