AI for Project Management: The $15 Plumber Problem
A few weeks ago, a plumber quoted me $900 to fix a toilet. A simple repair that involved one part. After sending them on their way, my partner and I found the fix ourselves: seven minutes of YouTube, $15 in parts, and about an hour of actual work.
I kept returning to that repair all weekend. Yes, because it saved me money, but something else was going on. There was a deep satisfaction in having figured it out. In having done the work.
I've thought about why that feeling was so strong. I think maybe it comes down to this: the dopamine hit from solving a problem yourself is measurably stronger than having the answer handed to you.
That's not a metaphor. It's neuroscience. And it has significant implications for how we're building high performing teams and leading project teams in right now.
What AI is doing to your team's capacity to think
There is a version of ai adoption in the workplace playing out in many project environments right now that looks like progress and functions like atrophy. It is often framed as ai for project management, but the risks are rarely discussed clearly enough.
Someone on the team has a problem. They ask an AI tool. They get an answer. They move on. The work gets done. The box gets ticked. But the cognitive process that would have built capability, the wrestling with ambiguity, the forming and testing of a hypothesis, the discomfort of not knowing yet, never happens.
Neuroplasticity research tells us the brain adapts in direct response to how it is used. A 2019 study published in Frontiers in Neuroscience found that the anterior mid-cingulate cortex, the region of the brain most associated with persistence and grit, grows more active when people do hard things they don't particularly want to do. It is the neural substrate of tenacity. And like any capacity, it atrophies when it's not exercised.
When AI consistently removes the hard part of thinking, it doesn't just save time. It gradually reduces your team's tolerance for the kind of effortful reasoning that complex project management challenges depend on. It can also affect long-term employee skills development in ways many leaders underestimate.
Project management challenges beneath the attention problem
This is compounded by what's happening to attention.
Gloria Mark, Professor of Informatics at UC Irvine, has spent nearly two decades tracking how digital work environments affect our capacity to focus. Her research found that people now spend an average of just 47 seconds on any screen before shifting attention.
In studies using heart rate monitors, she found a direct correlation between attention switching and physiological stress: blood pressure rises, cortisol increases, and errors go up. When she cut off email access in one organisation for a week, attention duration increased and stress measurably decreased.
The implication for project teams is direct. Attention is being chopped into shorter and shorter windows. Switching between tasks carries a real cognitive cost, and those costs accumulate.
Johann Hari, in Stolen Focus, argues that the collapse of our ability to sustain deep work is not a personal failing; it is a structural condition, shaped by the systems and tools we work inside. In his view, ‘the truth is that you are living in a system that is pouring acid on your attention every day.’
For project managers and functional leads working across complex implementations, particularly within retail project management, the capacity to hold a problem in focus long enough to understand it fully is not a nice-to-have. It is the job.
The AI decision making problem leaders need to notice
The goal is not to remove AI from the workflow. The goal is to be deliberate about where human thinking remains non-negotiable. Knowing how to use ai for project management matters far less than knowing when not to use it.
In practice, that means:
Protect the problem-definition stage. Before anyone reaches for an AI tool, the team should be able to articulate what they're actually trying to solve. If they can't frame the problem, they're not ready to evaluate any answer. This is one of the overlooked risks of using ai in business.
Require explanation, not just output. When AI is used to generate an option, recommendation, or draft, the person presenting it should be able to explain why it's right, not just what it says. If they can't, they don't yet understand the problem.
Design for effortful moments. The discomfort of not knowing the answer yet is where capability is built. Structured workshops, scenario walkthroughs, and cross-functional problem-solving sessions create the conditions for the kind of thinking that AI shortcuts tend to bypass. This is how stronger teams improve employee skills development and judgement.
Watch for compliance without comprehension. Teams under pressure will use AI to produce outputs that look complete. The diagnostic question is not “did they finish it?” It's “do they understand it well enough to defend it under challenge?”
‘A dopamine hit is stronger when you've worked for the reward. When AI does the thinking for your team, it doesn't just save time. It takes something from them.’
Vendor project management and the cost of thin attention
A few years ago, I worked on a project where our counterpart on the vendor side was managing eight projects simultaneously, and the project we were working on was large enough for us to have two people on it.
He could barely attend our weekly work-in-progress meetings. His lack of attention and oversight led to significant reporting mismatches and real project slip. It was a clear lesson in vendor project management and the consequences of poor prioritisation.
That's an extreme version of what happens when attention is spread too thin. But a subtler version of the same dynamic is now playing out across teams that are producing volume with AI assistance while quietly losing the depth of understanding that good project work requires.
More outputs. Thinner judgment. In many cases, the short-term ai impact on productivity masks the longer-term cost.
In a complex retail implementation, ERP implementation is a serious risk.
The decisions that matter, the ones about process design, data governance, systems selection, supplier relationships, retail change management, change sequencing, supplier relationships, and vendor management in project management, depend on people who can sit with a hard question long enough to actually understand it. This is where experienced retail consulting support can add real value.
For organisations operating in project management in retail industry environments, those judgment calls are often the difference between momentum and drift.
‘AI adoption without attention to capability is just a faster way to scale shallow thinking.’
Why projects fail when thinking is outsourced
The Hidden Mechanism: AI doesn't eliminate the need for judgment. It hides the moments where judgment is most needed, while the underlying capability to exercise it slowly weakens from disuse. That is often why projects fail.
Diagnostic Questions:
When your team uses AI to produce an answer, can they explain why that answer is correct without referencing the tool's output?
Where in your project workflow are people doing the effortful cognitive work that builds capability, rather than retrieving answers?
Is your team's tolerance for ambiguity increasing or decreasing as AI use grows?
Decision Framework: Use this test. If a team member cannot explain why an AI-generated answer is right, they don't yet understand the problem. Don't accept the output. Go back to the problem.
Retail improvement, made practical.
Leadership thinking that drives change.
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