24.03 Liontrust Global Innovation Report - The Rise of AI 04.24 - Flipbook - Page 7
What are the main ways AI will affect the economy in terms
of the way we work, how companies are run and how
companies will compete?
AI is really at its core an advance in statistics, in particular
the statistics of prediction. What these new techniques
have done by virtue of clever algorithmic design and
also a massive amount of computing power is to start to
dramatically drop the cost of prediction. And every other
time we’ve had that sort of transformation, when you’ve
taken something that’s quite costly and made it really
cheap, there’s been a whole lot of flow on applications
and progress.
For things we’re already predicting, like the weather or
traffic, then we can improve our predictions and do it
better. But the more interesting applications are where we
didn’t previously realise that the problem we were facing
was a prediction problem, such as self-driving vehicles.
The biggest gains of all, however, come not from mere
applications but when you get true transformation, when
an entire industry is essentially reorganised by an input
becoming cheap. Historically, think of how the taxi
industry was effectively completely upended by the mobile
phone. No one saw Steve Jobs introduce the iPhone and
said “well, that’s it for the taxi industry”.
But these transformational impacts will take time because
human reorganisation always takes time. AI will improve
prediction, but it won’t necessarily improve all the stodgy
human stuff that slows down transformation. The real
exciting developments for AI, which we’re starting to get
a glimpse of, is still to come in ways that we can’t quite
anticipate at the moment.
Do you expect companies that are prepared to reorganise
to fully embrace AI to outcompete the companies that aren’t
prepared to do it?
There will be environments in which it’s a good way to go
to completely reorganise and design a technically superior
system. But, more typically, it will be about tacking on
automation and that will be quite difficult. It will be case
by case based on the benefits of AI prediction versus all
the costs that come from integrating this prediction. You
will have to decide whether the juice is worth the squeeze.
For example, I recently visited the factory of Lego in Billund
in Denmark. There is lots of automation in producing these
billions of different types of bricks, 24/7. We could use AI
to do better demand prediction for different models of Lego
and send that back to the factory floor in an automated
way so it can reconfigure and churn out the right bricks
accordingly. But think about all the other moving parts,
getting it onto the shelves etc. Lego has a very fine-tuned
process already and you need to make sure your new
overall system operates better.
Furthermore, even where it is appropriate to reorganise the
whole company for AI, you’ll still need some way during
the transition to take care of all the exceptions through
human intervention when AI makes mistakes. AI can
perform well, but only within the bounds of the laws of
statistics. So if you’ve got model instability or bad data, it’s
going to do bad things as well.
The rise of AI: Technology and Innovation Report - 7
