April 17, 2025 • 52 mins
Join this deep dive into Agentic AI with Phil Fersht of HFS Research on CXOTalk episode #876. Learn about the future of AI and how it impacts business strategies.🔔
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Episode Transcript
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(00:00):
Are AI agents an enterprise savior, workforce apocalypse, or
just another tech bubble waitingto burst?
Today on CXO Talk episode 876, we cut through the noise with
Bill First, CEO of HFS Research and one of the most respected
(00:21):
industry analysts in the world. When we talk about AI agents and
agentic AI, what what are we actually referring to?
What do we mean by that? It's really the ability to
replicate human behaviour in software.
It's as simple as that. Whether it's mimicking our
voices or supporting us in doingour day-to-day work, It's, it's
(00:44):
really like the augmentation of humanity and software.
And we we talk about the blending of, you know, humans
and technology. This is really where it's at and
it's it's it's something that, you know, we dreamt about for a
long time, but there's only really starting to come into
reality, but at an alarming pace.
I don't know, you know, we see, you know, a lot of fun things
(01:08):
flying around on X and LinkedIn and all these types of things.
It's just incredible, you know, how much development there's
been in voice and video in in just the last six to nine
months. So we're we're going through a
complete revolution and Agentic is right at the front of that
from a technology perspective. Why is agentic AI so important
(01:30):
and at this particular time? I'd like to rewind back to the
early 20 tens when you might have heard of a technology
called RPA, Robotic Process Automation, which got very big
and very height within the technology world.
We actually coined the phrase alongside a company called
Blueprism when we launched it in2012 and we did the first
(01:52):
analyst papers on it. And at the time we were talking
about RPA replicating human, human behaviour in software,
which would allow us to scale more effectively, threatened
elements like offshore outsourcing, because companies
could technically consider having less offshore resources
(02:14):
when you can automate a lot of this stuff.
But the problem with RPA was thetechnology didn't scale well.
It was very brittle. But the the concept was there.
But that was really all about following instructions, easy,
easily. It was about eliminating manual
effort waste, you know, which was wasted on repetitive tasks.
Then everyone remembers the influx of gem AI nearly nearly
(02:37):
it's going to be its third year with ChatGPT really came public
nearly three years ago and that really changed the game in terms
of it became the productivity amplifier that accelerates
creative and analytical work that really bottlenecks humans.
It's the ability to create content and this is like one of
(02:58):
the first times we've had non-technical people have that
ability to start to create content, create data, augment
their work, create code. Even.
You know, there's a lot, lot of discussions going on around how
much code can be eliminated now because of Gen.
AI. And that was all about creating
based on prompts. Now we're into the Gen.
(03:20):
AI phase, which is about understanding goals and figuring
out how to achieve them. So Gentic AI is a collaborative
actor that removes the need for constant human oversight of
complex processes. It's self directing in many
respects. It coordinates multiple tasks.
It transforms entire workforces,it creates new organizational
(03:44):
paradigms. But it's not about the fact that
it sounds great. What's exciting about a genetic
is it really does work and it's and it's working at an alarming
pace that is making in, in reality, many people are
comfortable. Some people are loving it and
they're embracing it and they'rerealizing, wow, I can do my job
(04:04):
so much better. And I'm I'm an analyst.
I can tell you how Jane Turk andJenna are helping me do my job.
But this is the most, I think, impactful wave in this AI
continuum that takes us to the next phase, which we're terming
artificial general intelligence,which is much more self-directed
intelligence that overcomes human cognitive limitations
(04:26):
across all domains. And eventually, you know,
artificial super intelligence, which is about computers
outperforming humans. We're not there yet, obviously.
But you know, I watched Terminator, Terminator One with
my son the other day. I hadn't seen that in about 30
years. And that brought me back.
They actually predicted in 2029 was when humans, computers
(04:49):
become self aware. So that if anyone's got nothing
better to do this weekend, watchthe rerun of Terminator One.
It's uncanny. Oh, they got the timings right
on this thing. So I want to latch onto a
particular point that you made. You said it really works, right?
Can you elaborate on that? Because that's kind of the magic
(05:11):
point, right? This is not a lab based theory,
it's something that actually works.
So tell us about that. Most companies right now have
created some stand alone single agents.
So that's but that's an agent that can handle maybe one
specific task or function. So that could be like an e-mail
writer or even a meeting scheduler, things like that.
(05:34):
Even like copilot, you can use copilot right now to summarize
your emails and remind you to dothings.
Or you can use Fireflies, which is a really popular tool, or
LinkedIn, not so much LinkedIn, Zoom AI needed to summarize
meetings. Those are single agents, believe
it or not, and they're already working.
People are already, you know, pretty excited about or did you
(05:55):
turn your fireflies on? So we got a good summary of this
meeting. Where this starts to get really
exciting is when we start to build functional multi agents,
which is multiple agents work together within a single
business function. So that could be a sales team of
agents handling prospecting or qualification and follow-ups,
that sort of thing. And eventually we get to
(06:17):
something we're calling horizontal multi agents, which
is where you get different agents collaborating across
various business functions and and even other supply chain
partners. So that could be sales agents
working with marketing and customer service agents.
So you're actually building out capabilities and business
(06:37):
functions beyond one single function.
It works because you just got totry it like I, I, I'd love to.
There's a demo, it's called Super Film, I think it was where
you could actually put an avatarof me in our research website
and ask me questions and I wouldliterally in my voice dig into
(06:58):
our research and communicate them back to you using using
voice. You just got to see it to see
how effective this is. Now, is it perfect?
No. Is it as accurate as talking to
a human being? Not yet.
But in many respects, we're creating agents that are
becoming very, very supportive in our jobs.
(07:20):
I mean, I'll, I'll tell you, forexample, I wrote a piece on
tariffs the other day and I put together here's like big
tournament and things about terrorists and people might have
even read it. And for a bit of fun, I pumped
it through Chatchy, BT pro and and outcomes.
I said, can you pump? Can you can you replicate this
using Phil first voice just for a bit of fun.
(07:42):
And it came out sounding like the sort of thing that I would
have written. And then I asked it to turn it
down a bit, that sort of thing. And then I produced another
piece. Well, I said, can you produce me
a chart that shows life expectancy in the US and versus
other countries and health issues?
And it starts putting information all over the place.
And then you start to train, train the model.
So start starting to become yourown personal agent.
(08:03):
And it's getting to know what I need.
And then it's like, can you produce this in the HFS?
Find some colours and you're programming in the colours to
use and everything. So you're really building out
something that can literally become your go to at work, you
know, so there's so many different uses.
And, you know, I don't even knowif we're going to call these
agents in another 6 or 12 months, but this is just how
(08:25):
we're partnering with technologynow where we don't have to go to
people to get things done all the time now.
We can get so much done ourselves and then we as human
beings become the creators of that content.
Like we say, I've got a big business meeting to go to
tomorrow. You're going to get so much of
content that you need you. You set your agenda to the
(08:46):
technology, it gathers you to what you need and it allows you
to then curate that to make you effective as a human being.
How is this different from having an interactive chat with
Chat GT? So what's unique about agents?
But before you answer, I just want to remind everybody, our
(09:07):
regular listeners know this, that you can ask your questions.
So right now, if you're watchingon Twitter, pop your question
using the hashtag into the into Twitter using cxotalk #cxotalk.
If you're watching on LinkedIn, pop your question into the chat.
This is your opportunity to ask one of the top analysts in the
(09:30):
world pretty much whatever you want.
So take advantage of it. And we have some questions that
are coming in now. But first again, so you're
describing an interactive process.
You go to a meeting, you give itnotes, you then say modify this
or modify that sounds like ChatGPT.
How are agents different from LLMS and and our usage as we
(09:53):
know and love it today? ChatGPT is useful because you
can use it for a specific prompt.
So I need some information on this or that get some
information quickly produce this, do that, do this, do that.
An actual agent is the virtual Co worker who is completing end
to end processes for you. So it's self directs and
(10:14):
coordinates multiple tasks. So once you've so say I'm using
the example of I could use an agent to help me with my
research. I would develop train this as a
virtual Co worker to be like my research assistant.
So it would start to overtime learn what I do, what I need,
how I do it. So you can start to interact
(10:34):
with this like a virtual Co worker, like a research
assistant, for example. And you can leverage this to,
you know, create whole new organizational paradigms.
I'm not joking. In 12 months time you can say,
hey, they feel I need to have a meeting with you next week to
talk about XYZI can literally have you talk to my agent like
who will look at my diary and coordinate what I need in time
(10:57):
and maybe ask particular questions.
So we are training virtual Co workers to do the jobs that we
either used to do ourselves or someone else did.
And we can start to get into real examples of this.
But the challenge is, you know, going to your staff in another
company and asking them to almost recreate their jobs into
(11:20):
software, which is very different than saying train up
another human being and transfertasks from yourself to another
human. We're now expecting people,
including ourselves, to transferhuman work tasks into software
That technically frees us up to do other things.
Oh, let's be honest, could make us redundant, right?
(11:41):
We're not needed anymore. We can actually leverage agents
to do the jobs of humans. And we're now seeing enterprises
who are really trying to have anAI first mindset.
They're now insisting before youhire any new staff, you need to
show that this work can't be done by AI.
We've reaching that point quite quickly.
(12:03):
This wasn't like maybe 20 years ago.
People used to say, hey, if you hire new staff, can we see if
that work can be done offshore in the Philippines or India or
something? Now C-Suite directives are, can
we not do this with AI? So the whole point of agents a
really this ability for companies to grow and scale in a
way that you don't need to keep adding more and more people.
(12:24):
You do a lot more with the people you have.
And I think that's the positive way to think about this.
It's, you know, I want to run a marketing campaign.
Can I develop a planning agent who can coordinate and breakdown
the campaign requests into specific tasks?
Can I create a research agent that gathers market
intelligence? Can I create a creative agent
(12:48):
that develops, you know, creative assets and messaging?
Can I develop a strategy agent that optimizes my campaign and
engagement across marketing channels?
Right. You can go on and on about
almost every new staff member you need.
You can create an agent for likeit could be, hey, I need
somebody to manage social media and I need to do automated
(13:11):
LinkedIn updates, that sort of thing.
Or it could be, you know, even acampaign coordination agent to
synthesise inputs from all agents into a cohesive campaign.
So it's it's creating people into software.
So, you know, we call this thing, you probably heard of
services as software, but this is what's happening in the
(13:32):
services industry right now is companies are starting to think
about how can I replicate the services I'm receiving from an
IBM or an Infosys or one of these companies and receive this
using agent agentic software versus why do I keep having to
add more people all the time. So that's the real nub of
(13:53):
Agentic and I think why it's causing, you know, excitement
and friction at the same time. Go to cxotalk.com, subscribe to
our newsletter and join us. Join our community and join our
live shows. So we have a a very interesting
question coming from Twitter from Anthony Scrifignano, who is
(14:14):
the former Chief data scientist of Dun and Bradstreet and has
been a guest on CXO Talk a number of times.
And he's asking about the unintended risks or unintended
harms that can emerge. Can you talk about that aspect
(14:36):
of it? I think lots of people are are
concerned about the impact of AIagents on the workforce.
You spoke about the positive aspects, but what about the
unintended risks? First thing is you've got the
more general risks of AI. So when you read something sent
to you now a lot of people are thinking was this written by AI
(14:57):
or human being right. That's that's a big problem and
I see that as an opportunity fora research firm like us, because
it's people need real more than ever.
And do you trust information that's all produced using agents
and genetic software? Is it is it truly trustable?
Is it reliable where the source is coming from and that sort of
(15:20):
thing. And I think a bigger risk right
now is can you trust informationfrom people?
The other issues, obviously withinteractions and hallucinations
and these types of things, all the types of teething problems
you'll have with honestly any type of technology.
You know, you, I remember when we were getting into more
(15:41):
sophisticated accounting applications 20 years ago when
people worried then about software malfunctioning and
producing, you know, incorrect calculations and stuff like
that. So a lot of it is trusting the
software, trusting the security of that software as well, and
understanding how to navigate your way around this climate
because it's only going to get more confusing and more
(16:03):
worrying. And, you know, three times, you
know, we're getting more sophisticated spamming phishing
stuff or, you know, we're getting them everyday and texts
and emails and quite convincing ones.
Sometimes it's like, you know, Iget I get my own staff coming to
me saying, hey, Phil, did you send me this text about getting
Amazon vouchers, things like that.
(16:25):
So we can talk about this for a very long time.
All the different risks, all thedifferent worries, all the
different concerns. And the other thing is, you
know, where do you want it to impact?
So you know, I sat in a room with 10 senior level AI decision
makers in banking just a couple of weeks ago and they had a lot
(16:50):
of common issues, which was we really want to leverage Agantech
to improve our customer experience function.
But you know, it's all about thecustomer experience with using
banking apps and technology and that sort of thing.
And a lot of their customers, they still want to talk to a
human being, right? Especially when you're getting
into your finances and, and, and, and loans and borrowing and
(17:10):
that sort of thing is, you know,how far do you go before you can
truly trust the technology versus versus the people?
And and I think, I don't think we have a full answer for that
just yet. We have a question from Arsalan
Khan, who says agentic AI requires the correct data at the
right time with the right human and systems integration,
(17:33):
eventually these agents become autonomous.
What happens to humans then? So he's asking about this
boundary between human work and autonomous agent work.
This isn't just about enterprises trying to cut costs
from the place people with bots.This is about us as human
(17:54):
beings. We're all, we're all threatened
by this and we all have opportunities with this.
And if you're in a job where youcan be effectively replicated
and replaced, you kind of you kind of know that and you need
to figure out how do I continue to add value in an enterprise?
And I think the value comes fromcollaboration.
(18:15):
It comes from people skills, it comes from empathy.
And if you can become a great person everybody likes to work
with and you become very thoughtful about what you do and
you start to collaborate beyond your existing area, you become
very valuable to your company. And you know, I can, I can go
through many examples of this. You know, I have a, a guy
(18:36):
running my IT systems who actually was a procurement guy
just a couple, about 3 or 4 years ago, But he, he broadened
his knowledge into understandinghow to manage HubSpot and
accounting software. He manages our stack of social
and Grammarly and all this sort of stuff.
And as part of his job, he started to get to know all his
(18:57):
colleagues in different departments in the company, like
analysts and, and, and finance and HR and all this sort of
stuff. And then you start to develop
real value to deliver across your organization.
And I think no matter what role you're in, if you're on a sales
role, you're on a delivery role,you're on a tech role, you need
to become broader and more aligned to your business to add
(19:19):
value there. Because if you if you become
just a replicatable solo task driven professional, you do run
a risk. So you know, you wouldn't
believe some of the conversations I have with CIOs
right now who are under immense pressure to wipe out costs
because of code. One major organization I spoke
(19:43):
to produces half a billion linesof code a year to keep that
organization functioning. And they've been tasked with
eradicating 90% of the effort because you don't need to have
armies of legacy coders anymore.A lot of these code code can be
rewritten using Gen. AI and other types of AI
(20:04):
software now. So, you know, we're, we're just
all facing the challenge of how relevant are we now?
I think you can't replace the humanity and the human ability
to be empathetic. To collaborate, to energize
people and to curate content that is real.
I still believe and I think morethan ever, we're going to be hit
(20:26):
with so much AI. Fake information, or could be
real information, but it's written by AI.
We want to read stuff written bypeople.
AI can help us as humans get much better what we do.
It can help us become better communicators, maybe want more
productive, get more done, like I told you earlier.
And I find I'm becoming way moreproductive as an analyst because
(20:49):
I've now got, you know, some AI tools which can develop charts,
synthesize data, get me some bits I want so I can, I can
answer my questions. Be specific on how these agents
help you and your job. Tell us the tools you're using,
and then we're going to go back and get some more questions.
(21:10):
Questions are coming in. If you want to develop real
value within your own organization, you have to run
boot camps with your own colleagues to present to each
other how you're using these tools to be more effective at
your job. We've even, we've even hired an
AI expert who's a full time employee within our company.
She's probably listening to thisthat who's actually working
(21:32):
across our operations people, our analysts, she's working with
Amazon and, and, and a company called Lizier, for example, to
identify how we deliver our research to our clients.
So while yes, I, I can go on about the personal Productivity
Tools I use, we're using agenticto transform our whole business
because we're in the informationbusiness and we have set up a
(21:56):
fairly complex system. We're using an agentic solution
called Lizier, which is a, it's a start up, but it's, it's in a
pretty mature phase. They're very popular and they're
powered by AWS to produce at scale the ability for we have
like 150,000 subscribers to go in and create their own research
(22:18):
support agents to help them leverage, get the most out of
HFS. So that's how we're using it
from a corporate standpoint, from a personal standpoint,
right now I use, I'm using chat CPT Pro.
So I paid the extra money. I'm not sure I need the $200 a
month package, but I'm loving itright now because it gives me a
lot of query time. It, the computing power is a
(22:42):
little challenging. Sometimes it takes a bit of time
to produce everything I need. I'm finding that effective.
I'm using deep research from Perplexity, which is pretty good
as well. And I've also been experimenting
rather tools like Claude, which is the anthropic tool, And I've
also looked at some other tools that can be fairly effective,
(23:05):
like Gemini, I'm still not completely convinced by, but
other people love it. So a lot of this is, you know,
people finding technologies thatthey think are better than
others and they like the way they're interacting with these
tools. But the new, the new suite from
ChatGPT Pro is excellent. You've got the image creation,
you've got the operations piece,you've got the deep research
piece. What I'm seeing right now, this
(23:27):
thing is pretty good and we're going to get to a stage fairly
quickly where we're going to be whittled down to maybe 3 or 4
powerhouses in this space who are going to be dominating the
progression here. I use so many different LLMS,
I'm always experimenting to see which one is better.
Here is a question from Wes Andrews who says you jokingly
(23:51):
referenced Terminator earlier, but given the struggles that
that AI and other sectors are having with establishing
frameworks, guardrail standards such as NIST and GDPR, what do
you suggest? And I'll just mention also to
folks that last week we had two members of the House of Lords
(24:14):
from the UK discussing these issues.
So if you care about these issues, listen to our last show
and you can get the transcript on our site.
But Phil, what what about this this framework and guardrails
set of issues? We look deeply into this because
we cover Global Services a lot with an HFS and every different
region has slightly different attitudes towards AI.
(24:37):
So obviously you mentioned GDPR is, is huge in the UK and
Europe. India is a little bit more of a
free fall right now with how they're accepting AI based
solutions and US, you know, thiscould be the second coming with
the tech Bros driving a lot of policy here.
(24:58):
So I think we're still waiting to see how a lot of this shapes
up. EU has typically been the most
closed from a framework perspective and demanding in
terms of compliance. And anyone running a business
knows how challenging running GDPR practices has been in
recent years to get to, to the other side.
(25:21):
But I, I do think that as this continues to evolve, the need
for common frameworks is going to become more and more
paramount and the need for cooperation is, is going to
continue to proliferate. They're really doing, look
what's going on politically across the world right now in,
(25:42):
in many ways, this is going to actually bring I think a lot of
regions close to the governmentsand regions close together and
which may actually drive better cooperation with AI.
So for example, I was hearing today about a strong movement to
create the China less supply chain, right?
So how can countries start to group together to manufacture
(26:05):
goods outside of China to avoid these potential tariffs, right?
And in that case, you need to sort of build a supply chain
competency that sensors and responds, that manages
inventory, that brings cooperation together and these
types of things. So I, I think the need to build
and supply chain standards, trading standards, you know,
(26:27):
around AI, I, I, I think this isjust going to, it's only just
beginning and we're going to seea lot more of this emerge in the
next couple of years. What about enterprise adoption?
Where are we today? AI agents are still relatively
new. There's lots of promise, but in
(26:48):
terms of actual usage and enterprise adoption?
I can share the latest and greatest that we've been working
with. We spoke to over 1000 major
enterprises looking at the adoption of of Gen.
AI and the Gen. tech and 45% of them are either worried about
(27:13):
job loss or they're resistant tochange and adoption is I'd say
fairly diminished. The other at the end of the
spectrum, only 15% of AI leadersare generally positive about AI
adoption and they have fairly integrated views of where
(27:34):
they're going. They have a strong culture of
support and they're they're embracing this.
And then in the middle, you've got about 40% of enterprises
where they're still in that sortof pilot purgatory phase.
Their culture is becoming more adaptive.
They're recognizing the benefitsof AI, but they're not there
yet. So in terms of actual adoption,
(27:56):
you've only got about 15%, maybea little more, who are getting
to the point where they have a real clear vision and
understanding of where they're going.
One thing that is crystal clear is we're seeing immense pressure
coming from the board level people and also C-Suite leaders
in organizations to drive AI adoption a lot faster.
(28:20):
There's real pressure coming right from the top to really
embrace and become more effective as you know, AI first
cultures. So, but the reality is we're
still at early days, You know, we, we, we've been talking about
this. It's, you know, for a long time,
but the reality is ChatGPT 35 only came in not even 2 1/2 to
(28:42):
three years ago. So we're playing catch up, but
what's happening is the technology is staring it on our
face. It is really here we've got big
firms really trying to get on top of it.
You've got the big software companies like Salesforce in
particular with their Agentforceroll out and service.
Now somebody's business is really trying to muscle in on a
(29:06):
gentic because they see that as an opportunity to take market
share away from services firms. And at the other flip side,
you've got services companies like Accenture really trying to
become more dominant in the services of software realm as
well. So adoption is alone is the is
the real answer to this, but thepressure is there and and it's
(29:26):
on like never before. I just want to invite everybody
listening to join the CXO Talk community.
Go to cxotalk.com and subscribe to our newsletter so we can
notify you of upcoming conversations.
We do this every week and you guys who are listening, you are
(29:47):
the cream of the crop. So join, subscribe to our
newsletter and join these conversations and add your
points of view and your questions.
So we have an important questionfrom Arsalan Khan who says how
do you convince the C-Suite thatagentic AI is not just a fancy
(30:08):
chatbot before they move on to the next shiny object?
What are the challenges and the opportunities associated with
this we're. Past that point where C-Suite
can keep denying that this is just another fancy chat bot.
I think if, if you're leading ACX function in particular, you
(30:32):
know, you, you, if you, if you're not familiar with how
easily replicable call centre Asians are with Jane, you know,
with, with smart agents, you know, right now you shouldn't be
in a job anymore. To be quite harsh about it.
I can tell you, you know, just an example of an organization I
(30:53):
spoke to with 50,000 onshore, onshore staff responding to
healthcare inquiry calls and the, the leader basically said,
look, the bottom line is, is there's the same 6 questions
being asked over and over and over again.
We've already run the analysis. We can literally replace half
these people with, with intelligent bots and they call
them, they call them empathy bots very, very quickly.
(31:16):
We're not going to do it straight away, but we know the
possibility is there. And I think this is this is a
typical case across a lot of companies is they're very aware
of what they can do, but they they're still yet to have that
burning trigger platform to go do it.
My concern is if we plunge into,you know, a, a deep recession,
(31:37):
you're going to see some organizations literally come out
and say, we're just going to start relying a lot more on AI
and we're going to let people go.
So my vain hope is we don't fallinto recession so we can have a
more positive view of people andtechnology.
But there is that risk there that a negative economy can
drive a lot more weaponized AI where companies would say, look,
(32:01):
well, let's just replace these people, we don't need them
anymore. So I, I don't think companies I
speak to are not aware of this. It's more how advanced they are
with embracing this. Are they prepared to do
anything? And my concern is I do talk to a
lot of enterprises. We have a lot of summits and
round tables on top of our research where, you know, people
(32:21):
want to talk. But when it comes down to what
are you actually doing, They're not doing, They're not doing a
lot. And I think what I just said to
about the 15%, that's not a big #15% are kind of on the path.
The rest are either still figuring it out or they're not
on the path. And and this is just going to
become more pronounced as we go through the next few months of
(32:43):
macroeconomic turbulence. You just made wild comment,
which is, and I don't want to put words in your mouth, but it
seems like you just said that the technology is becoming so
good at sufficient number of usecases that an economic downturn
(33:06):
can push many companies to replace many workers because
those use cases and the effectiveness are so broadly
dispersed even even today or if not today soon enough.
The technology is available. It's there.
I think companies are aware of it.
(33:26):
I do believe as well most enterprises don't tread lightly
on the fact that, hey, let's go replace 5000 people with, you
know, 1000 or 500, you know, or get augmented consultants who
can manage a team of bots. But one of the things that has
(33:48):
been looked at in industry is why do you need 500 people in
India, for example, running a bunch of coding or app support,
that sort of thing, when you canpotentially replace them with a
team of maybe 25 people who are local and onshore who are
supported and augmented by Gentic technology.
(34:10):
So it's this ability to reduce the scale of people numbers that
you have and they'll augment higher value, you know, people
with, with the Gentic to supportthem.
And you know, we, we put out some research recently around,
you know, the impact of tariffs,for example, that could have a
(34:32):
real impact on what we call it could drive the whole services
and software adoption curve, right?
Because suddenly it's like if itbecomes really difficult to
manage a disparate global workforce, manufacturing goods
and all over the world, you needto bring stuff back home.
(34:54):
You know, suddenly, hey, I can actually do what I need in the
US with a smaller number of staff.
They might be more expensive, but I don't need as many.
And they're supported by this technology.
So we are at a point where companies are starting to make
much more radical assumptions onwhat they can do.
(35:15):
You may have seen a recent announcement from the bank Citi,
Citibank, who have decided to reduce their 144 service
provider relationships down to 50.
And they've actually increased the numbers of staff that they
have on shore in the United States and some other regions
who are directly within the company.
(35:36):
Because they what they want to do is they want to spend less on
the legacy and more on the new. So I'm not trying to say
companies are just going to fireeverybody or replace them with
bots, but I think a lot of smartbusinesses are thinking, how do
we stop spending billions of dollars on maintaining legacy
applications and legacy systems when we really want to reinvest
(35:59):
that money in modernized thinking, modernized agentic
technology, that sort of thing. So what some companies are
doing, and I use the example of Citibank is they're trying to
stop the cost of the old so thenthey can bring you back in house
and then start to think about how do we reinvest in and the
technology they need to take them to a different place.
(36:21):
So I don't think companies are thinking right now about how do
we just get rid of people. They're actually thinking about
how do we break from the past. I did a great podcast with Jason
Aberbrook, who's one of the leading minds in HR technology.
You know, he, he's a Mercer these days and he talks there's
like this CHROS across all the big global 50 companies.
(36:41):
And he, he actually came out andsaid these companies have so
much data. They, they don't want to do with
it. They can't join it up.
They can't make decisions on it.It's got to the point where he's
got clients who are literally thinking, oh, just just get,
let's just trash this old systems and rebuild, rebuild
with the new. And I think this is where some
(37:03):
of these conversations are happening with a Gentech, which
is how do we start to really build out the new and and make a
break from from the legacy that's been holding us back for
so long. Anthony Scrifignano makes a
comment on Twitter directly addressing this point that you
were just discussing. He says it's equally likely that
(37:23):
the C-Suite is being taken to task for not adopting more to
drive down cost. He says the KPIs need to be more
than just cost savings. What new problems are being
addressed that were unaddressed before being enabled by this
technology? And it sounds like you're saying
(37:46):
the same thing, that cost savings is a part of it, but
there's also a whole set of new opportunities.
I would agree that the same fundamental issues have remained
for a very long time in terms ofchanging we, we call it, you
know, paying off your debts, your technical debts, your
(38:08):
people debt, your process debt, your data debt within companies
and, and, and, and this inertia of companies refusing to change.
And there's so many managers andleaders within enterprise who,
let's be honest, have got away with not having to do much
different for the last 20-30 years.
I mean, we still have companies operating the processes that
were designed before the Second World War, some even the
(38:30):
industrial revolution. So what is different this time?
I think what's different is the technology is much more
pronounced. It's much more ready, it's much
more scalable. And there's a final exhaustion
where you talk to CIOs off record, they'll all tell you one
(38:51):
thing. They are fed up spending 10% a
year on their services firms andthen 10% increase is every year
on their software license hikes.SAS is becoming a legacy
paradigm and services people just don't want to keep paying
more and more and more. You can't keep going up this
exponential cost curve. Eventually the chickens come
home to roost and, and, and I think C-Suite executives are
(39:14):
really being held to task now. Can you deliver an AI first
organization where a culture hasto change within the company?
And I think that's the problem we've got with a lot of these
businesses is they haven't got the right cultures to shift,
shift forward and really embrace.
And you know, while I would agree, I don't think the
(39:34):
fundamental issues have changed all that much.
What is changing is the onus on AI that's coming right from the
top. Because when RPA came in, in
2012, the reason why one of the reasons it failed was the CIO.
It would get dumped on the CI OSdocket and it would eventually
get dumbed down two or three layers into the what we call the
(39:55):
frozen middle within the organization.
And that's when technology solutions go to die.
That's not happening so much with Agentic.
But Phil, I remember those RPA days and I remember software
companies describing RPA just like you're talking about
agentic AI right now, which is it's going to save us money.
(40:19):
We're not going to need as many employees.
This is going to, it's going to be great.
But the promise was never fulfilled.
So what's different and how do we see our way through the hype?
I think what is different is that 15% of high performers and
I think the following 15% behindare organizations where the
(40:44):
leadership have realized they can no longer keep painting lip
service to not fixing their underlying problems with data
technology and legacy. And all our PA was really doing
was it was like a Band-Aid tech that stitched together old
systems to get them functioning more effectively.
It was very useful. But in terms of could you use
(41:06):
RPA to replace thousands of people unless it was a very high
throughput process, very repeatable, very predictable.
Of course you couldn't. No, this was like a patchwork
technology. You, you know, if you want to,
if you want to say, hey, we have1000 people answering calls in
the Philippines for our consumerproducts that we're selling,
(41:27):
right? That's people on mass at scale
where you need technology that can actually have some empathy
with clients, that can replicateCX behaviour, that can actually
do the job. And I think that's the big
difference right now is agentic is much, much closer to doing
the job of human beings than RPAwas, which it really wasn't.
(41:49):
It was a patchwork back office break fix technology that was
great if you wanted to keep, youknow, your old kicks mainframe
working with a cobalt system, working with ASAP system, for
example. But now it's much more, you
know, you can see where this is all shifting.
And I think there's a, there's areal exhaustion with companies
(42:10):
having to keep maintaining real,you know, creaking old systems
in a world where if, you know, competition is much more
cutthroat and you got to be really slick and on the ball if
you're going to be effective in this economy.
Phil, I get the sense that what you're really also saying is
that the the difference between RPA and agentic AI is that that
(42:37):
15% of early adopters of agenticAI have demonstrated that in
fact it really does work. It really can bring these kinds
of benefits and savings that you've been describing.
Yeah, you can actually create a virtual Co worker to complete
(42:59):
end to end processes. It's proven it works.
We've all seen the demos, we've worked with companies who are
piloting it, we have done it on ourselves.
And a lot of enterprises, more advanced ones in particular are
at least working with single agents and some move into multi
agent models. So they're on the path.
And it, it's a different type oftechnology that removes the need
(43:22):
for constant human oversight of complex processes.
It's a transformational tool rather than a task automation
tool, which RPA was. That was about tasks.
This is about human oversight, support and real process
capability and the fact that youcan build these Co workers.
(43:42):
You can, you can engage with these things, you can talk to
them, right? I don't even want to get into
some disturbing things about teenage boys building
relationships with AI girlfriends and things like
that. I don't know if you've been
reading about some of these things, but this, this stuff is
real. People are building
relationships with their software.
(44:02):
The software, you know, you can ask the question, if you ring up
customer service today, do you care that you're dealing with a
computer or dealing with a humanbeing?
When you're go checking into your airline, do you really want
to talk to the gate agent? No, of course you don't.
You just want to use your app and get on the plane, you know
what I mean? So we're getting to this whole
next layer of, you know, technology becoming part of our
(44:26):
daily lives much more than ever before, to the point where we're
actually engaging with technology in a much more
humanistic real way. And I don't know if you saw the
CEO of Google Deepmine the otherday, He was, I dig this out, I'd
saw it on X this morning. But he was saying how the new
(44:46):
way to develop code is inviting creators and people with
heuristic creative skills to develop the code.
Rather than in the old days you were going to like a computer
science engineer and having to kind of explain in a very clunky
way, this is what we need. We're getting to the point where
we can create code and we can create applications without
(45:07):
being technically proficient. And one of the things where you
still want to talk about the difference between agentic and
RPA is agentic is the first timeever really that we can take
non-technical C-Suite or leaderswithin enterprises and have them
dictate what they want from their technology.
But we are seeing technology that is pivoted towards the
(45:29):
business professional and we're already in a situation where I
think 46% of IT decisions are made outside of the CI OS walls
of their offices. This is the age where the CFO,
the head of supply chains, the head of marketing, these people
are making their own technology choices because they can start
to build technology that is verymuch answering the needs of the
(45:53):
business versus something that you're having to be dictated to
by engineers, that sort of thing.
It is extraordinary the level ofresearch support that, for
example, that these tools can provide.
I had a networking issue of my own here in our studio and doing
(46:18):
a little bit of research I was able to figure out a fairly
complex question having to do with routing.
Rather than need to call an IT person and bring a consultant
in. It is amazing, but we have
we're, we're almost out of time and we have a number of
questions that are left. So I'm going to ask you, Phil,
to answer these questions prettyquickly, pretty concisely.
(46:40):
First one is from Prem Kumar Aparanji and he says when LLMS
powering the AI agents aren't reliable or predictable, how do
we rely on them to automate unpredictable scenarios that
need to work? You have to train the model to
work is is my answer. So if there's something wrong
(47:03):
with the LLMS, then you need to really have a look at the
underlying technology that you're using and find the right
LLMS that can deliver the scenarios you want.
So I think there's a lot more technology based conversations
we got to have to get this, you know, and really enterprise
(47:25):
grade ready. And what I would say is, you
know, I know from a lot of friends in the industry that
like open AI, for example, is very, very obsessed with
becoming enterprise ready. Like, you know, you know, the
leadership within that company are spending all their time with
the C-Suite within Fortune to 500.
(47:45):
They're trying to figure it out.So I would say it's great
question and there are a lot of faults in the system right now.
And a lot of this is honing the models and training the models
until you get them working. I mean, as I said, we're doing
our own model, we're putting ourwhole business into into an
agentic solution. And it's it's taken us three
(48:05):
years to get to a point where westill haven't gone live with the
new system yet. But you've got to learn
yourself, you've got to learn your business.
You've got to learn these models.
You've got to try it and try it and try until you know what
works and what doesn't. And I remember when shortly
after ChatGPT came out, I remember that your company HFS
Research was one of the first analyst firms that I was aware
(48:27):
of that was making that attempt to put your research online into
an LLM. So you, you truly are an early
adopter at this. We have a question very quickly
now because we're just going to run out of time from Elizabeth
Shaw, who says CE OS and boards are driving the use of AI
agentic and beyond. There's serious implications for
(48:50):
worker and social society impacts.
What concerns do CE OS and othersenior business leaders have
with these concerns? And very quickly, please,
please. I know it's a complicated
question. Data privacy and cyber are the
number one problems and biggest concerns by country mile, to be
(49:12):
honest with you. And after that, it's, you know,
it, it's, it's other areas around transformation and
replacing process and compliance.
But cyber is by far and away I think the biggest headache as
companies look at shifting to these models is, is maintaining
a secure infrastructure. Arslan Com comes back and says
(49:34):
who gains the most value from magentic AI, small companies or
large companies? I would say at the moment small
companies, it allows I, I hate using my own example, but it
allows mid sized businesses to really punch above their weight
because you can scale fast, you can act nimbly and you often
(49:54):
don't have as much legacy withinthe business to change.
You don't have as many people resisting change.
And I think large companies can also be truly beneficial in
terms of how they can leverage this.
But I just found with a lot of large businesses, it's harder
for them to beset their legacy. You know, look at the technical
debt they have the the lock in they've got with legacy software
(50:16):
providers, you know, that sort of thing.
So I think it's harder for largecompanies to change because
there's a huge amount of training and, and, and cultural
change and shifting that needs to happen.
And I think SM ES a better placeto pivot and and I see a lot of
people I know wanting to go and work for smaller businesses
'cause they are more nimble and you got to be nimble in this
(50:37):
market. What advice do you have for
enterprise, technology and business leaders when it comes
to how they should be relating to this agentic AI world today
and very quickly, please? Get on top of it, learn it,
understand it, experiment with it, do boot camps with it.
(50:59):
You've got to educate yourself. The days of BS ING around
technology are over. You've got to be much more
proficient at knowing what is possible and engaging and
building relationships with the whole emerging AI ecosystem
around you, you know? And with that, a huge thank you
to Phil. First, he's the CEO of HFS
(51:22):
Research. Phil, thank you so much for
being here. I'm just so grateful to you.
Yeah, a pleasure, Pleasure, Michael.
Now I went quickly, enjoyed it very much and I look forward to
more interactions. And a huge thank you to
everybody who is watching today.You guys are an awesome
audience. You're so intelligent, so smart.
Go to cxotalk.com, subscribe to our newsletter and join us.
(51:46):
Join our community and join our live shows.
We have one next week and the week after that, so check it
out. Thanks so much everybody, and I
hope you have a great day and we'll see you next time.
Are AI agents an enterprise savior, workforce apocalypse, or
just another tech bubble waitingto burst?
Today on CXO Talk episode 876, we cut through the noise with
Bill First, CEO of HFS Research and one of the most respected
(00:21):
industry analysts in the world. When we talk about AI agents and
agentic AI, what what are we actually referring to?
What do we mean by that? It's really the ability to
replicate human behaviour in software.
It's as simple as that. Whether it's mimicking our
voices or supporting us in doingour day-to-day work, It's, it's
(00:44):
really like the augmentation of humanity and software.
And we we talk about the blending of, you know, humans
and technology. This is really where it's at and
it's it's it's something that, you know, we dreamt about for a
long time, but there's only really starting to come into
reality, but at an alarming pace.
I don't know, you know, we see, you know, a lot of fun things
(01:08):
flying around on X and LinkedIn and all these types of things.
It's just incredible, you know, how much development there's
been in voice and video in in just the last six to nine
months. So we're we're going through a
complete revolution and Agentic is right at the front of that
from a technology perspective. Why is agentic AI so important
(01:30):
and at this particular time? I'd like to rewind back to the
early 20 tens when you might have heard of a technology
called RPA, Robotic Process Automation, which got very big
and very height within the technology world.
We actually coined the phrase alongside a company called
Blueprism when we launched it in2012 and we did the first
(01:52):
analyst papers on it. And at the time we were talking
about RPA replicating human, human behaviour in software,
which would allow us to scale more effectively, threatened
elements like offshore outsourcing, because companies
could technically consider having less offshore resources
(02:14):
when you can automate a lot of this stuff.
But the problem with RPA was thetechnology didn't scale well.
It was very brittle. But the the concept was there.
But that was really all about following instructions, easy,
easily. It was about eliminating manual
effort waste, you know, which was wasted on repetitive tasks.
Then everyone remembers the influx of gem AI nearly nearly
(02:37):
it's going to be its third year with ChatGPT really came public
nearly three years ago and that really changed the game in terms
of it became the productivity amplifier that accelerates
creative and analytical work that really bottlenecks humans.
It's the ability to create content and this is like one of
(02:58):
the first times we've had non-technical people have that
ability to start to create content, create data, augment
their work, create code. Even.
You know, there's a lot, lot of discussions going on around how
much code can be eliminated now because of Gen.
AI. And that was all about creating
based on prompts. Now we're into the Gen.
(03:20):
AI phase, which is about understanding goals and figuring
out how to achieve them. So Gentic AI is a collaborative
actor that removes the need for constant human oversight of
complex processes. It's self directing in many
respects. It coordinates multiple tasks.
It transforms entire workforces,it creates new organizational
(03:44):
paradigms. But it's not about the fact that
it sounds great. What's exciting about a genetic
is it really does work and it's and it's working at an alarming
pace that is making in, in reality, many people are
comfortable. Some people are loving it and
they're embracing it and they'rerealizing, wow, I can do my job
(04:04):
so much better. And I'm I'm an analyst.
I can tell you how Jane Turk andJenna are helping me do my job.
But this is the most, I think, impactful wave in this AI
continuum that takes us to the next phase, which we're terming
artificial general intelligence,which is much more self-directed
intelligence that overcomes human cognitive limitations
(04:26):
across all domains. And eventually, you know,
artificial super intelligence, which is about computers
outperforming humans. We're not there yet, obviously.
But you know, I watched Terminator, Terminator One with
my son the other day. I hadn't seen that in about 30
years. And that brought me back.
They actually predicted in 2029 was when humans, computers
(04:49):
become self aware. So that if anyone's got nothing
better to do this weekend, watchthe rerun of Terminator One.
It's uncanny. Oh, they got the timings right
on this thing. So I want to latch onto a
particular point that you made. You said it really works, right?
Can you elaborate on that? Because that's kind of the magic
(05:11):
point, right? This is not a lab based theory,
it's something that actually works.
So tell us about that. Most companies right now have
created some stand alone single agents.
So that's but that's an agent that can handle maybe one
specific task or function. So that could be like an e-mail
writer or even a meeting scheduler, things like that.
(05:34):
Even like copilot, you can use copilot right now to summarize
your emails and remind you to dothings.
Or you can use Fireflies, which is a really popular tool, or
LinkedIn, not so much LinkedIn, Zoom AI needed to summarize
meetings. Those are single agents, believe
it or not, and they're already working.
People are already, you know, pretty excited about or did you
(05:55):
turn your fireflies on? So we got a good summary of this
meeting. Where this starts to get really
exciting is when we start to build functional multi agents,
which is multiple agents work together within a single
business function. So that could be a sales team of
agents handling prospecting or qualification and follow-ups,
that sort of thing. And eventually we get to
(06:17):
something we're calling horizontal multi agents, which
is where you get different agents collaborating across
various business functions and and even other supply chain
partners. So that could be sales agents
working with marketing and customer service agents.
So you're actually building out capabilities and business
(06:37):
functions beyond one single function.
It works because you just got totry it like I, I, I'd love to.
There's a demo, it's called Super Film, I think it was where
you could actually put an avatarof me in our research website
and ask me questions and I wouldliterally in my voice dig into
(06:58):
our research and communicate them back to you using using
voice. You just got to see it to see
how effective this is. Now, is it perfect?
No. Is it as accurate as talking to
a human being? Not yet.
But in many respects, we're creating agents that are
becoming very, very supportive in our jobs.
(07:20):
I mean, I'll, I'll tell you, forexample, I wrote a piece on
tariffs the other day and I put together here's like big
tournament and things about terrorists and people might have
even read it. And for a bit of fun, I pumped
it through Chatchy, BT pro and and outcomes.
I said, can you pump? Can you can you replicate this
using Phil first voice just for a bit of fun.
(07:42):
And it came out sounding like the sort of thing that I would
have written. And then I asked it to turn it
down a bit, that sort of thing. And then I produced another
piece. Well, I said, can you produce me
a chart that shows life expectancy in the US and versus
other countries and health issues?
And it starts putting information all over the place.
And then you start to train, train the model.
So start starting to become yourown personal agent.
(08:03):
And it's getting to know what I need.
And then it's like, can you produce this in the HFS?
Find some colours and you're programming in the colours to
use and everything. So you're really building out
something that can literally become your go to at work, you
know, so there's so many different uses.
And, you know, I don't even knowif we're going to call these
agents in another 6 or 12 months, but this is just how
(08:25):
we're partnering with technologynow where we don't have to go to
people to get things done all the time now.
We can get so much done ourselves and then we as human
beings become the creators of that content.
Like we say, I've got a big business meeting to go to
tomorrow. You're going to get so much of
content that you need you. You set your agenda to the
(08:46):
technology, it gathers you to what you need and it allows you
to then curate that to make you effective as a human being.
How is this different from having an interactive chat with
Chat GT? So what's unique about agents?
But before you answer, I just want to remind everybody, our
(09:07):
regular listeners know this, that you can ask your questions.
So right now, if you're watchingon Twitter, pop your question
using the hashtag into the into Twitter using cxotalk #cxotalk.
If you're watching on LinkedIn, pop your question into the chat.
This is your opportunity to ask one of the top analysts in the
(09:30):
world pretty much whatever you want.
So take advantage of it. And we have some questions that
are coming in now. But first again, so you're
describing an interactive process.
You go to a meeting, you give itnotes, you then say modify this
or modify that sounds like ChatGPT.
How are agents different from LLMS and and our usage as we
(09:53):
know and love it today? ChatGPT is useful because you
can use it for a specific prompt.
So I need some information on this or that get some
information quickly produce this, do that, do this, do that.
An actual agent is the virtual Co worker who is completing end
to end processes for you. So it's self directs and
(10:14):
coordinates multiple tasks. So once you've so say I'm using
the example of I could use an agent to help me with my
research. I would develop train this as a
virtual Co worker to be like my research assistant.
So it would start to overtime learn what I do, what I need,
how I do it. So you can start to interact
(10:34):
with this like a virtual Co worker, like a research
assistant, for example. And you can leverage this to,
you know, create whole new organizational paradigms.
I'm not joking. In 12 months time you can say,
hey, they feel I need to have a meeting with you next week to
talk about XYZI can literally have you talk to my agent like
who will look at my diary and coordinate what I need in time
(10:57):
and maybe ask particular questions.
So we are training virtual Co workers to do the jobs that we
either used to do ourselves or someone else did.
And we can start to get into real examples of this.
But the challenge is, you know, going to your staff in another
company and asking them to almost recreate their jobs into
(11:20):
software, which is very different than saying train up
another human being and transfertasks from yourself to another
human. We're now expecting people,
including ourselves, to transferhuman work tasks into software
That technically frees us up to do other things.
Oh, let's be honest, could make us redundant, right?
(11:41):
We're not needed anymore. We can actually leverage agents
to do the jobs of humans. And we're now seeing enterprises
who are really trying to have anAI first mindset.
They're now insisting before youhire any new staff, you need to
show that this work can't be done by AI.
We've reaching that point quite quickly.
(12:03):
This wasn't like maybe 20 years ago.
People used to say, hey, if you hire new staff, can we see if
that work can be done offshore in the Philippines or India or
something? Now C-Suite directives are, can
we not do this with AI? So the whole point of agents a
really this ability for companies to grow and scale in a
way that you don't need to keep adding more and more people.
(12:24):
You do a lot more with the people you have.
And I think that's the positive way to think about this.
It's, you know, I want to run a marketing campaign.
Can I develop a planning agent who can coordinate and breakdown
the campaign requests into specific tasks?
Can I create a research agent that gathers market
intelligence? Can I create a creative agent
(12:48):
that develops, you know, creative assets and messaging?
Can I develop a strategy agent that optimizes my campaign and
engagement across marketing channels?
Right. You can go on and on about
almost every new staff member you need.
You can create an agent for likeit could be, hey, I need
somebody to manage social media and I need to do automated
(13:11):
LinkedIn updates, that sort of thing.
Or it could be, you know, even acampaign coordination agent to
synthesise inputs from all agents into a cohesive campaign.
So it's it's creating people into software.
So, you know, we call this thing, you probably heard of
services as software, but this is what's happening in the
(13:32):
services industry right now is companies are starting to think
about how can I replicate the services I'm receiving from an
IBM or an Infosys or one of these companies and receive this
using agent agentic software versus why do I keep having to
add more people all the time. So that's the real nub of
(13:53):
Agentic and I think why it's causing, you know, excitement
and friction at the same time. Go to cxotalk.com, subscribe to
our newsletter and join us. Join our community and join our
live shows. So we have a a very interesting
question coming from Twitter from Anthony Scrifignano, who is
(14:14):
the former Chief data scientist of Dun and Bradstreet and has
been a guest on CXO Talk a number of times.
And he's asking about the unintended risks or unintended
harms that can emerge. Can you talk about that aspect
(14:36):
of it? I think lots of people are are
concerned about the impact of AIagents on the workforce.
You spoke about the positive aspects, but what about the
unintended risks? First thing is you've got the
more general risks of AI. So when you read something sent
to you now a lot of people are thinking was this written by AI
(14:57):
or human being right. That's that's a big problem and
I see that as an opportunity fora research firm like us, because
it's people need real more than ever.
And do you trust information that's all produced using agents
and genetic software? Is it is it truly trustable?
Is it reliable where the source is coming from and that sort of
(15:20):
thing. And I think a bigger risk right
now is can you trust informationfrom people?
The other issues, obviously withinteractions and hallucinations
and these types of things, all the types of teething problems
you'll have with honestly any type of technology.
You know, you, I remember when we were getting into more
(15:41):
sophisticated accounting applications 20 years ago when
people worried then about software malfunctioning and
producing, you know, incorrect calculations and stuff like
that. So a lot of it is trusting the
software, trusting the security of that software as well, and
understanding how to navigate your way around this climate
because it's only going to get more confusing and more
(16:03):
worrying. And, you know, three times, you
know, we're getting more sophisticated spamming phishing
stuff or, you know, we're getting them everyday and texts
and emails and quite convincing ones.
Sometimes it's like, you know, Iget I get my own staff coming to
me saying, hey, Phil, did you send me this text about getting
Amazon vouchers, things like that.
(16:25):
So we can talk about this for a very long time.
All the different risks, all thedifferent worries, all the
different concerns. And the other thing is, you
know, where do you want it to impact?
So you know, I sat in a room with 10 senior level AI decision
makers in banking just a couple of weeks ago and they had a lot
(16:50):
of common issues, which was we really want to leverage Agantech
to improve our customer experience function.
But you know, it's all about thecustomer experience with using
banking apps and technology and that sort of thing.
And a lot of their customers, they still want to talk to a
human being, right? Especially when you're getting
into your finances and, and, and, and loans and borrowing and
(17:10):
that sort of thing is, you know,how far do you go before you can
truly trust the technology versus versus the people?
And and I think, I don't think we have a full answer for that
just yet. We have a question from Arsalan
Khan, who says agentic AI requires the correct data at the
right time with the right human and systems integration,
(17:33):
eventually these agents become autonomous.
What happens to humans then? So he's asking about this
boundary between human work and autonomous agent work.
This isn't just about enterprises trying to cut costs
from the place people with bots.This is about us as human
(17:54):
beings. We're all, we're all threatened
by this and we all have opportunities with this.
And if you're in a job where youcan be effectively replicated
and replaced, you kind of you kind of know that and you need
to figure out how do I continue to add value in an enterprise?
And I think the value comes fromcollaboration.
(18:15):
It comes from people skills, it comes from empathy.
And if you can become a great person everybody likes to work
with and you become very thoughtful about what you do and
you start to collaborate beyond your existing area, you become
very valuable to your company. And you know, I can, I can go
through many examples of this. You know, I have a, a guy
(18:36):
running my IT systems who actually was a procurement guy
just a couple, about 3 or 4 years ago, But he, he broadened
his knowledge into understandinghow to manage HubSpot and
accounting software. He manages our stack of social
and Grammarly and all this sort of stuff.
And as part of his job, he started to get to know all his
(18:57):
colleagues in different departments in the company, like
analysts and, and, and finance and HR and all this sort of
stuff. And then you start to develop
real value to deliver across your organization.
And I think no matter what role you're in, if you're on a sales
role, you're on a delivery role,you're on a tech role, you need
to become broader and more aligned to your business to add
(19:19):
value there. Because if you if you become
just a replicatable solo task driven professional, you do run
a risk. So you know, you wouldn't
believe some of the conversations I have with CIOs
right now who are under immense pressure to wipe out costs
because of code. One major organization I spoke
(19:43):
to produces half a billion linesof code a year to keep that
organization functioning. And they've been tasked with
eradicating 90% of the effort because you don't need to have
armies of legacy coders anymore.A lot of these code code can be
rewritten using Gen. AI and other types of AI
(20:04):
software now. So, you know, we're, we're just
all facing the challenge of how relevant are we now?
I think you can't replace the humanity and the human ability
to be empathetic. To collaborate, to energize
people and to curate content that is real.
I still believe and I think morethan ever, we're going to be hit
(20:26):
with so much AI. Fake information, or could be
real information, but it's written by AI.
We want to read stuff written bypeople.
AI can help us as humans get much better what we do.
It can help us become better communicators, maybe want more
productive, get more done, like I told you earlier.
And I find I'm becoming way moreproductive as an analyst because
(20:49):
I've now got, you know, some AI tools which can develop charts,
synthesize data, get me some bits I want so I can, I can
answer my questions. Be specific on how these agents
help you and your job. Tell us the tools you're using,
and then we're going to go back and get some more questions.
(21:10):
Questions are coming in. If you want to develop real
value within your own organization, you have to run
boot camps with your own colleagues to present to each
other how you're using these tools to be more effective at
your job. We've even, we've even hired an
AI expert who's a full time employee within our company.
She's probably listening to thisthat who's actually working
(21:32):
across our operations people, our analysts, she's working with
Amazon and, and, and a company called Lizier, for example, to
identify how we deliver our research to our clients.
So while yes, I, I can go on about the personal Productivity
Tools I use, we're using agenticto transform our whole business
because we're in the informationbusiness and we have set up a
(21:56):
fairly complex system. We're using an agentic solution
called Lizier, which is a, it's a start up, but it's, it's in a
pretty mature phase. They're very popular and they're
powered by AWS to produce at scale the ability for we have
like 150,000 subscribers to go in and create their own research
(22:18):
support agents to help them leverage, get the most out of
HFS. So that's how we're using it
from a corporate standpoint, from a personal standpoint,
right now I use, I'm using chat CPT Pro.
So I paid the extra money. I'm not sure I need the $200 a
month package, but I'm loving itright now because it gives me a
lot of query time. It, the computing power is a
(22:42):
little challenging. Sometimes it takes a bit of time
to produce everything I need. I'm finding that effective.
I'm using deep research from Perplexity, which is pretty good
as well. And I've also been experimenting
rather tools like Claude, which is the anthropic tool, And I've
also looked at some other tools that can be fairly effective,
(23:05):
like Gemini, I'm still not completely convinced by, but
other people love it. So a lot of this is, you know,
people finding technologies thatthey think are better than
others and they like the way they're interacting with these
tools. But the new, the new suite from
ChatGPT Pro is excellent. You've got the image creation,
you've got the operations piece,you've got the deep research
piece. What I'm seeing right now, this
(23:27):
thing is pretty good and we're going to get to a stage fairly
quickly where we're going to be whittled down to maybe 3 or 4
powerhouses in this space who are going to be dominating the
progression here. I use so many different LLMS,
I'm always experimenting to see which one is better.
Here is a question from Wes Andrews who says you jokingly
(23:51):
referenced Terminator earlier, but given the struggles that
that AI and other sectors are having with establishing
frameworks, guardrail standards such as NIST and GDPR, what do
you suggest? And I'll just mention also to
folks that last week we had two members of the House of Lords
(24:14):
from the UK discussing these issues.
So if you care about these issues, listen to our last show
and you can get the transcript on our site.
But Phil, what what about this this framework and guardrails
set of issues? We look deeply into this because
we cover Global Services a lot with an HFS and every different
region has slightly different attitudes towards AI.
(24:37):
So obviously you mentioned GDPR is, is huge in the UK and
Europe. India is a little bit more of a
free fall right now with how they're accepting AI based
solutions and US, you know, thiscould be the second coming with
the tech Bros driving a lot of policy here.
(24:58):
So I think we're still waiting to see how a lot of this shapes
up. EU has typically been the most
closed from a framework perspective and demanding in
terms of compliance. And anyone running a business
knows how challenging running GDPR practices has been in
recent years to get to, to the other side.
(25:21):
But I, I do think that as this continues to evolve, the need
for common frameworks is going to become more and more
paramount and the need for cooperation is, is going to
continue to proliferate. They're really doing, look
what's going on politically across the world right now in,
(25:42):
in many ways, this is going to actually bring I think a lot of
regions close to the governmentsand regions close together and
which may actually drive better cooperation with AI.
So for example, I was hearing today about a strong movement to
create the China less supply chain, right?
So how can countries start to group together to manufacture
(26:05):
goods outside of China to avoid these potential tariffs, right?
And in that case, you need to sort of build a supply chain
competency that sensors and responds, that manages
inventory, that brings cooperation together and these
types of things. So I, I think the need to build
and supply chain standards, trading standards, you know,
(26:27):
around AI, I, I, I think this isjust going to, it's only just
beginning and we're going to seea lot more of this emerge in the
next couple of years. What about enterprise adoption?
Where are we today? AI agents are still relatively
new. There's lots of promise, but in
(26:48):
terms of actual usage and enterprise adoption?
I can share the latest and greatest that we've been working
with. We spoke to over 1000 major
enterprises looking at the adoption of of Gen.
AI and the Gen. tech and 45% of them are either worried about
(27:13):
job loss or they're resistant tochange and adoption is I'd say
fairly diminished. The other at the end of the
spectrum, only 15% of AI leadersare generally positive about AI
adoption and they have fairly integrated views of where
(27:34):
they're going. They have a strong culture of
support and they're they're embracing this.
And then in the middle, you've got about 40% of enterprises
where they're still in that sortof pilot purgatory phase.
Their culture is becoming more adaptive.
They're recognizing the benefitsof AI, but they're not there
yet. So in terms of actual adoption,
(27:56):
you've only got about 15%, maybea little more, who are getting
to the point where they have a real clear vision and
understanding of where they're going.
One thing that is crystal clear is we're seeing immense pressure
coming from the board level people and also C-Suite leaders
in organizations to drive AI adoption a lot faster.
(28:20):
There's real pressure coming right from the top to really
embrace and become more effective as you know, AI first
cultures. So, but the reality is we're
still at early days, You know, we, we, we've been talking about
this. It's, you know, for a long time,
but the reality is ChatGPT 35 only came in not even 2 1/2 to
(28:42):
three years ago. So we're playing catch up, but
what's happening is the technology is staring it on our
face. It is really here we've got big
firms really trying to get on top of it.
You've got the big software companies like Salesforce in
particular with their Agentforceroll out and service.
Now somebody's business is really trying to muscle in on a
(29:06):
gentic because they see that as an opportunity to take market
share away from services firms. And at the other flip side,
you've got services companies like Accenture really trying to
become more dominant in the services of software realm as
well. So adoption is alone is the is
the real answer to this, but thepressure is there and and it's
(29:26):
on like never before. I just want to invite everybody
listening to join the CXO Talk community.
Go to cxotalk.com and subscribe to our newsletter so we can
notify you of upcoming conversations.
We do this every week and you guys who are listening, you are
(29:47):
the cream of the crop. So join, subscribe to our
newsletter and join these conversations and add your
points of view and your questions.
So we have an important questionfrom Arsalan Khan who says how
do you convince the C-Suite thatagentic AI is not just a fancy
(30:08):
chatbot before they move on to the next shiny object?
What are the challenges and the opportunities associated with
this we're. Past that point where C-Suite
can keep denying that this is just another fancy chat bot.
I think if, if you're leading ACX function in particular, you
(30:32):
know, you, you, if you, if you're not familiar with how
easily replicable call centre Asians are with Jane, you know,
with, with smart agents, you know, right now you shouldn't be
in a job anymore. To be quite harsh about it.
I can tell you, you know, just an example of an organization I
(30:53):
spoke to with 50,000 onshore, onshore staff responding to
healthcare inquiry calls and the, the leader basically said,
look, the bottom line is, is there's the same 6 questions
being asked over and over and over again.
We've already run the analysis. We can literally replace half
these people with, with intelligent bots and they call
them, they call them empathy bots very, very quickly.
(31:16):
We're not going to do it straight away, but we know the
possibility is there. And I think this is this is a
typical case across a lot of companies is they're very aware
of what they can do, but they they're still yet to have that
burning trigger platform to go do it.
My concern is if we plunge into,you know, a, a deep recession,
(31:37):
you're going to see some organizations literally come out
and say, we're just going to start relying a lot more on AI
and we're going to let people go.
So my vain hope is we don't fallinto recession so we can have a
more positive view of people andtechnology.
But there is that risk there that a negative economy can
drive a lot more weaponized AI where companies would say, look,
(32:01):
well, let's just replace these people, we don't need them
anymore. So I, I don't think companies I
speak to are not aware of this. It's more how advanced they are
with embracing this. Are they prepared to do
anything? And my concern is I do talk to a
lot of enterprises. We have a lot of summits and
round tables on top of our research where, you know, people
(32:21):
want to talk. But when it comes down to what
are you actually doing, They're not doing, They're not doing a
lot. And I think what I just said to
about the 15%, that's not a big #15% are kind of on the path.
The rest are either still figuring it out or they're not
on the path. And and this is just going to
become more pronounced as we go through the next few months of
(32:43):
macroeconomic turbulence. You just made wild comment,
which is, and I don't want to put words in your mouth, but it
seems like you just said that the technology is becoming so
good at sufficient number of usecases that an economic downturn
(33:06):
can push many companies to replace many workers because
those use cases and the effectiveness are so broadly
dispersed even even today or if not today soon enough.
The technology is available. It's there.
I think companies are aware of it.
(33:26):
I do believe as well most enterprises don't tread lightly
on the fact that, hey, let's go replace 5000 people with, you
know, 1000 or 500, you know, or get augmented consultants who
can manage a team of bots. But one of the things that has
(33:48):
been looked at in industry is why do you need 500 people in
India, for example, running a bunch of coding or app support,
that sort of thing, when you canpotentially replace them with a
team of maybe 25 people who are local and onshore who are
supported and augmented by Gentic technology.
(34:10):
So it's this ability to reduce the scale of people numbers that
you have and they'll augment higher value, you know, people
with, with the Gentic to supportthem.
And you know, we, we put out some research recently around,
you know, the impact of tariffs,for example, that could have a
(34:32):
real impact on what we call it could drive the whole services
and software adoption curve, right?
Because suddenly it's like if itbecomes really difficult to
manage a disparate global workforce, manufacturing goods
and all over the world, you needto bring stuff back home.
(34:54):
You know, suddenly, hey, I can actually do what I need in the
US with a smaller number of staff.
They might be more expensive, but I don't need as many.
And they're supported by this technology.
So we are at a point where companies are starting to make
much more radical assumptions onwhat they can do.
(35:15):
You may have seen a recent announcement from the bank Citi,
Citibank, who have decided to reduce their 144 service
provider relationships down to 50.
And they've actually increased the numbers of staff that they
have on shore in the United States and some other regions
who are directly within the company.
(35:36):
Because they what they want to do is they want to spend less on
the legacy and more on the new. So I'm not trying to say
companies are just going to fireeverybody or replace them with
bots, but I think a lot of smartbusinesses are thinking, how do
we stop spending billions of dollars on maintaining legacy
applications and legacy systems when we really want to reinvest
(35:59):
that money in modernized thinking, modernized agentic
technology, that sort of thing. So what some companies are
doing, and I use the example of Citibank is they're trying to
stop the cost of the old so thenthey can bring you back in house
and then start to think about how do we reinvest in and the
technology they need to take them to a different place.
(36:21):
So I don't think companies are thinking right now about how do
we just get rid of people. They're actually thinking about
how do we break from the past. I did a great podcast with Jason
Aberbrook, who's one of the leading minds in HR technology.
You know, he, he's a Mercer these days and he talks there's
like this CHROS across all the big global 50 companies.
(36:41):
And he, he actually came out andsaid these companies have so
much data. They, they don't want to do with
it. They can't join it up.
They can't make decisions on it.It's got to the point where he's
got clients who are literally thinking, oh, just just get,
let's just trash this old systems and rebuild, rebuild
with the new. And I think this is where some
(37:03):
of these conversations are happening with a Gentech, which
is how do we start to really build out the new and and make a
break from from the legacy that's been holding us back for
so long. Anthony Scrifignano makes a
comment on Twitter directly addressing this point that you
were just discussing. He says it's equally likely that
(37:23):
the C-Suite is being taken to task for not adopting more to
drive down cost. He says the KPIs need to be more
than just cost savings. What new problems are being
addressed that were unaddressed before being enabled by this
technology? And it sounds like you're saying
(37:46):
the same thing, that cost savings is a part of it, but
there's also a whole set of new opportunities.
I would agree that the same fundamental issues have remained
for a very long time in terms ofchanging we, we call it, you
know, paying off your debts, your technical debts, your
(38:08):
people debt, your process debt, your data debt within companies
and, and, and, and this inertia of companies refusing to change.
And there's so many managers andleaders within enterprise who,
let's be honest, have got away with not having to do much
different for the last 20-30 years.
I mean, we still have companies operating the processes that
were designed before the Second World War, some even the
(38:30):
industrial revolution. So what is different this time?
I think what's different is the technology is much more
pronounced. It's much more ready, it's much
more scalable. And there's a final exhaustion
where you talk to CIOs off record, they'll all tell you one
(38:51):
thing. They are fed up spending 10% a
year on their services firms andthen 10% increase is every year
on their software license hikes.SAS is becoming a legacy
paradigm and services people just don't want to keep paying
more and more and more. You can't keep going up this
exponential cost curve. Eventually the chickens come
home to roost and, and, and I think C-Suite executives are
(39:14):
really being held to task now. Can you deliver an AI first
organization where a culture hasto change within the company?
And I think that's the problem we've got with a lot of these
businesses is they haven't got the right cultures to shift,
shift forward and really embrace.
And you know, while I would agree, I don't think the
(39:34):
fundamental issues have changed all that much.
What is changing is the onus on AI that's coming right from the
top. Because when RPA came in, in
2012, the reason why one of the reasons it failed was the CIO.
It would get dumped on the CI OSdocket and it would eventually
get dumbed down two or three layers into the what we call the
(39:55):
frozen middle within the organization.
And that's when technology solutions go to die.
That's not happening so much with Agentic.
But Phil, I remember those RPA days and I remember software
companies describing RPA just like you're talking about
agentic AI right now, which is it's going to save us money.
(40:19):
We're not going to need as many employees.
This is going to, it's going to be great.
But the promise was never fulfilled.
So what's different and how do we see our way through the hype?
I think what is different is that 15% of high performers and
I think the following 15% behindare organizations where the
(40:44):
leadership have realized they can no longer keep painting lip
service to not fixing their underlying problems with data
technology and legacy. And all our PA was really doing
was it was like a Band-Aid tech that stitched together old
systems to get them functioning more effectively.
It was very useful. But in terms of could you use
(41:06):
RPA to replace thousands of people unless it was a very high
throughput process, very repeatable, very predictable.
Of course you couldn't. No, this was like a patchwork
technology. You, you know, if you want to,
if you want to say, hey, we have1000 people answering calls in
the Philippines for our consumerproducts that we're selling,
(41:27):
right? That's people on mass at scale
where you need technology that can actually have some empathy
with clients, that can replicateCX behaviour, that can actually
do the job. And I think that's the big
difference right now is agentic is much, much closer to doing
the job of human beings than RPAwas, which it really wasn't.
(41:49):
It was a patchwork back office break fix technology that was
great if you wanted to keep, youknow, your old kicks mainframe
working with a cobalt system, working with ASAP system, for
example. But now it's much more, you
know, you can see where this is all shifting.
And I think there's a, there's areal exhaustion with companies
(42:10):
having to keep maintaining real,you know, creaking old systems
in a world where if, you know, competition is much more
cutthroat and you got to be really slick and on the ball if
you're going to be effective in this economy.
Phil, I get the sense that what you're really also saying is
that the the difference between RPA and agentic AI is that that
(42:37):
15% of early adopters of agenticAI have demonstrated that in
fact it really does work. It really can bring these kinds
of benefits and savings that you've been describing.
Yeah, you can actually create a virtual Co worker to complete
(42:59):
end to end processes. It's proven it works.
We've all seen the demos, we've worked with companies who are
piloting it, we have done it on ourselves.
And a lot of enterprises, more advanced ones in particular are
at least working with single agents and some move into multi
agent models. So they're on the path.
And it, it's a different type oftechnology that removes the need
(43:22):
for constant human oversight of complex processes.
It's a transformational tool rather than a task automation
tool, which RPA was. That was about tasks.
This is about human oversight, support and real process
capability and the fact that youcan build these Co workers.
(43:42):
You can, you can engage with these things, you can talk to
them, right? I don't even want to get into
some disturbing things about teenage boys building
relationships with AI girlfriends and things like
that. I don't know if you've been
reading about some of these things, but this, this stuff is
real. People are building
relationships with their software.
(44:02):
The software, you know, you can ask the question, if you ring up
customer service today, do you care that you're dealing with a
computer or dealing with a humanbeing?
When you're go checking into your airline, do you really want
to talk to the gate agent? No, of course you don't.
You just want to use your app and get on the plane, you know
what I mean? So we're getting to this whole
next layer of, you know, technology becoming part of our
(44:26):
daily lives much more than ever before, to the point where we're
actually engaging with technology in a much more
humanistic real way. And I don't know if you saw the
CEO of Google Deepmine the otherday, He was, I dig this out, I'd
saw it on X this morning. But he was saying how the new
(44:46):
way to develop code is inviting creators and people with
heuristic creative skills to develop the code.
Rather than in the old days you were going to like a computer
science engineer and having to kind of explain in a very clunky
way, this is what we need. We're getting to the point where
we can create code and we can create applications without
(45:07):
being technically proficient. And one of the things where you
still want to talk about the difference between agentic and
RPA is agentic is the first timeever really that we can take
non-technical C-Suite or leaderswithin enterprises and have them
dictate what they want from their technology.
But we are seeing technology that is pivoted towards the
(45:29):
business professional and we're already in a situation where I
think 46% of IT decisions are made outside of the CI OS walls
of their offices. This is the age where the CFO,
the head of supply chains, the head of marketing, these people
are making their own technology choices because they can start
to build technology that is verymuch answering the needs of the
(45:53):
business versus something that you're having to be dictated to
by engineers, that sort of thing.
It is extraordinary the level ofresearch support that, for
example, that these tools can provide.
I had a networking issue of my own here in our studio and doing
(46:18):
a little bit of research I was able to figure out a fairly
complex question having to do with routing.
Rather than need to call an IT person and bring a consultant
in. It is amazing, but we have
we're, we're almost out of time and we have a number of
questions that are left. So I'm going to ask you, Phil,
to answer these questions prettyquickly, pretty concisely.
(46:40):
First one is from Prem Kumar Aparanji and he says when LLMS
powering the AI agents aren't reliable or predictable, how do
we rely on them to automate unpredictable scenarios that
need to work? You have to train the model to
work is is my answer. So if there's something wrong
(47:03):
with the LLMS, then you need to really have a look at the
underlying technology that you're using and find the right
LLMS that can deliver the scenarios you want.
So I think there's a lot more technology based conversations
we got to have to get this, you know, and really enterprise
(47:25):
grade ready. And what I would say is, you
know, I know from a lot of friends in the industry that
like open AI, for example, is very, very obsessed with
becoming enterprise ready. Like, you know, you know, the
leadership within that company are spending all their time with
the C-Suite within Fortune to 500.
(47:45):
They're trying to figure it out.So I would say it's great
question and there are a lot of faults in the system right now.
And a lot of this is honing the models and training the models
until you get them working. I mean, as I said, we're doing
our own model, we're putting ourwhole business into into an
agentic solution. And it's it's taken us three
(48:05):
years to get to a point where westill haven't gone live with the
new system yet. But you've got to learn
yourself, you've got to learn your business.
You've got to learn these models.
You've got to try it and try it and try until you know what
works and what doesn't. And I remember when shortly
after ChatGPT came out, I remember that your company HFS
Research was one of the first analyst firms that I was aware
(48:27):
of that was making that attempt to put your research online into
an LLM. So you, you truly are an early
adopter at this. We have a question very quickly
now because we're just going to run out of time from Elizabeth
Shaw, who says CE OS and boards are driving the use of AI
agentic and beyond. There's serious implications for
(48:50):
worker and social society impacts.
What concerns do CE OS and othersenior business leaders have
with these concerns? And very quickly, please,
please. I know it's a complicated
question. Data privacy and cyber are the
number one problems and biggest concerns by country mile, to be
(49:12):
honest with you. And after that, it's, you know,
it, it's, it's other areas around transformation and
replacing process and compliance.
But cyber is by far and away I think the biggest headache as
companies look at shifting to these models is, is maintaining
a secure infrastructure. Arslan Com comes back and says
(49:34):
who gains the most value from magentic AI, small companies or
large companies? I would say at the moment small
companies, it allows I, I hate using my own example, but it
allows mid sized businesses to really punch above their weight
because you can scale fast, you can act nimbly and you often
(49:54):
don't have as much legacy withinthe business to change.
You don't have as many people resisting change.
And I think large companies can also be truly beneficial in
terms of how they can leverage this.
But I just found with a lot of large businesses, it's harder
for them to beset their legacy. You know, look at the technical
debt they have the the lock in they've got with legacy software
(50:16):
providers, you know, that sort of thing.
So I think it's harder for largecompanies to change because
there's a huge amount of training and, and, and cultural
change and shifting that needs to happen.
And I think SM ES a better placeto pivot and and I see a lot of
people I know wanting to go and work for smaller businesses
'cause they are more nimble and you got to be nimble in this
(50:37):
market. What advice do you have for
enterprise, technology and business leaders when it comes
to how they should be relating to this agentic AI world today
and very quickly, please? Get on top of it, learn it,
understand it, experiment with it, do boot camps with it.
(50:59):
You've got to educate yourself. The days of BS ING around
technology are over. You've got to be much more
proficient at knowing what is possible and engaging and
building relationships with the whole emerging AI ecosystem
around you, you know? And with that, a huge thank you
to Phil. First, he's the CEO of HFS
(51:22):
Research. Phil, thank you so much for
being here. I'm just so grateful to you.
Yeah, a pleasure, Pleasure, Michael.
Now I went quickly, enjoyed it very much and I look forward to
more interactions. And a huge thank you to
everybody who is watching today.You guys are an awesome
audience. You're so intelligent, so smart.
Go to cxotalk.com, subscribe to our newsletter and join us.
(51:46):
Join our community and join our live shows.
We have one next week and the week after that, so check it
out. Thanks so much everybody, and I
hope you have a great day and we'll see you next time.
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