IBM Consulting On Moving From AI Experiments To Economic Impact

[00:00:04] What happens when the AI conversation finally grows up, matures and moves from excitement to accountability? Well, for the past couple of years, AI has dominated boardroom agendas, conference stages and executive offsites. But something this year has shifted. The conversation is no longer about what AI might do one day. It's now about what is it delivering right now? Where is it creating real value?

[00:00:33] And how can leaders justify continued investment? Well, my guest today is Neil C. Hughes from IBM, and he joins me for a grounded and refreshingly honest discussion about why proof of value has replaced proof of concept, and why the next phase of enterprise AI will be won by leaders. Leaders who treat it as a strategic discipline rather than just another tech experiment.

[00:01:00] And we will also talk about the return on investment of every AI tech project, the operating model changes required to make it a success, and also the underestimated importance of leadership mindset. And what is it out there that is separating organisations that are genuinely moving the needle from those that are still stuck in pilot mode?

[00:01:24] So as AI becomes inseparable from how modern enterprises run, the real question is no longer can you deploy it, but can you turn it into a lasting advantage? And with that scene perfectly set, allow me to beam your ears directly to New York City, where my guest is waiting to speak with us today. So thank you for joining me on the podcast, Neil.

[00:01:51] Can you tell everyone listening a little about who you are and what you do? Sure. Neil Deharp. I am a senior vice president and global managing partner for IBM's consulting business in the Americas, which has responsibility for the US, Canada and Latin America. Before that, I was the growth leader for the entire business and did that for a good chunk of time to set up where we're going as a business.

[00:02:17] And then before that, I was at another consulting firm for a long time doing more finance-oriented consulting. So I have a more of a bent towards sort of ROI. And, you know, most of my clients for the last decade and a half have been private equity clients that are laser focused on return on investment. So, you know, I've done a lot of different things across large scale transformations, innovation, and again, cost with a real return on investment.

[00:02:48] Well, a massive welcome to the show. I'm looking forward to speaking with you today. That is a lot I want to talk about, especially as we're recording this at a time where enterprise AI conversations are finally shifted from just proofs of concept to proof of value. And having spoken to you before we started recording, I know this is a topic you're incredibly passionate about. So for what you're seeing firsthand, what has changed in the boardroom that made ROI the central question rather than a technical possibility?

[00:03:18] You know, maybe I'll just like give you a bit of a story. I like to tell stories to sort of make my point. So I just got back from Davos, you know, a couple of weeks ago and had a ton of client meetings, met with a number of partners, met with a lot of folks from the media.

[00:03:34] And if I were to compare the discussion this year against last year, right, 12 months before, last year, the discussion was all about AI as the big rave and, you know, pilots and proof of concepts. And, you know, it's going to change the world dramatically. And, you know, fear of missing out right now and the like this year, 12 months, you know, past the clear focus was, okay.

[00:04:01] It's obviously going to be a very central part of business, a very central part of society in general. Investments are going to have to be made. What is the real return? Like, what am I going to get for this? My boards, my CEOs, even the CEOs, you know, that I spoke to were very focused on. And we have to do things. We have to transform our business. But remember, we have to transform our business, not just do AI for vanity's sake.

[00:04:26] And with that, how am I going to change my business, right, either from a productivity standpoint or from an innovation standpoint to make my business better? And what sort of leaders do I meet to go drive that business transformation? So a very, very different conversation in the span of 12 months.

[00:04:46] So, and that story I share, Neil, with you is prevalent with every client, every investor, every partner that I talk about around AI and the potential of the technology. And it's so refreshing to hear that. And before you came on today, I was doing a little research on you. And I read that you said that the companies pulling ahead right now are the ones that are treating AI as a strategic discipline rather than just another technology purchase.

[00:05:16] And I'm curious, what does that mindset change look like in practice inside those large organizations that may have been guilty of jumping on the AI bandwagon on that AI goldfish doing it for the wrong reasons, just wanting to be part of the narrative? What needs to change there? Yeah. So, look, let's start with, I don't think anyone deliberately gets up and says, I'm going to do this for the wrong reasons.

[00:05:36] I think what happens is a lot of times someone may say, look, AI is going to be superimposed on a bad process or AI is going to be superimposed on bad data versus how do I look at AI transformation from totality beginning to end?

[00:05:58] How do I really rip apart a process flow to see where I could reinvent that process with technology to really create return on investment? And again, Neil, like my mindset, just working with professional investors for a long time is for every dollar that's spent, right? You need to yield a significant return, i.e. $3 at least, right, of return.

[00:06:26] So with that, I call it, you got to start with AI transformation and then leverage the technology, you know, gen AI automation, you know, soon to be quantum, right? Whatever it's going to be as a tool to go drive that transformation that you need. And with your background in finance and private equity, I think you got this unique vantage point here of seeing AI through a capital allocation lens.

[00:06:56] So how are CEOs deciding where to invest in AI or just as importantly, where not to? So if you look at the CEO journey, I mean, it's only been a couple of years. So I remember very, very fondly, like when, you know, the first model, you know, came out and hit mainstream media and like all of that. It was only a few years ago.

[00:07:20] And if you sort of follow the evolution in through the CEO's eyes, the CEO initially had to figure out what it was, you know, gen AI, how it may impact their business. They were getting lots of questions from their boards. They had to educate themselves. They had to educate their management teams. They had to figure out if they were going to be leaders or fast followers. They had to work through proof of concepts.

[00:07:47] And that all took about two years to work through that entire cycle, through that entire cycle. Now we're in a cycle where the conversation has changed. Every board is asking their CEO, public and private, what are you doing with AI to actually figure out what the AI risk is in your business and what the AI opportunity is in your business? I think it's really as simple as that. What's the AI risk?

[00:08:15] What is the AI opportunity? And with that, to your point, CEOs, because of their boards asking and because they have to sort of make changes in a very ambiguous world and make choices in a very ambiguous world, have to make capital allocation decisions on is this something you want to explore and how do we want to lean in? And do you want to lean in big? Right. We want to mean it leaving big.

[00:08:39] I think it's safe to say probably over the last 12 months, 18 months, there's been a big focus on productivity. Right. And making cost, you know, models more efficient through the use of technology and AI transformation. We are seeing now a shift. So I see it in my client conversations. I see it in a survey we just did.

[00:09:05] We released a survey a couple of weeks ago called Enterprise 2030, where it really, really digs deep into this. We interviewed over 2000 executives across the world. And what we found was that shift now is moving from productivity to innovation. Innovation is a proxy for growth.

[00:09:25] And we hear CEOs now saying from a capital allocation standpoint that a massive chunk of AI spend now will start going more towards innovation, i.e. growth. And that's like up 50% from where it was, you know, not that long ago when we did the survey last. So you see these capital allocation decisions being made very purposefully now.

[00:09:50] And the other thing I'd say, Neil, is boards are being very focused. Investors are being very focused on show me return in a logical time frame. Don't tell me that you're going to need all this spend and you're going to show me return in two years time, three years time. So the most successful companies are putting in processes to say, I'm going to show you real gains, real returns on a quarterly basis, no more than a six month basis.

[00:10:18] So so that is the trajectory now to say, OK, I'm going to use the technology. It's going to make my processes better. And I'm going to show real shareholder return, you know, on a quarterly basis. Wow, that certainly answers the ROI question there, especially at a time when many early AI initiatives stuck or stalled in pilot purgatory because the workflows and operating models largely stayed the same.

[00:10:42] So if you look back at the journey that a lot of these companies have been in, what were the hard decisions that enterprises needed to make to redesign how work actually gets done rather than just building it on top of the way that all things have always been done? So what I see with the most successful companies is, including ours, including our own, is there has been a unique marriage that's come together between the CTO.

[00:11:13] The chief technology officer, the CHRO, the chief human resource officer, and the COO, the chief operations officer, the chief transformation officer in a company, depending on the company in the structure. Those three have to work seamlessly together. So what do I mean by that? The CTO obviously has the tech agenda and it has to be tied to business. And the CTO has become more relevant than I've ever seen in my career.

[00:11:40] The CHRO has to play a critical role because a lot of this is change management and developing next gen leaders. And then lastly, the COO, the CTO many times is the proxy from the CEO to get stuff done. Right. So you have that holy trinity that's come together and they've in many ways become the most important people at the company to keep the businesses honest around driving real transformation. So that's big picture.

[00:12:08] Then if you dig in deeper, what we're finding is to do a real transformation and actually get real return, either cost or revenue innovation, you need to start with the business process versus technology. A lot of companies start with technology. And that usually is a fool's errand.

[00:12:32] You get stuck and basically you're slapping technology onto bad process and you're not moving the ball forward, you know, meaningfully versus actually ripping apart a sales function, ripping apart an HR function, a finance function, you know, a front office function. And then looking at everything that needs to be done and then really aggressively figuring out where AI and automation can make a difference to drive that return and make the process better.

[00:13:04] And I went to a lot of tech conferences last year around the world. And one of the key phrases that I heard again and again was no data, no AI. And you work closely with global brands across so many different sectors. So what separates organizations that turn data into meaningful intelligence from those that remain stuck in experimenting phase or even stuck trying to clean their data?

[00:13:30] So, look, I start with good data, good results, bad data, bad results. Like it's as simple as that. And what do I mean by that? So from a data standpoint, you need to have clean data. You need to have organized data. And you need to protect that data. And companies many times don't even know what data they're sitting on, their own proprietary data, right? Let alone how do you superimpose their proprietary data with other data to get meaningful results?

[00:14:00] You know, our belief is for large enterprises, they really only uncovered like 1% of their enterprise data that they've actually meaningfully sort of organized and put through real transformation. So massive opportunities still left in front of us. What I love about IBM is IBM is, you know, it's a massive consultancy, massive tech arm, like incredible research.

[00:14:28] We marry that together to sort of drive value. We're like in a category of one. So why do I tell you that? What we have done is we have sort of looked at data and said, how can we make data more connected, cleaner? How do we even like move data from place A to B? We just did a really unique acquisition, Confluent, that allows us to exactly do that, you know, stream data from point A to point B in a seamless way.

[00:14:54] Um, so if you don't deal with your data, then AI is meaningless. Like it's really meaningless, right? You have to have, you got to identify your data. You got to clean your data. You got to organize your data. You got to protect your data. Only then do you have something that you could then put against the, the technology to actually drive meaningful, meaningful results, um, to, to create the value we've been talking about.

[00:15:19] And most companies, most companies, um, miss that, uh, in a, in a significant way. And IBM spoke very publicly about applying many of the principles that we're highlighting today internally. And as a result, you've, you guys have unlocked billions in productivity gains.

[00:15:38] So behind the scenes, what lessons from IBM's own transformational journey that you've been on are most relevant for other large enterprises and C-suite members that might be listening to this, this interview today. So, so the IBM journey, the last several years under Arvin's leadership, um, has been incredible. So, um, if you think about our journey, um, which is only, you know, a couple of years old, right?

[00:16:05] We like the punchline is we've taken out four and a half billion dollars of productivity savings, um, run rate that's run rate annual. And that's, you know, it's publicly reported in our 2025 results. And we've done that by identifying hundreds and hundreds of workflows.

[00:16:27] We have then chosen the ones that are going to maximize best return and not just economically, um, best returns from our process standpoint to make the function better. Be it HR, be it finance, be it, you know, procurement, be it, you know, sales and marketing, be it, you know, research, right? We figured out like, what are, what are the workflows that we're going to go prosecute and invest in.

[00:16:54] And then that's what drove the four and a half billion. And it required like, yeah, not obviously great technology, right. But it inquired incredible leadership, incredible leadership and discipline leadership to get after stuff. Right. And I explained like the, the, the, the three main architects that, that makes something like this happen, you know, the CTO, the COO, the CHRO. But it really all starts with the tone, the CEO sets. Right.

[00:17:23] And, and that's what like Arvin has done here at IBM, where it's, it's meticulous around the urgency of reshaping, you know, the function. And again, the economic benefits are like a by-product and what the beauty of this is, Neil, is you take that savings and you're able to reinvest in the business. You're able to do more R and D you're able to do acquisitions. You're able to invest in, in, in, in people.

[00:17:50] Um, and with that, you're able to move the needle on an ROI basis. Hence, Neil, I'm sure you followed the stock price is, you know, tripled in a, in, in, in a relatively short period of time. And we still think there's massive upside still to go as well. Right. Like we still think there's significant, um, opportunity to continue to reinvent the company, um, you know, to, to, to, to drive that shareholder return. Love it.

[00:18:18] And again, for those business leaders listening as AI and agentic AI becomes woven into just about every aspect of their enterprise. How does leadership responsibility, how does that change? Especially for executives who, who may not automatically come from technical backgrounds. Any big changes there? So, so, um, I will go back to my survey again, you know, the 2030 survey. There's some interesting data in there.

[00:18:43] Almost 70% of senior executives believe that mindset will matter more than skills going forward. Just think about that. Nearly 70% of executives agree that mindset will matter more than skills. Um, and almost 60% expect most employee skills to be obsolete in the next four years. Okay. So, I mean, those are some significant points, right? So, so let's dig a little bit deeper on that. Um, I believe we believe that, um,

[00:19:12] um, AI will create an entirely new set of leadership roles going forward. You're going to just need different people with different skills. And the thing I don't think we're talking about enough is the whole art of reskilling, upskilling workforces. Um, and by the way, that's a, that's a moral responsibility. Yeah. That is obviously a significant economic responsibility. And like, as leaders, we have to be like laser focused on that.

[00:19:40] And by the way, the employee base, right? The soon to be workforce and the current workforce has to also invest in themselves, right? Like at IBM, we take upskilling, reskilling very, very seriously. Not only our own people, but like society as a whole, right? We're making massive investments around tech, upskilling, reskilling. Um, and that has to be like a massive, massive undertaking, um, as we all get ready for this

[00:20:10] significant change that's upon us. And I don't think we're talking enough about the leadership skills that one will need to go drive this sort of change. That is such an important point. That entire pipeline for the workforce there, especially as entry-level roles disappear, it's going to be harder for new people to come into that pipeline, the next generation, and also the people in the existing workforce, they need to, to reskill and adapt and evolve as well and need a bit of investment in time.

[00:20:39] It's, it is a huge responsibility, isn't it? On all sides. Yep. And, and look, I mean, here at IBM, again, through Arvin's leadership, we're spending a lot of time, like with our leadership team, like our senior leadership team around this point, right? Around how do you do things differently? Um, how do you lead differently? Cause that is such a critical skill as we move the business forward. So technology is great, but like you got to lead from the front and actually drive these changes.

[00:21:07] And as someone working right in the heart of this space, you must hear a lot of myths and misconceptions. See a few untruths on your LinkedIn feed now and again. So I'm going to give you a virtual soapbox to stand on now and lay to rest some of these. What, what do people most misunderstand about your industry? Are there any myths about your job or field of expertise that we can lay to rest today? What would that be? So look at, I'll look at it through a principle and also through a client size.

[00:21:34] So for as a principle, I think the whole tech industry consulting is going through a massive change and the business model is going to change dramatically. Like we here at IBM believe that, you know, consulting is going to move clearly to software as a service model, which sounds chic, but basically the model is going to change dramatically. And there's going to be winners and losers that come out of this. The industry is going to get totally redeveloped. The way we create value for our clients is going to be like totally remapped.

[00:22:01] Um, and I'm really glad I'm on a platform that is a tech centered platform to go drive that change, right? So, so, so let's start there. Um, I think from our client standpoint, I think some of the things that we talked about in this conversation, I think I'll just reiterate that is putting the best technology on not so good process is not going to get you anywhere. Putting great technology against bad data is not going to get you anywhere.

[00:22:31] You have to start with a mindset of how am I going to have AI transformation really change a business and drive ROI and ROI comes in a, you know, a couple of different formats, right? It comes in productivity, which is cost innovation, which is growth. And obviously risk and compliance in a way that you have to protect the reputation of the company and do the right things around that. But it's like, those are your three value levers around ROI and creating that ROI.

[00:22:59] And as a finance guy and a numbers guy, I suspect you are well-versed in looking for trends, looking for signals at what would impact future investments. But if we look across the, the enterprise tech landscape, are there any signals that you think leaders should be looking out for over the next 12 to 18 months to know whether their AI strategy is truly driving growth and competitiveness rather than just more activity? Any, any signals or anything that you're keeping an eye on here?

[00:23:26] I think it comes back to like the key growth levers. Instead of looking at like new growth levers, I would look at how is the company changing their revenue model? Is it growing and what are they doing to innovate? Are they actually driving more efficiency, you know, um, net profit EBITDA margins, right? Depending on if you're a public or private investor. Um, those are the key levers, right? Are you doing more, uh, with less, right?

[00:23:54] Are you able to reinvent new product, new channels? Those are the metrics that you have to continuously go back to versus like tech only metrics. Fantastic. Well, I've loved chatting with you today. Before I let you go, we've covered a lot in a short amount of time. We did mention the 2030 survey. I will get a link to that and add it to the show notes, but for people listening, want to connect with you and your team or, or keep up to speed with some of those big announcements that will inevitably coming out of IBM this year. Anyway, in particular, you'd like to point everyone.

[00:24:23] I think the best way is I am really proud of our IBV or Institute of Business Value. I think, um, it's some terrific survey information, thought leadership information. Um, obviously we are a global entity and we have our folks on the ground, you know, all over the world. So obviously connect with folks locally as well. And, you know, if you want to chat, reach out to me directly. You know, I'd love chatting about this and I think there is so much opportunity, you

[00:24:50] know, in the marketplace to, to get after so much upside. Well, I will have links to everything you mentioned there. So I'd urge anybody listening that wants to find out more, go to the show notes. You'll find links to everything that you need there. And so many big takeaways from our conversation for me today. The fact that leaders are focused on proof of value, tying AI directly to growth and competitiveness and ROI is so refreshing to hear and also how the competitive edge that

[00:25:16] belongs to organizations that treat AI, not as a tech investment, but as a strategic discipline woven into every part of that enterprise. Just thank you for bringing all this to life today. Really appreciate your time. Anytime, Neil. Really good to be on with you. Looking forward to doing it again. I think today's conversation landed at a moment where many enterprise leaders universally will recognize and that is AI is no longer optional.

[00:25:43] But then again, neither is discipline, clarity or accountability. And one of the things that stood out to me here is a simple but demanding idea. Real returns come from redesigning how work gets done, aligning leadership around outcomes and treating AI as a long term capability rather than just another short term initiative. And the organizations that appear to be pulling ahead, they're the ones that are quietly doing the hard miles.

[00:26:13] They're not chasing headlines. They're making deliberate choices about data, workflows, talent and capital allocation. So a massive thank you to Neil there for taking the time to sit down with me and also bringing a finance led results focused lens on a topic that too often drifts into abstraction. And I think we've all seen that over the last three years. And if you're a business leader listening and you're navigating the next phase, I think the

[00:26:42] message from Neil today was very clear. AI will reward those that are willing to do those hard miles. So it's your own organization will also inevitably move from experimentation to execution. Hopefully this year. What would you change if every AI decision had to earn its place through real tangible value? Something for you to think about there. Please remember, go to techtalksnetwork.com. There's nearly 4,000 interviews over there at the moment.

[00:27:11] There's also a myriad of ways you can contact me. You can hit an audio message. Send me a quick recording there. And also you can connect with me on social. Send me a DM. Whatever it is, I'm nice and easy to find. And all my conversations I treat as a dialogue, not a monologue. It's not just about me and the guest. You've got some very real world experiences out there that I would love to get out of your heads and share to many ears listening around the world. But that is it for today. So thank you to Neil for joining me.

[00:27:38] A bigger thank you to each and every one of you for not only listening and subscribing, but making it to the end of the episode. Kudos to you. But meet me here. Same time, same place tomorrow. I'll be waiting in your podcast feed with another guest. Speak with you now. Bye for now.