What If The Real Competitive Edge Is Staying Human? With Susan Sanders

“Susan Sanders: To me, that's what AI is, it's this capability, and I think it could be really powerful, but we're really underutilizing it and misunderstanding. We're over trusting it, maybe even becoming over reliant on it, where there is a lot of usage. High usage could also equal something negative.”

[EPISODE]

Alex Cullimore: Hello, Cristina.

Cristina Amigoni: Hello. I don't know what day it is. Oh, it's Wednesday.

Alex Cullimore: Is it? Wow. Okay. We're Wednesday, Wednesday today, I fear, fear.

Cristina Amigoni: I know. Wednesday for Wednesday.

Alex Cullimore: If you’re seeing this on a release day, you're also on the same – exactly seven days. Not sure how many seven days since then.

Cristina Amigoni: Yeah. Yeah. It's another Wednesday. Don't know what month, what year, but it's another Wednesday.

Alex Cullimore: And speaking of another, we have another episode with a guest who's returning to us, Susan Sanders, who helped talk about – we talked with her and Rae over COVID, and she's had a whole trajectory of working with a lot of AI in companies and trying to understand adoption of it. She has a lot of great perspective to share on what the industry looks like now and what our hopes are for what can be different and what maybe should be different in a world where everybody feels behind. But what are we actually behind on?

Cristina Amigoni: Yes. Yes. What are we behind on is a big question. Yes, it's another episode about AI, because what else are we going to talk about these days?

Alex Cullimore: It's a good topic.

Cristina Amigoni: Yes. It's a good topic. Good conversation rules.

Alex Cullimore: More than enough to say.

Cristina Amigoni: Yes. Enjoy.

[INTERVIEW]

Alex Cullimore: Welcome back to another episode of Uncover the Human. We're joined by a return guest today, Susan Sanders. Welcome back to the podcast, Susan.

Susan Sanders: Thank you. It's been a while. It's been a while.

Alex Cullimore: It’s been a bit.

Cristina Amigoni: It's been a while. Yes. It's been a bit.

Susan Sanders: But better late than never, right?

Alex Cullimore: Yeah.

Cristina Amigoni: Yes. Yes.

Alex Cullimore: I think last time was sometime in COVID.

Cristina Amigoni: Lots have happened since.

Susan Sanders: What? What?

Cristina Amigoni: Yeah. It was a pants episode.

Susan Sanders: It was the pants off that episode, for sure. I do miss Rae. I'm sorry, she's not on our call today. Always an interesting lens or so.

Cristina Amigoni: It was COVID, because it was about soft pants. Yes. Wearing soft pants, because we are just neck up on videos. Nobody needs to know what's happening under that.

Susan Sanders: No.

Alex Cullimore: Susan, a lot has changed in the world. Obviously, we are out of the pandemic, so that's one huge positive change, than the other more ambiguous change, both some goods and some bads, it is the advancement, advancing of AI.

Cristina Amigoni: The advent. Yeah.

Alex Cullimore: Advent. Yes. The advent of AI and it growing. I understand you've been doing a lot of work in this space. I'm curious what your perspective is on the last crazy two years we've had of AI exploding in workplaces.

Susan Sanders: Well, it's been a bit of a journey. I made the transition to start focusing more exclusively on AI and my work three years ago, both in research, hands on experiences, thinking about how the world might change, how our work might change, and working towards creating some methods and frameworks and visuals that we can bring into the workplace to help us through it. I've seen a lot and thought about this a lot, and it's still ever changing. Talk to me in two weeks, and I might have an ever-evolving perspective on this. I might think about things a little bit differently, because while much of what's happening, we can look back in history. I say this all the time, I've seen this movie before. We have a lot of things we can look back to and we can learn from. Yet, it's like nothing that I've ever done before. It's nothing I've ever experienced before in terms of the range of impact, the pace of change that's happening with AI.

It gives us some unsettled expertise, if you want to call it that, where you're an expert, but only for a moment, because as something new enters the picture, you're going to have to adapt and pivot and think about things a little bit differently. That's on my personal journey.

Then comes around what I'm seeing with the impact on individuals that I help, or talk to along the way in this space and coach, and then the organization, which is where a lot of craziness is happening right now around what happened with AI and is AI going to take my job and we're behind, we need to have more AI, all these kinds of conversations coming out. It's nice to hear that we're getting a little bit of pushback on that, I feel like, of maybe we should slow down. We're hearing it from government officials, hearing it a lot from people. Even now, from some organizations that are saying, “Maybe we moved a bit too fast in our first attempt at this.”

Cristina Amigoni: Yeah. Like how you brought up the behind piece, because we talked about it offline and how there's this thing. I think I heard it recently. Somebody was saying like, “Yeah, everybody keeps saying like we're behind.” Oh, actually, I was at happy hour with a friend of mine and they're trying to figure out – it's an nonprofit, and they're trying to figure out how to integrate AI. That's still the pool. The pool is like, we're behind, we're behind, we're behind. We all talked about. We said like, behind what? Can we establish what we're behind? Who's behind? Who's ahead? Who's behind? What does it look like when you're ahead? What does it look like when you're right on time? What does it look like when you're behind? Because there's just this fear of being behind. But then, when you ask behind what, oh, we don't know. We haven't established behind what.

Susan Sanders: Now there's immense pressure, immense pressure to learn. I mean, I think my favorite ones are ads that come up on YouTube, or other kinds of things where it's like, master 28 AI applications in 10 days, whatever it is, right? I'm like, it's so unnecessary. Well, first of all, you'll never master it, because something new is coming around the corner. You don't need 28 AI applications. That's just a tool that's not really understanding AI. I find it interesting that with a non-profit organization, there's probably a lot of opportunity and value that one might have and they need it, because they always have to do more with less. I mean, that's just part of, I feel like, non-profit DNA. But there's a lot of ways to do that. It's not like they're going to fall behind the competition. This is a non-profit organization.

Cristina Amigoni: At least that's a what for the behind. No, they're not going to fall behind the competition, because in non-profit, it's probably more or less at the same pace. But it is interesting. The good thing is that as an organization, they've actually forced themselves to slow down and I don't know, clean their data first. I know. I know. It's a very strange concept. So that when they do choose the right tool and they figure out what we would use, which tool for what and how do we get more for less, they also have a good foundation ready for it.

Susan Sanders: Right. Now, I think that's a good point, and that might be a legit thing you could be behind on. What I mean by that is just jumping in without intention, without building that foundation, without building fluency and knowledge, because that could lead you to a conclusion you don't need to really adopt much in AI right now, but you should know whether you do or you don't and where it would create value. If you haven't done that as an organization, to me, that would be a mark of being behind. I bet you, there's a lot of organizations that are farther on the arc of what Microsoft is now calling AI absorption, not even adoption, and feeling you’re behind because you're not at that point on the arc. I'm like, that is not a measure, because you might not need to be there. A better measure would be, do you have your foundation in order? Do you have that understanding? That could be a mark to be behind on is being ignorant about AI and not developing fluency and how to judge when and where and how to use AI. That part could be detrimental.

Unfortunately, a lot of the leaders right now, a lot of the winners right now are losing, because they're winning at a race that's more like running on a treadmill. They're burning calories. They can see what their pace is and all this stuff. At the end of the day, they're still in a gym. If the goal is to get somewhere two miles away, they're never going to get there. They lost sight of what the goal is and more focused on those competitive indicators that really aren't determining whether in a good competitive position or not.

Alex Cullimore: Yeah. I was going to say, there's definitely some that are behind.

Susan Sanders: They're behind.

Alex Cullimore: They're behind in that, even when they aren’t, they are using AI, they're still put.

Cristina Amigoni: Yes. They’re still behind.

Alex Cullimore: They haven't done exactly what you're talking about, which is assessing, do we need this and do we have the foundation for this? Have we actually made the right choices of what tools we're using? Have we kept an eye on whether this is accomplishing any of the goals? We've seen lots of companies that just jump into like, “Oh, we're now using AI. We have to use AI. Everybody do it. Everybody do it.” Does that really clear what to do other than use it? Well, I asked ChatGPT a question and I'll send that off. I've now used AI today, so I'll check my usage mark. Yes, they have AI tools. They've bought licenses. They might be blowing through tokens, but they don't still have that clear view of why did we do this? And what are we aiming for? Are we actually making progress towards that? They’re very much in that treadmill feel of like, “Oh, now we're tired, but we're in the same place.”

Susan Sanders: Exactly. I mean, the good news is I think we've all seen it personally in the work and conversations that we have, but they're starting to be real evidence in different reports that are coming out, where all of a sudden they go, “Wait a minute. We get all that. We have really high adoption level and we've done all those things and we don't see any value in it. As a matter of fact, we're making a lot of investment without a lot of gain anywhere. We don't understand what we're doing.”

There's starting to be evidence of that. The Microsoft Work Trends Report speaks to that. McKinsey speaks to it. MIT. There's starting to be more study around what is really happening as a result and getting complaints from leadership that says, “We don't see the value. Where's this value? Where's the magic?” You're like, “But we're winning, because we have 100% adoption.”

Cristina Amigoni: Right. We’re winning. We're behind at the same time. We're winning and we're behind.

Susan Sanders: Right. A lot of that, though, was because that's not a horrible measure. If no one's using AI, you're never going to discover opportunities and creating value, that's for sure, but that's not the goal. That's just a means to an end, so to speak. Yes, of course, people have to understand it. How they're going about that, though, and that adoption is really unusual as well. Because they’re treating as if it's learning Excel, or learning an application, as opposed to learning a completely different way to maybe go about your day and your work. It's unfortunate, because they learn the tool. Even individual employees don't see the value, because they're like, “Well, that was fun, but that wasn't so big deal. That's not going to take my job. Ooh-hoo, that didn't really do all that much.” Because they're in this, I know how to use Copilot, or I know how to use this feature functionality, as opposed to what could be different in my day-to-day or my team's day to day and our work processes.

Starting to look more on, let's take a look at what we do. Let's redesign work, because we have this great opportunity now. AI gives us the potential for value, not inherent value. AI is not valuable. It's like electricity in a lot of ways, because electricity is there, right? You might use it for a lamp. You might use it to drive your car. I mean, those are two very different applications of what's happening when you use electricity and it can also be expensive. I mean, all of these things that we do with it, it's what you do with it. Until you turn on that light switch, you don't have light. I mean, to me, that's what AI is. It's this capability, and I think it could be really powerful, but we're really under-utilizing it and misunderstanding. We're over trusting it, maybe even becoming over reliant on it where there is a lot of usage.

High usage could also equal something negative, if it's being used in the wrong ways. If it's generating inaccurate content, if it's not delivering on your customer experience proposition, right? There could actually be a negative consequence to high usage and adoption, too.

Alex Cullimore: Yeah. I think there's a big issue of trust when so many companies tried to go straight for like, “We're going to replace jobs.” Trying to get people to think about how this integrates into their work is now particularly challenging. They're not only not being told to do that. They're being told to think of it as a feature and generally just like, go try it out and see if it's a tool that helps. The opportunity you're talking about about redesigning, where it feels even further away, because if it feels like it started to encroach on what I currently do, how far does that get pushed before a senior leader decides, “Oh, we're going to go do a riff. Now we're going to take everybody out, because the AI can do enough of this.”

I think that level of trust is both a huge missed opportunity, because you could just expand the capabilities of people instead of replacing them. It's going to be really hard to have anybody even know whether you could replace people, because why would people spend their time working themselves out of a job when there's no fallback? We've seen all of the headlines about the scary job market and how long it takes to get a new position. There's so many obstacles to the opportunity that I think that we have been introduced somewhat unnecessarily, definitely unnecessarily in the case of, hey, we're just going to create this giant layer of mistrust around this tool that could change how we do things, could improve how we do things. Now there's a new psychological barrier on top of just usage expense and regular business decision barriers.

Susan Sanders: No, definitely. It's an interesting concept, because it's like, on one hand, people once they start using it might even get a little addicted. They have their aha moment, and they love that chat, or Copilot ever does this one thing for them that they hated doing, or that it just makes something they're doing better, richer. Because it's not always about time savings, right? I mean, it could lead to better decision making. I think it enhances a lot of my thinking and output, because I can go broader than I did before. There's lots of things that you get that aha moment.

Then at the very same time, you're like, “Oh, what's going to happen?” We can see the writing on the wall. What happens when someone says, “Oh, I can just use AI. I don't need Susan anymore. I can just set up an analytical model that would think similar to her. Now, what do I need that thinking for?” Given that some thought. If I can personally feel being what we call the phobo – fear of becoming obsolete, there's so much uncertainty around, we get excited about this, we take it to the next level. We help the organizations win whatever that looks like. What happens to us? Because we know that many organizations, even, I think, good intention ones aren't going to keep people on just to keep people on and aren't going to keep people in their existing roles just to do that and be nice. We still have to have a business and we still have to have a business model.

I mean, this is where I'm curious in terms of your thinking around this becomes a leadership dilemma, right? How do we lead people through this change, so that we're doing it fairly, honestly, and doing right by, first of all, keeping our heads on that we have a business to run, that the race isn't about how quickly can we deploy AI. It's going back to, what are our competitors doing, right? Maybe you let your competitor go first, so they can sputter out and then maybe you have that. All these things we learned about competitive advantage and the importance of customer input and status, focus on that and then think about where you need adjustments to make that change.

Even then, if you do have to make changes to remain competitive, what happens to the people? I don't know how many leaders are thinking about that and what the position on that is. More importantly, are they being honest with where they think they may be going in the future, versus don't worry about it. AI is not going to take your job. Only people who don't use AI will lose their jobs. That's not true.

Cristina Amigoni: Until there's 10,000 layoffs tomorrow morning.

Susan Sanders: Yeah. Until the agents do all your work.

Cristina Amigoni: Yes. Yes. Yes. Which has nothing to do with AI usage. Yes, no, you're not going to lose your job.

Susan Sanders: I mean, I think honesty is always the best policy, but is anyone doing that? Is anyone really leading, maybe even to say, we don't know, but we care, or we don't know, but we're going to do this together? I think it starts from a position of leadership that has nothing to do with AI and everything to do with the humans that are already being affected by this. The anxiety, they're not working less hours. They’re working more hours, because they've got to learn AI on top of everything else and it hasn't really drastically changed their job. Worried about, are they going to be obsolete? What kind of skills should they be? All these things.

We've talked about mental health and well-being. I think this is making that worse, because it's a whole new level of anxiety and especially for, I think, older, for people who are on the other end of their career. Then also for those just coming in, right? There's a lot of fear and anxiety. What are leaders doing? I mean, are they thinking about this in your experience?

Cristina Amigoni: It depends. Very few are. Fewer than probably needed. I think there is, as you said, now that not too shockingly, reports are coming out that like, “Oh, we're actually not getting our ROI from this.” Now there's a little bit of it. Maybe I should pause and think through this from a human-centric perspective. So, how do I do that? The gap is still definitely there. It's interesting, because there's definitely, the race is taking over logic almost. It's like, the whole long-term view, it's out the window. It's all very short-term. It's like, I got to show my shareholders. I got to show my board. I got to share whoever it is asking for whatever they're asking for in the next two months, because that's all I have. I'm like, and what's going to happen to your business in two years, in a year, in six months? Because you're not running a business for two months. You're running it for as long as you can, right? Isn't that the goal?

There's this pressure. Again, there is this like, we're going to be behind that somehow, somebody said at some point and everybody is worried about from all sides. Everybody's struggling through that. Like you said, the adoption piece, I find it interesting, because yes, they're measuring adoption. As we explain how it's being measured, they're not measuring adoption. They're measuring usage. Adoption is a different definition. Adoption does include, hey, how can I improve my job, so that I can do other things? How can I become more productive? Adopting that into a process, into an organization, into how people work, that's adoption. Measuring how many times you use ChatGPT, that's usage, not adoption. Just because you use it, you didn't adopt it to make your job.

Alex Cullimore: I think there's also just a huge incentive mismatch there. Like you said, the ROI is not there. People are now saying like, “Oh, tokens are actually maybe more expensive than the employee and the healthcare that I was paying before now.” There's a huge issue there. There's also the incentive of, I don't know that a lot of people are thinking, “Oh, I want to run this business for as long as possible.” There's a lot of people who just trade leadership positions every few years into a new company. They want to just try and improve whatever short-term value they can for this quarter, this year, this two-year stint before they leave. Then they can put a flashy item on there on their LinkedIn and move on.

If there's no incentive to actually keep that long-term, then human centric ends up just being this secondary feeling for the leaders who are only driven by the ROI portion. If you only think of ROI in the very short term, then you end up with, oh, let's try and cut costs as soon as possible. We saw that with Salesforce. Let's lay out 4,000 people, even though we don't know the AI is going to be able to pick this up. You see like, oh, we can get through this quarter. We can get through this very quick portion. Until you actually decide, “Oh, no, we're committed to this,” then you can do something like you're saying, Susan, saying, “Oh, we don't know where all this is going to land. We intend to try and keep as many people as possible. We want this to be a boost to everybody.” Then you can have some leaders like that.

There are some who just care about the people who do want to have that longevity. It's just we're not seeing, either they're not the loud voices, which is probably pretty common, because they're busy caring about their internal company and not spewing it on LinkedIn. Or they are just not as populous. It's hard to tell which one it is. We have gotten to surround ourselves with a lot of people who do care about people, but it's not necessarily the common case within the larger corporate space. It's hard to know where leaders are landing, but there are definitely different types out there choosing which way they're going to go.

Susan Sanders: Yeah. It's just interesting to think about where it all started, because it's relatively easy to assume that it's the AI tech companies that started the hype, right? Because it's in their interest to get people to use their technology. It becomes harder to make that argument when it's your shareholders, when it's just the ecosystem as a whole, saying it over and over and over again, it starts to feel very real. I mean, I can imagine that even there are probably some leaders that want to slow down and they're struggling with getting their board on, board onboard. I work with a lot of senior leaders that get it but are having a hard time getting the C-suite onboard.

To your point, we've lost – well, I said before, we lost our mind. We've totally forgot that we're not an AI company. We're a business. I don't know if we can shift that tide. I mean, maybe some of this failure that's coming out, maybe showing a better way. I mean, I think there's a better way. I think you can take care of your people and take care of your business and implement a whole bunch of AI. I think those things can coexist. It's when you are one tracking this, when it's just only about the AI and how quickly you can get it in.

The one thing that surprises me though is, like, have we forgotten about value in performance? It seems really obvious to me when we drive usage and let's say, we get a lot of people, and maybe they even say, “I save two hours a week now that I have Copilot.” You can multiply those two hours out and multiply it by the average salary. This is what a lot of people do. They're like, “See, there's the ROI.” I'm like, “No. No.” Because you're still paying those people for those two hours. What did they do with that saved time? Then they're like, coming up with all these other things. Well, they're using it for higher value work. Okay. What is higher value work? How is that helping you improve as a business?

It all comes back to, so even when we say to employees, go use copilot, use it a bunch, experiment, figure out what you want to do, are they working on the right things that are going to have the most impact? Or are they working on the things that they find most entertaining? I mean, even in a work perspective, right? Maybe they love doing deep research, but that's not their job. But they really enjoy and going down the rabbit hole. That's not their job, but they really love Copilot, like don't take it away.

No one's thinking about how is that converting to something of value. I think the problem is you have to flip it. What are we trying to do? Then give people the task of saying, how can you use AI to improve this, to make this better, to make this faster? We're starting with people just experimenting on their own, not aligned, or tied to anything. I don't know why we've lost that sense of performance.

Cristina Amigoni: Yeah. It's definitely something got lost. You said before, maybe before we started recording, how we've been here before. Change AI and call it the cloud, call it something else. We've been here before. This is not new in history. Somehow, there's a loss of memory happening in what actually happens when we go down this path and the cost that it takes to come back from it. Especially when there are cases, like the IKEA case that we've talked about on the podcast, where if you can do it right, you can do it right for the humans, you can do it right for the business, you can do it right with the technology. It's not a one-track thing. You can do it very well for all three.

Susan Sanders: Yes.

Cristina Amigoni: So, why not? Why not try to do it that way?

Susan Sanders: I mean, I'm as puzzled as you. Because, I mean, I think, originally, I got it. I get that we're at where we are today, because in the beginning, when this was being rolled out, when AI was being rolled out over the last three years, the change leaders in this were Microsoft. Because if you think about it inside of an organization, Microsoft was pushing the change, not the organization. I think a lot of change leaders and change management people wouldn't even know where to begin. It's scary. It's AI. Organizations like Microsoft come out with their playbook like, this is how you drive adoption. It's hard to push back and say, well, I could have given some examples, past experience examples, but it's hard to come out definitively and say, that's not going to work. Even though a lot of us felt like that's not going to work.

Now there's data that says that didn't work. And there's data that's starting to point to what is working and we have history, and I see people just pushing on the gas pedal harder on what they were doing before, not pausing to absorb that. That's the part that's really puzzling to me. I get it why people weren't clamoring to do something different a year and a half ago. But we have data, anecdotal and some of these studies and things like that that are proving out what works and what doesn't.

Alex Cullimore: I think maybe some of it is what we're talking about the whole time, the feeling behind, that sense of urgency, it makes people want to push that gas pedal and then make even fewer of those complex, or critical thinking decisions about how are we going to apply this to try and balance out all of our three pillars. There is some of that just hype and urgency that I think has pushed past the idea of, “Oh, we should maybe be more diligent about how we're doing this.”

Susan Sanders: Right. Well, a little tortoise and the hare. I was going to say, rabbit and the hare. That doesn't make sense. Those are the same thing. I think about it in that sense, right? That you go there really fast, but maybe the organization's going a little bit slower, waiting, learning, seeing. Not stopping, not getting started, but not rushing to the finish line, but taking the time to be intentional and strategic around how they're going to bring AI in the organization and are still focused on their business and the value. I mean, hopefully, and I think we're starting to see that those should be the winners. Maybe it will self-correct when we finally realize the leaderboard was all wrong and we get a new leaderboard of what AI success really looks like.

I was calling it AI. It's just business success, right? Who's going to come through this and be the leaders and be intact? One of the things about people is I feel like the organizations that don't care about people, then there's a consumer issue. That, too, that says, you could have a whole brand that says, we are not moving fast on AI. We're going to take our time, because we care about you. We want to make sure it's safe. I'm waiting for that brand message to come back. I think a lot of people will gravitate if it's a consumer choice organization to say, that's the kind of company we want to work, we wanted to support, versus, I know when Duolingo did their thing, a lot of people canceled their subscription, because they fired all of the translators, because AI could do it better.

Same thing with Klarna, these experiences where people are like, “No, we want to talk to people.” Forgetting about your customer is one place where then the consumers can come back and that helps shape who the winners are as well.

Cristina Amigoni: I find that also very hard and hypocritical to have it, especially as a big advertising marketing token, that, “We're customer first. We care about our customers.” But then the internal people get treated like shit. Because if you cared about humans, you would care about your own humans too, not just the external humans. I actually don't believe you when you say, “I care about you as a human as my customer,” because you're not even showing me that you care about the humans that you're responsible for.

Susan Sanders: Right.

Alex Cullimore: It's funny that those organizations also call their support groups overhead.

Cristina Amigoni: Oh, yes, yes.

Alex Cullimore: We care about our customers, but the one that calls the customer is an overhead department. We stopped building the product.

Susan Sanders: Again, we've seen that coming, we've seen that happening before, whether it's changing our investment portfolio to be with more green, or more B Corp. We have choices as consumers. It's hard for sometimes a consumer to push back, because of maybe a little dependency on – we're seeing it, like quasi boycotts, or complete boycotts, cancellation of subscriptions. I do think that the consumer can play a part in this. I think employees can, too. I did see an article, and this isn't necessarily something that I would say is rampant, but where people are quietly quitting using AI, because they're starting to see the light. They're concerned about data centers popping up. They're becoming an opposition of AI. Some organizations, they're allowed to say they're not going to use AI in their job. They're starting to be a little bit of a pushback, not just from consumers, but from employees as well.

I just wonder, as they get used more for extraction, how do we compensate people when they're going to design the systems that move them out of a job eventually? I think there's going to be more pushback. If I can add one thing though that's a bright spot is I do believe the one thing that hasn't changed a lot, and we've seen this movie before is organizations are slow to change.

Cristina Amigoni: Yes.

Susan Sanders: As quickly as they are getting people to use Copilot, the fact that they're doing it in this feature functional kind of way is slowing down agentic AI, which is we had power automate. We could have automated a lot of our workflows before and just chose not to do it. Essentially, agentic AI is an evolution of that. A scary one, but still an evolution of automation. We couldn't even get it right when it was power automate. But now we expect the same employees that couldn't figure out how to become citizen developers for power automate to be designing agentic systems that are going to be safe and trustworthy and productive. The good news is we don't move very fast to change. We've got a little bit of time before bigger disruption, I think, happens, before we see these orchestrated agent teams, where there's some wizard making sure they're all doing what they're supposed to be doing and all the people that used to do that weren't going away. I can see that happening, but not at the pace that I think a lot of people predict it. The capability is there. If we could figure out how to all do it and safely things like that. But the reality is the human capability isn't there, and the ability of leaders to really drive change in the organization.

Cristina Amigoni: When you said “wizard,” all I could think of is the image of the part in Fantasia, where Mickey Mouse is a sorcerer's apprentice and uses magic the wrong way to multiply the brooms and the buckets, so that he doesn't have to do the work. It's what's happening now. The movie is 40 years old. That is what's happening now. Let me not do my job, because I don't want to do it the way I'm supposed to do it. Let me – I’ll apply, all these things. It becomes a disaster, until it all has to be reversed. You’re like, no, take the broom, take the bucket. Let's go back to that.

Susan Sanders: Right. I hadn't thought about that, but that's a perfect analogy. Then pretty soon, isn't there a big dragon or something, right, that comes out that has to be battled as a result of all of that magic.

Cristina Amigoni: Probably.

Susan Sanders: Yeah.

Alex Cullimore: That's exactly what's going to happen with AI. We're going to get a dragon.

Cristina Amigoni: The dragon.

Susan Sanders: You need dragon slayers.

Cristina Amigoni: Yes.

Susan Sanders: Then to be in the center of this is a challenge. I question my sanity a lot. Because on one hand, I am helping organizations accelerate. My acceleration model is also about doing it in a more human-centric way and an honest way. Nonetheless, it's still accelerating AI. Then on the other hand, I'm like, “We need to slow down. What do you mean you didn't have capacity? Why are you making us do all of this stuff and you didn't really have the capacity to keep up? What's all the data center stuff?”

It's a really weird place to be in. I think the one thing is it's not – I mean, and they're starting to be consensus around this. We don't know. We don't know exactly what's going to happen, when's going to happen, how big the disruption is going to be? A big part of that is because it hasn't been done yet. I always talk about it's not inevitable. When someone says, “What's going to happen?” I'm like, I don't know, but we still have a say in this. I feel like we should use our agency more. I think I am trying to get more of a collection of voices to agree that this is the framework we should use, just like they have it for responsible AI when you're building an LLM. There's these frameworks, there's governance, all these things. We need that same responsible AI effect on how we're dealing with the humans and thinking through these models and how we're thinking about customers. I think we can shape it. I think we can shape it, but the key is how do we break the AI spell, since we were talking about magic, which AI is not magic, but how do we break the spell so that we get back to business? Let's get back to business.

Doesn't mean we kick AI out, but let's get back to business, let's flip this and get back to our priorities and then say, how could AI help us achieve that, remain competitive, be more competitive? What opportunities might this bring? Duning that, they not, how quickly can we deploy AI and reframe it?

Alex Cullimore: I did like one thing you had a little bit ago, Susan, about you don't win AI. You just win as a business. When we all adopted the cloud, we weren't like, “Oh, look at this cloud winner.” We were just like, “Oh, look at this business, which has done better since the cloud.” Or was able to utilize the cloud to do better. But there's this weird primacy of AI of we consider it the thing, instead of the business that was running this whole time. You do healthcare services, you make warehouses, you make outdoor gear, you are not an AI company. You can use AI, you might use the cloud, but you're still an outdoor gear company.

Cristina Amigoni: Yes.

Susan Sanders: That's a really good point, because the other thing is there might be some organizations that literally are in a race, right? It's going to be to automate for autonomous AI, or die. Or to really develop some deep research, especially if you're thinking about biotech. There are some places where use cases and bringing AI in the organization are mission critical, but not everybody. I think about one of my peers, fellow Microsoft partner in Germany, and he works with the lumber industry. They're like, “What do we do with AI?” He's like, “Not much.” Like, we'll figure it out, but this messaging is not for you, right? Because there'll be some things maybe that'll help behind the scenes, or managing customer, or whatever planning, or whatever it would be. But in the core of your business, AI is not essential.

I even think about a non-profit that I worked with, where what I was going to give them the ability to do is they're not going to cut headcount, because they never have enough headcount to do. If they have more time, or they could add more staff, that means more people working with their constituents. They train service dogs for veterans, right? So, they would have more people in the field, can't have a robot or AI go do that with the veteran. It has to be a human and there's no need to invest in something to take their place. If they didn't have to do a lot of things that take a lot of time from them administratively, they could each have more veterans that they're working with.

I see opportunity there on how they can deliver more on what their mission is. I think that's a cool thing. If they went in and said to everybody, “Go learn,” which we didn't do. We didn't say, everyone's got to go learn Copilot. We started with that use case, so that they could already see the benefit and it happened to be AI, but they didn't even have to really learn AI. They needed to learn the process in which we brought AI in to be the time saving on some of their administrative tasks.

That's an example where AI does create a really compelling opportunity and I would never tell anyone to completely ignore it. That's not a race for them, but there is a lot of opportunity and value. Or in healthcare, a lot of behind the scenes, maybe, but we still need patient care, people physically in the room and they might get assisted by some things that are AI, but they don't need to learn Copilot, right? They need to learn a new process that's AI enabled, forgetting that everything is not chat. I call it beyond the prompt.

AI has been a long time behind the scenes and we've been using it without being aware and that has created value and benefit for us. Well, the same thing applies. This idea that everyone's got to have a Copilot license and engage in so many chat activities a week and I'm like, no, it's situational and it needs to have a purpose. I think that's one of the distinctions. A race for some, but very few.

Cristina Amigoni: Debatable, but yes.

Alex Cullimore: I like that example of the service dogs, because it's – There's definitely are the ones where you just can't replace the human in it. The human has to be the dog trainer, the human has to be the health care provider, or whatever the person in the room making those decisions. But there's also stories like IKEA, where they realized AI could answer 70%, 80% of all customer support questions. But instead of just laying out their entire customer support team, they retrained them all to be interior designers and introduced an entire billion-dollar line of revenue for themselves. I mean, this is not even like, oh, you have to have a human do that.

There's also just opportunity to repurpose the intelligence and creativity of the humans into a billion-dollar business line for IKEA. There's huge opportunity if you treat it that way, instead of just being like, “Great, this is a cost saving measure.” I worry that there's too many people currently who are just choosing cost savings, instead of choosing growth, choosing cost savings as a measure of, “Oh, well, we aren't spending as much.” Instead of what, else could you do?

Susan Sanders: Well, this might surprise you, but that way of thinking about just pure productivity and time savings is part of the lexicon. Like, when you point out to someone that that's not what this is all about only, or that simply saving time doesn't create value, again, it's one of those things that are under the spell. It's like, save time, save time, save time. Then I'm like, for what?

Cristina Amigoni: For what? Exactly.

Susan Sanders: If you save time, but you impact quality of something, is that good? There’s lots of ways to save time, doesn't mean that that's the right call to make. But we've again, lost our minds on this too, because you come into any conversation about ROI and it's save time, save time. To your point, I think save time is always a part. Well, no. Because like I said, I do things I never would have done. I actually take more time on certain things, but I give much better outcome.

What is the goal here? If we want better diagnoses in health care, right? What if you improve the accuracy, spending a little bit more time with a patient? AI enabled, but you improved your diagnostic capabilities exponentially. That's valuable. That's really valuable. You may add more staff to do that, but then you may get more business, because you are delivering higher value to your patients. I think everyone wants to take the way we work today, pop AI in it somewhere to save a bunch of time and call it a day. I'm like, that doesn't make sense either. Because –

Cristina Amigoni: Success. We’re no longer behind. We made it.

Susan Sanders: Yeah. But we don't need AI to work like we work, because it has some special skills. We don't need to layer AI into a process. We need to rethink, how do we do our work? I like a model, too, that I work with, where it says like, just because you can doesn't mean you should. You're redesigning a process and you get to a point where your AI results are pretty close to the level of quality of human judgment, right? But should you? You still have humans. You could have a good customer angle on that that say, just because we can, doesn't mean we did. We still have people here.

What are you giving up? Because you still have to pay money for that AI. That AI can drift, by the way. It's not like you create that AI and then it's going to work perfectly. You've got to continuously have experts in checking and the results, because it could drift. While it might give really great judgment at one point in time, something could shift. Now it's not. How do you know that? If it's a high-risk thing, maybe it's better to leave it in people's hands, even though, technically, AI could do it. Discerning around what part of the process do you give to humans and what part do you give to AI? Just think of them as another contributor, right?

It wouldn't be like you'd have your intern. Like, okay, great. Now we have an intern and their judgment is pretty good. We're going to let all of the SMEs go. You wouldn't do that, right? You wouldn't do that. That's where we're at with AI is this over trust. If it's something really important, I don't think you can be so hands off yet.

Cristina Amigoni: With what's proven that you can't. The data is wrong. What comes up is wrong. There's major issue.

Susan Sanders: Yeah. The thing is it gets better and better, but it also means it's changing.

Cristina Amigoni: Yes.

Susan Sanders: What ends up happening is by the time you have an agent that you feel you can trust, it actually ends up being more rules based than truly agentic AI, right? As soon as you engineer everything that it can and can't do and draw from and things like that, you have a hybrid. In my mind, you have a hybrid of what AI is really autonomously doing. There are cases where that can work, but it's consistently inconsistent. Meaning, it will do the same. It will process things the same way, but everything else changes around it than can deliver an inconsistent outcome. You can't ever get it to say the same thing twice when you're dealing with gen AI.

Anyway, so I think as we think about it as capabilities and contributions and what we value and costs, both in risk and in actual tangible costs to hire consultants and developers and play for tokens and consumption and licensing and all that, versus just have a person, right? In some cases, I know there's an education. They're talking about robots. Some people are, because of teacher shortages. I'm like, maybe we just figure out a way to get more teachers, right? Maybe you pay a teacher more.

Cristina Amigoni: Pay a shit load money for the robot. Why would you pay the teacher what you would pay for the robot and the tokens of the robot is going to use?

Susan Sanders: Yeah, yeah. That was one I'm like, I think there's a lot of ways that you can help teachers have more time using AI than to leap to having AI become a teacher. Because there's things that teachers teach that AI will never be able to teach. The cost to develop a well-managed robot that can perceive and do all the things that a human can do, because it's embodied in this thing to create that. I'm just thinking about the cost to do that when, like you said, just hire another teacher and maybe –

Cristina Amigoni: Pay the teacher more.

Susan Sanders: Yeah, pay them more. Maybe make it easier to become a teacher. These are things that might start to change as we have people looking for work. We decide that there are certain fields where we want them to remain human. How do we make that shift, too? That's a whole other episode of societal concerns.

Cristina Amigoni: Yes, that is. Of the end of society, yes. Yeah, and the teacher piece I can think of is in my own experience, and I'm sure both of your experiences, but especially with my kids and I see it every day. The teachers that make the impact on my kids are not the ones that teach them the knowledge, or the most knowledge. They're the ones that show empathy and make them feel seen and make them feel like humans and care about them as a human, not about, oh here's a worksheet. You memorize it all. Good for you. That is not the teachers that are making the impact.

Susan Sanders: Well, and there's even studies that are saying that teachers can have a bad day, right? Maybe they're a little bit snippy that, whatever it is, right? Teachers are human and they're like, exactly. Students need that, because they need to understand how to interact in a world of humans, because humans are not going away in their entirety. Again, all of this might get better. I'm not going to say never, ever, ever, because we can take those problems and maybe there would be a good use case and maybe we can create something that makes more sense. But you get it all the time like, way to go.

You get over when you're dealing with ChatGPT like, “Well, that's a really interesting insight, Susan.” You get this over – I can't remember what the word is, but it's a –

Cristina Amigoni: Validation, or –

Susan Sanders: Yeah. Validation and sycophancy. That's the other one too, where if you don't specifically say, don't do this when we're having conversations. A teacher will be a better barometer of that of what's real and that's a skill to learn conflict. We saw what happened during COVID when kids were doing everything on computer and were not interacting with other kids. Now there's some schools who aren't going to have any computers, whatsoever, in that theme that we need to really develop our human capabilities.

The bottom line is to be thoughtful and intentional and apply good judgment around, this is my short summary of around when and why and how we bring AI into our workplaces and into our schools and into our lives. If we can do that as organizations, if that can come from the top, I think that'll be the winner. I want that to be the leaderboard, or I want IKEA to be in top spot on the leaderboard and then let's see who fills in underneath that, and let's use that as the indicator.

Cristina Amigoni: Yes. That's a great indicator. Yes, most of you are behind IKEA. Go race.

Susan Sanders: Well, let's change the race. Let's change the course.

Cristina Amigoni: Exactly.

Susan Sanders: Let's change the racing course.

Cristina Amigoni: That's your measure of success. Go do that. Until you're like that, you're behind. Outside behind, behind that one, yes, definitely behind. Oh, so much to unpack. A couple of last questions for you, Susan, and we asked you this five – Did we establish when it was? 22 maybe? It was COVID, so 21, so four or five years ago.

Alex Cullimore: Yeah.

Cristina Amigoni: What is your definition of authenticity?

Susan Sanders: Oh. Takes out a whole new meaning now.

Cristina Amigoni: It does.

Susan Sanders: Well, I think the other thing about, especially if you think about authenticity and being human, one thing that AI is doing that I think is good is reminding us what it is about us and being human that is great, right? How we can bring that forward. The anti-authenticity is blatant now, right? When I think about it in the sense of as images and responses and conversations that are AI-generated start to proliferate into our day-to-day, do we start to forget about what's authentic and what's not? As AI helps us co-write, and I have to stop myself and say, I'm just going to write. I'm not going to use that, because I want to make sure I have my authentic voice. In one sense, being more aware of what does authentic mean. Then keeping that authenticity and keeping us what we value as humans distinct, rather than just always being a capability conversation.

Cristina Amigoni: That's a good one, for sure.

Susan Sanders: Yeah. I do think of it very differently than I would have in 2020. Yeah.

Cristina Amigoni: Yes. That was probably very different in two weeks when something else changes. Where can people find you?

Susan Sanders: Oh, I think the easiest place to find me is on LinkedIn. I don't know if that gets shared in that.

Cristina Amigoni: Yes, we'll share it in the show notes.

Susan Sanders: Yes, on LinkedIn. Then also to learn more about what I actually do, because I talked a lot about more of the view of things, but I actually have an approach in sessions and things like that that are pretty easy to bring inside of an organization to help drive these kinds of conversations and mindset shifts around how to really bring AI into organizations responsibly. That's at spintherock.com. Then while you're on LinkedIn, I have a newsletter called Is This Thing On? Well, it represents the fact that I've been saying things for over – It literally is. Well, I'm not going to say how many years it is, but this year is an anniversary year. I think it's 30 years that I came out with an idea that in, this was in graduate school, around a model that in human resources would treat employees more like humans and customers. Flipping that script.

A lot of things that I've carried forward have been from that philosophy and that research. A lot of things I talk about today, I feel like I've been talking about them for 20 years. I was saying like, is this thing on? Because I'm saying very similar things. Just talk data. You talked earlier about data on television. You we're talking about change management. These are all things that don't go away.

Cristina Amigoni: Same things.

Susan Sanders: It's just –

Cristina Amigoni: The same things.

Cristina Amigoni: Different flavor, same thing.

Susan Sanders: Yeah, so subscribe, or check out the newsletter and I think for the best ways to connect.

Cristina Amigoni: Excellent. Thank you.

Susan Sanders: You are welcome.

Cristina Amigoni: Thank you, Susan. Thanks, everybody, for listening.

[END OF INTERVIEW]

Alex Cullimore: Thanks so much for listening to Uncover the Human. We Are Siamo, that is the company that sponsors and created this podcast. If you would like to reach out to us further, reach out with any questions, or to be on the podcast, please reach out to podcast@wearesiamo.com. Or you can find us on Instagram. Our handle is @wearesiamo, S-I-A-M-O. Or you can go to wearesiamo.com and check us out there. Or, I suppose, Cristina, you and I have LinkedIn as well. People could find us anywhere else.

Cristina Amigoni: Yes, we do have LinkedIn. Yes. Yeah. We’d like to thank Abbay Robinson for producing our podcast and making sure that they actually reach all of you. And Rachel Sherwood for the wonderful score.

Alex Cullimore: Thank you guys so much for listening. Tune in next time.

Cristina Amigoni: Thank you.

[END]