ESCP ALUMNI magazine - Special Issue 2022 - 68

Campus ❙ Opinion piece
On using artificial
intelligence to achieve
circular economy
It is really only a matter of time before the two main trends, artificial intelligence (AI) and circular
economy, would come together. AI can be used as a tool to help accelerate and scale our
transition to a circular economy.
The pace will be slow
No doubt these are very good news. It also
shows how innovative technologies can
take circular economy to the next level. Yet,
I believe the path leading there will be full
of challenges, not least because, contrary
to what general media would like to get
us to believe, the development of AI is, in
reality, really slow. There are several reasons
attributable to this sluggish pace.
First, there is a general shortage of
AI-proficient graduates. Training up AI
researchers takes time. Universities are not
churning out data scientists fast enough
to meet the job market demand. For those
who are graduating, they will most likely
be snapped up by the technology giants.
Indeed, it has been estimate that some
60% of AI talent are in the employment of
technology and financial services companies,
leading to a " brain drain " in academia,
which in turn, slows down the production of
qualified graduates. Small circular economybased
companies (as well as AI start-ups)
will struggle to have the same hiring power,
as they often lack the ability to match the
levels of salaries and prestige offered by large
organisations.
Another reason why circular economyaimed
companies, large or small, will
struggle to deploy AI is that the technology
remains a very expensive investment. AI is,
at the moment, far from a plug-and-play
technology. Arguably, there are off-the-shelf
68 ❙
AI applications available in the market. But
what these one-size-fits-all technology
solutions can really do is often very limited
and their effectiveness low. Inevitably, for
AI to work at an acceptable, value-creating
level, it is necessary to integrate it into the
existing wider IT system. Customising AI
application to be embedded in the system
architecture is very complex and hence
very costly.
To make matter worse, the market is
seemingly inundated with self-proclaimed
AI companies. A recent report has suggested
that 40 percent of start-ups in Europe that
are classified as AI companies do not actually
use artificial intelligence technologies in a
way that is " material " to their businesses.
As someone who researches and works in
the business of AI, I can readily observe this
phenomenon has already eroded the trust of
many companies, making them increasingly
cautious when proceeding with investment
and deployment of AI.
Gradual developments, not quantum
jump
or these reasons above, the adoption of
AI, and by extension, in the area of circular
economy, will be slow. This, however, does
not mean there will be no advancement.
Instead of " big bang " new business model
creations, AI will most likely produce
circular advantages through baby steps in
operational enhancement gradually. For
instance, one of the important elements
ESCP Alumni Magazine Special issue ❙ 2022
in achieving circular economy is better
asset management. In a recent research
project for the European Defence Agency,
my colleagues and I have discovered that
there is a wide spectrum of operations for
ministries of defence to save money and
practise circular economy, from refurbishing
and repurposing small military equipment
items to reduce waste and minimise the use
of virgin materials to extending the service
years of capital assets. Unquestionably, the
same be applied to civilian activities. For
example, combining the power of AI and
drones can extend the longevity of major
infrastructure such as reactors and bridges.
Advancements in drone technologies have
allowed them to be deployed to take pictures
at heights that are dangerous for inspectors
to reach. The contributions of AI come from
its ability to analyse and identify cracks as
well as defects on assets that are not always
visible to human eyes from captured images.
Consequently, problems are detected before
the assets become irreparable, thereby
lengthening their lifetime.
A seemingly insignificant but potentially
huge possibility of waste reduction would be
saving on paper use. In the insurance industry,
for instance, there is still a huge reliance
on actual paper, with the communications
between various stakeholders including
the underwriters, brokers and insured
passing on a large amount of physical
documents. AI techniques in particular

ESCP ALUMNI magazine - Special Issue 2022

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