AI Found What McKinsey Missed for 20 Years

Last week I wrote about the Hubble archive. Thirty-five years of data hiding 800 cosmic anomalies. This week, McKinsey found its own.

Here’s what happened.

McKinsey receives 1 million applications a year and hires less than 1% of them. They wanted to know: What distinguishes candidates who become successful partners from those who don’t?

They analyzed 20 years of hiring data with AI.

The pattern jumped out immediately. Candidates who’d experienced setbacks and recovered were more likely to become partners than candidates with perfect records.

For two decades, McKinsey had been optimizing for the wrong signal. They were looking for flawless trajectories. They should have been looking for resilience.

This isn’t a McKinsey story.

It’s an everyone story.

Every company has decades of data that was analyzed once, with the tools available at the time, and then filed away. Customer behavior, sales patterns, product experiments, performance reviews.

You looked at it, drew conclusions, built processes around those conclusions and moved on.

The economics just changed.

Reanalyzing 20 years of data was always technically possible. You could have hired analysts to comb through everything, challenge assumptions, look for hidden patterns.

You didn’t. Because the cost-benefit didn’t justify it. You were already successful. Why question what was working?

AI changes that.

But only if you have the data.

McKinsey could do this because they had 20 years of structured hiring records. Hubble could do this because NASA systematically archived images.

Most companies don’t have this. The reality is FAR messier.

The data might technically exist. But can you actually access it? Is it complete enough to analyze? Does anyone remember the context?

You’ve got access to the algorithms. But do you have usable data?

If you don’t, that’s your first unlock. Not analyzing what you have. Building the foundations for what you need going forward.

If you have the data then every settled conclusion is worth revisiting. Every archive becomes a new dataset. Every “best practice” becomes a testable hypothesis.

This is the exponential I wrote about: the multiplication of past human effort by current machine intelligence.

What changed is the cost of asking: What did we miss?

McKinsey had been screening for perfection. They missed resilience.

What have you been screening for? What patterns have you missed? What conclusions deserve a second look?

AI won’t find your blind spots unless you have the courage and curiosity to reopen the archives.

Until next time,

Ram

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Ram Srinivasan MIT Alum | Author, The Conscious Machine | Global Future of Work and AI Adoption Leader published in Business Insider, Fortune, Harvard Business Review, MIT Executive Viewpoints and more.

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Ram Srinivasan currently serves as an Innovation Strategist and Transformation Leader, authoring groundbreaking works including "The Conscious Machine" and the upcoming "The Exponential Human."

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