Piet Buyck Podcast Transcript
Piet Buyck joins host Brian Thomas on The Digital Executive Podcast.
Brian Thomas: Welcome to Coruzant Technologies, Home of The Digital Executive Podcast.
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Welcome to The Digital Executive. Today’s guest is Piet Buyck. Piet Buyck is a global technology executive with over 30 years of experience in managing and positioning high level IT applications that disrupt current practices.
And author of a new book, AI Compass for SC Leaders, he is well known as an influential and strategic business thought leader and entrepreneur with significant achievements and expertise in artificial intelligence. Demand sensing and demand planning as senior vice president Innovation Strategies at Logility and Aptean Company, Piet is on a crusade to make artificial intelligence for planning easy, accessible, and explainable while keeping human decision makers in control.
Well, good afternoon, Piet. Welcome to the show.
Piet Buyck: Oh, thank you. Thank you, Brian, and glad to be here.
Brian Thomas: Absolutely, my friend. I appreciate it. You are in Ant Belgium at the moment. I am in. Kansas City. So, we’re about seven hours apart, give or take. And I just appreciate you making the time. It’s hard to juggle the calendars these days, but Piet, if you don’t mind, I’m gonna jump into your first question.
You’ve introduced the idea of an AI compass that combines generative agentic and narrow AI to reshape supply chain planning. What inspired you to frame AI adoption and planning as AI Compass rather than just better tools? And how does that metaphor help leaders navigate uncertainty?
Piet Buyck: Very good question.
Thank you Brian. So, I choose for Compass because Compass helps you to navigate by giving direction, not describing every step the way a map does. And that’s a good metaphor for supply chain planning. Because planning, especially in supply chain, is a constant flow of decisions with competing interests, sales versus production, stability versus exception, growth versus margin.
But the roles of those decision makers have been implicit defined for 35 years already in the traditional and OP processes. And the technology behind it hasn’t fundamentally changed either. And because of that the functional silos in which decisions have to be made. Make it very hard for operations to work at their full potential in these changing times.
And when people think about AI at this moment, they often jump to a binary view, either fully automate their current function or keep it like it is. And that’s exactly why I framed this as a compass. We are navigating really to a new world and people are generally okay with change, but don’t really like the steps it takes to do that change end.
As decisions are always a combination of people, process, and technology, now we have to realize that AI has the potential to be all three at once. It can be as well processed as a decision taker, as the pe as being able to, be defined as, as technology and it can now combine numbers and insights at the, at the same time.
So, it’s a true transformation. And so, we’re, it’s almost like moving from horses to cars and that transition didn’t work until roads, gas stations, driver license and the whole traffic rules existed. And therefore it’s important that we can look beyond. What is current been doing and look like a compass to look more broadly.
Yeah. And every organization for the second part of your question has now its own DNA and the environment is very challenging and changing. So with the help of ai. It’s gonna be possible to be connected to those utterly change and use that power to support the necessary decision to be the best you can be in that environment.
Brian Thomas: Thank you. I appreciate that. And I love the metaphor, an analogy, right? You know, you talk about that compass as a navigation tool, a navigating into a new world, as you said. But change is hard, and we all know this. People are stubborn, especially when they don’t wanna change or move. And it doesn’t help when the communication’s not there or there are silos as you mentioned.
So, and there is a lot of change coming with ai of course. So, appreciate your insights. And Piet, you emphasize that AI shouldn’t replace human decision makers, but amplify them, keeping humans in control. How do you operationalize a human plus AI model in supply chain planning? And what are the governance or cultural shifts required to avoid over automation or blind trust in that machine?
Again, very good question.
Piet Buyck: Brian, it’s very important that machines should not replace humans and humans, and machines should collaborate. I know it almost sounds like a kindergarten slogan and play nicely together, even with the kids from the other neighborhoods, but it’s true. We also have to think humans are not perfect.
We are slow. We are a bit political, we are tribal and we can only work with limited sets of data, but machines. On their side are also imperfect to when if you think of machines of ai, they’re, they can be unethical. They are, they hallucinate, they need huge amounts of data and they’re not very popular with their colleagues and definitely not popular as possible.
So, the first step is to understand which value is broad and which value you actually want to achieve and arm everybody be the best capabilities to achieve those values. And so, you need to look at your organization as your partner. You need to map out the old way, and then you need to look at ai, what the new possibilities could be, and a very helpful framework.
Is there a balance between accuracy, transparency, and fairness? Let me explain. If you don’t understand something. Then you don’t wanna give it to AI to, to, to solve it because it’s gonna be too apac so that you want to supervise. But if it’s easy and you understand it, let’s automate it. And if it’s very complex, let’s automate it, but supervise it.
So, if fairness matters also in this triangle, then keep humans in the loop. You don’t want your production planning to plan. Over Christmas, for instance. So, the cultural shift is moving basically from this siloed KPIs in the, to a more horizontal approach where you look at the total value concept.
It means recognizing where people have a capability bias and where they can support it with processes and the rules that, that can look at the whole value creation. So now we train people in almost to be the stereotype type of what their current roles are. Defined sales, for instance, they are social, but they not be trusted with finances.
Finances, they’re not commercial. But if we support them in the right way, we can give people more holistic functions. Looking at. The total and the more complex the problem is, uh, contrary the more human supervision you need. But once you get the model right, of course, the speed and the impact is comparable to the horse and the car going from 20 miles to 100, uh, miles an hour.
But if you don’t understand the problem yourself, then don’t trust the machine.
Brian Thomas: Great. I appreciate that. Just to kind of step through some of the things that you highlighted obviously machines cannot replace humans and, and both are imperfect, working hand in hand. This partnership could be unstoppable, but you talked about those processes or tasks.
If it’s easy and you understand it, automate it. If it’s complex, you must automate, but must supervise. And the more complex, there’s more supervision required. So. I really appreciate that. And Piet, in your book announcement, you reflect that planning has remained painfully manual and analog despite evolving technology.
What are the most entrenched legacy habits or systems that hold companies back in becoming AI first, and how do you recommend breaking those habits? Thank you, Brian.
Piet Buyck: Very good question again. We have to think again. So, in our world of planning right now, we only communicate our numbers. We say budget wise, uh, revenue might be 10 million, but if it’s realized at 10 million, maybe it’s a different country has done the 10 million or a different product.
So, every time we need to explain in our meetings what’s behind. And not understanding the why takes away the possibility to react timely when things are different. So that means that every decisions need to be documented. And on top of that, an important hurdle is that all these planning levels are separate as well, the strategic, tactical, and, operational levels as the planning by department sales, marketing and so, so bringing that together needs an enormous amount of work, and that’s why I call it painfully manual. So, if we can bring a language to it that is able to translate what’s behind the numbers. We can create an understanding across the whole spectrum, and each decision can now be mapped against what is the plan to be so.
In order to do that we need to step away from entrenched habits. Like, the process is more important than the outcome. And no, the outcome is more important than the process and the power base. Like for instance, finance cannot be defined as so difficult and nobody else with the help of AI would be trusted with a financial decision.
The bringing the power of decisions to the decision takers is one of the opportunities that AI can bring. And by, decolonizing the numbers in the underlying drivers, it’s also possible to understand where exactly we’re deviating from. We thought what’s gonna happen so we can step out of this, okay, we’ve made this plan and now it has to stay for a year.
We can go back to okay, if our assumptions change and the reality show that the change is permanent, then we should change
Brian Thomas: our plans. Thank you. Appreciate that. And you know, it is a complex world and we all know we’ve all been there in whatever it is, a board meeting or some other type of meeting.
And our world, as you said, is communicated in numbers and every number needs to be explained. Now, if we can bring a map to provide that translation behind the numbers that is gonna help decisions, decision makers make better decisions, faster decisions. And obviously you talked about the outcome being important, so I appreciate that.
And then Piet, the last question of the day beyond technology, you argue that culture and change management are major barriers to AI adoption and planning. What skills or mindsets do you believe supply chain professionals must develop today, and how should leaders build a culture that’s ready for AI augmented planning?
Piet Buyck: Absolutely fits, absolutely with the previous question, it’s a changing world, so you have to have the attention to change with it. Almost like, complete company overall, not in what your core skills are, but it’s a bit like restarting your business in a world where nobody speaks English anymore suddenly, and everybody has to learn that new language that exists in that environment.
That means, maybe other roles in management. Training management to have a feel for data science, not knowing they don’t have to become data scientists, but understanding what’s that environment and redefine what now that you have more data available, more that you have more decision power available, what could be new objectives and results and you don’t need to start.
Too big because, and otherwise it takes too long before you get any return or too small. And because then when it’s too small, basically you just automate some tasks, that could be done already by a little bit more discipline, eh, so you have really have to look at the process, refine a new one, and make sure that there is an owner of the total process.
Because one of the risks is of course, that we didn’t. If you go across different silos, as long as there is no improvement in the silo, that people say, okay, the process has failed. And you need, like I said, to retrain the organization. And a lot of barriers come from fear, not fear, you know, like I’m scared for height, but worries about if I reskill my people, will they stay?
Will they move to another department? Maybe I will lose control. Or maybe the KPIs, that have. Being always the driver of my my department will go away. ’cause in some cases serving a customer might mean more inventory and I don’t like that. But when you get it right, the business case is really enormous.
It’s almost like 50 times a traditional business case for a billion dollar company. If you, it could be, if your accuracy, you’re talking about 2 million. If you are talking about creating your organization in the best. Possible way you are talking about like, like hundred million as a potential. So this mind shift need to come from this understanding and the curiosity about the why with a willingness to unlearn old habits and of course confidence to, uh, work side by side with AI rather than, uh, so, so this culture, change needs, almost like to go like an embracing and think through how it could impact your environment.
Brian Thomas: Thank you, appreciate that. A lot to impact there, but I agree there’s a lot behind messaging and change management. The culture there, there’s a lot that goes into that, but we as an organization need to be able to adapt to this changing world.
And it’s important you highlighted, helping your organization to understand and train before you move forward when you, for example, redefine objectives. But it’s important to identify an owner of a process as well. You talked about that. Again, that communication helped people understand that why is so, so important.
So, I appreciate all your insights on that and Piet, it was such a pleasure having you on today and I look forward to speaking with you real soon.
Piet Buyck: Okay. Thank you very much, Brian. The pleasure was totally on my side and thanks for having me.
Brian Thomas: Bye for now.
Piet Buck Podcast Transcript. Listen to the audio on the guest’s Podcast Page.
