HR insights | Series 9 Episode 3: AI, trust and the future of human-centred work

In this episode of the HR Insights Podcast, Stuart Elliott is joined by Marc Ramos, Chief Learning Officer, Harvard Learning Innovation Lab Fellow and MIT-trained AI strategist, to explore how organisations can approach AI adoption without losing sight of the people behind the technology.

The conversation looks at the growing tension between AI-driven efficiency and employee trust, why some businesses may be moving too quickly into implementation, and how leadership communication can shape whether employees feel included or threatened by change.

From trust and transparency to cognitive overload, workplace culture and the future of human connection, this episode offers practical insight for HR, talent and learning leaders navigating AI transformation inside their organisations.

Key timestamps:

• 01:06 - Introduction to AI and Learning Development
• 03:58 - The Current State of AI in Business
• 07:02 - Leadership and AI Implementation
• 09:49 - The Spectrum of AI Adoption
• 13:08 - Risk and Reward in AI Investments
• 15:55 - Communication Strategies for AI Adoption
• 18:56 - Change Management and AI
• 22:00 - The Role of Trust in AI Acceptance
• 25:34 - Trust and Society's Insular State
• 29:19 - The Leadership Gap in Trust
• 33:10 - Human Agency and Cognitive Traps
• 37:27 - AI's Impact on Human Connection
• 42:41 - A Human-Centric AI Strategy

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Transcript

Stuart Elliott (01:06)

Hi Marc, welcome to the Alex Scott podcast. How are you today?

Marc (01:10)

Doing well Stuart, thank you so very much for the invitation.

Stuart Elliott (01:13)

xNice, really good to have you. Now, for the audience, can you tell us your name, where you come from, what do you do? It'd be nice to get a little summary from you.

Marc (01:23)

Sure, so Marc Ramos, Chief Learning Officer, helping to support Fortune 500 companies really, really develop the capabilities of their workforce and their partners. Most recently worked with Google, Microsoft, Accenture.

I actually spent some time in Europe supporting learning strategy and innovation at Novartis, which was great. And then moved back to the US, where I'm dialling in now from Cape Coral, Florida, where I was most recently the chief learning officer at Cornerstone. And again, really excited to be here.

Stuart Elliott (01:46)

Amazing.

Yeah, that's very cool. Cape Coral Florida. Where exactly in Florida is that? Some people listening won't know exactly where that is.

Marc (02:02)

Yeah, you... So Florida is one kind of big peninsula, for lack of better words, and on the Gulf side, not the Atlantic side, if you go about a third of the way up, that's Cape Coral, in between Naples and Tampa.

Stuart Elliott (02:15)

I it's pretty nice. I'm guessing you've got some pretty nice weather as well in terms of nice hot, warm conditions too.

Marc (02:22)

Yeah, yeah, yeah. It's a little subjective to a certain degree. I don't mind the heat, but I don't like the bugs. And the Gators, the famous Florida Gators, they're actually roaming around places that they shouldn't. That's okay. It's great to be here. My wife loves golfing and there's a ton of golfing, so that's another benefit.

Stuart Elliott (02:29)

Yeah.

I'm very jealous. I'm a huge golfer for myself. So yeah, am very, very jealous of that. today's topic, we're going to talk about all things AI. So we're going to go through a number of different questions, a number of things. Your experience with that, before we get into the questions, do you want to touch on that in a little bit more detail for our audience?

Marc (03:01)

Yeah, I think in terms of the current state of AI and I think that the birth, so to speak, of the current state of AI was really around November 2022 when Chat GBT OpenAI's 3.5 kind of came out. So that's when everything kind of exploded. So that was roughly three years ago or so. But even before that, when I was at Google in the teens, a big focus in

from an L &D perspective was trying to figure out machine learning and how machine learning, know, deep use of data can actually benefit learning programs, people programs and so forth. And that eventually evolved to the current state of AI. Where I'm at currently is I'm lucky enough to be appointed as a fellow at Harvard's Graduate School of Education supporting their learning innovation lab, which is a really, really deep focus and exploratory investigative focus on the use of AI.

Stuart Elliott (03:34)

Yeah.

Marc (03:58)

for L &D and for talent and HR, but mostly from an L &D perspective. And then I've also been doing a lot of consulting and advising of young, AI ed tech startups, giving them some perspectives, not only on the product, but also on the go-to-market motions and so forth. And then I've been doing a ton and ton of writing and research. think that's been most of my hours is just researching because

Stuart Elliott (04:12)

Very cool.

Marc (04:27)

AI and I know we'll talk about this too, it is traveling so quickly and I'm a fan and I'm curious, but I'm also a little concerned and in order for me to have the right mindset and to help folks, I'm just a research maniac. So based on what I'm researching, I do a lot of publications as well, published in a Harvard Business Review and some other publications as well. So that's really exciting is to really understand what's going on.

Stuart Elliott (04:51)

Very cool.

Marc (04:56)

try to make sure that it makes sense in my mind and put it on paper so that way, you know, people can critique and make sure that I'm on the right path.

Stuart Elliott (04:58)

Yeah.

Yeah, very, very cool. And I suppose to our audience a little bit, I'm sitting here now and we're surrounded by these articles around AI. It gets mentioned pretty much every day. was going to say almost every hour, every minute almost in some respects. But the conversation here is we're in different phases of where AI is. And different companies are in different phases. Some are more advanced. Some are not so advanced. Some are nervous about it. So for me,

I suppose when the topic of AI comes up as a podcast topic, a lot of people might say, another one. But part of me thinks there almost has to be, because it's an ongoing conversation that continues to change and evolve as we go through. So the reason we want to go there is because we want to be part of that continual conversation. And that's part of the reason why we thought this was such a good topic today to talk about. So I want to kick us off then. Now, AI.

Just mentioned, we talk about it every day, every minute, every hour, every day, it's getting mentioned somewhere. From your side, Marc, do you think that businesses are moving too quickly into AI implementation without fully understanding, I suppose, that the consequences for their organisation and the potential wider environment? And I mentioned that in the backdrop of the Standard Chartered CEO comments just literally yesterday around removal of lower value. Value human capital.

Marc (06:36)

Well, that wasn't exactly the most thoughtful comment. I'm actually looking at it right here. So the full comment is, it's not cost cutting. It's replacing in some cases, lower value human capital with the financial capital and investment capital we're putting in. We don't have job losses, but we do have job role reductions. Like there's a difference in favour of the machines.

Stuart Elliott (06:39)

No.

Yeah.

Marc (07:05)

So this is coming from Bill Winters, about 8,000 folks cut. I get it. That's a brutal part of our reality broadly. But one of the things that I'm hoping that we can talk about is leadership's responsibility to provide the right tone and the right messaging to their people. It's one thing saying that you're

Stuart Elliott (07:08)

Yeah, see ya.

Marc (07:26)

going to have to cut roughly 8,000 people. I don't know if I'd be as dramatic as calling those 8,000, you know, lower value human capital. I don't think that's the leadership's right message because there's other messaging that's happening. Commonwealth Bank in Australia, you know, they basically said they're going to be letting, they're going to be reducing by roughly 90 million in Australian dollars between now and 2030. But the messaging was radically different. The messaging was

We're taking that money to rebuild and reskill our existing people. And so I think that's a really, really big tone that we need to, I think, set. But getting back to your question, Stuart, wow, you know, there's just a lot going on right now. And I feel for folks who are listening to this podcast and maybe watching on YouTube in the sense of if you're in an HR or talent or L &D function, you're on this spectrum, right?

And the spectrum hasn't been changing, but now I think it's getting radically a little funky for lack of better words. The spectrum basically is, wow, I'm so excited. I'm so enthusiastic. This AI thing is going to radically change how we can provide better programs and services and content to our workforce. And so I'm so excited, right? This is the best time to be in HR. This is the best time to be in L &D or whatever it might be. And I love that. And I agree with that a thousand percent.

But there's this brutal reality of folks on the other side of the spectrum, right? The folks on the other side of the spectrum, they're loaded with anxiety. Is this AI thing gonna take my job? Is this gonna radically change my vocation and my role? Is it going to really have more outbound impacts negatively on society, on my community, on my family? And so where are we on this spectrum? And I think those folks that are kind of in the middle-ish,

where I'm trying to play, but there's so much noise, to be honest, and there's so much great things that are getting stuck in the noise. It's hard to diffuse exactly where do I want to play? How can I benefit my company? How can I benefit my team and then the people that I'm supporting, the workforce? And it's just a tough, tough, tough, tough time, tough situation. And I think that spectrum in itself is getting a little frightening and we can talk about that as well.

Stuart Elliott (09:49)

Yeah, it's a really difficult one, I think. To your point, I think people have been so quick on the enthusiastic side to sort of dive straight in. And even quotes or comments that I was seeing yesterday, I think there was one around, it won't be long before we have the first billion dollar company that has just one employee. And I think the fear of that is, what are we creating? And I think this is definitely

Marc (10:10)

Yep.

Stuart Elliott (10:18)

there is a worry. do you think then people are moving too quickly into it? Or do you think that, again, it's just a spectrum? Some are too slow, some are too quick, and some are doing it just right.

Marc (10:32)

Yeah, I think getting back to your prior question, think, you look at it at an enterprise level, regardless if you're a big company, small company, or you'll do that from the individual level. Am I responding enough to stay in the groove related to what is going on right now with Anthropic and Claude and whatever it might be? For me, I guess I have a little bit of a jaundiced perspective in the sense of

I, you know, 60 % of my time these days is just reading a whole bunch of academic stuff and then commercial stuff and then stuff from, you know, our big five consultancies, right? And I'm trying to read between the lines and my takeaway is absolutely, I think companies and individuals are frankly moving a little bit too quick.

to respond, you know, on the enterprise side, we've heard many, stories of companies, you know, we need to, you know, we need to remove X hundred or X thousands of people because the efficiency gain is just loud and clear. And I get that. And I think that's also part of business growth. But in many cases, and this is coming back big time in many cases, the people that you let go tend to be

call center or customer facing maybe on the sales side. And now there's a really big swing of that pendulum where now they have to rehire those folks back. So the message to me, think Stuart is, yeah, I think on the enterprise side, people are, think, you know, it's a knee jerk. It's an AI knee jerk, right? I think they're just trying to respond to what the board or what, you know, the macroeconomics at play are basically saying what they need to do. And then on the individual level, it's kind of,

Stuart Elliott (11:53)

Yeah.

Marc (12:15)

equally confusing, maybe not as dark if I can say that, because it's an opportunity and I'm looking at it from an L&D perspective, you know, it's an opportunity to upskill and to reskill and to make yourself the best, awesome instructional designer or program manager or facilitator will still need human facilitators, folks. And how can it be best in that domain to partner with AI? So I think the net for me is in both domains, but particularly in the large scale corporate perspective.

I think a lot of companies are still moving a little bit too quickly. And it kind of comes down to just not really understanding the true consequences that might not be monetary or commercial. It could be around culture, the voice of our people. It could be customer responses. It could be the industry's kind of macro view of, wow, we get it, but aren't you cutting a little bit too much? Because I need these people. I need the John and Jane and Stuart and Mary that's been supporting me.

Stuart Elliott (13:08)

Yep.

Marc (13:13)

and my success for the past few years. So, yeah, think people and companies are moving a little bit too quickly.

Stuart Elliott (13:19)

Yeah, my only thoughts on that a little bit, I always think the stock market's a really good one to look at when you're sort of looking at different companies. And the one thing I'd point everybody to is you look at companies like Microsoft and Meta and their share price. The share price hasn't gone anywhere purely because people are nervous. It's just, yeah, it's gone up a little bit, but not massively over the last year because of the amount of investment they put into AI with no return. But then you look at Apple, who have gone up in greater percentage terms.

Marc (13:33)

It's actually going up.

Stuart Elliott (13:48)

And they haven't really gone heavily into AI. And then you look at Nvidia, who have gone very heavily, obviously, into it because of what they do. And their share price has gone up dramatically. So I think the moral of the story really is that a lot of it depends on your risk appetite. A lot of it depends on how risky you want to be to get the return. And the riskier you are, obviously, the greater upside. But the riskier you are also means the greater downside. I think.

That would be where I would stand if I was looking at it from a company perspective. It all depends on your risk appetite. And that is something that I think it comes back to the culture of your firm, who you are as a business. And that is where HR should be talking to the C-suite around how this exists within their whole framework.

Marc (14:32)

Yeah, I think you're spot on the risk appetite, the risk mitigation. What's really interesting is, and I don't know if this is like a US specific thing, but I think one of the reasons why our economy, so to speak, can handle all of this risk is because when you're looking at firms investing into series A, series B startups, that level of risk tolerance, 1 out of 10 of you startups is going to succeed, but that's going to be a blockbuster. And I'm willing to take on the risk of that 90 % of the investment that's not going to truly happen. I think that's really, really there. I will say the risk thing, it's kind of 360 to a certain degree. So, I mean, there's like companies that are taking the risk to engage or not engage, whichever, but there's the investment community that's also taking a risk into where do I invest and where do I not invest? I mean,

Big players, look at OpenAI. So OpenAI, think, was a week ago or the week before. OpenAI, and by the way, Anthropic, they're starting a new services deployment businesses related to this whole kind of concept from Palantir called Forward Deployed Engineers or FDEs. So they're taking the Forward Deployed Engineer model and they're building a whole new services unit.

for OpenAI and then I know Anthropic and interestingly, it's lot of the same investors that are sharing that risk for both entities. But then you think about ultimate economics, right? I think OpenAI, for example, they're getting $4 billion to build out this new services deployment unit. $4 billion and that's the investment over the next four years to 2030. But you know what?

Stuart Elliott (16:16)

Wow. Wow.

Marc (16:22)

OpenAI's estimated loss for 2026 is still around $12 billion. They haven't made any profit since they started. So when you look about this risk, it's kind of this really funky dimension where the providers of the money are taking even a bigger risk for a company that's already devoted and announced their risk of, hey, we're only losing $12 billion this year.

Stuart Elliott (16:49)

Yeah.

Marc (16:50)

And it's

not as bad as last year where it you know, 11.75. So this risk economics thing is just really, really scary. And getting back to your prior question, you know, are companies moving too quickly? This is like empirical evidence, man. It's like there you can, I don't know if the agreements are public, but if this amount of investment is actually being fortified and deployed for that type of model that is still questionable.

Stuart Elliott (17:07)

Yeah.

Yep.

Marc (17:21)

We are in some really, really interesting times.

Stuart Elliott (17:23)

Yeah, I totally agree. And it's so interesting when you look at that. I want to jump to another question. I'm moving around a little bit. But I want to touch on the communication piece that we mentioned. Actually, standard charted, the timing of that is just ironic in terms of how that's played in here. But I want to talk about how leaders then should communicate AI adoption if they want employees to feel included rather than threatened. And that's going to be tough, isn't it? Because

Everything that we're hearing about, and I think you mentioned the good and bad, and we might go back to reference that in terms of how someone does it well and how someone does it badly. But that's tough, isn't it? Because generally, everyone's looking at AI as a way of cost saving, correct?

Marc (18:07)

I think so. I don't know if it's 80-20, but it's probably more 51 than 49 in terms of that group, if it makes sense. That's a good question. mean, looking at it from a leadership perspective and the decisions that key leaders, whether you're with Standard Charter or Commonwealth or IBM. IBM, what IBM's doing, their CHRO Nickel is just announcing some phenomenally positive stuff. But when it comes down to leadership traits,

Stuart Elliott (18:13)

Yeah.

Marc (18:36)

I don't think there's anything like super, super, super radically different. You need to establish trust. Trust is established by being transparent. Transparent is also being fortified and established by walking the talk. So C levels, they got to say, hey, I built an agent over the weekend. no, don't say over the weekend. I built an agent one morning. And it kind of does this. All it does is really help filter my outlook or my Gmail better.

Stuart Elliott (18:56)

Yeah.

Marc (19:06)

But it's one thing to say that you're in it, but I think in terms of transparency, you should show your teams exactly what you built. So you need to be trustworthy, transparent, and I think you need to be authentic. Good old classic authenticity, but truly authentic in the sense of, this is what I built. Can I share it with you? It's kind of ugly, I know, but this is what I'm doing, this is what I'm trying, and this is what I'm trying to do from a self-learning perspective.

And so I think that's another attribute because I think once, once leaders actually show that level of authenticity and that they're not the perfect AI agent builder, that is totally fine. And that's expected. And I think getting back to the 95 % of C levels that are not doing that, there is short-term, medium-term and definitely long-term damage because of all the other factors of anxiety and job displacement and so forth. So yeah, it's again, really interesting time,

Stuart Elliott (20:05)

Yeah, it is. But with, suppose, this adoption then comes, I suppose, a lot of change. And change is something that I've always found that people struggle with. historically just don't like change. It's something that everybody does struggle with. is there a reason for why people or so many organisations change efforts, I suppose, struggle today? Is there a general reason for that?

Marc (20:37)

I'm someone said this before I was born. So some very famous individual, know, humans don't like change just in general, whether it's a, fight or flight kind of construct. And I think there's like some, some DNA parallels there. What's really interesting. And I mentioned this from at the onset, you know, from an L & D guy's perspective, this is the best time to be in this vocation.

Stuart Elliott (20:39)

Yeah

Marc (21:03)

because we've been striving to have the mechanisms and the tools and the data that AI now provides and provisions for us if we're willing to kind of jump into the pool. And so that willingness to change, I kind of think it's at an individual level. But then getting back to the kind of leadership examples, if leadership and culturally we are providing, this is what I'm doing. I'm whomever and I'm the CEO or I'm the board whomever.

And these are my frailties. And this is where I do think the optimism and the opportunity is. By conveying that very, very simply without ideally damaging the vanity or the ego of C-level people. Nothing against vanity or ego of C-level people. But I'm just saying that's kind of part of it. And I think we need to get out of that. And I think this is the opportunity to also be a phenomenal, phenomenal leader.

because now there aren't any excuses. I mean, we can call AI a tool, we can call AI a friend, we can call AI your therapy, your therapist, whichever. It isn’t going away. And so I think the, companies that are starting to be a little more on the cutting edge, I think they're, they're providing those attributes that I mentioned and a whole bunch more. So we'll see what happens. I mentioned Nickel at IBM. So the chief,

a people officer, CHRO, and she announced, I think like five weeks ago, counter to what IBM has been saying, you know, I am going to triple the amount of entry level people starting in the US as soon as possible. That is the opposite message of a lot of other companies. But her point, real brief, is just common sense. You know, it's middle management that really, really is the fuel and the engine of any successful enterprise.

Stuart Elliott (22:45)

Yeah, is. Yep.

Marc (22:55)

The reality is if we don't bring in those new people that are going to become middle managers, we are diluting that critical aspect of our future. The other thing she said, I think was really, really bold, particularly coming from a chief people officer, she said, and by the way, half these people we bring on board, we're not even sure what they're going to do. But her point was, I recognise that.

Stuart Elliott (23:18)

That's fair though.

Marc (23:22)

But here's the thing, in three months, 90 days to whatever, the ballgame's gonna change and there's gonna be a whole bunch of new roles where we want to be ready. And the conversation is what's the dilemma between readiness and risk? And at some point you gotta play both sides, but it's both sides of the same coin, right? It's survival at the corporate level. And then I think it's also growth at the individual level.

Stuart Elliott (23:47)

Yeah, interesting. What role does trust play in whether people embrace or resist AI inside an organisation?

Marc (23:58)

Oh boy, we need like a... We need another...

Stuart Elliott (24:00)

Yeah.

Another 45 minutes on top of this 45 minutes to talk about it.

Marc (24:08)

I've been thinking and I've been speaking a lot on this and this whole thing about trust, it's obviously critical, right? But we need to look at it from, I think, different segments. I think when you look at it from a global perspective, I don't know if you read the Edelman Trust Barometer, it's a really, really good annual view of trust. And basically, when you're looking at it kind of at the highest level, 49 % of the world does not trust AI.

And what's interesting is that number is getting worse. But what's super interesting, I have the report right here, what's super interesting, and by the way, if you're not familiar with the element trust barometer, they're looking at roughly 40,000 individuals in roughly 30 countries. So it's really crisp data, and I think it's fairly accurate. What's really interesting is what they're basically saying is there's like this certain momentum related to trust, and we can break that down geographically, commercially, whatever. 2016, their focus was on the fact that we're

where trust is being diluted because of the class divide. 2023, they were saying that trust is being diluted because we're living in more and more of a polarized world. Last year, it was trust and the crisis of grievance. Now this year for 2026, they're basically saying that we're in a state of insularity. And what that basically means is the one question that they asked, which I think is just mind blowing, the one question that they asked in the survey for these folks,

When it comes to trusting someone who lives by my core values, has different perspectives, but they respect mine, I am generally unwilling or hesitant or open to trust that person. So unwilling, 30%, hesitant, 40%, open, 30%. So 70 % of our society is in a insular state.

where they're not only trusting AI, they're not trusting their neighbourhood and their friends and their peers and their colleagues. Good, bad, or wrong, if this is fairly accurate, think the impacts are huge. That's like this global level. What's interesting too about the 49 % is it's actually 72 % of folks in the People's Republic of China have a higher level of trust versus the 32%.

and the 28%, 32 in the US and 28 % in the UK. And that probably feels about right, but you gotta think about where the heck did that 40 % difference kind of come from? So that is a really, really interesting question. And then when you look at trust, I think not only at the global level and then at the regional level, there's this gap. Microsoft has its annual work report.

And those numbers are striking in the sense of, know, executives at the C level believe that AI is trustworthy when you're doing this kind of programs, whatever. And it's like a 35 % difference of the worker saying, no, that's not right. So there's like this leadership gap. And then there's the gap even within your peer community. There's a article that came out by HBR. It's really, really fascinating, Stuart, and it's a little scary. Basically the article was one where they did a survey of, I think, 2,000 engineers. And they had some observers of the people that are going through this test. Not really a test, but basically they asked these folks to do these particular kind of programming exercise or activities. And they were observed by these folks. And the thing is, the observers didn't know if any one of those engineers used AI or not. And so what was

I don't know you're familiar with the survey, but what's really interesting is the result was one where if an observer felt that someone used AI in this exercise, they were deemed by, I think it was 9 or 10 % of not being as competent as someone who didn't use AI. Then the numbers got worse. When the same kind of folks were looking at women programmers and engineers,

it moved from that  9% to 13%. So just from a gender perspective, if you're doing the same type of work, if you're using AI, your competence is deemed even more. And then it got even kind of worse, worse, where there was observers that were older male engineers evaluating these other folks. And if they found out that it was a woman engineer, the number moved to like 34%. So there's like this trust.

even within ourselves, that's getting really, really questionable. the trust thing is, I think, probably core to adoption. there's this great quote by, I think it's someone in Google, and they basically said, the number one area of adoption is trust through transparency. And I really think that's so spot on. So I don't mean to kind of go.

Stuart Elliott (29:14)

Right.

Marc (29:19)

into the trusted zone, but I think it's a really, really big deal.

Stuart Elliott (29:23)

Yeah, it's super interesting because I think you've got two real issues, haven't you? A little bit of that seems to be cropping up more so now. Obviously, there's the internal company issue for the adoption piece, but there's also this external negativity that seems to be cropping up.

more generally. that's a couple of articles that I looked at just prior to this call that sort of implied that there's the negativity towards the data centres themselves with the amount of energy potentially that they might take up and what that might mean for, I suppose, countries moving forward. And then there's the, I suppose, the broader negativity around general jobs, et cetera, as well. And some of the stats are quite interesting. Like 64 % of 18- to 29-year-olds think it's

too quickly. 67 % of 30 to 44 year olds think it's moving too quickly. it's amazing that something a couple of years ago seems so inevitable. The negativity now seems so high, which is what surprises me the most.

Marc (30:25)

Yeah, I think you're spot on. And I, so like there's negativity to me. Negativity is like a, it's somewhat of a passive response, but I think it's getting worse. Uh, there's an article that came out just this week from, uh, uh, the researchers at writer and workspace intelligence. And it's a really, really phenomenal survey. And what the one big takeaway is a lot of it is one is 29 % of employees admit.

Stuart Elliott (30:38)

Yeah.

Marc (30:54)

actively sabotaging their company's AI strategy.

Stuart Elliott (30:58)

Wow.

Marc (31:00)

What are they doing? So they're using unauthorised AI tools. They're feeding proprietary data into public AI systems on purpose. They're deliberately generating poor AI outputs beyond slot, right? They're refusing mandated AI tools. What's really interesting is we were talking about middle management for like IBM for something, middle management anywhere. That's kind of basically coming from Gen Z folks, Folks that are currently 15 to 30 years old or something like that. And

Their sabotage exceeds the 29 % to 44%. And this is really, really scary. think it's particularly scary for the HR folks and the talent folks on this podcast and on the video. One of the stats of the same group at the executive level, 60 % of executives plan on laying off employees who cannot or will not use AI.

Stuart Elliott (31:35)

Right. Wow.

Marc (31:59)

92 % of C-level leaders admit they are cultivating an AI elite employee class. And they are more likely to be promoted three times faster than if you're not in that elite class. So I think you can both read between the lines and you just say, this is kind of malarkey, right? It's not really fully there. But there's something there.

Stuart Elliott (32:12)

Wow.

Yeah. Well, it's something that, again, I suppose companies have to deal with or think about when they're implementing AI. think we mentioned, there's a trust from both an internal and external perspective. And that's a tough one to manage because you've got this negative sentiment externally that seems to be coming through. And I'm guessing that negative sentiment probably sits internally as well. Somebody internally is not going to be very happy about this.

Marc (32:51)

yeah.

Stuart Elliott (32:54)

So how do you do that? you as a board member are moving ahead with this, this is the future. This is what you think. How do you deal with that? Do you have to fix cultural and trust issues? Is that the first thing you do before you do the announcement?

Marc (33:10)

Yeah, I think you definitely need to tackle the whole kind of trust issues. I think there's two other issues that also need to be tackled. And one of them has to do with human agency. So human beings in general, we like this thing called control. We like to make our own decisions. So just at a high level, generally speaking, that's human agency, right? And human agency is being attacked.

And I think what's fascinating is, so that's just one thing in general, is that we need to think about how to consider new people measures just related to human agency. And I think it's a really, really big deal. I think we need to think a lot more about how we measure the choice being made related to an asset that's actually being built or a program that's kind of being built. I think it's also really, really helpful.

is really thinking about, again, from the context of a measurement perspective, what I'd also kind of look at is, and I have a list here, I'm going to jump, I'm just going to say something really, really quick. So it's one thing to measure consumption. It's one thing to measure AI activity, but from a human agency perspective, I think it's really important to have measurements related to, you feel the ownership of your output?

Ownership meaning getting back to agency. Do you feel that you have any say from an L & D perspective in which L & D or talent tasks are being automated versus no, that should stay with human. You know, are you doing the parts of your job that you find most meaningful? You know, meaningful, meaningfulness. I mean, that's a word. It's kind of like this loss attribute. And where is the real meaning? Cause AI.

Stuart Elliott (34:55)

It is what?

Marc (35:02)

I'm sure this is an argument somewhere. AI doesn't really provide meaning in a sense of human meaning. So I think the human atrophy thing, think is just, I'm not atrophy, the human agency thing is really important. The other thing, just really, really quick, because this is a really hot topic. There's like these cognitive traps that are happening for a lot of our workers. So I'm just going to...

highlight what I think are like the four biggest ones that are kind of like in a certain cadence. So there's like cognitive offloading, which basically means I'm just going to rely on AI to do X percentage of my work. Okay, so that's like the act that occurs. Then there's the consequence of that. So the consequence of that is what I consider was a lot of papers on cognitive atrophy. So after you've offloaded some of the work,

Is there any, are there any skills that are actually now being consciously or unconsciously offloaded as well? So that's part of this is atrophy. And then you're familiar with work slop. You know, that's another result. as a result of the first two, now I don't really have to do accurate stuff. I'm going to lean on AI. But what's really interesting is from a work slop perspective, a lot of workers don't know that the slop being created now.

is not intentional. I don't know that I'm building all that slop. Or really, you found that 30 % of my slide deck is incorrect? Really? I didn't know that. Which kind of leads to this fourth point that I call delusional spirals. Delusional spirals in the sense of, you start to have like this schizophrenic, schizophrenic, that's the word, relationship with AI.

Stuart Elliott (36:22)

You

Marc (36:36)

where now it's my buddy, of course it's trustworthy. Yeah, give me some advice on the dating scene or whatever it might be, right? And so think these cognitive dimensions are really, really starting to really overspin in the sense of A, not being viewed by your coworkers in the company, and then C, and then B, not understanding the impacts of the business.

Stuart Elliott (36:37)

Right.

Yeah, yeah, it's really interesting. I want to touch on then, talk about a little bit in terms of the human connection within organisations and

Will AI enhance it or does it risk diluting that human connection? Because part of the reason I like going to work or doing what I do is for the connection that I get with people. But are we running the risk of removing that completely by the adoption of AI?

Marc (37:27)

I don't think completely. I mean, one of good things about AI is it helps you understand what you're good at. Now, it might only be 80 % correct because of all these other factors. But you know what? If I didn't know that I actually have really good analytical skills related to seeing patterns, I kind of thought so, maybe intuitively. But now AI is saying, actually, if you use the right prompting,

Stuart Elliott (37:51)

Yeah.

Marc (37:56)

Give me some advice in terms of skills that I'm not recognising. Did you know that you're... So if you have a certain... So that's great. So like this new found or unleashed expertise is now becoming explicit or visible. I know that's definitely happening. It's definitely happening to me all the time. But anyway, I know that's happening. You're finding out that you're actually pretty good at something. Well, if that's the case, getting back to how I work with my buddies and so forth, if that's the case, do you have an obligation

to take that unleashed newfound expertise and share it. One of the things I think really is fascinating, this is outside of the L & D domain, there's a lot of companies that are increasing their spend dollars related to having the average person teach something to others. This whole aspect of teaching is learning twice. And this is something that was very predominant at Google. We had a program called Googler to Googler. And the whole premise here is anyone

can teach any topic at any time to scale or just to individuals. And so getting back to your question about collaboration and community, I am kind of seeing that pop up because AI is kind of maybe unleashing is not the right word, but AI is providing something that new about Marc and Stuart that we weren't even aware of, we didn't know about, but here's the trick. Are you sharing it?

So the dimensions there of personal recognition are tremendous. The dimensions there of personal growth, wow, I didn't know that from a mobility standpoint, I'm a sales guy or gal and I can actually get into engineering. Well, yeah, because you have to those analytical skills that. So there's something really beautiful there. And then there's something also beautiful, if I can use that word. If you find new found time,

Stuart Elliott (39:49)

Yeah.

Marc (39:52)

because AI is creating something faster, better for you, whatever. But there's like some really good studies basically that say in general, there's a 20 % new net new gain in your hourly work because AI is doing all this other stuff. Okay, well you should take that 20 % to make yourself better. You should take that 20 % to teach somebody else. You should take that 20 % to improve what currently exists. You should take that 20 % to fill in gaps related to how we work with the business where we weren't resourced beforehand.

So those opportunities are becoming more apparent just being glass half full. And the glass half empty side is, yeah, you know, whether it's one of those cognitive ploys, to a certain degree, people are getting lazy. And the laziness can also equate to, you know what, normally I would walk down the hallway to talk to Mary because Mary's really cool and Mary has this stuff. Now it's kinda like, what the heck. I'm just gonna look it up online.

Stuart Elliott (40:36)

Yep.

Yeah.

Marc (40:51)

So that human engagement, think at some point it should be increasing, but on some other real points it is decreasing.

Stuart Elliott (40:58)

Yeah, OK. And I suppose you could chuck in whether we're all in the office together or whether we're not. I guess if you're not in the office together, chances are you're going to look it up online, because that's probably quicker, versus, yeah, I'm guessing that becomes a factor too.

Marc (41:09)

you'll return to work.

I think so. I think so. But I think you're starting to see like this. I think people are basically saying, yeah, because of AI, we are tripling. You got to come back to the office, not three days a week, but five days a week, because there's a new opportunity. Then there's the whole Google, Apple, Amazon mantra is, well, innovation occurs where humans can have a real conversation. Got it. Then there's the other people that say, you know what? I understand that you can actually perform.

Stuart Elliott (41:18)

Yeah, it's crazy.

Yeah.

Marc (41:45)

Better if you're focused and just doing the stuff that you need to do, whether it's AI related or not. And maybe they're kind of backing back from the return to office thing. I don't know. I think a lot of it comes down to the historical cultural nuances of the workplace where, hey, you know what? We work in manufacturing. I think you better show up to the office. Versus no, we're all a 100 % knowledge-based company, whatever that means. And you know what? No, do what you gotta do. And then there's all the stuff in the middle.

Stuart Elliott (42:06)

Yeah.

Yeah, that's fair. So we've got about five minutes left. there's one particular question I want to get to. And that revolves around, I suppose, a genuinely human-centric AI strategy. So I suppose the question to you there is, what does that look like in practice?

Marc (42:41)

So I wrote this series a little while ago called The Purpose as People. And it's exactly in this vein, Stuart, and that AI's purpose is to benefit mankind, period. But what does that mean in practice, particularly from a corporate perspective? So that's a really good question. I don't know if anybody has found the answer. I do know that.

a lot of what I would consider smart and savvy C level folks, they understand that at the end of the day, is about the humans. It is about the people and AI has a certain play to make the humans or the people kind of better. But what does that actually kind of look like? One of the things that I think is really interesting is the whole aspect of culture and that culture really didn't originate by a machine.

Right? think culture probably originated by some smart, savvy humans that said at the end of the day, this is what we believe in. These are our values and this is what it looks like in practice. So I think that's one really paramount attribute. think another one is, it's just kind of organisational. think one of the things about technology, one of the things about AI is it's flattening the org structure. And there's a lot of benefits of that, right? Speed.

access to more relevant information, accelerated support of the business, whether it's a regional or functional or whatever it be, business unit. So I think there's like this organizational play. And I'm not saying that a lot of companies are taking this on, but I'm starting to see this in Microsoft and IBM. At ServiceNow, it's becoming flatter and flatter, which is great. Why? Because now decision-making.

This is maybe like the third thing. I think the decision-making is now becoming louder and louder in terms of where humans play and call it, know, augmented work, you know, the AI agent with the human worker. get it. But at some point, decision-making still remains at this time, a human quality, a human attribute. So kind of getting back to your questions, how do we bring in like this, these humane or humanistic kind of attributes?

Stuart Elliott (44:56)

Hmm.

Marc (44:59)

You know, I think, I think culture, I think organizational structure really, really helps. And then understanding where do humans, where will humans continually play related to the right skills? And I think the top, top, top, it's maybe two top parallel skills. is decision-making. And then the other one is just understanding how to think. Because if you don't understand how you think related to how AI is trying to figure out how you think, then AI is going to commandeer your approach.

I guess there's maybe one last thing and that is related to augmented work and AI agents being embedded into the work to do some steps with you or for you. What's really, really interesting to me is, know, say you're working for BMW and you're building the engine. don't know. And you're in the assembly line. And let's just say there's 18 steps to build that. Okay. Now from an augmented perspective, maybe an AI is now doing nine of those steps. And so the human.

is doing the other nine, but not in reality. The remaining nine are now a brand new nine, but they're still for the human to figure out. So my last point here is if we understand how work is being rewired, where does AI play, where does humans play, whatever, but how work is being rewired, we must answer the question, where does the human fit?

And once we have that, then we have this kind of this humanistic humane kind of perspective. But that's just original thoughts. I don't think anybody has really, really figured it out, but some initial thinking.

Stuart Elliott (46:38)

Yeah, I think it's really powerful. And I think it's something for everyone to think about. Because I think the message probably one, two, three years ago that is coming, so everyone's got to deal with it. And it was almost like a get down with it or get out almost approach if you're not comfortable. And it feels like that message has shifted. It feels like that message isn't like get down with it. It now feels like.

people will start pushing against it or rebel against it if you don't deliver the message in the right way, if you don't have a human approach to it. And I think that is what I think is coming out and what I'm hearing from yourself, from everybody else, is that think about the message from a people perspective. Think about that first before you then go and bully is probably the wrong word, but I'm sure a lot of workers will feel like it's being there being

Marc (47:07)

Exactly.

Stuart Elliott (47:31)

bullied into doing this because that's what the board wants because it's going to save on profit, save money, it's going to increase profitability. But think about the people side of things first before you then mention the gain you're going to save from a profitability standpoint.

Marc (47:49)

Yeah. And then, you know, don't call people what's, what was the, the phraseology? Don't, don't call them, you know, lower value human capital. I remember in maybe 15, 20 years ago, know, HR wouldn't now be human capital. And I was thinking, really? that the, we want to use that word? But I totally hear what you're saying. And I think that's maybe the fifth element is, you know, got to treat people with respect. You know, that's ageless.

Stuart Elliott (47:53)

Yeah.

Yeah, yeah.

Yeah.

Marc (48:19)

And respect looks like this, this, and this. And I think new forms of leadership showing respect also means being uber authentic. I'm playing around with Claude code. I'm building an AI agent. totally fumbled. This is what it looks like. It doesn't work.

But you've got to keep on trying as well. And you've got to experiment. And we're protecting your time. We're protecting your manager to protect your time to make this happen. But there's no choice. We've got to get really, really good at this. But we need to think about humans and people first.

Stuart Elliott (48:49)

Yeah,

completely. I think that's a lovely way to of sign off on the show. But Marc, it's been an absolute pleasure to talk to you about this. And it is it's a topic that's going to sort of run and run. So I'm almost giving you a virtual invitation to come back maybe next year and repeat this conversation, because I think we'll potentially be in a totally different space than where we're at right this second.

Marc (49:11)

Yeah, happy to have it to support and I'm saying more than from a glass half full perspective, maybe from a glass two thirds full perspective, it will get better. It will be really, really resonating to individuals and then to company. So very happy to come back and I really appreciate the invitation and the time Stuart

Stuart Elliott (49:32)

Excellent. Good to have you on the show. Thanks ever so much. Cheers, Marc. Thanks.

Marc (49:34)

Thank you. Thank you.