When considering AI's impact on jobs, it's more effective to analyze individual tasks rather than focusing solely on the most iconic or obvious aspects of a role, as the greatest AI opportunities may lie in less apparent tasks.
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I found that for many job roles people
have a mental picture of that iconic TSS
that uniquely defines that job R for
example computer programmers right code
doctors maybe see patients lawyers go to
court to argue court cases and I think
that when people think about AI
opportunities often is instinctual to
say Can AI do that most iconic role or
that most iconic task of that job but I
found that when we actually systematic
an ize the task that the particular job
is made up of the best opportunities may
or may not be that first initial
Instinct let's take a look at a few
examples if you look at the task the
computer programmer does they do write
code and so it's tempting when thinking
about AI for computer programming to ask
can AI help write code but it turns out
computer programmers do many other
things they have the right documentation
they sometimes respond to user support
requests they often review others codes
and they often gather request
requirements for what a piece of
software is intended to do and if you
were to evaluate the generative AI
potential for this job you may find that
writing codes can be help of AI but it's
a relatively difficult task but maybe
writing documentation is actually easier
to do with Gen of AI and so on don't
take the Gena of AI potential column to
seriously in these examples since these
are informal evaluations and if were to
do a rigorous evaluation based on
technical feasibility and business value
your specific conclusions may be
different but I think that is actually
easier to get gen of AI to write
documentation for code than to actually
write the code itself and in many
different job rowes the best potential
for AI may not be the most obvious first
TS you might think of let's look at
another example lawyers spend a lot of
time Drafting and reviewing legal
documents they often have to to answer
client's questions on how to interpret
laws if preparing for a court case
they'll have to review evidence and
sometimes they're involved in
negotiating settlements and sometimes to
represent clients in court and I find
that a systematic listing out of these
toss as well as the systematic
evaluation of the potential May
sometimes lead to interesting
conclusions so I think that there's a
high potential of regen of AI to help
with Drafting and reviewing legal
documents as well as maybe with
interpreting laws whereas I can't see a
lawyer sending a robot to court to argue
on their behalf at least not for some
time and so if you work with the law
firm and Analysis like this might help
you decide where you actually want to
use genter VII one more example
Landscaping a landscaper has to maintain
and care for plants purchase and
transport plants maintain equipment Comm
with clients maintain a Business website
and so on I'm listing of course just a
subset of the TSS that any of these job
rules do if you were to do an analysis
yourself you may end up with anywhere
from five to 15 to 30 toss per job row
and in this case I think most of these
toss actually have pretty low genive AI
potential and so the work of a
landscaper may be less impacted in the
next few years by genitive AI compared
to computer programmers and lawyers so
that's how you can analyze jobs by
breaking it down into tasks and I
encourage you to think through what are
the tasks in your work and where gvi may
be able to help or for the business you
may be involved in to think about how
gender VII could help many different
tasks in that
business when people think about
augmentation or automation people's
minds often go initially to cost savings
because if you automate something seems
like you can you know save money but in
most ways of Technology Innovation going
back all the way way to say the
invention of the steam engine to
electricity to the computer many
companies started off thinking about
cost savings but ended up actually
putting even more of the effort into
pursuing Revenue growth and that's
because growth has no limit but you know
you can only save so much money and when
certain tasks are automated it turns out
sometimes you can rethink the workflow
of how the business creates value so for
example if you could do something a
thousand times cheaper because of
automation say answering queries from
customers then rather than just taking
the cost savings you may be able to
build a new type of customer service
organization that serves people a
thousand times better and this type of
thinking can lead to growth
opportunities that go well beyond cost
savings let's take a look at some
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