Easy in theory, difficult in practice

Born in the U.K. in the late 60s, I became a lifelong Doctor Who fan during my early years there. For many, David Tennant’s Tenth Doctor is a favorite, but for those of us who grew up watching in the 70s and 80s, Tom Baker’s Fourth Doctor was the one who captured our imaginations. When his Doctor regenerated in Logopolis, it was a heart-wrenching moment. His final words (the title of this article) resonated then, and they’ve stuck with me ever since.

I retired at the end of December 2024 (and no, that is not the reason why I haven't published any new articles in over six months). But the moment was prepared for.

Having had first-hand experience of what happens when someone retires and doesn't have a plan, I did not want to spend my year or two of retirement hoping to come up with sufficient pastimes to fill a suddenly empty calendar.

My planning started with defining what I wanted to replace paid work with.

Retirement isn’t the end. It’s a means to a more fulfilling end. I knew I wanted to give back - both to my local community and to the project management profession. I also wanted to reconnect with hobbies I’d long neglected during my working years.

So, I ran some experiments.

Some activities resonated with me; others didn’t - and that was an expected outcome of the process.

I wanted to help a local food bank and tried sorting and boxing donations in their warehouse and helping folks address food insecurity needs at the food bank itself. While both activities were enjoyable, I preferred the warehouse work as it took greater advantage of my skills.

What helped with these preparations was that my journey to retirement was over a period of a couple of years during which I progressively replaced paid with volunteer work as well as other pursuits. This is not something which everyone might be able to do so it is important to do this planning as and when you find time as you approach your retirement date.

Planning is an ongoing activity with projects and the same is true for retirement. While I have a sufficiently varied set of activities to occupy me now, I realize that I may need to replace some of them over time, hence it is important to keep exploring what else might be of interest.

One important insight: you don’t need to replicate a 35- or 40-hour work week. In fact, one of the most valuable aspects of retirement has been the white space - the slack time that allows for reflection, rest, and appreciation.

Retirement, like any meaningful journey, requires thoughtful preparation. It’s not about filling time, but enriching it. And in doing so, I’ve discovered that the greatest freedom comes not from a blank calendar—but from one filled with purpose, flexibility, and joy. In the words of my favorite Doctor: the moment was prepared for.

The term "critical path" is unfortunate. 

While a critical path does denote the longest sequence of activities through a project network diagram from start to finish, the activities along the critical path (also unfortunately named "critical activities") might not be those which possess the greatest uncertainty.

Why should this concern us?

It can be tempting to focus on critical path activities from a schedule variance perspective, but if sufficient contingency buffers have not been added to non-critical paths, realization of negative risks impacting those will turn them into critical paths.

This phenomenon can be simulated by use of Monte Carlo simulation. By providing the level of confidence or an estimate of duration distributions for activities and then running a Monte Carlo simulation with a reasonable number of iterations we can get a prediction as to how frequently a non-critical path becomes the critical path. This can then be used to appropriately size contingency buffers for those paths to reduce this likelihood.

While on the topic of critical path, schedule variance calculations in earned value can be fooled. Because most network diagrams will have multiple parallel paths, some of which are critical and some not, if sufficient non-critical paths are ahead of schedule relative to critical paths, schedule variance might be positive but the project will still be late. One way to avoid being caught off guard by this is to also calculate schedule variance on your critical path activities alone.

The term "critical path" is unfortunate. 

While a critical path does denote the longest sequence of activities through a project network diagram from start to finish, the activities along the critical path (also unfortunately named "critical activities") might not be those which possess the greatest uncertainty.

Why should this concern us?

It can be tempting to focus on critical path activities from a schedule variance perspective, but if sufficient contingency buffers have not been added to non-critical paths, realization of negative risks impacting those will turn them into critical paths.

This phenomenon can be simulated by use of Monte Carlo simulation. By providing the level of confidence or an estimate of duration distributions for activities and then running a Monte Carlo simulation with a reasonable number of iterations we can get a prediction as to how frequently a non-critical path becomes the critical path. This can then be used to appropriately size contingency buffers for those paths to reduce this likelihood.

While on the topic of critical path, schedule variance calculations in earned value can be fooled. Because most network diagrams will have multiple parallel paths, some of which are critical and some not, if sufficient non-critical paths are ahead of schedule relative to critical paths, schedule variance might be positive but the project will still be late. One way to avoid being caught off guard by this is to also calculate schedule variance on your critical path activities alone.

I'm midway through reading Jeremy Kahn's book "Mastering A.I. - A Survival Guide To Our Superpowered Future". While I find the title aspirational (can you truly master anything which is evolving as rapidly as A.I.?), the author has done a good job of providing a balanced assessment of some near and longer term benefits and risks of A.I.

What has resonated with me as it relates to project management are the following three cognitive biases:

• Automation bias - the inclination to assume that recommendations or information presented by a computer system are more accurate than that produced by a human being, even when we are presented with contradictory evidence.
• Automation neglect - the tendency to discount and ignore what a computer system is telling us, especially when it runs counter to our beliefs or desires.
• Automation surprise - the tendency to rely on computer systems and to be confused or surprised when they fail.

I've witnessed the impact of the first two biases multiple times over my career with traditional project management applications.

I've seen senior executives trust the information provided in a Project Portfolio Management solution's sexy dashboard telling them that a particular project was healthy even when the data used to populate that dashboard had undergone significant green-shifting and it was clear to any stakeholder remotely close to the project that it was on fire.

I've seen a sponsor refuse to accept a project manager's recommendation to push back a milestone date based on a Monte Carlo simulation which showed that meeting the desired date had an extremely low probability of success.

I haven't run into automation surprise yet mostly because many project management applications have the unfortunate tendency of failing regularly as the complexity or volume of data or queries increases.

In the near term, we are unlikely to fall prey to such biases when it comes to A.I.-based project management solutions. It is being well drilled into us to employ techniques such as human in the middle to verify that A.I. generated outputs are valid.

But lets fast forward a few years to when the growing pains of the current generation of A.I. tools are but distant memories.

As the reliability of the tools improves, our vigilance diminishes. The likelihood of automation bias affecting project managers, team members, and senior stakeholders will increase, especially as our ability to understand how the A.I. tools are coming to a conclusion gets harder. This will go hand-in-hand with automation surprise. When A.I. tools fail, we might lack the experience or knowledge to understand how to troubleshoot it and if we have become too reliant on the tool doing what we would have done manually in the past, our ability to take over might have atrophied.

The impacts of automation neglect are likely to remain fairly constant. For stakeholders who have a preconceived belief that they don't wish to have challenged, a high confidence contrary answer from a more reliable A.I. is unlikely to sway them. Mandating that users are required to follow the A.I.'s guidance is not the solution as it just increases the potential impacts of automation bias and automation surprise.

So as you contemplate your future as a project manager, what will YOU do to reduce the impacts of these biases as A.I.-enabled project management continues to mature?

Based on the extensive media coverage, YouTube videos, TED Talks, and books published, many might agree that 2023 has been hailed as the year of artificial intelligence, at least in terms of mindshare if not market dominance.

Throughout the past year, online project management communities have frequently discussed the potential impact of A.I. tools on the role of project managers. While concerns persist about potential negative effects, such as new project risks and potential job displacement, there's also optimism. A.I. tools, when used appropriately, are seen as potential assistants in delivering projects more efficiently and effectively, akin to other professions.

However, let's maintain perspective. Like previous project management tools—such as schedulers and knowledge management platforms—some aspects of our work won't be affected by A.I. until projects can be entirely completed by machines without human involvement.

Certain challenges will persist:

1. Commitments will still be made prematurely: A.I. might provide better reasoning for unattainable completion dates or funding amounts, but it's unlikely to deter senior stakeholders from imposing unrealistic constraints.
2. What you don't know will still hurt more than what you do know: In the near term, we won't have sufficiently advanced A.I. capabilities to identify all the possible risks which could impact our projects. And as complexity continues to increase, the likelihood remains that unknown-unknowns will affect our projects to a greater extent than the known-unknowns.
3. Stakeholders will continue to surprise us: Provided sufficient context, A.I. tools might be able to improve our forecast of how stakeholders will respond to a given decision or project approach. However, if we've learned anything from The Matrix, even if humans are part of an A.I. system, they'll still find ways to behave unexpectedly.
4. More concurrent work than can be effectively delivered: A.I. tools might give us a better understanding of the capacity within our teams and our throughput potential, but with the exception of those who use product-centric delivery models or who embrace the flow guidance of Dr. Goldratt or Don Reinertsen, most will still welcome more work into their system than should be permitted, so multitasking, work overload and the inability to accurately forecast people's availability will persist.
5. The single biggest problem in communication: A.I. tools will eventually help us to bridge communication gaps with real-time context sensitive translation and guidance to make better choices about messaging tone, medium and other factors. Nevertheless, some gaps, as demonstrated in 'Star Trek: The Next Generation's' episode 'Darmok,' may remain insurmountable.

So as the dawn of 2024 approaches, lets greet it with the confidence that while some things are likely change in project delivery, most won't.

"The art of progress is to preserve order amid change and to preserve change amid order." - Alfred North Whitehead