Why success can be a liability when you ignore the unknown

learning from the unknown

When Steve Jobs bought the computer division from Lucasfilm and started an independent company called Pixar in 1986, no one could have anticipated its runaway success.

Until Pixar came along, the commercial success of computer-animated movies had been hit or miss. It was rare for a production company to have a string of hits, let alone more than ten in a row.

learning from the unknown

What set Pixar apart was its willingness to use post-mortems to explore why it was successful. For the 33 years that Ed Catmull led Pixar as President, he wouldn’t let them fall into the trap of resting on their laurels.

Compare this to Ducati who in 2003, their first year fielding a bike in the MotoGP, they declared the year would be all about learning rather than winning. They quickly forgot their intention when they started achieving podium finishes. They started 2004 with high-performance expectations that were quickly dashed when previously unrecognised design flaws started to impact performance. Because they didn’t understand why they’d been successful despite their risks, they couldn’t effectively respond when things started going wrong.

When Filippo Preziosi from the Ducati Corse team was asked to reflect on what happened, he said, “In racing, when you make a change, you only care whether or not it leads to superior performance. You tend to care less why something works. But over the long term you need to know why. This is the science.”

Preziosi had realised a common trap that has claimed many successful organisations and teams: people like to focus on what went right rather than what went wrong.

However, Catmull’s insistence on rigorous post-mortems, regardless of a movie’s success or failure, contributed to Pixar’s long string of hits.

learning from the unknown

In their HBR article, Why Leaders Don’t Learn from Success, Francesca Gino and Gary P. Pisano explain, ‘You should use success to breed more success by understanding it.’

Their research across multiple industries and organisations found that success can breed failure by hindering learning at both the individual and the organisational level.

When nothing seemed to be broken, they found people often asked, ‘Why fix it?’

They identified that many successful organisations tended to focus on applying what they already knew to solve problems, and this prevented organisations from expanding their knowledge about why something worked.

When things work, it’s not always because we know what we are doing; we could have just got lucky and didn’t know it. Unfortunately, those same conditions may not exist the next time we use the same recipe.

It’s akin to using a GPS that only shows the last turn you made correctly, not the full journey ahead.

Ideally, we want to get beyond success being like a spotlight on a stage where the light only shines on what’s working and the risks are in darkness. To get there, Gino and Pisano recommend we examine successes with the same scrutiny that applies to failures.

That means adopting a simplified five-step model for learning that tests the assumptions, models, and theories that underpin how we approach problems.

They suggest we:

  1. Celebrate success but examine it using four key questions: 

a. What did we set out to do?
b. What actually happened?
c. Why did it happen?
d. What are we going to do next time

  1. Use systematic project reviews and avoid sticking with the same approach. Learn from Pixar and mix it up to keep it fresh so people stay interested and curious.
  2. Use the right time horizons and consider time lags between the action taken and its consequences so you don’t attribute success to random occurrences. Remember the lag between cause and effect can sometimes take years.
  3. Avoid assuming that replication is learning by exploring the root causes of success and understanding what is under your direct control and what is affected by external factors.
  4. Experiment, even when nothing is broken, by pushing boundaries and continually testing until you understand the breaking points. Consider your specific context and the risks and costs associated with pushing to breaking point – simulations may be a better option when the risks are high.

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