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Re-thinking How AI Can Actually Drive Business Value
116 Plays7 days ago
Eric Pilkington joins the show to cut through the noise around artificial intelligence and deliver some hard truths about what's actually working—and what's just expensive theater.
AI isn't new; it's been around for 70+ years. The current generative AI boom is democratization, not innovation—and 95% of AI projects are still failing.
Startups with no product, no customers, and no revenue raising $30-100 million. Companies are getting massive funding without a single dollar of revenue.
The real AI leaders aren't the loudest voices on conference stages. They're the ones quietly embedding AI into workflows, building better products, and closing the gap between pilots and actual impact.
Most companies chase cost savings instead of using AI to drive top-line growth. You can't cut your way to growth. Real business transformation comes from understanding the actual problems you're solving, not from chasing the newest shiny object. The superheroes of AI aren't prognosticating on stages—they're in garages and labs building things that'll matter five years from now.
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Transcript
Introduction to Eric Pilkington: Human-Centric AI
00:00:08
Speaker
This is Bare Knuckles and Brass Tacks. I am George Kay. My co-host George A. is out for this week, but today's guest is Eric Pilkington, and he is a digital transformation executive focused on human-centric AI.
00:00:23
Speaker
This conversation could not be more timely, more merited.
Why Do Generative AI Experiments Fail?
00:00:27
Speaker
We dig into why a lot of generative AI experiments are failing, where business leaders should be looking to operationalize, where they should focus on building out transformative change in their businesses rather than just trying to bolt chat pots onto things and hope that it just reduces costs.
00:00:47
Speaker
um We're talking about what it takes to really leverage ai in a meaningful way that impacts a business. So without further ado, I will turn it over to Eric Pilkington.
00:01:05
Speaker
Eric Pilkington, welcome to the show. Thanks, George. It's so nice to be on. Thanks for the insight and looking forward to our conversation. Absolutely. Yes. We are very excited to have a conversation with you.
AI's Historic Perception vs. Democratization
00:01:19
Speaker
ai is in the zeitgeist. I'm sure people are tired of talking about it, but I'm looking forward to this conversation as a way to dig into more of the brass tacks behind what is and is not working. um i have a lot of strong opinions. I only have strong opinions, but let's start with a where you as an executive who works with businesses trying to do these transformative projects,
00:01:42
Speaker
thinks the current state is like sort of like where is the hype where is the real and um yeah just give us a brief overview because and then we'll dig into some of those specifics Yeah, well, I think you know AI is an interesting topic.
The Indispensability of AI and Human-Centered Design
00:01:56
Speaker
um And it's not new.
00:01:58
Speaker
um you know I think the ah thing that you know I'm always sort of perplexed by um is the fact that so many people treat it as ah as a new technology, something that kind of burst on the scene a year or two ago and when ChatGPT kind of um you know came online. But the reality right is it's been around for 70 plus years.
00:02:17
Speaker
Um, I mean, God, it's already been 14 or so years since Watson, uh, won on Jeopardy. and Um, and, ah and of course I was early days with, with Watson, um you know, back in the, um, you know, and you know, 2012 through 2016. Um, so I've kind of seen the hype cycle, um, you know, become, you know, to you know basically break ah the cycle where it was everything, nothing, and now it's kind of everything again. I think the the reality this time around, and maybe what makes it different, is the democratization that technologies or platforms like ChatGPT have brought and OpenAI have brought to the to the market.
00:02:58
Speaker
But, um you know, my jury's still out. I think, you know, the reality is, is that while AI is not going to go away, maybe the way it it did, you know, five or six years ago, um i think it's definitely here to stay.
AI Mandates and Executive Guidance
00:03:12
Speaker
My personal point of view is that, you know, we still live in very much in a human to human world. So, um As an executive who is a problem solver, I always say that you know we are humans solving problems for other humans. And the way we deliver those um the solutions against those problems ultimately may be run and may be managed by a machine. And certainly there are some efficiency gains that um those types of technologies bring online. But you know in the end, I still feel like um technology is not a supplant. um
00:03:46
Speaker
to you know human intuition, excuse me, and um and and just human-centered design, which I still think is is very much a an important ingredient in a lot of the things that businesses today are are trying to do in terms of you know the markets they serve and the customers
IBM's Position and the Fragmented AI Market
00:04:03
Speaker
they serve too. So um yeah, I mean, you know the reality is You know, when I talk to executives, I think the mandate is coming down certainly from the top, from their boards of of directors.
00:04:15
Speaker
They're pushing the mandates back, you know, down to their workers to do more with AI. But to be frank, I don't really know that companies, executives or otherwise really know what that means.
00:04:26
Speaker
um That's good news for people like me who are basically, you know, getting hired. to help figure that out. But at the end of the day, um you know, it's, it's a great question because I i don't know where we really are in the hype cycle. I see a lot of companies obviously coming on online. I see a lot of dollars being thrown at companies who are, but in the end, um,
00:04:47
Speaker
You know, I still think digital transformation, business transformation, the way we work, the way we operate as businesses, um you know, even the way that we deploy, you know, humans to to solve
The Experimental Nature of AI Investments
00:04:59
Speaker
real problems. I still think those are the the things that um in my mind are are far, you know, far more important um than the technologies that are, you know, bursting on the scene, as they say.
00:05:12
Speaker
Yeah, I think you raise a good point. You know, Watson, I remember that Jeopardy game, sort of the age of expert systems. And then I don't think anything and encapsulates the cycle that you talked about more than Watson itself, right? It was going to solve healthcare. care Then IBM broke it and sold it off for parts.
00:05:31
Speaker
And then yeah generative AI burst on the scene. and They're like, you know what, we have this new product called Watson X. ah like That's right. That's right. OK. OK. And the reality is, and I say this you know and with all due respect to IBM, which is a company that, you know i i as I say, I bleed blue, like a lot of folks do. And you know I hold them sort of near and dear to my heart. But they're still the market leader in in AI, surprisingly. um But the truth is they own 9% of the market.
00:05:59
Speaker
So it also tells you how fragmented the market's become in the last, you know, give or take, you know, decade. um There are a lot of technologies. I do think it's the generative note, right? Because you've as you mentioned, we've had machine learning, recommendation engines, all sorts of stuff for ages. But it was that what captures the popular imagination. I remember when ChatGPT first came out, all these very serious people on LinkedIn are like,
00:06:23
Speaker
look at this sonnet I made, you know, like this ability to generate new data based off of the training data is just so um intoxicating, I think. Right. And so that is, the that is, yeah, that's the appearance of this intelligence. And then when you start wrapping it around these terms, like AGI suddenly, yes, that's when like, oh, this is the, this is the transformational moment I think is very easy buy into. Yeah.
00:06:50
Speaker
And I think that, you know, what's interesting for me is that, um you know, I think history always has a ah way of repeating itself. I think, you know, early days in in AI was extraordinarily experimental. I think it still is today.
00:07:06
Speaker
um You know, there's a recent article published, it was actually a study by MIT, um published in, you know, multiple outlets. But the the point being that 95% of you problem yeah projects are failing. and I think it's failing in part because so much of it is experimental.
00:07:23
Speaker
Folks are you know learning to do things with you know agents and you know training agents and training models and doing all of the things that you know we read about and see you know maybe online every day.
00:07:34
Speaker
But at the end of the day, um a lot of these things aren't scaling, part and parcel, because I think companies are are not putting enough dollars and cents behind the investment and the long-term viability of some of these things.
00:07:46
Speaker
um In other cases, the opposite is completely true, where you've got some startups that are literally in in A and B rounds getting you know tens, if not hundreds of millions of dollars thrown at them without a product. Some of the seed seed raises are like $30 million. This is bananas.
00:08:02
Speaker
It's insane. I was actually um lucky, fortunate enough to have gone to a um ah light ah speed event in New York about a year ago. And, you know, this was sort of an AI showcase. um If they called it that, maybe not. But at the end of the day, there were 10 companies on stage all talking about their AI you know future. Mm-hmm.
00:08:23
Speaker
And you know the one thing that they all, a few things that they all had in common, one was that you know most of them had multi-time um co-founders. So these were companies mainly started by co-founders who have you know two, three, four times in, who've done this successfully in years past in different capacities maybe.
00:08:40
Speaker
um Very few, if any, had a product. um Very few, if any, had a customer um and none of them had revenue. um Yet the average, and I'm not not exaggerating, the average deal flow for these companies, all of whom were pre-A and a maybe 1B amongst the 10, was on par 700 million euro.
00:09:02
Speaker
um I, at the time, was working for a startup trying to raise 5 million and fail and failing miserably. So um you can imagine how it made me feel sitting in the in the crowd, you know watching these companies sort of swim and in dollars that were flowing in from you know dozens of VCs um without a product, without any revenue, without customers.
00:09:25
Speaker
um But they all had a vision. um And you know seemingly, a lot of these VCs bought into that vision. So I guess good for them. But I think that's you know also part of the problem. You've got sort of the tale of two tapes where corporations are trying to do more you know based on the mandates that their shareholders and their boards and their CEOs are effectively mandating.
00:09:47
Speaker
And then, you know, you've got, you know, folks, frankly, just trying to figure it out. And, yeah um you know, in the end, I always say, look, it's not a hammer nail scenario. At least it shouldn't be. So for me, it's always about, you know, what problem is worth solving? Can we solve it in a 10X way? And if technology can be an assist to that, then amazing, right? But um but I think that's where, you know, a lot of my time these days is kind of spent um in that form, you know, really trying to help um clients really figure that piece out.
AI's Impact on Corporate Strategy and Operations
00:10:18
Speaker
Yeah, i do think also some of those mandates coming either from shareholders or boards are predicated on beliefs in so-called productivity gains. But quote I put that in air quotes because the the real ah desire is cost efficiencies, yeah which means you end up chasing sort of at the margins rather than thinking bigger picture, like you said, like a true transformation.
00:10:48
Speaker
so um i I want to get your take there in terms of like You know, instead of thinking of these like one time moonshot, we're going to change all the things overnight.
00:11:01
Speaker
What is it like when you're advising clients to think about AI as more of a core capability? Like what does that shift in mindset require in terms of leadership, thinking about resources, timelines, success metrics?
00:11:15
Speaker
Yeah, I mean, I think the, I mean, AI in a sense is is changing everything for companies. and It's not just the ah ways in which they um operate, um but it's also and in in terms of how they think. I mean, you think about most corporations, they operate quarter by quarter.
00:11:32
Speaker
Um, you know, they always kind of focus on their fiscal year and, you know, ultimately what value, if you will, they can return to shareholders in the in the same you know cycles. I think with AI and the propagation of these technologies, um, the massive swell of data that we're, we're now privy to, um, you know, the reality is, is that, you know, decisions and, and strategies and,
00:11:56
Speaker
you know, market signals and shifts in in in your business happen almost daily, right? um And I think being able to, you know, to analyze that data, rationalize it and action it is is hard.
00:12:10
Speaker
um It was hard for companies to do that quarter by quarter, year by year. now they're being asked to do that, you know, at at the pace of of of which this technology is is coming online. And to some degree, the the pace of which, you know, customers are expecting and these companies to sort of shift with those ties.
00:12:29
Speaker
um And to operate in those forums is incredibly hard. i mean, you know, no surprise and it's no knock on most Fortune 1000 companies. I think most of them operate, if not, you know, almost all of them operate with antiquated operating models, um massive silos between departments, um you know, fiefdoms and decrees of ownership that at the end of the day slows progress, um governance that often is looked at as ah as an inhibitor more so than an enabler.
00:12:59
Speaker
So I think the mindset and mechanics of how organizations operate in this you know brave new world need to change. um And then you add to that the fact, too, that you know I think there are CEOs who recognize the value and and and recognize the fact that there are real pressures to to do, you know quote unquote, more with AI.
00:13:20
Speaker
But, you know, if they're five years into their seven year tenure, you know, it's the next guy's problem. Right. So there's a much little bit of that that exists too. So it it is, it is massively challenging. And then, you know, you add to, to that ah point I made earlier about, you know, the fact that so much of this is, you know,
00:13:38
Speaker
I would say more remedied in in in this ideal of experimentation. You know, what I say to clients all the time is like, look, if we we find an idea that is worth investing in, you need to be ready to go all the way, right? This is not something where you're going to take $2 million dollars or create a ah slush fund that has a few more in it um and, you know, sprinkle a little here and sprinkle a little there. at the end of the day, we were talking about products that, um you know, again, are are, you know, moving at massive, you know, can move at at at more massive acceleration um ah trajectories that the end of the day, um yeah just really fundamentally changes the way that that people um operate these things. And so I think those operating models, the way that, you know, companies are rallying around these technologies, the amount of investment that is required in order to sufficiently scale these things um is is is incredibly important.
00:14:36
Speaker
um and And at the end of the day, it will it will drain um to some degree shareholder value. And that's that's to me that what's at odds with most fortune companies because, um you know again, not everything's gonna be a success. You almost have to treat it like a startup.
00:14:54
Speaker
Um, some will make it through and some won't. And that's just the, you know, that's just just life. And so I think the, um, the way in which, you know, companies are, are, you know, again, experimenting with, um, you know, who understand, you know again, the, the,
00:15:11
Speaker
we're in it for the long haul. And, and that at the end of the day, um that's going to require, uh, even a new balance of folks coming into the organization that are just fundamentally, if they can act and work fundamentally differently than, than most of the traditional folks who have maybe been in the company for decades.
Integrating Human Insight with AI Outputs
00:15:28
Speaker
Um, that's a, it's a challenge. Um, yeah, I'm glad that you, I'm glad that you mentioned operational, um models and organizational problems because I have this pet theory that like there's a friction point at which the AI and in tooling is okay, but it's running into a human wall of meat, right? Like like you could detect things, you could ah analyze things at lightning speed, but if it runs into the way we currently organize things,
00:16:06
Speaker
It's just going to run into just these roadblocks because we have organized human labor along lines of specialization, right? Like you mentioned silos. We have those silos because that's what we're used to. That's the paradigm we've always been operating for centuries.
00:16:20
Speaker
And once you introduce these things that like machine learning that can just do one of those functions at orders of magnitude faster, Like what, what does, it's not a one-to-one replacement, but what does that require of your teams to take in that information and like do something with it?
00:16:37
Speaker
You know, that's an interesting problem to solve. And it requires, you know, you know, sounds cliche because every company talks about retraining and reskilling and, you know, all, I mean, in some cases, maybe even replacement. Right. But I always equate it to, you know, it's kind of a brain with, you know, without a nervous system.
00:16:54
Speaker
It it doesn't work, right? You still need people. humans Humans are very much in the loop. I mean, that's that's the bottom line of where we are. I mean, if we have this conversation 10 years from now, who knows, right?
00:17:05
Speaker
but um But at the end of the day, you know, humans are very much in the loop. And I think Um, you know, the ability to, to rationalize the data and the signals are coming through the technologies, um, how to apply them, you know, how at the end of the day, how to service your customers better, um, how to, you know create better product, better service, you know, all of it, um, to me is still very much a ah human, a human challenge.
Good AI Governance and Experimentation
00:17:28
Speaker
Um, again, the accelerated pace by which AI and technology is enabling us to do some of those things is, is pretty incredible. um But again, harnessing that, you know, putting the right governance in place that again is less of an inhibitor, more of an enabler.
00:17:42
Speaker
It's giving, you know, folks within the organization permission to experiment and to do things that in the end will push the business um to a better place. um You know, those are the things that, um like I said, I spend a lot of time, you you know talking with clients and working with clients to to do it these days.
00:18:10
Speaker
You've mentioned governance twice now. So what does good AI governance look like when it's working in terms of that balance between innovation speed and controls? Yeah. I mean, again, i I always say this, you know, maybe ad nauseum, right? That it's, you know, good governance is an enabler to progress. It's not an inhibitor.
00:18:30
Speaker
And so, um you know, putting, you know, setting guardrails and setting, you know rules of engagement, so certainly important, but, you know, um almost turning your folks loose in in in in ways in which they can apply those guardrails sensibly,
00:18:44
Speaker
um you know play by some set of rules, but at the end of the day are are free to experiment, are free to do you know some of the things that frankly make startups so so viable.
00:18:55
Speaker
um It's kind of taking that that mindset and and and approach that um you know can in some cases be indoctrinated into large ah you know all scale businesses.
00:19:07
Speaker
To me, it it's it's it's those types of things. And and not being... um ignorant of the fact that operating models are very much part of governance. To me, those two things tie together quite, you know, um, quite importantly.
00:19:22
Speaker
Um, and so being able to look at, you know, how you sort of modernize, um, the ways in which organizations work again, knowing that tech data, uh, marketing sales, um,
00:19:32
Speaker
um I mean, even back office departments like legal all need to kind of work tangentially together. And so you know how do you start to put the the rules of engagement in place that becomes more cooperative and collaborative?
00:19:48
Speaker
than prohibitive. um And I think that's, to me, what, you know, harmonious governance um in in the in the age of of AI kind of kind of looks like it needs
AI's Transformative Potential and Challenges
00:19:58
Speaker
to be. And that's a little nirvana, um in a sense.
00:20:01
Speaker
But, um you know, the way... i think that's i think that's the hard work. I mean, that that, again, we just have a human organization, like getting those... It's stakeholders to work in harmony.
00:20:13
Speaker
and and and And almost, you know, forgetting what you know. It's like, you know, um I think that, I mean, look, we went through this, by the way. I mean, this is not new. um You know, we've been going through this for years.
00:20:25
Speaker
um You know, years and years and years ago, we started with, you know, something called digital transformation. And then that transcended into business transformation. And I would argue that most of those quote unquote transformations, whatever you call them or whatever, you know,
00:20:38
Speaker
noun or verb or, you know, other, uh, descriptor you put in front of that, you know, we were, we were mid journey if we were even that far in most of those journeys. And so now all of a sudden you've got this thing called AI and you've got this, you know, this whole other thing to, to, to think about. And, and, you know, obviously that opens up a lot of challenges. I mean, we, we,
00:21:01
Speaker
you know, a lot of my time also is spent, you know, talking about legal and privacy and all of the things that, you know, come to mind when you start thinking about AI and data. um But again, in the end, I think common sense rules, right? And so if you're, you know, if you're applying some common sense to the business and you're, you know, enabling your people to be, to experiment and to, um you know, to do things that in the end are,
00:21:26
Speaker
um you know, are proving hypotheses maybe that they or you as a business have, um you know, working together to deliver those products and services to market in inequitable ways. To me, that's, you know, that's how companies will win.
00:21:40
Speaker
um That's how markets will continue to be shaped. and um And that's the, you know, that's the space that I love personally playing in because it becomes inventive, it becomes inquisitive.
00:21:51
Speaker
um it's not necessarily rooted in, you know, what got you here. um It's really rooted in what's going to get you to whatever that place is over the next, you know, 10, 15, 20, 50 years.
Addressing Data Readiness for AI Success
00:22:05
Speaker
um yeah and that and and Yeah, you mentioned data. I remember you said, i mean, those buzzwords sort of haunt me from my earlier days. I remember big data was going to solve all the problems, right?
00:22:18
Speaker
Yeah, it actually created a lot more. Yeah, it turned out just like creating data at massive scale and then trying to figure out where to store it was part of the problem. yeah But to that point, I think there's probably a case to be made for experiments falling flat because...
00:22:35
Speaker
organizations have either underestimated the level of infrastructure required to make use of that data? Like, do they know where it all is? Is it labeled? Is it structured? Is it unstructured? Just like not really yeah knowing it, right? they They're like, I got it somewhere.
00:22:50
Speaker
and's right And so you have ah written about operational plumbing and data readiness. yeah So could you talk a little bit about what that means in practice or or what are the things that you've seen your clients underestimate in terms of like all the stuff they have to get ready in order to get on the AI train.
00:23:11
Speaker
Yeah, I mean, you know, it's um it it kind of is mind blowing, mind numbing when you think about all of the things theoretically you need to get right as an organization. i mean, obviously there's the technical aspect and the investment in tech and infrastructure and modernizing your tech stack in a way that is, you know, cloud enabled, API driven, all of it that we've been talking about for years.
00:23:36
Speaker
You hit it on the head. I mean, I still think the biggest challenge so many companies have is is is a data problem. Their data is everywhere. um Data lakes obviously didn't didn't solve that.
00:23:47
Speaker
um you know In some ways, they've actually created more more problems than than then folks um thought perhaps they would. and um And you know to boot, right I think the amount of monies that have been invested erroneously in some of those projects have also drained and fatigued the CFO and their willingness to write checks. And so, hence even the AI, the great AI experiment where you know CFOs are writing you know checks $2 to $3 million dollars at a time. Well, that's great. But at the end of the day, that's not going to progress your business forward.
00:24:22
Speaker
um And then there's the people quotient. right I think you know at the you know in the end, you you need operators. And I think you know you go back to you know what I mentioned earlier about digital transformation. I think one of the the big divides in in those who maybe did it better than others and those who who you know maybe fell on their face and failed is the fact that...
00:24:45
Speaker
um technology was sort of seen as the as the great savior, right? If we make the right investments in technology, our world will be great. If we make the right investments in you know data, our world will be great.
00:25:00
Speaker
um And in reality, you know none of that really proved to be true um in part because again, organizations, I think more you know more more often than not, just frankly, didn't have the operators or the operating muscle to to to, you know, advance those things sufficiently in ways that that paid them back, right, in terms of their return on investment.
00:25:22
Speaker
And I think AI is, you know, frankly, that on on steroids, I think the the challenge is, you know, as you know, these technologies are not inexpensive. um The compute power that's required to power a lot of these solutions is extraordinarily expensive.
00:25:37
Speaker
And in the end, if you're not prepared to do, know, in a sense, what you're investing to do, can be crippling for organizations. I mentioned earlier. Yeah, think sometimes I want to pause people and be like, know you want AI in your business.
00:25:53
Speaker
Yes, totally. if you were to like take a first step, my question is, does it need to be a generative model? Probably not. Maybe you just need some deterministic modeling first. I don't know. Does it even need to be AI? I mean, you know, like that's the other thing I, you mentioned earlier the first, the, and the Lightspeed event that I went to. I mean, I can't tell you how many um products I pry the hood open only to learn that it, that's not even AI. Yes.
00:26:21
Speaker
um good you It's quasi ML at best, but um but a lot of it is, you know, it's decision tree, it's modeling, it's algorithmic. I mean, great.
00:26:31
Speaker
um It works and it's functional. But in the end, I think lots of folks have, you know, there's been a great AI washing and, you know, a lot of buzz just, you know, sprinkled on top that I think has disillusioned a lot of folks.
00:26:45
Speaker
Um, and I mentioned that the nomenclature is like overly broad, well right? No. And it also doesn't help when you've got, you know, again, VCs throwing hundreds of millions of dollars at quote unquote AI companies. Right.
00:26:58
Speaker
Um, when in the end, like I said, at the end of the day, every, kind every great company has to solve a problem that enough people have.
Strategic AI Investments for Growth
00:27:04
Speaker
That's, that's, you know, that's the quintessential philosophy of business, right?
00:27:10
Speaker
Now, without that, it doesn't matter what technology, what data, you know, you know, what else you have that, that you know, if if, if you, if you're not solving a big enough problem in a 10 X way, then you're not, you're really not doing much, um,
00:27:23
Speaker
you know, um, period. So, you know, I think when, you know, talk about, you know operations, again, it comes down to, you know, people, operators, it comes down to investment, it comes down to patients, right? Things, things are not necessarily going to, you know, ah materially create, you know, tens of millions, billions of dollars of, of, uh, of revenue instantaneously, if they ever do at all.
00:27:49
Speaker
Um, you know, you kind of need to be prepared to lose as much as you're hoping to win. And I think that's, that's the reality. I think, again, you sort of treat it almost like a startup. Um, you know, you gate things in the way that, you know, VCs gate, gate funding. I think corporations need to do the same thing.
00:28:05
Speaker
Um, you know, that said, I think when, when you start to think about, you know, how you govern this, I mean, to me, again, it becomes, uh, uh, and a moment of enablement, again, where you're you're almost enticing or um incentivizing your your people to be an you know imaginative and inquisitive and and inventive in a sense.
00:28:28
Speaker
um and But if the signals aren't there, the signals aren't there and no technology in the world is going to change that. and And to your point about, you know, business has to solve a problem.
00:28:39
Speaker
I think some of these experiments are probably a microcosm of that. It's like, totally do you understand the problem inside your business that you are trying yeah to solve for?
00:28:51
Speaker
And are you designing for that outcome? Or is it just like, I was told to put this thing here. Let me see what juice I can squeeze out of it. Well, yeah, and that's a great point because I think a lot of the investment you know that I've seen in a lot of these cases even that I hear almost daily, it's all about cost takeout and savings. And I can save time or save costs or I can do more with fewer people.
00:29:15
Speaker
So therefore, you know i'm going to you know save my company you know tens of millions of dollars a year And that's going to excite my shareholders versus, you know, what can I actually be doing to contribute to my top line that frankly, I think if you do that right and well, it excites shareholders even more.
00:29:34
Speaker
Right. So yes, it's like, sort yeah and can I, can I, yes, can I boost the P&L by nibbling at the margins or can I completely transform how fast I deliver to customers?
00:29:47
Speaker
Yeah, that's right. And look, and I got into this game a few years ago where, you know, if you looked at the, you know, I haven't looked at the, you know, at the, at the Fortune 1000, you know, revenue, you know,
00:30:02
Speaker
numbers, you know, year on year for the last couple of years. But there was a moment in time between 15 and 24, I believe. You looked at the, at the top, um you know, 500 companies in the world, the average margin or the average growth was about 3% annually year on year, right? There were ah very, very few number in the Fortune 1000 growing by double digits. Most of them were were sort of new, new emergence, if you will. So they were still kind of young and and youthful in their in their business trajectory.
00:30:31
Speaker
Um, you know, the only thing they were winning on, frankly, was Marvin, right? They were extraordinarily profitable, but they weren't really growing, um, you know, uh, significantly year on year. And I think, you know, I think to, to some degree, um,
00:30:45
Speaker
AI has kind of fed that that um you know that that model. I think everybody seems to think it's ah it's a it's a um both an equalizer as well as something that at the end of the day is going to enable organizations to do more with less. And maybe it will on some level.
00:31:01
Speaker
But um like I said, i I'm most interested in how can we use technology advantageously to drive you know top line growth, to provide better products and services to customers who consume them, provide a better level of service, you know customer experience and service to the same customers who are consuming those those products.
Identifying Real AI Leaders
00:31:20
Speaker
um And how can you use it to also build, you know, some operational efficiency into the, into the model where, you know, you're both, you know, saving money at the bottom line, but you're also, you know, gaining it at the top.
00:31:33
Speaker
Yeah. um Okay. I want to close out here with something that you had recently written on LinkedIn about how, the real AI leaders, meaning companies that are implementing it well and designing it well, will likely not be the loudest voices on conference stages, right?
00:31:53
Speaker
Sort of, it's right I guess, being performative and hand wavy about it. So what is, what should we be looking for in terms of separating operators from evangelists?
00:32:04
Speaker
Well, I think again, I mean, I, I, that, that, um, the scenario that you just described is not new. I think this has always been the case in, in big tech.
00:32:15
Speaker
Um, so AI is really, you know, just the, the new shiny thing that folks are theoretically putting on stage. But, you know, i always say that like real business leaders, really AI leaders, if you will, aren't selling the potential, right? They're delivering progress, right? They're the ones who are, you know, really translating, um the ambition into adoption. They're aligning technology,
00:32:37
Speaker
um with with business value. They're they're closing the gap um you know between pilots and impact. um And the operators yeah you really want to watch are the ones who are talking about aren' aren't necessarily talking about the future of AI, but they're quietly sort of embedding it into workflows um into building better products, better services, better outcomes for businesses, for customers.
00:33:02
Speaker
um And you know that's how I think real value is is being created. um It's not the chest pounders on stage who are talking about you know the future and the envisioning of it. I mean, we need those folks too, don't get me wrong.
00:33:16
Speaker
um I always say so what made Steve Jobs great is that you know he was a visionary that inspired people around him to to build his vision and and certainly you need that. But still needed Wozniak and Johnny Ive and all the others. 100%.
00:33:30
Speaker
That's right. And I think, you know, the folks that are really the the superheroes, if you will, of of AI are the ones that are, you know, there they're the ones building in garages and in basements and in labs and doing some things that, um you know, five years from now, I think we'll be talking about.
00:33:46
Speaker
Yeah. those are the real breakthroughs and it's not necessarily the ones that are, you know, again, prognosticating what, you know, what this future ultimately is going to be or look like that at the end of the day, I, I look at it as a, um, you know, to, to some degree, I i look at that as, you know sort of pandering. I i mean, again,
00:34:06
Speaker
I also would argue that the companies that I mentioned on stage all had you know incredible visionaries who have have done this multiple times over. So there's charm to that. But it's the people in the trenches that you know understand data, who understand technology, who understand customers, who understand markets.
00:34:24
Speaker
um To me, you know those are the folks to watch and to see what they in the next, let's just say, five years do with this new tech, a new ish technology, um, and the application of it to me, those, those are the leaders that I'm paying the most attention to.
00:34:40
Speaker
Um, you know, not necessarily the ones that are, are amusing about it on, you know, on, on, you know, in forums and blogs. And mean, I do that too, but I i feel like I'm in the trenches to trying to figure it out with, you know, with clients, customers, technologists, strategists, all of them.
00:34:56
Speaker
Yeah. All right. Well, Eric, thank you for the time. We wish you well in the trenches. Oh, thank you. Thank you very much. And thanks for having me on. I'm happy to join anytime and, you know, certainly, um you know, look forward to to future conversations.
00:35:12
Speaker
All right. We'll catch you later. Yeah, George, thanks so much.
00:35:18
Speaker
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00:35:31
Speaker
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