Kim Warren on Strategy

Strategy insights and living business models

I did say - how to make bigger mistakes faster with AI

I did say here that - although AI offers huge benefits - it can bring big, big problems if a project's knock-on consequences are not thought through.

... and not just thought through, but worked out:

  • what exactly, could be the wider impacts of that AI project?
  • what scale could those impacts be?
  • how fast could those consequences play out (often super-fast!)?
  • so what other things should we do to avert problems?
  • and how fast do we need to do those things?

Here's a case ...

Last year, the buy-now, pay-later provider Klarna claimed that its AI chatbot could do the work of 700 representatives. But Klarna is now turning back to using people to handle more of its customer service work.

Just to be clear - this is not a disaster story. The chat-bot initiative did indeed both cut the need for humans on customer support, and so saved costs. And it did improve customer support quality - mostly!

It's just that some users needed to interact with a human. And the poor experience of those few users who did not get that option did the company some reputational damage.

So user-numbers, revenue and profit are likely lower today than they might otherwise have been.

Here's the company's growth in 'gross merchandise value'. We can't know how much higher the last two numbers might have been without that AI mis-step.

And more of a growth penalty could show up in future reported results.

Just a simple question to ask

The initiative could have done better if Klarna had just asked "What fraction of customer interactions might still need a human?", then planned a transition to that much smaller, but higher-skilled support team.

... and you'll know, I am sure, that this would be very easy to plan, test and manage with a digital-twin business model

AI roll-outs are a specific form of performance-improvement program, which we already know how to simulate.
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