Last month, a resounding theme emerged among the CPOs and CTOs in the room while I was emceeing the Product Led Alliance’s CPO conference: a concern about the future of the mid-level manager, and the impact of AI on the leadership pipeline. There was palpable tension around the topic.
But it wasn’t a panicked conversation or a rejection of AI, nor was it a sentiment of disappointment in the youth of today. Instead, it was a dawning realization among human-first and tech-first leaders that AI is posing risks to our long-term talent pipeline.
As the conversation unfolded, it became clear that two simultaneous factors are impacting the future of our talent pools.
The 2x Risk Factor of AI on the Talent Pipeline
AI has afforded us a lot of efficiencies and opportunities, but there is still a cost to replacing human-led work.
As AI replaces front-end developers, product owners, junior-level talent, and beyond, we’re quite literally shrinking the pool available to move upward toward middle management roles.
And even if those roles aren’t being completely replaced, they’re being heavily augmented by an abundance of AI tools. Talent is now able to skip over the sometimes messy but usually necessary speed bumps that help build strategic muscle memory.
Think about how you got to where you are today. When we started out as product people and engineers, we got to make mistakes, take calculated risks, and learn through doing. And we accumulated 20+ years of acumen that allowed us to rise into positions of leadership.
The emerging situation right now presents a conundrum: Junior talent landing jobs are getting fewer chances to learn by failing. The result is less experience and less strategic know how, and that translates to fewer future leaders.
Many of the optimizations AI tools have afforded organizations are great—my own business, as well as the work we do with teams is a testament to that! So yes, some roles can be automated, but at what point are we eliminating a chunk of the pool who we’ll need 10 years down the line?
Talent Strategy is an Operating Model Problem
At Tuckpoint, talent is one of the four dimensions we look at when we assess an organization’s operating model maturity (I write about the foundations here). No matter what technologies come along, people and teams will continue to be at the heart of the work.
While mulling over this broader topic, something occurred to me: Right now, there are a lot of good people looking for work. At the same time, a lot of companies say they can’t find good talent. How can both of those things be true?
I can’t help but wonder if we’re already seeing the early signs of this pipeline problem. And is it only going to get worse?
Four Questions to Reflect On
I know a lot of you are feeling pulled in two directions right now.
On one side is the budget. Your board and executive team want to see the return on your AI investments, and automation is one of the clearest ways to show it. Fewer hours, lower costs, faster output. It’s a hard case to argue with.
On the other side is the responsibility to develop the people who will give you a competitive edge, and make decisions five and ten years from now.
As you think ahead to building a high-performing org, are you going to have the volume and skill of people qualified to make it to mid-level manager, and then to go on to become effective leaders?
I don’t think you have to choose one over the other, but I do think you have to be honest about the tradeoff, because right now many organizations are only counting one side of it.
So before you greenlight the next round of automation, here are a few questions worth sitting with:
When you build the business case for automating a role, does anyone account for the cost to your future leadership bench? Or does only this year’s savings make it onto the slide?
Where do your early-career people get to fail? Can you point to real decisions they own, where they could get it wrong and recover?
If you cut entry-level hiring, how will people with real experience get to the next level? Or are you counting on hiring them from somewhere else?
Are your AI tools helping people learn the work, or doing it for them? Do your junior people understand why the AI’s answer is right, or just that it was accepted?
While I can’t answer those questions for you, I know many of you still believe human judgment is irreplaceable. What I’d encourage you to consider is how to keep building that judgment when the old ways of earning it are disappearing.
That alone makes this an operating model question, not a hiring question. It’s about how roles are designed, how decisions get made, where people get room to stretch, and what you choose to measure.
If you’re a tech or product leader wrestling with how to push forward on AI while still investing in the leaders you’ll need 5, 10, or 15 years from now, send me a message. I want to hear what you’re seeing, what you’re trying, and what’s keeping you up at night.