Andreas Welsch: 0:30

Today, we'll talk about the top skills for working with AI agents and who better to talk about it than someone who's actively working and thinking about that. John Thompson. Hey John. Thank you so much for joining.

John Thompson: 0:40

Hey, Andreas. It's nice to see you. How you doing today?

Andreas Welsch: 0:44

Doing very well. Hey, I'm so excited to have you on the show. I've been following your content on LinkedIn for a number of years. So I'm really thankful that you're making time for me and making time for us.

John Thompson: 0:54

Yeah. As we said in the pre-show I've followed your content for a long time, and when I got the invitation, I was really excited. I'm really looking forward to the conversation.

Andreas Welsch: 1:04

Fantastic. Look for, those of our guests in the audience who don't know you yet, maybe you can share a little bit about yourself, who you are and what you do.

John Thompson: 1:12

Sure. John Thompson. I'm the Senior Vice President and Principal at The Hackett Group, and my job there is to set up a Gen AI services practice and help build out the Gen AI products. Previous to that, I was the Global Head of AI at EY. Previous to that I was the Global Head of AI and Advanced Analytics at the second largest biopharmaceutical company called CSL Bearing. Before that I was at Dell and IBM and a few other companies, and at the same time I've written, you can see over my shoulder, I've written five books. About data, AI and analytics, and I'm a professor at the University of Michigan as well.

Andreas Welsch: 1:47

Hey you've been in the industry for quite some time. You've seen it from many different angles. This will be a really good and meaty conversation. I'm super excited. Why don't we jump straight to the questions that we talked about for today's episode. There's so much hype and so much buzz in the market and it's been there for the last nine months probably around the topic of AI agents, Agentic AI. We can delegate goals to software now all of a sudden, and they will. Work on all these magical things that we have been working on ourselves before. And they will give us predictions and recommendations. They can also implement actions. And while that sounds all very nice and very lofty in terms of goals, I think there's also a very tangible aspect to it. And that is what happens when you actually introduce that kind of AI into a workplace. What changes if anything? So I'm curious, what are you seeing in your conversations with businesses and with leaders? What changes?

John Thompson: 2:42

There is a lot of buzz and a lot of hype around gen AI and large language models and large reasoning models and AI agents and, they're all intertwined. Some people say, oh I wanna talk about agents only. I don't want to talk about Gen AI. And I said you, really can't do that. They're intertwined they're the same. And if we've had agents for a long time. We've had agents for maybe 40 years or something like that. Those are symbolic neuro, they're not neural network, but there's symbolic driven rules based agents. So agents are not new. We've had them for quite some time. But what changes with AI agents is that AI agents can do anything a person can do. If you think about an AI, a Gen AI driven agent. You can have them do pretty much anything. They can spend money, they can book airfares, they can book hotels, they can fire people, they can hire people. We've all seen those missteps. I think the Canadian Airlines is probably the most famous one where they're putting out tickets for 79 cents or something like that. And the airline, to their credit, honored all of those. So the thing is, that a lot can change. What has to change? Not much really,'cause you can build an agent and you can control that agent much more in a much more fine grain way than you can control an employee. So if you're building an agent. I always tell people, don't anthropomorphize models. Don't use pronouns like he and she and talk to them as if they're John or Andreas. They're just models. They don't think they don't have emotions. But if you think about what a model can do, it's helpful to think of a model as a proxy for a person. So anything a person can do, a model can do. So the breadth of change is either nothing to everything.

Andreas Welsch: 4:37

I love that and I must say I'm in the same camp or of the same opinion that we shouldn't anthropomophize systems and, say, call him Ava or Eva or something else or John or Andreas, because it's it doesn't mirror what they can do. They're still based on data. They're still working with probabilities and making predictions. But I find a lot of times now that we talk about agents and these systems that can take over more complex tasks that we've been. Doing so far I a lot of times find that these analogies to, how would you manage a team of, people actually work quite well or what are parts of the tasks that that you are doing that we could now delegate or that you would now delegate to an agent? What do you think about that? How far can we actually compare humans and the way we work in an organization with how AI is now working?

John Thompson: 5:30

Yeah, I think the one thing that's gets in a parallel path or very close to what we're talking about here is, then, all the buzz in the last week apple put out their paper, does AI really think and I've been very vocal about this for the last three years, and the AI does not think, AI infers, it infers on steroids. It can infer billions and billions of times a second, but it's not thinking. It's clearly not doing that. So if we look there's a great deal of conversation about the crisis of AI. Is it gonna take all the entry level jobs? You mentioned it in the intro, and Dario Amodei of Anthropic stirred the pot a couple weeks ago by going in a big forum and saying, nobody's gonna hire any kids outta college anymore, which that's not true. That's not the case. I think what we're getting confused here is that many people are looking at what AI does now, and they're saying, oh, they're taking over the entry level jobs. That's not true. What AI does is it does a good job, as you said moments ago, of predicting what is the next letter, the next word, the next frame, the next concept, the next paragraph, whatever it is. So if you are a person or an employee that just does rot. Tasks, A, B, C, that kind of thing. Your job is in at least roles. Parts of your job is, going to be automated away. But if you're a higher level thinking thinker, someone who's doing creative work in synthesizing different parts of information and coming up with original thoughts and adding value to the organization, you have no worries. AI doesn't do that. AI is nowhere near that. So what AI will take over is the very low level mechanistic, repeatable work that's gone. So if you have a role and half of your role is mess mechanistic, and half of your role is creative, stop doing that part and fill out your whole job with the roles over here.

Andreas Welsch: 7:30

Now this is a pretty big change to some extent, right? On one hand, we've always had this technological change where things have been automated that we've done before. That's the whole premise of software to begin with. But it feels like there's a sense of urgency. On one hand, there's a order of magnitude that seems to be a lot bigger than what we've seen from previous waves of innovation. And so not all leaders are comfortable with this change, right? First of all, their employees, their team members are not comfortable. If you read the news and the headlines, whether it's the entry-level roles or if you say that has some ripple effects and goes up the hierarchy. What's the role of a professional? What's the role of an expert? What's the role of a leader? You see the CEO memos from Shopify, from Duolingo. We want to be AI first. We're not hiring new people unless, and until we can do that with AI, there's a lot of angst and confusion and fear there. So how do you see leaders becoming more comfortable with this situation and also helping their team members go through that transformation? What are the skills that leaders need?

John Thompson: 8:39

I see it all the time. We've seen it with Klarna and Shopify and all these different companies. We're AI first we're and I talked to many companies, I think I'm over 500 companies over the last three years that I've spoken with. Managers have said, when I go to hire a new person, I have to justify why I'm gonna hire this person and I can't do it with AI. Fair. I get it. But the VP, SVP, EVP, C-level executives need to understand that you can't really just turn these things off. It's more you can migrate to it. You still need to hire people. You still need to do your jobs. You still need to build the technology. We're not there yet. We're large language models and the whole debate over the last week about large lms large resource models, or large reasoning models, sorry, talking about, hey, these things are gonna take over thinking positions. That's not the case. Again, I'll say it over and over again. Large reasoning models do not think, they do not reason. They infer at a high rate. So you just can't say, Hey, we're not gonna hire any more people. We're gonna do it all with AI. What you can do is say, we're gonna look at some of the more mechanistic roles. We're gonna automate those. We're gonna hire different people. Maybe you don't hire the same profile you did before. Maybe you hire a different profile, but you're still gonna hire people. You're still gonna bring people into the business, you're still gonna grow them and you're gonna augment them with AI. So to answer your question directly, I think that leaders at the top of the organizations at the top of the pyramid need to have a more nuanced view of this. It's not an on or off question it's more of a here's a continuum, we wanna take AI, this far, we're gonna still hire people over here. But where we were hiring people that had no. No knowledge of our skills or no knowledge of our industry. We're gonna need people with a little bit more nuanced understanding and subtle understanding of what we do. So I don't think it's gonna turn things on or off. I think it's gonna change them materially.

Andreas Welsch: 10:42

That deeply resonates with, honestly, how I'm thinking about this too, right? I'm visualizing this as a sliding window basically, from where you're further to left more basic research, gathering information, gathering data. If we talk about entry-level roles now that shifts further to the right. You have software that does the legwork for you. Yeah. But you are still in charge to ensure is it complete, is it accurate? Are there any areas in any aspects that we have not explored that, again, maybe AI can help us explore or we should be exploring, synthesizing, and then working more on the decision proposal as opposed to can you get as much data as you can and put this together?

John Thompson: 11:21

I think a really good example is David Solomon at Goldman at the last World Economic Forum, the last Davos meeting. He got up in front of all the world leaders and said, Hey we generate all these different documents for investment banking. I think it was like a prospectus or something like that. He said 95% of that can be done with AI and that is true. A lot of it is gathering information from the SEC and getting information from the company and the market and competitors and Bloomberg and different places, and putting it all together in a predetermined form that everybody understands very well. And then the last 5% of the processes you bring in experts who review it now, that's exactly how all these processes are gonna work. If it's all about just gathering and collating and stacking up information, AI can do that all day long.

Andreas Welsch: 12:10

Now that brings up another question and, to me that is we've talked so much about automation for the last 10, 15 years, right? Obviously we've had rule-based automation for a very long time and we said there are some some automations between different systems logging here. Download a vendor invoice, save it locally, extract information, put it into your ERP and then kick off of the process. So things like robotic process automation, are we now talking about agents as an RPA 2.0 or, 3.0? Or is it a big fundamental shift in how we work?

John Thompson: 12:44

Andreas, you're the first person that's put it that way, and I love it. Because I've been positioning it that way for about a year and a half now, is that when people ask me about automation, the first thing I say to them is, how are you thinking about automation? Are you thinking about automation in the old line, RPA way, which a lot of people do and I say you could do it that way, but that's A to B, C to D, X to Y never changes. It's always the same. So I say, you really should think about this as an agentic workflow. Which is then very dynamic, okay, the conditions are so much so that this really shouldn't go to the governance department. This should go to the legal department. Or the velocity of change is such that you really need to talk to someone who's managing the plants. Because we're seeing the demand signals ramp up significantly, but our production signals, our production plan is not changing. So automation from an AI perspective can be very dynamic and very interesting and very exciting and sensing what's going on in the world. As you said earlier in RPIA to B, c d, it's just it just runs. It doesn't change. Automation from a gen AI and an AI perspective can be really exciting and dynamic. That automation conversation really needs to be grounded in a context of what are you expecting automation to do for you.

Andreas Welsch: 14:29

I like that part. First of all, that we're throwing each other the ball back and forth on this. But the part specifically around where you said, making a decision should this go to route A, to route B to route C and using AI to make that decision, right? We've used machine learning for a while to make decisions, for example is this an expense that can be approved easily because it's below a certain threshold where it's in a neutral or category or this is actually needed person to review this. Now we're taking this steps further by saying, based on the information we have and all these different input factors. Which route is the optimal one to make a decision. So great to, to hear that from you as well. Now, when we do that and we outsourcing a lot of our thinking to AI, there've been some recent studies about this. What do we actually retain? What's the role of us as team members, as team leaders if we just say, Hey, AI, agentic AI, whatever thing, agentic workflow. Yeah. Go figure this out for me. I want to think about this. Is it taking it too far?

John Thompson: 15:35

No and I don't think at this point with the technology, as we've talked about, large reasoning models and the inability of LLMs to think I don't think you can take it too far. I think it's really mechanistic the way that the AI that we have today works. It's great, it's fantastic. It says a lot of things, many things that we can't do. If you brought to me, let's say 25, 30 page legal documents and you said compare them all and let us know where the privacy shortcomings are and all these documents, it would take me weeks and I would be very bad at it. AI could probably do that and. I don't know, 10 minutes, five minutes, six seconds. I don't know It depends on the model and the compute environment. No, I don't think we're taking it too far. I think what we're doing is we're still feeling around in the dark trying to understand where, what is the domain of computing, which we've always had to do and what is the domain of humans, and once we have that, and we, and in general, we understand it, but it's not a widespread understanding. Once we get that more clear and say, okay, these kind of comparative exercises against large corpuses of information, definitely in the domain of computing. The idea of creativity and keeping things under control and making decisions with the, vast context we have in our minds. Definitely human. We just need to bring those together.

Andreas Welsch: 17:01

Do you see, or to what extent do you see people just more or less blindly or in good faith using AI and say I let the thing do the groundwork for me? I don't need to think about this. And then I just sent this off and we end up with AI slop.

John Thompson: 17:14

Yeah, it's that's, a great question and a great point. I've been, feeling around in the dark myself trying to figure out what is my next book, my sixth book, and it's actually gonna be on on how people need to be, developing and refining the critical thinking in the age of AI, which is just what you described. It's one of those things that when you ask people to critically think, you can almost see them go blank. They're like. I don't even know what you're talking about. I think that's an in indictment of our education system. We're not teaching people to critically think and we need to do a better job on that.

Andreas Welsch: 17:49

It's difficult, right? Even before Gen AI, you could make the argument that critical thinking or, the value of critical thinking has been diminishing through social media in the mix and compartmentalizing or living in your own social bubble. Now you add AI in when we're already not as critical thinkers anymore as we used to be. That's true, right? That gets more difficult. One of the other questions that I that I keep pondering with experts in the industry is agent AI so much different from how we've usually and traditionally used software? Are there even new skills that we need to learn? Or is this just another conversation with a more capable chat bot, with a more capable assistant and there's just a lot more, almost like an iceberg underneath the surface that we don't see as users, but I just ask it a question, does the legwork for me Then comes back before I did have to do that. Where do you see this? Is it just another evolution on a continuum? Or is that a revolutionary thing that we now need to train everybody on and change how they think about it, how they articulate their goals?

John Thompson: 19:01

Another great question. I think, Andreas, we could be here for about five hours doing this, but we're gonna run outta time. I always get painted with the brush of being a pessimist, and that's not true. I'm an AI optimist. I think we have over the last three years seen a step function. It's not a smooth evolution. It is definitely a step function in the capabilities of AI, but the way we're going to use it and spread it more widely across the world, professionally, personally, educationally, academics, universities, research. Is that we're gonna understand that AI in this respect, what I'm talking about now is no different than any other system, at least any other AI system we have built. It's all about the data. It's all about managing it correctly. It's all about integrating it. It's all about bringing an ensemble of data together in an intelligent way that allows AI to understand, analyze, and project from it. What we've done over the last couple years, three years, is we've gone from using 10% of the world's information to a hundred plus percent of the world's information that people have not got their head wrapped around. But once everybody, in, at least in information management, understands that in systems and development and AI, we're gonna have a, just a golden age of AI, in my opinion. So is it different? Yes. Is it really different? No. I'm on both sides of that equation.

Andreas Welsch: 20:28

I like that. That's wonderful. So, maybe then to bring it home and, make it tangible for business leaders, again, maybe those that are not quite sure is this real? How big of a thing is this actually? Where should I even start? Are my people using this and do I even want them to use this? What's the one thing that they can implement or encourage the team to do today?

John Thompson: 20:49

You know what? It's, I don't know. I think this is a great idea. I did it at EY and it worked out exceptionally well. Every organization, every company, no matter what size you are, you should build a safe, secure, private gen AI environment, and you should let everybody use it. For unfettered reasons. Let them write their weekend holiday agendas. Let'em write sonnets to their puppies. Let'em use it for anything, because what happens is you raise the level of capability across your whole organization. Now, they're gonna use it for work reasons too. Of course, they're not gonna waste a whole lot of your time and money, but you really should have every part of your organization using Gen AI in a safe, secure way. You really should. Don't wait. This is not a hallucination. This is a real transformation.

Andreas Welsch: 21:40

Great. And again, very actionable. And I've seen many large enterprises do that in fact, make different models from different vendors available to their teams. And then it's up to the transformation team, transformation office, to get people enabled and on board to rather use our safe and secure environment than some public version of whatever assistant where we don't know what's happening with our corporate data and so on. Great point. Now, we're getting close to the end of the show. John and I was wondering if you can summarize the key three takeaways for our audience today of how can you build the top skills for working with AI and agents?

John Thompson: 22:15

Right? First and foremost, you should have some part of your technical team spending their time learning anthropics model context protocol. And Google's a to a protocol because that's how agents are gonna talk to each other. MCP is going to be at the low level, A to a is gonna be across multi-vendor, multi-agent. So you should have your teams learning that now. That's, they're gonna need it. It's like a one-on-one skill, they have to have it. So get'em, learning that. Now, secondly, if you're concerned about AI and you're reticent about it and you're worried about moving forward, you gotta get past that. You need to talk to Andreas or me, or whoever on your team or whoever, whatever consultant you wanna bring in to make you feel comfortable. But now's the time. Every day that you wait, the further from further behind you fall. And then lastly, as I said, everything I implied this, but I'll be explicit. You really need to build environments that are safe and secure. And it's not hard to do. It's quite easy to do. The one thing that and non-technical people need to understand is that Gen AI and models and all these different kinds of agents and chat bots and all this kind of stuff are easily knitted together. You and I, Andreas have been doing this for decades. You remember when it was really difficult to get two systems to talk to each other as n impossible. Now, you put a model repository together, you drop in Claude, one day, you drop in GPT, another day you drop in and coheres model, anybody can point at any model and use it. You know that, that's just, it's wild, it's great. It's, the tech has really enabled us to do some great Things. build stuff that your people can use and let'em go in an unfettered way, but make sure it's safe.

Andreas Welsch: 24:06

Wonderful. I love that. That really brings us home. So John, thank you so much for joining us and for sharing your experience with us. I really appreciated the conversation.

John Thompson: 24:14

Thanks, I've really enjoyed it. Hopefully we'll do it again soon.

Andreas Welsch: 24:16

Yeah, that'd be great. So folks, for those of you in the audience, thank you for joining us as well, and see you next time for another episode of What's the BUZZ? Bye-bye

John Thompson: 24:24

Bye.