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In Q4 of last year, an HR leader I know attended a tense performance review meeting. One engineering manager wanted to give an employee an 'exceeds expectations' rating because their output had doubled. Another manager disagreed, pointing out that the increase was just from using ChatGPT, while the employee's poor design choices had caused two production errors. Because the team lacked a framework to resolve this kind of conflict, the discussion quickly broke down into an argument about fairness. If you haven't faced this situation yet, you will soon. In an AI-driven workplace, performance reviews often turn into arguments your current goal system can't solve. I recently published a four-part framework covering outcomes, verification, judgment, and capability in this article. In this edition, I'll focus on how to guide your team through the goal-reset process in the AI era. Both Managers Are Right and That’s the ProblemMost performance disputes moving forward will boil down to the same issue: your goals measure output volume, but AI has taken over most of that work. One manager looks at the numbers while the other looks at the decisions behind them. Both are right, yet they are describing the same employee. Until the goals change, you will keep settling disagreements that have no correct answer in your current performance system. Stop trying to resolve these disputes during the review cycle. You cannot fix a broken measurement system through calibration. Instead, acknowledge that your current system is transitional, document where the goals failed, and use that data to drive your reset. The Reset Is a Change Program, and You Own ItRolling out new goal categories is simple; managing the change is hard. When you reset targets, your team will worry that you're just raising the bar to justify layoffs. In today's climate, that's their default assumption. Here is how to handle it:
What This Does for Your Seat at the TableDo this right, and you’ll earn more credibility than any other project you deliver this year. You’ll head into executive meetings armed with role-specific productivity data that no one else has. You’ll know exactly who is worth promoting. And when the board asks how AI is affecting your output, you’ll have a clear, evidence-based answer. This changes the conversation during headcount planning, allowing you to make strategic decisions about funding growth rather than just making reflexive cuts. Action Items for This Week
That’s it for this week. If you are in the middle of a reset and hitting roadblocks, just hit reply and let me know where you are stuck. I read and respond to every email. |
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Read on the website The last two editions of this “layoff analysis” series covered Block's 40% cut and Atlassian's 1,600-person restructuring. Both generated incredible feedback and conversations, so I'm keeping this going. This time, Snap is in the hot seat. If Block was a story about overhiring and Atlassian was a story about a broken cost structure, Snap is something worse: a company that keeps making the same workforce planning mistakes over and over again, repackaging each round with a...
The last edition, where I broke down the real reasons behind Block's layoffs, got a lot of great feedback and a few of you reached out asking me to keep going with this kind of analysis. So here we are. This time, Atlassian is in the hot seat. Like Block, Atlassian is not a startup, but the lessons buried in what happened there are directly relevant to anyone building and scaling a team right now. If anything, the Atlassian story hits closer to home for HR and people ops leaders because it...
Everyone Is Blaming AI for Block's 40% Layoff. The Data Tells a Different Story. I'm sure you've heard this news by now. Jack Dorsey cut more than 4,000 employees from Block, taking the company from over 10,000 people to just under 6,000. I wasn't planning on writing about this, but the conversation on LinkedIn and elsewhere has been dominated by fear, hot takes, and very little actual analysis. So I decided to dig into the data and share what I found, because the story the numbers tell is...