Turtles all the way down
Every big mistake I've made in my career has been a people mistake. The same is true for every leader I know.
Contrary to what you might think, the rise of AI is going to make this truer than ever.
Case study lies
It's common for business school students to analyze business successes and failures. The goal is to identify the patterns that drive success and failure respectively. Typically, B-school case studies focus on the most impactful levers, such as new products, unique manufacturing processes, or a team's ability to innovate quickly.
Occasionally, these levers include organizational factors, like how compensation is structured or how big teams are. But even when business case studies look at org factors, the analysis is generally structural, not interpersonal.
This is striking, since every decision about a major business lever is made by an individual person. In other words, the choices you make about personnel determine your ultimate business performance.
When I think about my own successes and failures in business, all the big errors, like the ones that have been messiest to come back from, have been mistakes about individual people. Things like:
- Betting on the wrong person
- Not engaging with conflict when I needed to
- Making decisions about individuals based on hope rather than facts
- Not prioritizing growth mindset highly enough in the people I hire
There are many things I wish I'd known sooner: Most yellow flags are red. The more talented the person, the more harmful it is if they're toxic. By the time you're consistently questioning someone's work or character, it's too late.
But AI is machines, not people. Right?
The theory of an agentic AI world is that AI will be increasingly able to take autonomous actions in the workplace. So it might seem strange to assert that in an AI world, all the biggest mistakes are still people mistakes. But there are three reasons that your people bets matter in an agentic AI world even more than they did before.
#1: AI doesn’t decide what to optimize. People do.
If your team asks the wrong question, sets the wrong objective, or feeds biased data into a system, the model will faithfully take the wrong action faster and at scale. When things go wrong, it isn't the AI per se that has failed. It's the responsibility of whoever scoped, framed, and validated the system inputs.
The net of this is that betting on the wrong person in an agentic AI world hoses you faster and more comprehensively than ever before.
#2: Trust and adoption are human decisions.
Even the best AI systems are worthless if no one trusts or uses them.
Resistance to change, poor communication, unclear accountability, and lack of psychological safety can all derail adoption. Culture, incentives, and leadership clarity are human dynamics, not technical gaps.
For instance, it doesn't matter if your AI forecast tool can improve accuracy by 20%. If your sales leader ignores it because the tool's forecasts run counter to their intuitions, you won't get any of the benefits.
Conversely, if your sales outreach tool produces absolute dreck but your sales leader insists that you use it anyway in a misguided efficiency play, you're doomed.
Betting on the wrong people means you'll make the wrong AI bets in the first place. The bigger your AI bets, the bigger the risk to your current operations, and the more you're counting on individual people's judgment.
#3: Ethical and strategic judgment can't be automated.
AI can execute, but it doesn’t own (or care about) consequences.
Decisions about fairness, privacy, transparency, and acceptable risk come back to human judgment. If an AI system makes a harmful or reputationally disastrous decision, it’s because people failed to set or enforce boundaries.
For example, if a generative AI campaign violates copyright because no one reviewed its training source policy, that's a people oversight, not a model glitch.
Don't think you're off the hook just because you're using a credible third party's AI, either. As we're starting to see in cases like Mobley v. Workday and Harper v. SiriusXM Radio, both the AI vendor and their customer may be legally accountable when things go wrong.
Because AI accelerates the impact of decision-making to scale a lot more quickly, making the wrong ethical decision on the human side causes a lot more harm.
The bottom line: AI represents a tectonic shift in how work will get done. The potential to rapidly scale the wrong system, process, or ethics is like nothing we've ever seen before. That makes it more important than ever that you make the strongest possible decisions about individual personnel.
Kieran
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