Are AI agents an enterprise savior, workforce apocalypse, or just another tech bubble waiting to 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 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 doing our day-to-day work, It's, it's 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 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 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 in 2012 and we did the first 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 when you can automate a lot of this stuff.

But the problem with RPA was the technology 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 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 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.

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 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're realizing, wow, I can do my job so much better.

And I'm I'm an analyst.

I can tell you how Jane Turk and Jenna 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 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 become self aware.

So that if anyone's got nothing better to do this weekend, watch the 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 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.

Even like copilot, you can use copilot right now to summarize your emails and remind you to do things.

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 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 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 functions beyond one single function.

It works because you just got to try 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 avatar of me in our research website and ask me questions and I would literally in my voice dig into 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.

I mean, I'll, I'll tell you, for example, 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.

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 your own personal agent.

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 know if we're going to call these agents in another 6 or 12 months, but this is just how we're partnering with technology now 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 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 regular listeners know this, that you can ask your questions.

So right now, if you're watching on Twitter, pop your question using the hashtag into the into Twitter using cxotalk #cxotalk.

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This is your opportunity to ask one of the top analysts in the 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 it notes, you then say modify this or modify that sounds like ChatGPT.

How are agents different from LLMS and and our usage as we 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 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 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 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 software, which is very different than saying train up another human being and transfer tasks from yourself to another human.

We're now expecting people, including ourselves, to transfer human work tasks into software That technically frees us up to do other things.

Oh, let's be honest, could make us redundant, right?

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 an AI first mindset.

They're now insisting before you hire any new staff, you need to show that this work can't be done by AI.

We've reaching that point quite quickly.

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.

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 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 like it could be, hey, I need somebody to manage social media and I need to do automated LinkedIn updates, that sort of thing.

Or it could be, you know, even a campaign 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 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 Agentic and I think why it's causing, you know, excitement and friction at the same time.

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So we have a a very interesting question coming from Twitter from Anthony Scrifignano, who is 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 of it?

I think lots of people are are concerned about the impact of AI agents 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 or human being right.

That's that's a big problem and I see that as an opportunity for a 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 thing.

And I think a bigger risk right now is can you trust information from people?

The other issues, obviously with interactions 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 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 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, I get I get my own staff coming to me saying, hey, Phil, did you send me this text about getting Amazon vouchers, things like that.

So we can talk about this for a very long time.

All the different risks, all the different 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 of common issues, which was we really want to leverage Agantech to improve our customer experience function.

But you know, it's all about the customer 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 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, 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 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 you can 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 from collaboration.

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 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 understanding how 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 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 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 to produces half a billion lines of 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 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 more than ever, we're going to be hit with so much AI.

Fake information, or could be real information, but it's written by AI.

We want to read stuff written by people.

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 more productive as an analyst because 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.

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 this that who's actually working 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 agentic to transform our whole business because we're in the information business and we have set up a 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 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 it right now because it gives me a lot of query time.

It, the computing power is a 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, 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 that they 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 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 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 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.

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, this could be the second coming with the tech Bros driving a lot of policy here.

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.

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, in many ways, this is going to actually bring I think a lot of regions close to the governments and 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 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, around AI, I, I, I think this is just going to, it's only just beginning and we're going to see a 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 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 job loss or they're resistant to change and adoption is I'd say fairly diminished.

The other at the end of the spectrum, only 15% of AI leaders are generally positive about AI adoption and they have fairly integrated views of where 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 sort of pilot purgatory phase.

Their culture is becoming more adaptive.

They're recognizing the benefits of AI, but they're not there yet.

So in terms of actual adoption, you've only got about 15%, maybe a 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.

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 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 Agentforce roll out and service.

Now somebody's business is really trying to muscle in on a 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 the pressure is there and and it's on like never before.

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So we have an important question from Arsalan Khan who says how do you convince the C-Suite that agentic AI is not just a fancy 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 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 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.

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, 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 fall into recession so we can have a more positive view of people and technology.

But there is that risk there that a negative economy can drive a lot more weaponized AI where companies would say, look, 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 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 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 use cases that an economic downturn 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.

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 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 can potentially replace them with a team of maybe 25 people who are local and onshore who are supported and augmented by Gentic technology.

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 support them.

And you know, we, we put out some research recently around, you know, the impact of tariffs, for example, that could have a real impact on what we call it could drive the whole services and software adoption curve, right?

Because suddenly it's like if it becomes really difficult to manage a disparate global workforce, manufacturing goods and all over the world, you need to bring stuff back home.

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 on what they can do.

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.

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 fire everybody or replace them with bots, but I think a lot of smart businesses are thinking, how do we stop spending billions of dollars on maintaining legacy applications and legacy systems when we really want to reinvest 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 then they 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.

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.

And he, he actually came out and said 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 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 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 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 of changing we, we call it, you know, paying off your debts, your technical debts, your 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 and leaders 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 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 thing.

They are fed up spending 10% a year on their services firms and then 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 really being held to task now.

Can you deliver an AI first organization where a culture has to 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 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 OS docket and it would eventually get dumbed down two or three layers into the what we call the 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.

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% behind are organizations where the 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 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 have 1000 people answering calls in the Philippines for our consumer products that we're selling, right?

That's people on mass at scale where you need technology that can actually have some empathy with clients, that can replicate CX 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 RPA was, which it really wasn't.

It was a patchwork back office break fix technology that was great if you wanted to keep, you know, 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 a real exhaustion with companies 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 15% of early adopters of agentic AI 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 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 of technology that removes the need 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 you can build these Co workers.

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.

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 human being?

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 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 other day, He was, I dig this out, I'd saw it on X this morning.

But he was saying how the new 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 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 time ever really that we can take non-technical C-Suite or leaders within enterprises and have them dictate what they want from their technology.

But we are seeing technology that is pivoted towards the 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 very much answering the needs of the business versus something that you're having to be dictated to by engineers, that sort of thing.

It is extraordinary the level of research support that, for example, that these tools can provide.

I had a networking issue of my own here in our studio and doing 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 pretty quickly, pretty concisely.

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 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 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.

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 our whole business into into an agentic solution.

And it's it's taken us three years to get to a point where we still 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 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 worker and social society impacts.

What concerns do CE OS and other senior 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 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 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 don't have as much legacy within the 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 providers, you know, that sort of thing.

So I think it's harder for large companies 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 place to 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 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.

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 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.

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