17 Apr 2025 · 52m · Michael Krigsman, Phil Fersht · 20 quoted facts
Phil Fersht, CEO of HFS Research, argues that agentic AI (software that pursues goals and completes whole processes, not just answers prompts) is a real step beyond earlier automation. He says it works today, that boards are pushing for it, and that it will change how companies staff and buy services. Humans stay valuable through collaboration, empathy and breadth, but narrow, repeatable jobs are exposed.
The main tension is between pressure and action. Fersht's research shows roughly 15% of enterprises have a clear vision, about 40% are stuck in pilots, and 45% are worried or resistant. The technology is available, boards want speed, and a recession could turn cautious experiments into layoffs. Host Michael presses him on why this differs from the over-hyped robotic process automation (RPA) wave.
Fersht says ChatGPT answers a prompt, while an agent acts as a virtual co-worker that self-directs and coordinates multiple tasks. He describes a path from single agents to multi-agent teams in one function, then agents working across functions and partners.
Fersht cites HFS research on over a thousand enterprises: 45% are worried about job loss or resistant to change, about 40% are in 'pilot purgatory', and only about 15% have a clear path. Pressure from boards and the C-suite is high, but action lags.
Fersht describes a healthcare call operation with 50,000 onshore staff whose leader believes half could be replaced by 'empathy bots' but has no trigger yet. He fears a deep recession could supply that trigger.
Fersht calls RPA 'patchwork' back-office technology that followed instructions and stalled in the 'frozen middle' of management. He says agentic AI can handle empathy-heavy work like customer calls, and executives now push it from the top.
Fersht says firms ask why 500 offshore coders are needed when 25 onshore staff backed by agents might do. He points to Citi cutting service providers from 144 to 50, shifting spending from legacy systems to new technology.
Phil Fersht says ChatGPT responds to individual prompts, while an agent works like a virtual co-worker that completes whole processes, self-directs and coordinates several tasks. In his words, it removes the need for constant human oversight of complex work.
Fersht says some roles that can be easily replicated are at risk, and the technology to replace many call-center and coding tasks already exists. He thinks people who collaborate, show empathy and work across departments stay valuable, and that most firms are aiming to stop adding headcount rather than fire everyone.
Fersht's research on over a thousand enterprises found about 15% have a clear, integrated vision, about 40% are stuck in pilots, and 45% are worried about job loss or resistant. Most are at best using single agents, with more advanced ones moving to multi-agent setups.
Fersht says RPA was brittle 'band-aid' technology for stitching old systems together, and it died in the middle layers of management. Agentic AI comes closer to doing the actual job of humans and is being pushed from the board and C-suite down.
Fersht says data privacy and cybersecurity are by far the biggest concerns for executives. He also raises trust in AI-generated information, hallucinations (confident but wrong output) and scams as general risks.
Stated by the speakers. Each quote matches the transcript, which shows what was said, not that it is true.
Phil Fersht says HFS Research coined the term robotic process automation alongside Blue Prism in 2012 and published the first analyst papers on it.
“We actually coined the phrase, um, alongside a company called Blue Prism when we launched it in 2012, and we did the first analyst papers on it.”
Phil Fersht says robotic process automation did not scale well and was brittle, although the concept was sound.
“but the problem with RPA was the technology didn't scale well, it was very brittle, but the the concept was there.”
Phil Fersht says the film Terminator predicted that computers would become self-aware in 2029.
“they actually predicted in 2029 was when humans were, uh, computers would become self-aware.”
Phil Fersht cites Microsoft Copilot summarizing emails and setting reminders as an example of a single-task agent.
“even like Copilot, you can use Copilot right now to summarize your emails and remind you to do things.”
Phil Fersht describes horizontal multi-agents as agents collaborating across business functions such as sales, marketing and customer service.
“something we're calling horizontal multi-agents, which is where you get different agents collaborating across various business functions”
Phil Fersht says some enterprises now require managers to show that work cannot be done by AI before hiring new staff.
“they're now insisting before you hire any new staff, you need to show that this work can't be done by AI.”
Phil Fersht says companies are exploring replacing services bought from firms such as IBM and Infosys with agentic software, a trend he calls services as software.
“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?”
Phil Fersht says one major organization he spoke to produces half a billion lines of code a year to keep running.
“one, um, major organization I spoke to produces half a billion lines of code a year to keep that organization functioning.”
Phil Fersht says he pays for ChatGPT Pro at $200 a month and is unsure he needs it.
“I'm using ChatGPT Pro, so I paid the extra money. Uh, I'm not sure I need the 200 a month package”
Phil Fersht says an HFS Research survey of over a thousand enterprises found 45% were worried about job loss or resistant to change.
“we spoke to over a thousand, uh, and major enterprises, um, uh, looking at the adoption of of gen ai and agentic, and um, 45% of them, uh, either worried about job loss or they're resistant to change.”
Phil Fersht says about 40% of surveyed enterprises are stuck in pilot purgatory with generative and agentic AI.
“you've got about forty of enterprises where they're still in that sort of pilot purgatory phase.”
Phil Fersht says software vendors such as Salesforce, with Agentforce, and ServiceNow are pushing into agentic AI to take market share from services firms.
“You've got the big software companies like Salesforce in particular, with their agent force rollout and ServiceNow.”
Phil Fersht describes a healthcare organization with 50,000 onshore staff answering inquiry calls whose leader sees half as replaceable by bots.
“an organization I spoke to with 50,000 onshore, onshore staff responding to healthcare inquiry calls.”
Phil Fersht says Citibank decided to reduce its service provider relationships from 144 to 50.
“You may have seen a recent announcement from, um, the bank, Citi, Citibank, who have decided to reduce, um, their 144 service provider relationships down to fifty.”
Phil Fersht says industry is considering replacing 500 offshore coding or support staff with about 25 onshore staff augmented by agentic technology.
“When you could potentially replace them with a team of maybe 25 people who are local and onshore, who are supported and augmented by agentic technology.”
Phil Fersht says robotic process automation stalled because it was handed to the CIO and diluted in the frozen middle of management.
“It would get dumped on the CIO's 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.”
Phil Fersht says about 46% of IT decisions are now made outside the chief information officer's organization.
“I think 46 of IT decisions are made outside of the CIO's walls of their offices.”
Phil Fersht says the CEO of Google DeepMind argued that creative, non-engineer people can now help develop code.
“But he was saying how, um, the new way to develop code is inviting creatives and people with heuristic creative skills to develop the code.”
Phil Fersht says building HFS Research's own agentic system has taken three years and it had not yet gone live.
“and it's it's taken us three years to get to a point we still haven't gone live with the new system yet.”
Phil Fersht says data privacy and cybersecurity are the biggest concerns executives have about adopting agentic AI.
“Data privacy and cyber are the number one problems, uh, and biggest concerns by country mile, to be honest with you.”
Fersht sets out what the term means and why voice and video progress makes it urgent now.
“the ability to replicate human behavior in software. It's as simple as that”
A short history shows why Fersht sees agents as the next wave after robotic process automation and ChatGPT-style tools.
“agentic AI is a collaborative actor that removes the need for constant human oversight”
Fersht lays out the maturity ladder from meeting summarizers to agents collaborating across business functions.
“multiple agents work together within a single business function”
The discussion explains how agents differ from chatbots and how hiring rules are starting to change.
“you need to show that this work can't be done by AI.”
Fersht shares survey numbers showing a small group of leaders and many stuck in pilots.
“only fifteen of AI leaders are generally positive about AI adoption.”
A healthcare example shows how much call-center work could be automated and what could trigger it.
“the same six questions being asked over and over and over again”
Fersht argues firms want to stop paying for the old, using Citi as an example.
“their 144 service provider relationships down to fifty”
The host challenges the hype, and Fersht explains what changed in capability and leadership backing.
“agentic is much, much closer to doing the job of human beings than RPA was”
Closing answers cover security concerns, who benefits most and what leaders should do.
“I would say at the moment, small companies.”
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 Phil Fersht, 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, uh, the ability to replicate human behavior 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. Uh, this is really where it's at, and it's it's. It's something that, um, 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 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 2010s, when you might have heard of a technology called RPA, robotic process automation, which got very big and very hyped within, uh, the technology world. We actually coined the phrase, um, alongside a company called Blue Prism when we launched it in 2012, and we did the first analyst papers on it. And at the time, we were talking about, um, RPA, replicating human human behavior, uh, in software, which would allow us to scale more effectively.
Um, threatened, uh, elements like offshore outsourcing, because companies could technically consider having less offshore resources when you can automate a lot of this stuff. Um, 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. Um, it was about eliminating manual effort waste, you know, which was wasted on repetitive tasks. Then everyone remembers the influx of gen ai, nearly nearly it's gonna be its third year, uh, where chatgpt, uh, really came public, um, nearly three years ago, and that really changed the game in terms of um.
It became a 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 agentic 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. Um, but it's not about the fact that it sounds great. What's exciting about agentic 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 agentic and gen ai 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 superintelligence, which is about computers outperforming humans. Uh, we're not there yet, obviously. But, you know, I watched, uh, terminator, Terminator one with my son the other day. I hadn't seen that in about 30 years, and that brought me back. Um, they actually predicted in 2029 was when humans were, uh, computers would become self-aware. So that, and if anyone's got nothing better to do this weekend, watch the rerun of Terminator One. It, it's uncanny how they got the timings right on this thing,
Phil, I wanna latch on to a particular point that you made. You said it really works. 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 standalone single agents, so that's, uh, uh, that's an agent that can handle maybe one specific task or function. So that could be like an email 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. Actually, Zoom AI needs it to summarize meetings.
Those are single agents, believe it or not, um, and they're already working. People are already, you know, pretty excited about, Oh, did you turn your fireflies on? So we got a good summary of this meeting. Uh, 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 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. Um, it works because you just gotta try it like, Uh, I, I, I'd love to. There's a demo, it's called Superfilm, 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, uh, 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, um, is it as accurate as talking to a human being? Not yet. But um, 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 um, I put together.
Here's like the big ten or ten things about tariffs. Some people may have even read it. And for a bit of fun, I pumped it through ChatGPT Pro and and outcomes. Um, I said, can you pump? Can you? Can you replicate this using Phil's 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 where I said, Can you produce me? A chart that shows life expectancy in the US versus other countries and health issues? And it starts pulling information all over the place. And then you start to train, train the model, so it starts 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? Font, some colors, and you're programming in the colors 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, I don't even know if we're gonna call these agents in another six or so 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're saying, I've got a big business meeting to go to tomorrow. You're gonna get so much content that you need you. You set your agenda to the technology, it gathers you 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 ChatGPT? So what's unique about agents? But before you answer, I just wanna 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 CXO Talk, hashtag CXO Talk. 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 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, say 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 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. Um, I would develop, train this as a virtual co-worker to be like my research assistant.
So it would start to over time 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 twelve months time, you can say, Hey, Phil, I need to have a meeting with you next week to talk about XYZ. I can literally have you talk to my agent, right?
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 an in a company and asking them to almost recreate their jobs into software, which is very different from 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, or, 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 are really this ability for companies to grow and scale, uh, 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 wanna run a marketing campaign. Can I develop a planning agent who can coordinate, um, and break down the campaign requests into specific tasks? Can I create a research agent that gathers market intelligence? Can I create, uh, a creative agent that, um, develops, you know, creative assets and messaging? Can I develop a a strategy agent that optimizes my campaign and engagement across marketing channels?
Like, 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, uh, a, you know, even a campaign coordination agent to synthesize inputs from all agents into a cohesive campaign. So it's it's creating, uh, people into software, so what, you know, we call this thing you've 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, um, 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.
Go to CXO Talk dot 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 Scriffignano, 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 a 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, uh, using agents and agentic software? Is it, is it truly trustable?
Is it reliable? Where the source is coming from? Um, and that sort of thing, um, and I think the bigger risk right now is, um, can you trust information from people? Um, the other issues, obviously, with interactions and hallucinations and these types of things, all the types of teething problems you'll have with honest, on, honestly, any type of technology, you know, you. I remember when we were getting into more sophisticated accounting applications 20 years ago, and people were worried then about, um, 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, um, understanding how to navigate your way around this climate. Cause it's only gonna get more confusing and more worrying. And, you know, through times, you know, we're getting more sophisticated spamming, phishing stuff, or, you know, we're getting them every day on texts and emails. And the quite convincing ones. Sometimes it's like, you know, I get p. I get my own staff coming to me saying, Hey, Phil, did you send me this text about, uh, getting Amazon vouchers, things like that?
So, um, we can talk about this for a very long time. All the different risks, all the different worries, all the different concerns, um, and the the other thing is, you know, where do you want it to impact? So, um, you know, I sat in a room with ten senior level AI decision makers in banking just a couple of weeks ago, and, uh, they had a lot of common issues, which was, uh. We really want to leverage agentic 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 wanna talk to a human being, right? Especially when you're getting into your finances and and loans and borrowing and that sort of thing. Is, um, 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 Arsalon 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 and replace 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 kinda, you kinda 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's 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. Um, uh, you know, I have a a guy running my IT systems who actually was a procurement guy just a couple about three or four years ago. But he he broadened his knowledge into understanding how to manage HubSpot and, um, 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 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 in a sales role, you're in a delivery role, you're in a tech role.
You need to become broader and more aligned to your business to add value there. Because if you, if you become, uh, just a replicatable, uh, solo task-driven professional, uh, you do run a risk. So, um, 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. Uh, one, um, 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 co code can be rewritten using, uh, gen ai and other types of AI software now. So, you know, we, 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 gonna be hit with so much AI fake information, or it could be real information, but it's written by AI. We wanna read stuff written by people. AI can help us as humans get much better at what we do, it can help us become better communicators, be more more productive, get more done. Like I told you earlier, um, I find I'm becoming way more productive as an analyst.
Uh, because I've now got, um, 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, uh, these agents help you in your job. Tell us the tools you're using, and then we're gonna go back and get some more questions. Questions are coming in.
If you wanna 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, but, uh, who's actually working across our operations people, our analysts. She's working with, um, Amazon and and and a company called Lyziar, for example, to agentify how we deliver our research to our clients.
So, while, yes, I, I can go on about the personal productivity tools I use, uh, we're using agentic to transform our whole business, cause we're an information business. And we have set up, um, a fairly complex system. Uh, we're using an agentic solution called Lyziar, which is a it's a startup, but it's it's in a pretty mature phase. They're very popular, uh, and they're powered by AWS to produce at scale. The ability for we have like 150 subscribers to go in and create their own research support agents to help them, um, 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, um, I'm using ChatGPT Pro, so I paid the extra money. Uh, I'm not sure I need the 200 a month package, but I'm loving it right now, cause it gives me a lot of query time. It it. The computing power is a little challenging. Sometimes it takes a bit of time to produce everything I need. I'm finding that, uh, effective.
I'm using deep research from Perplexity, uh, which is pretty good as well. And I've also been experimenting rather tools like, um, Claude, um, which is the Anthropic tool, and um, I've also looked at some other tools that can be fairly effective, like Gemini. I'm still not completely convinced by it, 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 three or four 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, uh, 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 within HFS. And um, every different region has slightly different attitudes towards AI. Um, so obviously you mentioned GDPR is is huge in UK and Europe. Um, India is a little bit more of a free for right now with how they're accepting AI based solutions, and the US. W 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 shakes out. EU has typically been the most, uh, closed from a frameworked perspective and demanding in terms of compliance. And anyone running a business knows, um, how challenging running GDPR practices has been in recent years to get to to the other side. Um, but I I do think that as this continues to evolve, um, the need for common frameworks I is going to become more and more paramount.
And the need for cooperation, um, is, you know, is going to continue to proliferate. I really do. And look what's going on, uh, politically, across the world right now. In in many ways, this is gonna 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, uh, today about, um, 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 and in that case, you need to sort of build a supply chain competency that senses and responds, that manages inventory, that brings cooperation together and these types of things. So I I think the need to build, um, supply chain standards, trading standards, uh, you know, around AI. Um, I, I I think this is just going to, uh, it's only just beginning, and we're gonna 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. Um, we spoke to over a thousand, uh, and major enterprises, um, uh, looking at the adoption of of gen ai and agentic, and um, 45% of them, uh, either worried about job loss or they're resistant to change. And adoption is, um, I'd say, fairly diminished. At the other end of the spectrum, only fifteen of AI leaders are generally positive about AI adoption. And they have, um, fairly integrated, uh, 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 forty of enterprises where they're still in that sort of pilot purgatory phase. Um, 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 fifteen, maybe a little more, who are getting to the point where they have a real clear vision and understanding of where they're going.
Um, one thing that is crystal clear is we're seeing immense pressure coming from the board level people and also C-suite, uh, 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 three, five only came in not even two and a half years ago.
So, um, we're playing catch up. But what's happening is the technology is staring it in our face. It it is really here. Um, 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 agent force rollout and ServiceNow. Some of these businesses really trying to muscle in on agentic, cause 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, uh, really trying to, uh, become more dominant in the services and software realm as well.
So adoption is a low is the is the real answer to this, but the pressure is there and and it's on like never before.
I just want to invite everybody listening to join the CXO Talk community. Go to cxo talk dot 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 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, uh, an important question from Arsalon 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 chatbot. I think, um, if if you're, um, leading a CX function in particular, um, you know, you, you, if you, if you're not familiar with, uh, how easily replicable call center agents are with, um, a j a, you know, with with smart agents. You know, now you shouldn't be in a job anymore, to be quite, um, you know, harsh about it. Um, 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 leader basically said, Look, the bottom line is, is there's the same six questions being asked over and over and over again. We've already run the analysis. We can literally replace half these people with with, um, intelligent bots, and they call them, they call them empathy bots. Um, very, very quickly. We're not going to do it straight away, but we know the possibility is there. And I think this is a 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 re a deep recession, um, you're gonna see some organizations literally come out and say, we're just gonna start relying a lot more on AI, and we're gonna let people go. So my vain hope is we don't fall into recession, so we can have a more positive view of of people and technology. But there is that risk there, um, that a a negative economy can drive a lot more, uh, weaponized AI.
Where companies would say, look, well, let's just replace these people, we don't need them anymore. Um, so I d. 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 and my concern is I do talk to a lot of enterprises. We have a lot of summits and roundtables on top of our research where, you know, people wanna talk. But when it comes down to what are you actually doing?
They're not doing a, they're not doing a lot. And I think what I just said about the fifteen, that's not a big number. Fifteen are kind of on the path, the rest are either still figuring it out or they're not on the path. A 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 wanna 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, uh, downturn can push many companies to replace many workers. Because those use cases and the effectiveness are so, uh, broadly dispersed, even even today, or, if not today, soon enough.
The technology is available, it's there. Um, I think companies are aware of it. Um, I do believe as well. Most enterprises don't tread lightly on the fact that, hey, let's go replace five zero, zero people with, you know, a thousand or 500, um, you know, or get augmented consultants who can manage a team of bots. But, uh, one of the things that has been looked at in industry is why do you need f uh 500 people in India, for example, running a bunch of coding or app support, that sort of thing?
When you could potentially replace them with a team of maybe 25 people who are local and onshore, who are supported and augmented by agentic technology. So it's this ability to reduce the scale of people numbers that you have and, uh, augment higher value, you know, people, uh, with, uh, with agentic, to support them. And, you know, we we put out some research recently around, um, you know, the impact of tariffs, for example. Um, that could have a real impact on, um, what we call, um, it could drive the whole services and software, um, adoption curve, right?
Cause suddenly it's like, if it becomes really difficult to manage a disparate global workforce, manufacturing goods in all over the world, you need to bring stuff back home. Um, 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, uh, 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, um, uh, assumptions on what they can do.
You may have seen a recent announcement from, um, the bank, Citi, Citibank, who have decided to reduce, um, their 144 service provider relationships down to fifty. And they've actually increased the numbers of staff that they have, uh, onshore 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 gonna fire everybody and replace them with bots.
But I think a lot of smart businesses that's thinking, how do we stop spending billions of dollars on maintaining legacy applications and legacy systems? Uh, 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'll use the example of Citibank, is they're trying to stop the cost of the old. So then they can bring it back in house. And then start to think about how do we reinvest in in 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? Um, I did a great podcast with Jason Averbrook, who's one of the leading minds in HR technology. He, he's immersed these days, and he talks to, like, the CHROs across all the big global fifty companies. And he, he actually came out and said, These companies have so much data, they they don't know what to do with it.
They can't join it up, they can't make decisions on it. It's got to the point where, um, he's got clients who are literally thinking of 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 agentic, 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 Scriffignano 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, um, have remained for a very long time in terms of, um, changing. We we call it, you know, paying off your debts, your technical debt, your people debt, your process debt, your data debt within companies. 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 2030 years.
I mean, we still have companies operating with processes that were designed before the Second World War, some even the industrial revolution. Um, so what is different this time? I think what's different is, um, the technology is much more pronounced, it's much more ready, um, 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 ten a year on their services firms and a ten increase is every year on their software license hikes.
Uh, SaaS is becoming a legacy paradigm, and services people just don't wanna 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 the 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 would. It would get dumped on the CIO's 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 gonna save us money, we're not gonna need as many employees. This is gonna, it's gonna 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 fifteen 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 RPA 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, um, 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 a thousand 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 behavior. Um, 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 COBOL system, working with a SAP 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, uh, real, you know, creaking old systems, uh, in a world where, if you know, competition is much more cutthroat.
And you gotta be really slick and on the ball if you're gonna 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 fifteen 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, um, a virtual co-worker to complete end-to-end processes. It's proven, it works. We've all seen the demos, uh, 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 moving to multi-agent models. So they're on the path and it, it's a different type of technology, um, 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, um, 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 wanna 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, um, but this this stuff is real.
People are building relationships with their software, um, 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, uh, do you really wanna talk to the gate agent? No, of course you don't. You just wanna 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 um, uh, CEO of Google DeepMind the other day, he was, I did this out, I just saw it on X this morning. But he was saying how, um, the new way to develop code is inviting creatives 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. A And one of the things where you see want to talk about the difference between agentic and RPA, is agentic is the first time ever. Really, that we can take, um, non-technical C-suite or leaders within enterprises and have them dictate what they want from their technology. Like, 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 CIO's walls of their offices.
Um, 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, uh, 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 gonna ask you, Phil, to answer these questions pretty quickly, pretty concisely. First one is from Prem Kumar of Aranchi, 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, um, then you need to, um, 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 gotta have to get this, you know, and really enterprise grade ready. Um, what I would say is, you know, I know from a lot of friends in the industry that, like OpenAI, for example, is very, very obsessed with becoming enterprise ready.
Like, you know, um, you know, the leadership within that company are spending all their time with the C-suite within four zero to five zero. They're trying to figure it out. So I would say it's a great question, and there are a lot of faults in the system right now. Um, 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 we still haven't gone live with the new system yet.
But you gotta learn yourself, you gotta learn your business, you gotta learn these models, you've gotta try it and try it and try it 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. Uh, we have a question very quickly now, cause we're just gonna run out of time from Elizabeth Shaw, who says, CEOs and boards are driving the use of AI, agentic and beyond.
There are serious implications for worker and s social society impacts. What concerns do CEOs 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, uh, and biggest concerns by country mile, to be honest with you. And after that, it's, um, you know, it, it's. It's other areas around transformation and replacing process and compliance, but cyber is, um, by far and away. I think the biggest headache as companies look at shifting to these models is is maintaining a secure infrastructure.
Arselon com comes back and says, W Uh, who gains the most value from agentic AI, small companies or large companies?
I would say at the moment, small companies. It allows, um, uh, 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 find 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 locking they've got with, uh, legacy software providers, you know, that sort of thing. So I think it's harder for large companies to change. Cause there's a huge amount of um, training and and cultural change and shifting that needs to happen. And I think SMEs are 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 gotta 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. Um, you've got to educate yourself. The days of BSing around technology are over. You've got to be much more proficient at knowing what is possible and, um, engaging and building relationships with the whole emerging AI ecosystem around you, you know?
And with that, a huge thank you to Phil Fersht, he's the CEO of HFS Research. Phil, thank you so much for being here. I'm just so grateful to you
Yeah, pleasure, pleasure, Michael. Uh, that 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 are so intelligent, so smart. Go to cxo talk dot com, subscribe to our newsletter and join us, 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.