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The AI rollout is here - and it's messy | FT Working It | Financial Times | YouTubeToText
YouTube Transcript: The AI rollout is here - and it's messy | FT Working It
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Core Theme
Despite massive investment and hype surrounding Artificial Intelligence (AI), widespread adoption and tangible productivity gains are lagging due to a significant gap in workforce training, strategy, and practical implementation, suggesting a potential bubble if these challenges aren't addressed.
The last big tech bubble burst at the
start of this century and we may be
heading that way again.
>> The kind of investment wave in AI we've
seen is like probably nothing ever
before in history.
>> Hundreds of billions of dollars are
being spent on automating workplaces.
>> We have this amazing technology.
However, we're not seeing adoption fully
yet in every pocket of the the economy.
Only 1% of CEOs have a fully formed AI
strategy. With such high stakes, will
businesses see a return on investment?
I'm Isabelle Barrett. I lead the FTS
working at Grand speaking, presenting,
and writing about management,
leadership, and workplaces. In this
series, I'll explore some of the most
pressing issues around the future of
work and talk to senior leaders about
how they are making work better.
>> 3 5 years from now, I think things will
look quite different
I'm here at the Charter Workplace Summit
in New York in rooms filled with senior
leaders from some of America's biggest
companies. These are the people tasked
with AI roll out and preparing the
workforce for the skills needed for the future.
future.
>> Last is David.
>> Every 6 months a new model is dropping.
Every 6 months something shifts within
the marketplace where you have to stay
up to date. With AI, we're still in like
the very very very early days of
everything happening. We have this
amazing technology with the promise of
productivity enhancing gains. Roughly
10% of companies are fully starting to
integrate AI into their processes. But
there's going to be years of this
happening. We have to figure out exactly
how we can use it and where it makes
sense to use it.
A staggering amount of investment has
been made in AI over the last few years,
and it now accounts for a 40% share of
US GDP growth this year. Over 75% of
businesses worldwide are using
generative AI in at least one function.
But despite this, a study by MIT Media
Lab found that 95% of Gen AI pilots in
the workplace failed. I spoke with
editor-inchief of charter Kevin Delaney
about the state of AI rollouts in industry.
industry.
>> Think about how AI is different from humans.
humans.
>> Companies are adopting AI at two
separate speeds. You have the tech
companies who are actually quite far
along to the point where they think of
AI agents as co-workers. On the other
hand, you have companies that are still
getting their heads around what AI
adoption means. And these are the
companies that are still trying to get
their employees to use chat GPT or
claude. A lot of them are not seeing
gains in productivity at this point. So
you have these two extremes.
>> So we hear a lot about um the need to
upskill the workforce for AI. What does
that actually mean? Are people actually
doing it or are they just letting people
get on with it?
>> People are trying to figure out what
exactly that means. And I think part of
the challenge is that we don't actually
know what the ideal worker skills will
be in 3 years or 5 years as AI is rolled
out more pervasively. There's a lot of
discussion about is the ideal worker in
a more AI deployed environment someone
who is a real specialist in a field or
is it someone who is a generalist who
kind of knows a little bit about the
business and how business operates and
who can communicate clearly and knows
enough to be able to check what the AI
is bringing back.
>> So we need a lot more experimentation
and possibly failure.
>> Yeah. And so that's uncomfortable for
leaders too. To be comfortable with
failure is something that you are not
generally taught in business school.
Failure generally is something that
executives are allergic to encouraging
in their workers.
>> After a day of off thereord discussions,
panels, and big picture sessions, what's
emerged is that there's no clear path
forward for Genai at work. It's still
all to be decided. the reimagination of work.
work.
>> Leaders have spent billions on preparing
for an augmented future, but for what gain?
So, at the FT, we wanted to look at how
is this roll out actually going and what
are companies saying about how they're
using AI. And so, we did this massive
analysis looking at um SP500 companies
in the US. Um we went through thousands
of earnings reports and um regulatory
filings and the s- the results were
quite surprising. Um in earnings reports
CEOs would often say you know AI is
amazing. It would bring incredible
productivity gains a Cambrian explosion
of innovation things like that. Um but
then in the filings which to be fair
tend to be more measured and um risk
averse no one really had anything
concrete to say of how they're actually
using it. And uh in those filings, the
risks outweighed the benefits very very
clearly. If you look at the SP500 index,
it's obviously going up, but a lot of
that growth is driven by seven big tech
companies. And the other companies on
the SP500 haven't necessarily grown that
much when they've said they use AI. AI
use is often phrased in their filings as
being something quite abstract. Um they
talk about productivity but don't really
offer any concrete examples of how
they're using it. Coca-Cola is one
example where in their earnings reports
they raved about how they're using
generative AI to transform their
business. Um, but in their filings, the
only example they could give was using
generative AI to create a Christmas ad.
It's definitely a mixed bag. The growth
of AI has led to a boom for
consultancies and learning platforms who
are keen to show business how to harness
the powers of AI at work. I visited the
HQ of AI upskilling platform Multiverse
and met with their CEO and founder Euan
Blair. So what are the ways in which
companies I guess your clients are
engaging with AI skills? Are they
hesitant? Are they all in? How is it
what does it look like?
>> So I I I think it's it's almost the kind
of polar opposite of hesitant. The kind
of investment wave in AI we've seen is
like probably nothing ever before in
history. So the big challenge a lot of
organizations are facing is how to turn
kind of potential AI gains into actual
realized AI gains. And that's where the
kind of training gap comes in because
what a lot of people are doing with AI
at the moment is the equivalent of
having an iPhone and just using it to
send text messages and make calls,
right? They're missing out on loads of
the capabilities that these tools
actually have. So we've seen a lot of
companies spend a lot of money on AI and
>> really a lot of money
>> and there haven't been right
>> particular productivity gains that I'm
aware of.
>> What's what's the where's this gap?
What's the gap?
>> We've seen accounts teams for example um
process invoices 50% more quickly and
with half the number of errors because
of introducing AI. We've seen um
software engineering teams increase
their speed of shipping code uh by 75%
in some cases. Those are big tangible
things that do actually have an impact.
One of the reasons we're not seeing
gains at the kind of big macro level yet
in terms of economic growth is this sort
of training and capability gap. Because
with previous versions of software, it
was often deemed enough to go and invest
in the technology and then over a period
of several years, people would figure
out how to use it and where to use it
and everything would be okay. The
difference this time is the inherent
capability of the systems is so much
greater. You need a lot of training to
be able to kind of fundamentally change
the way you work, but also the amounts
being spent are so much greater. So the
stakes are higher and that kind of
creates this this perfect set of
conditions where people realize the
people who spend the most on AI are not
the ones who are going to win. It's
going to be the people who have the most
AI enabled workforce and that's the kind
of space multiverse is playing in.
Everyone feels like they're behind the
curve when it comes to AI and they all
feel like they're not doing enough and
could be doing more. And that is kind of
creating this sort of um it's not even a
hype cycle but it's a just a desire to
kind of do more faster.
So when you think about the financial
gain of AI, a lot of that money is
flowing into tech companies. AI
companies, management consultants, and
companies adopting AI aren't necessarily
seeing those magical financial gains
that they were promised. But it's worth
bearing in mind that it's still really
early on. Um it's really early in the
deployment stage of these technologies.
Just a few years ago, they were still in
the lab. And so we have to be patient.
But obviously the question is how long
do we have to wait? Obviously,
businesses are hoping that these use
cases and gains will come sooner rather
>> The number of people turning to
commercial AI platforms on a daily basis
has been astronomical. The rate of
adoption for chat GPT alone outpaces the
rise in use of the internet when it was
first launched. But the gulf between
work related and personal usage is growing.
So what you often see are these shadow
use cases where official uh corporate AI
initiatives are often untouched or
unused and people just use the AI tools
they like and this is often because
there there hasn't been necessary
communication between leadership and
staff about what they need and what kind
of tools they actually want but
different rules apply at workplaces
right workplaces often have sensitive
information or accuracy really matters
and so you have to pay attention to the
fact that these models often do make
factual mistakes and that could be
really embarrassing or even catastrophic
for an organization. So, every
organization needs to be thinking about
this and thinking about how these tools
apply to them and what they want their
employees to know about how to use them.
Some of the biggest challenges that
businesses face are that they just
aren't ready for this digital transformation.
transformation.
To use AI well, you need good structured
data, good cyber defenses, and perhaps
most importantly, AI literate staff.
I went to Google's newest campus in New
York to meet Amanda Broofphy, director
of Grow with Google. It's Google's
professional training arm and offers
courses to businesses and individuals on
how to use AI. What's your advice for
leaders who have maybe a cohort of staff
who are still very skeptical of AI or
slow to adopt? I think you need to find
how to make the AI work for that
specific person in their role and what
they're doing. What makes AI so powerful
is when you can translate it into what
you are doing today and now that's
specific to you. So if a marketer is
trying to use AI and we're helping them
figure out how to use this to write
social captions for their social media
posts for customer service to think
about how they use this to write
responses back in a way that's polite
when someone's getting upset and it's
escalating. Making it custom to that
person and role is when you actually see
the real benefits. And so being able to
test that for you is what allows that
skepticism to go away and see the real
benefit from it. One of the big problems
with AI rollout is that people aren't
really getting trained. So what do you
say to employers? You need both the
technology and the training, right? You
need the tools in the training. It's an
and not an or. And so what we're finding
is just rolling out the technology isn't
enough. We have a course, the Google AI
essentials course. And what we've seen
is that being able to teach people how
to use the technology, how to prompt and
make sure that they're using it in an
effective and reliable way helps them to
get to use it every day to upskill and
reskill. What I think makes AI different
is it's not learning about it. It's you
have to use it and do it. You have to
have the daily practice to make it a
regular habit in the work that you do.
It's one of those ones that you need to
have the intrinsic interest to be able
to see the value of AI in the day-to-day
of your professional and personal
benefits and the employer needs to be
able to deliver and have this available
for employees so that people are
consuming this information for their for
the company. And what's your best tip
for anyone watching this who wants to
get better with AI in their job? You
need to be able to prompt the AI
effectively to make sure you get the
desired output that you want.
Highlighting pieces like who's the
audience you're trying to reach? What's
the goals in the context? What's the
reference materials? And so being able
to prompt AI effectively is critical to
get the output that you will then see to
make this a regular habit and the
efficiencies that you want.
>> So do you think journalists make good
prompters? I bet we do.
>> I think you make excellent prompters
because you're good at the questions.
It's exactly what it is. You understand
who the audience is, what the questions
are. I think journalists are excellent prompters.
prompters.
>> Perhaps not surprisingly, the tech
sector has been an enthusiastic AI adopter.
adopter.
I met with Cisco's UK and Ireland CEO
Sarah Walker to see how it's working for
them. So internally at Cisco, it's a
tech company ahead of the curve.
>> What does AI usage look like generally
internally here?
>> Really, really broad spectrum. So if I
think of it in terms of our product
development, um things like our WebEx
platform have AI agents built in and
they do some fabulous things which have
made my life a lot easier and more
efficient. We've also then got some
really great platforms that we use as as
employees. There's different levels of
adoption of that. As you can imagine,
some are super proficient, some still
are trying to get to grips with with
what that means. But that's where
adoption becomes key because for for us
to really capitalize on the efficiencies
that those investments um can and should
deliver um our next task is, you know,
how do people adopt that and and make
that a part of their kind of DNA and how
they operate on a daily basis.
>> From talking to people, there's a kind
of you know, people bring in AI systems
and then they don't really monitor
adoption. How can leaders get over that?
>> Well, first of all, you have to lead by
example because my team will never adopt
those sorts of platforms if I'm not
talking about it and using it myself.
So, we did a master class actually with
our senior leadership team across the
UK. And I speak really really positively
about pro workforce and pro AI. It's not
an eitheror and using AI doesn't mean
that at some point in the future your
role will be replaced by it. This is
about using these applications to say
how do you become more efficient in the
things that you can and should automate.
And candidly, it's human nature to want
to find a quicker and more efficient way
to do things. We've always been like
that just because it's now called AI or
that's more kind of broadly known. We
shouldn't be we shouldn't be fearful um
of that. But it is a a common mistake
that businesses make that thinking just
because you've got the applications or
the opportunity that adoption will
follow. Everyone should definitely try
these tools. They're a lot of fun to
play around with and that's the quickest
way to learn how these might work for
you or how they might not work for you.
You have to use them for use cases where
the tools are actually beneficial
instead of expecting it to be some sort
of magic wand that can fix all problems.
And so currently we're operating in the
fact that this all will work and it'll
lead to amazing things in the future.
But if that were to change, if this were
a ma massive bubble that were to burst,
um the reality is that a lot of these AI
experiments, only the use cases that
actually work and that bring benefits to
employees will stay. Everything else I
can't really see surviving.
The challenge of AI rollout in
workplaces doesn't have a
one-sizefits-all solution. Businesses
need input from staff, but equally those
staff need support and training from
their leaders if any of us are to
realize the financial and productivity
gains that AI promises. I'm old enough
to remember when the internet rolled out
in the mid 1990s, and it seems to me
we're at a very similar early stage of
the cycle with Gen AI. There's a lot of
boom and bust to come and with it
disruption and I hope excitement at work.
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