The Digital Project Manager

Why Automating Everything is The Wrong Move (And What to do Instead)

• Galen Low and Kelsey Alpaio

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AI is not eating all the jobs—but the story is more nuanced than the headlines suggest. In this episode, Galen Low sits down with Jim Iyoob (President of ETS Labs, CCO at Etech Global Services) and Manu Dwievedi (AVP at ETS Labs) to cut through the noise around large-scale AI transformation in customer experience. Together, they unpack what’s really happening in call centers worldwide, why AI is more about augmentation than replacement, and how organizations can implement AI without breaking the customer or employee experience.

From pragmatic strategies to leadership-level insights, this conversation tackles the real challenges of AI change management: how to start small, get quick wins, and bring people along for the journey. Whether you’re leading a CX transformation or navigating an AI project in any industry, you’ll leave with concrete strategies to make AI work for your team instead of against them.

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Jim Iyoob:

You gotta be careful about this fricking narrative that's out there, that it's eating all the jobs. It's a lot more complicated than that, and I think the problem is nobody wants to admit it.

Galen Low:

Some of these companies are taking away jobs that maybe they shouldn't have created in the first place.

Jim Iyoob:

It's not your decision to tell me how to contact you if I'm buying your product. I can buy your product from anywhere. So the only thing that differentiates you from anybody else is the customer experience.

Manu Dwievedi:

When you think about implementing AI, you don't implement AI to replace people. You implement AI to make things better for your customers.

Jim Iyoob:

Everybody's trying to automate everything. Bull crap. Pick three things that keeps you up at night. Start there. You should see an ROI in 30 to 45 days... if you do it properly.

Galen Low:

Welcome to The Digital Project Manager podcast — the show that helps delivery leaders work smarter, deliver faster, and lead better in the age of AI. I'm Galen, and every week we dive into real world strategies, new tools, proven frameworks, and the occasional war story from the project front lines. Whether you're steering massive transformation projects, wrangling AI workflows, we're just trying to keep the chaos under control. You're in the right place. Today, we are talking about large scale AI transformation projects and what we can learn from what goes right and what goes wrong. In particular, we're gonna be zeroing in on the world of customer experience where customer contact centers everywhere are trying to incorporate the right level of AI into the customer and agent experience. With me today are two experts with deep expertise in both AI and change management in the CX world — Jim Iyoob and Manu Dwievedi. Jim is the President of ETS Labs and the Chief Customer Officer at Etech Global Services. He's my go-to person when it comes to pragmatic, straight shooting AI innovation that leads to operational excellence, and he's also a prolific author as well as a member of the advisory board for our sister publication, The CX Lead. Manu is the Assistant Vice President of ETS Labs and the co-host of Etech's podcast where he does deep dive explorations into AI and CX with respected experts in the field. He's also the person that I call when I get asked about enterprise scale change management challenges in the AI transformation space. Jim, Manu — thanks for being with me here today.

Jim Iyoob:

Thanks so much for having us..

Manu Dwievedi:

Thank you.

Galen Low:

I love digging into our conversations. I've known you guys for a little while. I expect us to zig and zag a little bit in interesting ways, and that's totally cool. But just so our listeners know, here's the roadmap that I put together today. I was mostly excited because you two, you're in this space, you're deep in the CX AI transformation space. There's been a lot of headlines in the news lately about big companies trying to incorporate AI into their customer experience. Some have a good outcome, some have a bad outcome. So what I wanted to do is I wanted to start us off and just address the big elephant in the room about AI call center transformation projects. And then I wanted to just get a few levels deeper in the headlines about like what is actually happening in these headlines. And then I just kind of wanted to zoom out, just use CX as a lens to talk about three things. I wanted to talk about, kinda like the state of AI, human hybrid business models today. I know this is like a big topic, but I don't wanna just talk about the tech. I wanna talk about like the cultural impact as well of like what you're seeing, boots on the ground. Then I'd like to hear from you about, yeah, change management best practices around any project that's implementing AI related change. And lastly, I'd like to get your thoughts on how like an individual department head or project leader should approach AI led change of this nature, especially if they're like already mid-flight in IT and how they can help make it do more good than harm. How does that sound to you too?

Jim Iyoob:

That sounds awesome. It's gonna be a good one today.

Galen Low:

Well, let's start with the hot question, and here's the way I see it. Even outside of the world of customer experience, A lot of us have seen these headlines, like the one about Klarna replacing almost all of its human call center agents with AI only to like rehire most of their call center staff after a few months. Other examples, just like that. So here's the question. When we hear about layoffs and roles that actually appear to be getting replaced by AI as a result of a big strategic pivot, how much of this is exactly what it looks like on the surface and how much is perhaps like more complicated than it seems?

Jim Iyoob:

Yeah, I'll probably get in all sorts of trouble for this, but in 2019, I spoke in Montego Bay, Jamaica and basically said, AI is not replacing your people. But like you, you really gotta think about this differently. You gotta be careful about this fricking narrative that's out there, that it's eating all the jobs. I mean, it's a lot more complicated than that. And I think the problem is nobody wants to admit it. So if we look at it from a holistic viewpoint, most companies overcorrected when we had this pandemic, so basically people were showing up for work'cause they were all remote. Let's be clear. So let's, hey, people are showing up for work, let's hire more people. You know? And really what's happening is a lot of people are just right sizing right now.'cause you hired so many people. And now they're saying, we're pivoting to AI, which is why I'm getting rid of people. That's not really true. It's really not strategic when you think about it. They didn't plan properly and now you're reacting to what the market is telling you. You know, I can tell you Etech, as a company, we've grown jobs, even with AI deploying AI everywhere. What AI is doing now in, in my opinion. Is really it's productivity to get people to do things better, faster, but it's enabling them to do it. What it's allowing us to do though, is give more skilled people more roles, right? And I think the companies that are using these mass layoffs are using that because they're basically saying, Hey, it's not me, it's AI. That's why I'm laying off these people, and they just think it's wrong. AI is always gonna need human people. We talk about it all the time. Human in the loop. AI is only as smart as the person that programmed it. So guess what? You need people to do that. And that's really what I'm looking at it. Versus what everybody else is saying about it. Everybody's just blaming AI for why I am downsizing. And by the way, the reason you hear these stories about people bringing back is'cause they didn't know how to deploy it in the first place. So instead of taking that customer experience, you're pissed off half your customers. So now guess what? You gotta pivot back because you didn't do it right in the first place, which I'm sure we're gonna talk about today with change management.

Galen Low:

I actually, I really like that perspective of like, the cause and effect is a longer period of time than we're looking at. And also that notion that AI is being used as like an excuse. And you could as you were saying that, I'm like, I can see how that works up and down, right? You're like, you've got your shareholders, and they're like, why aren't you 10 Xing shareholder value by replacing everyone with AI and on when you're looking down, you're like, okay, well also I need to justify some of the changes that we're making, whereas. It is actually a correction. I remember it was early 2020 and like I was part of a massive layoff at a large consultancy and part of it was yeah, lockdown and like the pandemic and everything. But then, you know, when I got down to it, they're like, actually, we were planning this for months. It was just the circumstances that made us pull the trigger a bit sooner than we meant to. Then we had planned to, but it was always the plan and I was like, oh, that's interesting. And I suspect like, I'm glad that like we're talking about this 'cause I could see how that correction would take place, right? It's like, oh, well a lot of the contact centers didn't even think that they could do remote call center at the beginning of lockdown and then suddenly it worked and then you, I could just, I could imagine it perfectly. Okay, you're getting like higher call volume. It was hire more people. It's actually easy now. We don't even have to like get like workstation set up, like they're remote and actually this is like a correction of that. I think that's fascinating.

Manu Dwievedi:

Absolutely Galen. When you think about it Watson, and just like you said about stakeholders specifically, right? What's an easiest story to tell? Is it that, Hey, I had easy money, I hired a bunch of people and I don't need them now. Or is it that AI, I don't need more people now. So it's, people are actually trying to just use AI as a scapegoat.

Galen Low:

And that's like, it's interesting about the headlines and like what I learn about news media more and more every day is that it's political, right? Like the stuff that's in the headlines is political. It's going both ways. It's you're thinking about the stock or the investors, and you're also thinking about messaging to the broader population of what's happening in the world. And sometimes that gets muddled. And our takeaway is, oh my gosh, AI is taking all the jobs, even though it said it wouldn't. Whereas the answer is some of these companies are taking away jobs that maybe they shouldn't have created in the first place, and they're gonna use AI as the excuse, which is fascinating. I wanted to zoom out maybe a little bit because. Folks in my network and folks who listen to the show, a lot of them are being asked to reimagine parts of their business through the lens of AI. Like that's the sort of directive that they're given. But when they receive those marching orders, it's like pretty vague. It's more vague and like just directional than a plan. And a lot of them are sort of less scratching their heads about where to start. I was just, you know, thinking about contact centers and you know, AI assist. And what the technology's doing there. And I have been in the space for a little while, or I've touched it at least when it was kind of not super AI, it was just like chat bots, right? It was like how do we incorporate this and like create like more of a omni-channel customer experience that doesn't require like massive staffing changes. But CX has always been sort of re-imagining parts of this business around technology. For folks who are like just getting started when trying to imagine or reimagine a core part of a business operation using AI, what's the first thing that an organization actually should do before entering a planning and implementation phase so that when people receive it, they're not like, what do I do with this directive?

Jim Iyoob:

Yeah, it's a great question. So I'll tell you this. All you CEOs out there listening and all you people that are bosses are telling'em to automate everything. First of all, I have this quote since 2003. It's not your decision to tell me how to contact you if I'm buying your product, it's mine. So number one, stop trying to automate everything because you're gonna just upset me and I'm gonna leave you. In a global marketplace, I can buy your product from anywhere. So the only thing that differentiates you from anybody else is the customer experience. And if you destroy that, I'm gone. So that's the first thing I would say. Number two, if you're on Kindle, by the way, here's your guide. It's free on Kindle. This is my latest publication, AI in the Contact Center. That's a good step by step to tell you what to do. In the planning phase, I think where people get it wrong is they think they have to automate everything, and that is so far from the truth. You're sitting on the largest data set in the planet, which is your interactions. If you actually analyze those interactions, you can get low hanging fruit that can be automated. You might lose a few people, you'll improve your customer experience, or at least keep it the same, but give customers what they want. That's my 2 cents on it. And Manu, but is much smarter than me, as we all know. He can probably give you a lot more on the, but that's just my common sense is what I call it, approach to it, where every software company, I'm, they all came to me. They came to me. I can remove all your people. Oh, please. Like you're an idiot. And the funny part about it is the people that are telling you, you can remove all your people, never took a call a day in their life, never worked in a call center. And they're clueless on how it actually operates. Manu, I'll pass it to you.

Manu Dwievedi:

Galen, every time this happens, like a customer comes to us and says, Hey, I want to automate everything, because there are a lot of things that are wrong within the whatever is set up currently. I go back to something that Jim has always taught us. He said, if your process is broken. AI is not gonna fix it. AI is an efficiency tool. Its automation is not gonna fix something that is already broken. And I think that's inherently what's wrong with our approach. When you zoom out and when you think about implementing AI, you don't implement AI to replace people. You implement AI to make things better for your customers. Do I want my customers to wait for 10 minutes in a queue to be able to get a small simple answer like, Hey, what's the balance on my card? Or password reset, right? Once you understand that you are automating because you want to solve your customer's problem, improve CX, the entire thing changes. Now you're not thinking from that negation mindset that, Hey, I want to get rid of 200 people and that's why I'm gonna automate. So one thing that people can learn is go back, get brutally honest with yourself. Understand how I can help improve my customer experience and then see where you can drive that efficiency, which will result in a better sentiment.

Galen Low:

I love that. And like, there we go again with the idea that AI is just this sort of like, trigger for a thought that we should have been having or have been having for years. When you go to process design, like, yes, we, you know, we want to become more efficient. We don't wanna scale something that's broken. And to that same point, it's like, well, I hear you, Jim, about like the outreach. Like, I get it too. Right? It's like, and you can tell that it's like the generic ones. It's like as a CCO at, you know, Etech. I'm assuming that you'd like to increase profit margins by, you know, a hundred percent month over month, and I can help, and you're like, no, actually that's not my goal. That's not my goal. Because my goal is to improve customer experience everywhere by delighting the end user, not by dictating what the end user should experience. And it's funny because. Thiss lag in every industry, right. CX and others where, and I come from a world of like, you know, user-centric design, mostly digital, where that was sort of a big flip. Like, you know, when I started in like just before the, like the 2010s where it was like, wait a minute. But it's not just shove an experience down our users' throat. So it's actually like, think about what they want. And then I'm thinking about CX and like, you know, that the classic contact center experience where you're on hold, the music is there. They're trying to route you so that you kind of get lost in the system so that maybe you'll drop off and then we won't have so much call volume and it's like, oh, great. Like that wasn't the goal. The goal wasn't to have fewer calls hitting agents. It was to be able to like actually delight the customer, retain them, do good for your brand and do good for your people. It's like the reasons to redesign, the reasons to redesign are important. I guess if I were to boil it down, then I'd be like, A great place to start is not. What can AI replace in our chain? A great place to start is like, what's broken that we need to fix or what's working that we need to leave alone and like what is the actual end goal for the people we're trying to serve? Whether that's, you know, internal staff or customers or you know, maybe shareholders, I don't know. But it's, we kinda have to kind of take it back to the studs and figure out what the goal is, not kind of just do automation and AI for automation and AI's sake.

Jim Iyoob:

Agreed. Absolutely.

Galen Low:

I wonder if I could circle back around to the first question because like. I think a lot of people are being told like a very binary story about either AI is taking over your job, or AI is your helpful intern, and I suspect that like the reality is somewhere in between. I do think that like in the CX space, a lot of folks are actually ahead of other industries and you're really close to some of these transformations in that space. So I thought I'd ask you like. What is the state of human AI hybrid operations in contact centers around the world right now? And like what is working well and where does it have some room to grow? And just to make it even more complicated, like what are some of the implications you see for other industries?

Jim Iyoob:

So I can tell you firsthand, there was a big, even at Etech, we have 4,000 people in three countries. When we introduced it, everybody is gonna replace me. You know, you're trying to get rid of me, I'm not gonna help. I don't wanna support you because your end goal is to fire me, which is farthest from the truth. Here's what we found. Once you teach them that, my goal is to make your job better, more strategic, then you'll see the benefits. So as a live example, when you get that one call. Two calls a day. They're complex. Let's be honest. You learned it in training. You've taken 50,000 calls and this is one that you got training two years ago. Would it make sense if I give you an assistant to help you with that question that you get once every six months, they listen to that? Because that's what I'm trying to do. Now, should I focus that on automation? No.'cause you only get five of those a year, right? But let's help the agent with that, and then when you show them that part of it. And then the low hanging fruit, let's just call it password reset for an example, that should be automated. Let's be clear, right? So there are companies that still don't have it automated, which is fine. But when you think about that, when I take that volume away from you agent, what it's gonna do is it's gonna let us go to our customer and say, Hey, now that we saved you 30% of your call volume, what other things are you doing internally right now that can be outsourced? I can skill my agent up now to actually handle more complex interactions. Fraud prevention, fraud detection, things I know you're doing internally that can be outsourced for less money. And that's really the messaging that we're doing that seems to be working. But everybody's afraid because of, I guess they watch YouTube. I'm replacing all your jobs and you have to get over that to say what's in it for them. My goal is not to replace you. My goal is to augment you and then skill you up. So to give you a life example, Manu was an agent 10 years ago. He's an AVP today.

Galen Low:

You yourself are an agent. You started on the floor.

Jim Iyoob:

I had green screens, so nobody remembers what those are, but like, think about it. If Manu didn't learn more things, he'd never be sitting in an AVP role now. He challenged himself to learn new technologies, embrace them, and learn what he could, do more complex stuff, which is why he's so successful what he does. And that's really the messaging that has, 'cause you're always gonna have people, I have a customer that has 99% containment in their IVR. So think about it, 99% of the interactions are contained in the IVR, which means never hits a human being. I still have 135 agents answering those calls, that 1% because an IVR can't do it.

Galen Low:

And those are the high value interactions because the IVR can't do it. Correct? Correct. It's interesting what you're saying too, because random context for our listeners, one of my first jobs at a university was actually eBay Investigations contact center, where we were kind of like looking at the fraud folks, you know, we were the specialists. It was an email center. I wasn't on the phones. But what was true about the organizational sort of. Org chart at the time was that specialists, were a small team. It's like the generalists that are, you know, doing the like, you know, copy paste template, password resets or you know, low level customer support. That was a big team. And then like our team was the smaller team. And I could see how that could be like, oh great, yeah, you're gonna elevate me. But I see that the triangle, you know, upwards, there's fewer roles. So that definitely still gonna mean less jobs for me, Jim. So I could see it from that angle. But I also like the sort of the flip side, which is like. Maybe even to put it another way, it's like if you're being told, yeah, don't worry, you're gonna like add more value and be more strategic, but you're not being given any training to actually do any of those things, then that actually might be a signal that maybe there's something more going on there. Maybe you shouldn't trust that message as much, but if you're an organization, if you're leading an organization that wants. To level these people up, then I think it is a matter of like showing them that there is gonna be enough opportunity up there. It's not gonna sort of narrow and that you are gonna support them by providing training. They don't have to go and figure it out on YouTube, which is a hundred percent. Frankly, what a lot of people are doing. They're like, I know searching be more strategic in my role on YouTube and or TikTok and hoping for the best. Like is not exactly I think what would be healthy for the workforce at large. And I guess like the other side of it too, like you mentioned this, some of it's automation, right? Password reset. Those things are gonna happen and that just goes away. It gets delegated and you start doing, you know, a different part of your job in there. You also mentioned like the sort of like assistant, right? Like the AI assist of something kind of popping up on your screen going like, Hey, this question you don't get often, like last time you got trained on it was like two years ago. Here's like the script and the policy and like, you know, here's what you ought to do because it happens rarely, but it is high value and I thought that was a really interesting. If I was to kind of take that lens and look at it from the point of view of like, you know, a lot of my listeners are project managers or they're leading teams or project managers, a lot of them are in the digital space. They're trying to figure out the sort of like hybrid way of working internally and sort of externally in terms of like the projects are delivering that value. They might even be implementing a project where they're bringing AI into a workflow. But do you find that also gets some, like pushback in when you're training agents to be like, okay, you would normally right, like a veteran contact center agent who came from green screen. You have this new thing that's gonna pop up like clippy, you're gonna be like, Hey, it looks like you're writing a letter. And do you find a lot of people being like, I don't need that thing. Turn it off.

Jim Iyoob:

I don't see too many on the complex things because what happens when they're not prepared? They're going to look at something. Let's be clear, you're not a mastermind, that you're gonna remember everything. So you're going to your training manual, you're going to your handouts, you're going to your job aids. What if I just dropped a job aid right in your screen for you so you know exactly what you're doing? Wouldn't that help you? Wouldn't you be less stressed? When you wanna continue eating your cookie that you're not supposed to have at your desk right now, right? While you're doing this, I mean, that's really what it is. And I think if you have fun with it and explain to them that, I think it works well. To answer your question, there's always 1%. Doesn't matter what you do, they're just gonna be miserable and you can't fix those. Those are the people that are gonna be replaced, by the way.

Manu Dwievedi:

In fact while Jim was saying this, I just recalled something that just happened on a call before this where you and I, we were both there. I'll tell you a story, Galen, but I'll tell you the result first. So there is this call happening. A lot of people are sitting there and you will hear someone saying they have amazing retention. I've not seen retention like this. Agents are not taking easy calls. So agents are happier. So that's the reason I think they're able to retain. Tech is working out very well. Our customers are happy as well. They are not waiting in queues. And this is the end of the story. But the way it started was you have to ensure that the agent is educated enough about AI, that the first experience that they have with AI is honest and authentic. The moment that happens now they know that it's solving problems for me. I don't have to go and as Jim saying, I don't have to go and look at those five PDFs, right? The customer said, I'm looking this at this information and my screen tells me what to say. I don't have to answer those 50 calls every day that says the same thing. Hey, thank you so much for calling me. I reset your password. So, doing the exact same thing every day. And this is a story that we just came from a call where this is exactly what the customer was saying about us because of these changes and we are seeing a very positive shift in the demographic. We're now people who understand it. They actually want to be here because they know, hey, this job just became, you know, something more than that standard contact center job where I'm just picking up phone and answering it all the time.

Galen Low:

I love that. I actually remember I was recently at a conference and they put this sort of AI adoption chart on screen for their internal team, and the context was more on the coding side of things like using cursor. But basically they're like, we did nothing differently. We just we kept plugging along with sort of our messaging and driving adoption, and the line goes flat, spike. Because I think it's that, I think it was like, okay, well, you know, like I have my doubts that this is like actually gonna make my life better. I'm being forced to tinker with it. I guess I'll use it. Fine fine. And you're like, well, wait a minute. What? And then you're like, actually, parts of my job did get better. And I'm gonna tell my friends, or my friends are gonna tell me. And they're like, ah, like actually you should get into this. Because like now I don't have to do password resets, like for, you know, six hours a day, I'm feeling more engaged. And suddenly that spike I don't know. From a change management perspective, like we talk about this valley of despair where people kind of get stuck in change that has been sort of pushed upon them and they kind of need to like get themselves out of it. But this was kind of more like, it just seemed like a sort of cultural shift internally of like realizing the benefits after being told them and the benefits didn't change. But once they it took them time to sort of realize how it actually impacts them. And I like what you said about. My first interaction with AI should be delightful as well. Right? And like that's the true of the the customer in the customer experience and also like the employee for the employee experience, which, I mean, I was thinking about it, I was gonna ask you actually about like, sort of change management, like your sort of philosophy on change management, ETS Labs, like it's a services organization. You are helping your clients with this, so you might not always get to see. Change management from end to end. But in terms of like recommendations that you make to your clients about how they can make or break their project by doing change management, right or wrong, like what are some of the things that you're sort of recommending to folks to do, like before or during or after an implementation of your services and solutions?

Jim Iyoob:

My news, the brains behind that. I'll let him take that one.

Manu Dwievedi:

I'm gonna start with something again that, you know, Jim always make that a soft. If you spring up something on people as a surprise and say, Hey, this is AI, it's gonna help you. There will always be doubt because people fear what they don't know, but people will support what they have helped create, and that's very important when it comes to change management. If you buy in top down or bottoms up, doesn't matter if everybody in the organization is bought in. They understand that this change is coming to help me here is how it's gonna function. They understand AI as well so that it, they're not fearful that, Hey, it's gonna make me do something wrong and it's gonna impact me the first tier. And in fact, you know, we talked about this at CCW as well, any AI implementation. 80% is change management and only 20% is technology. You have to educate people. You have to make sure that they are providing their own, you know, input into it. That as an agent, this is what I want to do. Think about a guild. They are agents out there in the world right now that have been penalized for making spelling mistake or grammatical mistake on a ticket mode when you never have to summarize a quality. Think about it, it's still happening. So when you tell an agent, Hey, you know what? This will help you. Make sure that you never have to write those notes down anymore. You don't have to worry about spelling, you don't have to worry about grammar. It's perfect. It sends the notes over, and you can move on to solving next problem. Tell me one agent who's gonna say that, Hey, I don't want it. The problem is you never explain it. You never educated them enough and now they don't know what's gonna happen. They just know that they're including AI and there are all already these, you know, new story circling right. I just wrote about it on LinkedIn a few days back. There is this new story saying MIT's report says 95% of the AI project failed. Not one person went into that NANDA project report and actually read it. The report says that 95% of project that fail are because of change management or people and leadership problems. Yep. But the sexier story was saying 95% of them fail, right? Yep. So that's what, so the narrative is being, you know, driven either AI fearful or, you know, companies saying that AI is doing great, and it's actually turning into fear for, you know, frontline. So people are already afraid of it. So first point coming back, educate. Make sure that you understand what you want to implement and why you want to implement it. Have a very clear and narrow view of your use case. I want to implement AI because I want to save those 60 seconds that an agent takes after every call just to write things down. Now you have a very clear use case. It's helping your customers as well, because that 67 second, where is it going? It's going into making sure the next customer is waiting 62nd less. So it's helping your customers as well. Also, it's helping your customers because now agents are not making grammatical mistake or forgetting to write something in a ticket. So when the next agent picks up a call, anytime they would know exactly what the issue was. And what is all of this doing? It's saving your 62nd every call.

Galen Low:

And not getting penalized for having a typo.

Manu Dwievedi:

Exactly, and not getting penalized. So as a company, you have just solved the problem for your people, for your customers, and made money off it. So when you manage change management this way. You educate people, you find right use cases, you go very narrow, implement it, irate, fix what's the issue is, and then go, you know, wide and try to build the ocean. It always works out. That's what we are seeing everywhere.

Galen Low:

You know, it's funny you mentioned about that headline from the MIT report because you know, and I don't have the stats from before, but I'm willing to bet. That the same percentage is almost true of any project AI or not because of change management. Yes. So we're talking about this sort of like, you know, some folks listening might be like yeah, change management. I get it. Like it's important. But what I like about what you said in that change management process with the AI inflection is show them early what it can do. Tell them early what the goal of it is and create that delightful experience around AI. You know, it's not a slimy sales pitch. The reality is you do earnestly want to solve problems for your agents or whatever your staff and whatever industry you're in are doing. But also it's kind of showing them instead of just telling them because we can. And I think that's where a lot of the distrust is. A lot of folks, you know, some folks in my network haven't even, you know, they're like, I know I'm supposed to like crack the lid on AI, but like, honestly, I haven't yet. I'm using like Gemini for meal planning, and that's as far as I've gotten because, you know, like I'm scared. I don't know what it can do. And it's like, well, let me like just show you, because.

Jim Iyoob:

Let me take a note. You mean Gemini does meal planning? That might be something I've never used AI for meal planning, but I might have to start.

Galen Low:

I actually use a different LLM for different things. My Gemini is mostly inventing songs about Pokemon or Minecraft that we can then sort of record with my kid.

Jim Iyoob:

Oh, cool.

Galen Low:

That's nice. And then ChatGPT is all my work stuff and Claude. That's my sort of division of labor across my LLM staff. But I think it's really interesting that. Some people are talking about AI transformation as this big new thing. It's different and like it's, you know, we all have to figure it out and of course we're gonna fail. But actually that's like not necessarily true. What is true is that it's probably should go through almost the same process of any sort of large scale transformation. Yep. And it's just, we haven't been doing it right before either, so.

Jim Iyoob:

So it's funny you say that. When IVR came out, what'd they say? Gonna replace all the agents. Yeah. Bull crap. Then chat, oh, they're gonna be able to do two, three chats at a time. You won't need near as many people. Bull crap. Now they're saying the same thing here. It's just a different story and it's not true what they're doing with this fear mongering with AI. I dunno if you saw the latest video with the robot that was beating up the people that were working on it now. That was funny. I'm gonna use that one in Vegas next year. That one was a funny one. The robot went crazy and started beating up the guys that were working on it. Is that the corridor crew one? I forget which one it was, but it, I watched this like 15 times 'cause it was a riot and I'm saying to myself, whoever programmed that one didn't program it. Right. When that goes back to what human intelligence.

Galen Low:

I wanted to circle back on something because I think it's important and realistic. You said, sometimes you might need to lose people. It's not like this utopia where like, don't worry, there's gonna be more and more jobs like Etech has been successful at doing that, at creating more jobs. It's not necessarily true that everyone is gonna be able to like retain their job. You also mentioned that some of the folks who are like not high performers are probably gonna lose their job. But again, this is not a, the causality goes further back. It's not just because of AI, it's because you're a low performer and not taking your job seriously and not learning, not growing. So it's not really AI's fault, it's just the kind of trigger, and then the other thing you said was we need people to build these things too. We need them to program them. And you know, I'm watching these sort of waves of people that I never thought would code in their lives making lovable apps and you know, doing the vibe coding thing. And they're in N eight N. You know, internally, we just did a big sort of like Hackathon Quest week where we all sort of took on an AI experiment and did a bit of a show and tell. I think folks listening might be like yeah. So what, like a call center agent's gonna become like an AI developer. Is that a, like a logical step? Is that a bit of a reach or like, is that kind of what we're saying in terms of the workforce, or not necessarily?

Jim Iyoob:

So, great question. I say if you have the drive, like as I say this, you have the will, he can teach you the skill. Okay. But you have to have the will first. Manu, no disrespect to him, was an agent. He's a coder today. But it's because he had the will to learn those other skills. It's like my daughter. My daughter is, she graduated from college and now she wants to be a lawyer. And I told her, I said, ChatGPT is not gonna help you get those LSATs. So theory, you might be able to do it, but when you go for the actual test, understand you're gonna have to know what you learned. That's what some people, unfortunately in our society, I see a lot of people in school, like now they're banning, you know, my 17-year-old, she's a senior now, they're banned her phone from school. So now she has to actually think on her own. Well, it's good for you, right? Because what are you using the phone for? Like, I don't have my chat g pt, like so, oh my goodness. You're gonna learn. This is just a terrible thing.

Galen Low:

Actually that's a really interesting use case because you think about like the calculator or like, you know, when I was in whatever 11th grade sort of math, you get the like Texas instrument calculator that.

Jim Iyoob:

Yeah, I remeber the Texas Instruments one.

Galen Low:

TI 83 and then you know, at some point someone's like, yeah, because you don't need to know all the math. Like in the real world, in the workforce, you're gonna have a calculator like we didn't know at the time, but like on your phone at all times. Now we have AI assistant in the contact centers like. You know, is there a fathomable future where actually your daughter is a lawyer with like hologram AI assist with like, you know, the law just referenceable there with like a clippy there going like, oh, did you mean this part of the legislation? Yes. Okay. Oh, we could probably argue that this, you know, do they actually need to know?

Jim Iyoob:

They do. Here's why, because if you look, there's tons of articles out there. If you Google it on AI given wrong answers. So that's why you need that human being, like AI. That's why we say it's not replacing, it's the cystic. I use AI all the time. I have five different platforms like you that use it, different things. But the problem is, it's all based on who coded this thing to understand, right? And yes, can it learn from you eventually? Yes, it can learn your personality, it can learn your style of talk, your style of writing. But at the end of the day, you still gotta read it because it does make mistakes. I could tell 'cause people use it to send me stuff all the time. And you see it on LinkedIn. You can tell 'cause it's got the emojis, it's got the crap in there that means nothing to me. They copied and paste it. I got BPOs by the way, coming to me in LinkedIn saying, Hey listen, I looked you, I just had one today. I see you're in logistics. No, I'm not. Not even close. And they're trying to sell me their services. So that tells me the guy didn't even read it. He just copied and pasted. Probably used it for everybody and that's why you always need that human being part of it. That's the way I look at it.

Galen Low:

Actually, it's just a really clean picture of the future, whereas the workforce is changing. Education is important. We need people to upskill and we need to support them in upskilling. There has to be that will to do it. If you don't have that will to do it and you're a low performer, yeah, you're probably out of a job. And that's probably your own fault actually. But you know, not AI's fault. Yeah. And the mission is to get AI to be better and hopefully not take over the world like I don't think that's what we're saying, but I think that's. There is this sort of harmony that can be achieved. And even though you're taking on this big sort of AI transformation project that is gonna impact lives and people are gonna hate it and some people are gonna love it, at the end of the day, it kind of boils back down to any kind of change, any kind of transformation, you know, whether it's iiv or because of the pandemic or because of AI. It's still, you know, the approach is actually still the same. It's still a human in the loop at some point, and it's still about the people. I wonder if I can ask one last question because I wanna kind of take this back down to like a practical level, but I was talking about my listeners who might be sort of, you know, department heads or project leaders. What advice do you have for a department head or a project leader who is like already mid-flight on a transformation project? That is adding sort of AI into like a core business function, and they're listening to this and they're going, oh crap. I should have listened to this before I started. Now, like, am I screwed? What can I do mid-flight? That can make sure that from a change management perspective, from a tech perspective, from an operational perspective, that it'll do more good than harm.

Jim Iyoob:

I would tell you domain expertise means more than any tech expertise. Number one, I would say crawl, walk, run. Everybody's trying to automate everything. Bullcrap. Pick three things that keeps you up at night. Start there. Once you deploy AI and once you do it in a controlled environment, then you could pick up other things. But I would be going for what we call low hanging fruit, and I think that's where everybody messes up because these projects take six months, nine months, 12 months to see your ROI. You should see an ROI in 30 to 45 days if you do it properly, because if you're not going in with the big, huge, let me automate everything. Let me automate a piece of the business. If I drop my call volume by 5%, that's real money, real savings. Then again, scale it up. I think that's where everybody makes the mistake. Everybody makes the mistake'cause they think they, they have to do it all and that project never gets off the ground. I tell our customers all the time, they're okay, I wanna do this. Yeah, good for you. Let's pick three. Let's start with these three. Let's make sure it works first, then scale.'cause once you get it down, you'll learn there's, and by the way, learn from your failures. Failures don't mean bad things. Failures mean the way you tried it, it didn't work. Let's try something different. And when you're doing it with 3, 4, 5 little items, it's much easier to be adjustable, nimble than it is if you tried to do it all. And the third thing I la last thing I would say is don't copy the process. If the process today doesn't work.

Galen Low:

The scale failure and then automate it.

Jim Iyoob:

Yes. Mannu, what do you got to add?

Manu Dwievedi:

This is exactly it. You should actually take this, print it and put it somewhere if you're starting an AI process, because if you do these three things, everything is works out profit. In fact, I was just a friend of mine, he's playing around with a with GPT-5 old accounting firm. They're trying to put in troubleshooting using AI internally for help desk. And he calls me a few weeks back and he's like, you know what? This GPT-5 is amazing. It actually went through troubleshooting steps for one of my internal employees, even though I didn't train at all. And then he says, but how do I make it do things right every time? Well, that's your problem. You decided I'm gonna take a bot and tell it, Hey, troubleshoot for people, and it's just gonna do it every time. No, you have to define a very specific use case. My outlook is not working. Now, this is the use case that you work on email or something like that, so that's one of the big problem. People are using AI as a magic wand that's gonna solve everything. The moment you order the word AI. But that's not how it's gonna be.

Galen Low:

I love that, like it's almost poetic, but logical that the path to macro transformation is actually through micro. It's like we need to go through these motions. We need to know what our goals are. If we don't. Figure out what we're trying to do at a big scale. Wholesale transformation has always been hard, even from that first wave of digital transformation. I know organizations that are still journeying to cloud or trying to get onto that other CRM or you know, trying to digitize this part of their business like 10 years later, 12 years later. And the reality is things will change in the world of AI and the world of work during that period of time. We have to think in shorter cycles.

Manu Dwievedi:

Agree?

Jim Iyoob:

Yes.

Galen Low:

Awesome. Jim, thank you so much for spending the time with me today. This has been a lot of fun. Just before I let you go, where can folks learn more about you?

Jim Iyoob:

So easy for me. LinkedIn, Jim Iyoob. I put out a remarkable CX newsletter once a month, subscribe to my blog. Mine's all about content I love. I say things people think about, but don't say it. So I tell people what they need to hear, not what they wanna hear. So it's all educational stuff. Have some fun with that, by all means. That's probably the easiest way to find us. Try not to sell me on LinkedIn, and if you do, I'm gonna critique you like I did this morning.

Galen Low:

Yeah, do your research.

Jim Iyoob:

Do your research first before you reach out to me on LinkedIn. You can connect with me all day long, but if you're trying to sell me on LinkedIn, you better do your research first, or you're gonna get the message back from me that's critiquing how you did it.

Galen Low:

Amazing.

Jim Iyoob:

Manu.

Manu Dwievedi:

You can find me on LinkedIn as well. I think that will be the easiest way. So Manu AI, you can go on LinkedIn, linkedin.com/in/manuai/.

Galen Low:

It's a really good LinkedIn URL.

Jim Iyoob:

Yeah, all of you would have that.

Galen Low:

Amazing. I will include all those links in the show notes. And again, thank you for coming on the show, sharing your knowledge and insights. This has been great.

Jim Iyoob:

Thank you so much.

Manu Dwievedi:

Thank you. Thank you for having us.

Galen Low:

And that's it for today's episode of The Digital Project Manager Podcast. If you enjoyed this conversation, make sure to subscribe wherever you're listening. And if you want even more tactical insights, case studies and playbooks, head on over to thedigitalprojectmanager.com. Until next time, thanks for listening.