Easy in theory, difficult in practice

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

Whether it is in one of LinkedIn's project management discussion groups or in PMI's Projectmanagement.com community, one of the more frequent requests made by members is for mentoring. Sometimes the mentee has done a good job of articulating their needs which will increase their odds of finding a suitable mentor but this is the exception, not the rule.

Project management mentors are usually senior practitioners who tend to be quite busy, hence providing limited information almost guarantees that the request won't be fulfilled in a timely fashion.

So before you post a request for a mentor, take the time to answer these five questions:

What are my objectives for the mentoring relationship?

This is a good case of where the S.M.A.R.T. test for objectives should be used - are they specific, measurable, achievable, realistic and timebound?

This question will help you answer the next one.

Is the mentoring relationship I'm seeking short or long term?

This will help prospective mentors decide whether they are willing to commit for a longer period of time and will serve as a good sanity check on the achievability of your objectives.

The answers to these two questions might help you answer the next question.

Will I be better served with a mentor whom I can meet in person?

Depending on your objectives, you might find that geographic or temporal distance will significantly reduce the mentor's ability to help you succeed such as intimate knowledge of the local business environment. Thankfully many PMI chapters have well established mentoring programs which might help you to connect with a local practitioner.

How much effort will my mentor need to commit to help me achieve my objectives?

You might think that you have found the perfect practitioner from a personality and experience perspective but if they are too busy to effectively support you, you may need to connect with someone that has more time but less experience or you might need to adjust your expectations of the mentor's time commitment.

Finally, while many mentors provide their services on a voluntary basis, others might treat it as billable work.

Am I willing to pay for mentoring support, and if so, what is my budget?

If you don't know what you want to get out of a mentoring relationship, no mentor can help you achieve your goals.

I've almost finished reading "Gigs, Hustles & Temps" by Jason Foster which is about precarious work and the negative impacts it creates on individuals, their families and society in general. While we might think of precarious work as something limited to Uber drivers, home cleaners and other gig workers, such work covers multiple industries spanning both public and private sector employment.

The author does a great job of highlighting the personal impacts of precarity such as reduced wages, reduced leverage with employers and delay or deferment of capital purchases, but the chapter on the economic impacts of precarious work resonated with me as it covers the productivity impacts when a large proportion of a company's work force is experiencing precarity.

The author identifies three reasons for this:

• Leadership teams don't have the same motivation to train and develop precarious employees
• Workers experiencing precarity are less likely to be invested in the organization and its long term success
• There is a ripple effect on permanent, non-precarious staff as they see how precarious workers are treated by their company and are more likely to be concerned about how well they will be treated in the future

The author also writes about the link between precarity and reduced physical and psychological safety as employers are less inclined to invest in exceeding health & safety standards and workers are more like to experience high levels of ongoing stress.

But the kicker for me is this quote from the International Labour Organization of the United Nations (ILO): "The use of temporary workers can over time erode the motivation that workers have to contribute to the organization, and can lower the level of ability available in the organization to innovate or in other ways contribute to firm performance."

This made me think about the companies I've worked with over the years that had tried to increase their delivery agility and the relative differences in success between those which had few precarious workers and those which had much more.

While I wouldn't consider it the sole cause, it is safe to say that those companies which had a higher percentage of workers in precarious positions were more likely to struggle with the transition, especially the organizational commitment to ongoing continuous improvement.

Precarity reduces safety, and without safety, nothing else matters.

During a presentation I delivered today to members of the PMI Nova Scotia chapter on cultivating psychological safety, one of the attendees asked how would she be able to assess whether the team she was going to join was safe prior to joining.

This is a great question because whenever we move to a new company or even a different division in a sufficiently large company, our access to verifiable information is quite limited. For obvious reasons, the leadership of our new team will usually not want to provide evidence of a poor team culture and unless we have trusted connections within the team itself or have access to someone who has recently left the team, it can be difficult to feel confident that we aren't jumping into a snake pit.

It is certainly worth asking your potential new manager questions such as:

• "How much turnover had there been within the team?"
• "Could I see a copy of the team's working agreements?
• "Can you give me an example of a recent time when a team member challenged the status quo?"
• "How frequently do your team members challenge a decision you've made?"

But, I'd also recommend asking the manager to speak one-on-one with a few team members.

If they resist that request, walk away.

But let's say they are open to it.

Here are a few questions to consider asking when you meet with each team member:

• "Think back to the last time you made a mistake with the work you do in the team. How was the news of that mistake received by your manager and your fellow team members?"
• "When was the last time you provided constructive feedback to a fellow team member? How about to your manager?"
• "Can you describe a situation where you challenged a decision which your manager and the majority of the team were endorsing?"
• "Can you think of a time when someone from outside of the team was being overly critical of you or another team member and what did the rest of the team or your manager do?"

While it is quite feasible that one or more of the team members you speak with might be under the manager's thrall, active listening while you ask these probing questions might reveal something different than what the person is saying.

Joining a new organization is fraught with risks but with a little bit of due diligence you can reduce the odds of snake bite!