Designing Supply Chains for Volatility - with Dr. Gopalendu Pal of Target

Summary

Podcast Summary: The AI in Business Podcast – “Designing Supply Chains for Volatility” with Dr. Gopalendu Pal of Target
Host: Daniel Faggella
Guest: Dr. Gopalendu Pal, Director of Operations at Target
Date: April 28, 2026

Episode Overview

This episode delves into how volatility in the global supply chain landscape exposes “seams” between teams and operational processes, challenging traditional scenario planning and decision-making. Dr. Gopalendu Pal discusses the limitations of human-driven, siloed forecasting and the necessity for supply chains to adopt advanced simulation, AI, and automation—with an emphasis on process simplification as a prerequisite for successful AI transformation. The discussion is rooted in retail, but the underlying principles are broadly applicable.

---

Key Discussion Points & Insights

1. The Nature and Impact of Volatility in Supply Chains

Global Sensitivities and Modern Challenges

• Volatility in supply-chains has always existed, but recent years—exacerbated by COVID-19, tariffs, and other disruptions—have increased sensitivity.
• Supply chains are now highly efficient, operating with little margin for error, making even small perturbations ripple enterprise-wide.
  • Quote:
    “We are now much more efficient. We are operating on the very cusp of... as efficient as we actually can get... So that means whenever there is a small perturbation... it really ripples through the entire chain...”
    — Dr. Pal, [02:06]

Seams and Siloes

• Efficiency has created vulnerable “seams” where different departments (with their own KPIs and goals) interact—often leading to suboptimal, disconnected decision-making.
  • Quote:
    “Those decisions often at the seams of the organization cause problems. When there is volatility... individually each team might try to maximize their own outcome. And that’s where the breaking sometimes happens.”
    — Dr. Pal, [03:40]

---

2. Departmental Tensions and Real-World Examples

Types of Volatility and Organizational Response

• Not all volatility is equal. Dr. Pal compares:
  • COVID-19:
    • Demand shifts drastically (e.g., working from home) while supply is choked.
    • Procurement wants to buy more; supply side can’t deliver.
  • Tariffs:
    • Procurement wants to buy ahead, demand is stable, but operations may not have warehouse or workforce capacity.
  • Quote:
    “Now procurement team wants to buy more, but we can’t have it. That’s a big problem.”
    — Dr. Pal, [06:33]

---

3. Challenge of Siloed Data and Inadequacy of Traditional Human Scenario Planning

Limits of Siloed Data and Human Planning

• Teams do communicate, but mere communication isn’t enough:
  • Quote:
    “Communication doesn’t lead into an effective decision... the problem is so massive that humanly if I optimize one segment over the other, rather than run hundreds of entire system design of experiment simulations, then we don’t know to really cause an optimized outcome decision.”
    — Dr. Pal, [08:32]

Necessity for Simulation at Scale

• Traditional playbooks focus on just a handful of scenarios—no longer sufficient at modern scale and speed.
• Retail and related sectors must simulate hundreds or thousands of interconnected scenarios (e.g., buying more, capacity limits, truck availability).
  • Quote:
    “These four scenarios are not good anymore because we operate at such a thin margin... We have to run hundreds if not thousands of design of experiments... run hundreds of the simulations and then figure out... what is the specific theme each of these teams are going to do considering the entire enterprise goal.”
    — Dr. Pal, [10:00]

---

4. From Enterprise Outcomes to Departmental Action

Optimizing for the Whole—Not Just the Parts

• Simulate and optimize for enterprise-level outcomes, then break down into actionable targets for each team.
  • Quote:
    “It is good to understand what are the key optimization criteria at an enterprise level. But once those are done, also break it down to the individual team. Okay, so now with this one, I do want you to hire more people…”
    — Dr. Pal, [13:42]

Ownership & Accountability

• Enterprise strategy is set by C-suite, then cascaded down to operations, marketing, and distribution leaders.
• C-suite must be forward-looking but avoid pure reactivity; process design and execution happen at the departmental level.
  • Quote:
    “C-suite is a very strong stakeholder on that vision. Enterprise strategy team owns that direction. But then they work with the individual functional departments and segments to understand those individual milestones and KPIs.”
    — Dr. Pal, [15:03]

---

5. First Steps for Leaders Toward Volatility Readiness

Ruthless Process Evaluation

• Leaders must critically assess and simplify current processes before layering on AI or automation.

  • Quote:
    “I typically always advise operational leadership team to ruthlessly—and I mean it—not being just aggressive, but really, really critical on the process that exists today. Right. That is the step number one.”
    — Dr. Pal, [18:14]

• Metaphor on AI and Good Processes:

  • Clark Kent (robust processes) + superpower (AI/automation) = great results (Superman).
  • Weak processes + automation = amplified problems (Homelander).
  • Quote:
    “If your process is not robust enough… Any workflow automations you put on top... is going to amplify the good and the bad both. And unfortunately, the bad quite often gets amplified even more.”
    — Dr. Pal, [19:02]

Simplify, Then Automate

• Simpler, stable processes are more likely to benefit from automation—complicated processes deter understanding and usage.
  • Quote:
    “The simpler the process is, the much better chance you’re going to have for transformation. If you build a very complicated process... nobody’s going to use it and that’s where the problem... happens.”
    — Dr. Pal, [20:29]

---

6. Choosing the Right AI Tools—Problem Before Solution

Solution Fit Over “Shiny Objects”

• Don’t adopt AI for its own sake; identify clear problems first.

  • Quote:
    “Really understand where the nails are and understand what hammer do you need to buy. So think about AI... as a fantastic hammer… But we have to really use that good hammer for the right set of nails.”
    — Dr. Pal, [22:14]

• Sometimes, simpler models are the best solution; only use complex AI if justified by the problem.

  • Quote:
    “Your solution might be as simple as a linear regression model, and that’s fine… Or your solution might be that you need a 7 billion parameter large language model... then do that...”
    — Dr. Pal, [23:41]

• Avoid technology for technology’s sake—a common pitfall in the current AI-hype climate.

---

Memorable Quotes & Moments

• On the Amplification Effect of Automation:
  “If you don’t have a good person with that superpower, you become Homelander—very strongly not a good person.” [19:17]

• On the Pace of AI Adoption:
  “We were thinking... if our research ever sees the light of the day in the next 50 years—it’s going to be magical. And then 15 years later here we are seeing all the work... being used at hyperscale.” [22:44]

• Actionable Leadership Advice:
  “Don’t go find a hammer looking for nails. Really identify the problem and then go after the right solutions.” [22:14]

---

Important Segment Timestamps

• [02:06] – Volatility and exposure of organizational seams
• [05:07] – Example contrasts: COVID-19 vs. tariff disruptions
• [08:32] – Siloed data & communication vs. true enterprise simulation
• [12:21] – Why traditional scenario planning fails at scale
• [13:42] – Breaking down enterprise-level wins into departmental KPIs
• [14:59] – Who owns enterprise transformation and response?
• [18:14] – The imperative of ruthless process evaluation
• [19:50] – Process robustness as the foundation for successful automation/AI
• [22:14] – Choose the right AI for the right problems (avoid “AI for AI’s sake”)

---

Final Takeaways

1. Visibility at the Enterprise Level: Volatility exposes crucial gaps—leaders must enable visibility and scenario planning across the whole system, not just within teams.
2. Simulation and AI Readiness: Traditional forecasting falls short—organizations should run extensive, interconnected simulations to optimize enterprise-level outcomes.
3. Process First, AI Second: Stable, simple processes are necessary for automation and AI to generate value—otherwise, technology amplifies existing weaknesses.
4. Problem-Led Adoption: Identify real business problems and process weaknesses before seeking AI/automation as a solution; don’t adopt technology based on hype.

---

This episode provides actionable frameworks and real-world perspectives for leaders tasked with future-proofing supply chains in an era of relentless volatility.

Transcript

[Dr. Kopalindi Powell]

Foreign.

[Podcast Host - Intro/Outro]

Welcome everyone to the Emerge AI and Business Podcast. Today's guest is Dr. Kopalindi Powell, Director of Operations at Target. Dr. Powell joins us on today's episode to unpack why volatility exposes the seams between forecasting, procurement and operations and why traditional human driven scenario planning can't keep pace with the scale and speed of modern REITs. He explains that leaders need the ability to run hundreds of interconnected simulations, understand enterprise level trade offs, not just team level KPIs. He also underscores that simplifying and stabilizing core processes is what allows automation and AI to strengthen decision making rather than magnify existing operational weaknesses. Just a quick note for our audience that the views expressed by Dr. Koppel and Nicole on today's program do not reflect that of Tolkien or its leadership. Today's episode is part of a special series on AI in supply chain design and network strategy sponsored by Optilogis. Supply Chain Leaders Mark your calendars. Optilogic's Opticon 2026 is taking place June 2nd through 4th in Detroit. The event centers on AI driven design in action, including demonstrations of how teams are collapsing modeling timelines from months to days. You'll hear from organizations using design as a competitive Edge. Connect with 300 supply chain professionals facing similar challenges and take part in hands on training to run network experiments with arc risk. Learn more and [e-mail] Opticon26 that's Optilogic.com Opticon2 now the conversation with Dr. Pohl.

[Podcast Interviewer]

Dr. Powell, welcome to the show. It's great to have you.

[Dr. Kopalindi Powell]

Thank you very much for having me today.

[Podcast Interviewer]

Supply chains have obviously always mattered, but it seems over the last couple of years it's become a real issue in the boardroom. We've seen a lot of volatility on the global scale and it's created a situation where executive leaders do need more insights and it's more than planning because it's so volatile. And I'm very interested to see what you're seeing on the ground as being the breaking point in the supply chain at the moment.

[Dr. Kopalindi Powell]

Yeah, that is a great question. So volatility in supply chain exists in multitude of scales. If you look at past not just two years or so, not just because of COVID 19 and the tariffs, but even in the past. Supply chain is a connected global economy. So any kind of perturbation happens anywhere, it really ripples through the entire chain across multiple organizations, multiple countries very much. But what we have recently seen, the volatility are becoming more Predominant. All the systems are becoming more sensitive to these changes. And part of the reason it's happening is because we are now much more efficient. We are operating on the very cusp of how efficient as, as efficient as we actually can get with these systems. So that means whenever there is a small perturbation, that is from the run state of the applications, run state of everything, how it operates, a very simple example, right? So if you have a big rock and you want to break it up into two parts, the best way to do it is to split it wherever there's a crack, right? Of course we're not trying to split the system in a supply chain. We are trying to make it more integrated, to make it work. But the seams of the organization is where the fault typically shows now seems of the organization by that means is that in a supply chain there are multiple teams, multiple groups and every group has a very specific functionality. They have critical miles, they have KPIs, they have business goals. Now those goals are typically connected across the enterprise, but they still have their own criteria that they optimize. When there is a volatility and we try to make a change, not all those change quite often ripples through the entire strategic organization. And different teams might try to optimize different goals differently without creating an interconnected decisions that is most effective for the whole whole organization, whole enterprise, rather than one or two or maybe five teams instead of many, many different teams. So some team might have to win, some team might have to lose. So those decisions often at the scenes of the organization causes problem. When there is volatility and these environments is large, it's very, very large. You want to talk about solving the largest data problem, come to retail and you're going to see how volatile these situations are, how many teams make decisions. And whenever there's volatility, those decisions may not always get optimized. That means individually each team might try to maximize their own outcome. And that's where the breaking sometimes happens.

[Podcast Interviewer]

And that makes sense to me because as you said, especially in retail, there are a lot of functions involved and department has their own KPIs and that's what they are worried about. Do you see specific points of tension in certain departments? Are there two or three departments that are usually vying for results which do fight against each other?

[Dr. Kopalindi Powell]

It depends on the volatility itself, right? Not all volatilities are the same. So let's just talk about two different volatilities such as COVID 19 and global tariff driven volatility. So very large, strong geopolitical reach on both cases, very strong impact on the society as we know it. But they are not the same and here is why. So let's just talk about COVID 19 pandemic, right? So what caused some of these seams to crack open? Whenever there is a procurement or buying team involved, they are trying to buy and they're trying to understand from the forecasting team what the demand signals should be so that they can very clearly understand what is the consumer behaviors are. Now before COVID 19 pandemic, we are always not thinking clearly that how the supply side might get choked. So we are taking the demand from our consumers. We are finding okay, so if this is the demand supply side within plus minus certain percentages will be able to provide these goods and products pretty easily within the generated lead time. But when the pandemic hit, the demand first of all shifted, right? Because working from home cooking more at home, not going to restaurant, that means we are talking about a big pressure on the retail. So the demand spike. But the problem is on the supply side, the supply also plummeted. So that means the previous assumption that yes, we can change or adjust to that demand indicator, the supply side will be able to manage completely broke down and that caused the seam to crack open because now procurement team wants to buy more, but we can't have it, right? So that's a big problem. Now if you look at the tariff side, very different situation now we want to buy ahead of time so that we can actually not be subjected to some of the tariffs that we think. And the demand side is more or less flat. I would say it's not as drastically has changed. But the problem is here it's just not the forecasting versus procurement. It's about procurement buying more. But do I have the capacity to receive it? Do I have the workforce to properly process it? Now the operation team and the procurement team might have a two different conflicting ideologies of how to get around that business, but just too different. It really, really depends on what is the cause of the volatility that can, that can cause two different sets of problems.

[Podcast Interviewer]

So it sounds to me like it is so many different systems and so many different, different volatilities that affect each of those departments so differently that it's almost impossible for humans that are often siloed to actually see the patterns and to start intelligently as much as you can plan for volatility, actually planning for that. So am I hearing you correctly that it's each department might have their Data sets, but they're not necessarily speaking to one another, the data sets per se. So overall there's not one view of exactly what's going on and what each effect each small adjustment would make to every department. You might have enough intelligence in one department to understand that this is going to be a heavy problem. On the procurement side, there is just no stock. But you do not realize what it does to the demand side or what it does even to warehousing and storage. So is it the fact that it's just so much information and it's that you don't have one place where it

[Dr. Kopalindi Powell]

all speaks to each other in part that is true. The different systems or different types of the operations, different sides of the operation, all these are really operation, right? From procurement to warehousing operations to distribution, transportation, forecasting, all the different teams of this operation. And yes, each of them has different sides of software platforms, tools they use. And they do communicate, right? It's a misunderstanding, might be that they don't communicate effectively. No, they do. But the problem is just communication doesn't lead into an effective decision. So here's a background. Before the retail side, I worked in technology development. Specifically worked on the simulations and design of experiments. So think about that way. When engineer creates a car or an airplane, right? So they're thinking about not just design the engine, right? I can make a powerful engine. The moment I put it on a car that doesn't have the right suspension, no matter how much power I can make, it's going to slip and slide on the road. I can make the biggest gas turbine ever, put it on a plane. But if it is too heavy, then the plane cannot fly or it's just too costly. Fuel, effectively the very similar thing, we know the data, we have the data, we communicate such a way. But the problem is so massive that humanly if I optimize one segment over the other, rather than run hundreds and hundreds of entire system design of experiment optimization simulations, then we don't know to really cause an optimized outcome decision. What has to give that means some team has to make X decisions, some team has to make Y decisions. Some of the KPIs might to get strategically lower down. Some of the KPIs needs to be augmented in a different way so that the entire enterprise wins. And that's where it becomes very difficult, right? So if you look into a simulation based decision making, typically, you know what traditional, if you go to a school and if you have been a business, business executive, you typically think about like four different scenarios. You think about my baseline scenario. You think about your best case scenario, your worst case scenario. And if you're someone like me, I always ask my team, have you planned for the train wreck scenario? So even if you talk four scenarios, but these four scenarios are not good anymore because we operate at such a thin margin, such a hyperscale activities, such a very, very efficient process that spans across multiple partners, multiple global network. We have to run hundreds if not thousands of design of experiments. Design of experiments meaning simulate the environment. If I buy more, do I have the capacity? Do I have the people, Do I have the number of trucks available? Is this how much are we going to hold? And if the holding period is not justified in the warehouse, then what's going to happen to the end of those products? Can we recuperate some of the cost? What is the E commerce look like? If you have hundreds of parameters to optimize, run hundreds of the simulations and then figure out okay, now with that uncertainty level, what is the specific theme each of these teams are going to do? Considering the entire enterprise goal, that is the best possible outcome scenarios and also understand what is the probability of that not being worked out and if it doesn't work, what is my plan B? So that's where a manual scenario analysis breaks down today. And that's where I believe more automations, more AI, better decision making can really help the organization.

[Podcast Interviewer]

So if I understand you correctly, it's about running so many different scenarios exactly at the same time and tweaking, making decisions, but pulling the levers in different spaces and seeing how that affects the overall business outcome. Even though like you said at the start it might cost some departments something, the overall could be a win for the company is not the capabilities that you're seeing in all all of the supply chains is the capability to run so many simulations. So, so maybe one or two or three or four. So if you had the capability to run hundreds of simulations, choose the best scenario and then design the supply chain for that, that's where you'll make small wins within the thin margins you're working in.

[Dr. Kopalindi Powell]

Yeah, I think you are quite right on that. But I would also think that just running the baseline scenario will not be enough. Right. So we have to run few scenarios but across multiple teams, multiple organizations and really optimize for the enterprise outcome. Not a team outcome, but once you optimize the enterprise outcome then you have to break it down into the individual team outcome. Because if you say like hey, I want to grow my sales for the enterprise by 5% within next two quarters. That's great. The next thing that operation team is going to ask you, okay, what does it mean for me? Are you going to process more volume? Are you going to hire more people? Are you going to get more automation? Yes, it is good to understand what are the key optimization criteria as an enterprise level. But once those are done, also break it down to the individual team. Okay, so now with this one I do want you to hire more people and that might show up that okay, you have an increased budget and then communicate effectively that okay, with this increased budget for you, I'm actually lowering my holding time somewhere else. So your increased budget is getting offset by somewhere else. So the entire enterprise wins. So this kind of breakdown actually helps the people buy into the process more. So yes, run design of experiments, optimize from the enterprise outcome, but then break it down to the individual team's executable criteria. And those criteria under volatility will look very different from your standard run state operations.

[Podcast Interviewer]

And looking at where you'll need to work your way down from the enterprise win and then what that looks like into the different departments who would own that process in a large enterprise.

[Dr. Kopalindi Powell]

So typically there are always teams that are involved, right? So we, if you look into any organization, retail, manufacturing, pretty much anything you're going to find there is an enterprise strategy department. But the enterprise strategy department might look into, okay, what is the total direction of the enterprise that we are going into? They're going to look into like, you know, what is my sales outcome look like, what my net promoters could look like, what is directionally, what is my total strategy that would look like. But then within that team they connect with maybe operational leadership team, they're going to look into the marketing team. They're going to understand, okay, so for individually how those different criterias kind of break down. But at the end of the day, the C suite is the owner of pretty much the high level strategy. They work with the enterprise strategy team to understand what is the vision for this. Whenever there is a volatility, how we are going to assess that volatility, how we are going to, how are we going to respond to it, what is the timeline that looks like, are you going to respond to it too reactively or are you going to try to predict it and set up the systems and processes so that when that comes, and it will come, we know that from the trend that has been established very well for past 10, 15 years now. When that comes, how quickly are you going to respond to it without being Too reactive. Right. So it is nature for us to be reactive. Short term vision. Something happens, oh, let's just fix it now. Because we are just problem solvers. We love problem solving. But also sometimes it is okay to take a stand back and say, okay, where is it going? How thoughtfully envision and then take steps, but set up the system and processes so that it can be mitigated. So definitely C suite is a very strong stakeholder on that vision. Enterprise strategy team owns that direction. But then they work with the individual functional departments and segments to understand those individual milestones and KPIs. And then it kind of breaks down into further and further and further operation team. We learn, okay, how my warehouse capacity is going to look like. Do I need to build more, do I need to shrink? Or do I need to think about the operational processes a little bit differently? Do I have to more human involved? Do I need to create more autonomous robots and human kind of being like a codependent system more so that we can handle these uncertainties better. But that will be really the decision of the operations team rather than the CEO deciding how that will be executed. I mean simply he doesn't have the bandwidth to do so.

[Podcast Interviewer]

I can imagine myself being a supply chain or a distribution leader now and thinking to myself, I'm working with very thin margins. I have to make the wins where I can make the wins. Having the capability to be able to look at the entire system and test multiple different scenarios at the same time. That will put me in a rather confident position to at least make more intelligent decisions should volatility come up, which it will, as we as we know. But maybe you're not as far in the transformation. What are those first steps that you would see suggest leaders to take to get to the point where they can get to that the wonderful future where you can actually plan as far as you can plan and design for volatility.

[Dr. Kopalindi Powell]

Yeah. First of all, I typically always advise operational leadership team to ruthlessly and I mean it not being just aggressive, but really, really critical on the process that exists today. Right. That is the step number one. So here's why I like to give a very simple example. Think about Superman and Clark Kent, right? The reason Superman is such a good superhero is not because of his superpower. It's because that Clark Kent as a human being harbors the good qualities. Really has a vision and optimistic view for the world. It should be. And now you give it superpower and now something really awesome emerges from that. On the other hand, if you watch the show by prime called the boys. If you don't have a good person with that superpower, you become homelander very strongly not a good person. My point is think about the current processes as Clark Kent your processes need to be really at par, needs to be scalable, needs to be able to accept any efficiency, gain, transformation, agility. Otherwise whenever the volatility happens, you put a bunch of automations in there or you change certain other processes. If your process is not robust enough, if it is processed, is not stable neutrally, that means it can handle some changes on both plus and minus side. Any workflow automations you put on top of that is going to amplify the good and the bad both. And unfortunately the bad quite often gets amplified even more because those amplification typically gets noticed by the people and then people typically think the problem is more than it should be and that erodes the value the culture, so the bad part gets amplified more. So my advice to all operational leaders is to ruthlessly understand what your processes are today. Not everything needs to be solved, but really work on asking yourself should the volatility come? Should I change the process? Will it survive? Will it amplify the goodness in that process that is already there or will it amplify some of the broken points there are today and then solve it, Solve it in a simple way, but simplify the process. I can tell you one single thing that anybody should walk away from. The simpler the process is, the much better chance you're going to have for transformation. If you build a very complicated process that people don't understand, they can physically comprehend what that should be. If you put a whole bunch of automation, nobody's going to use it and that's where the problem typically happens. So simplify process, make the process stable, add automations and then the goodness of the process get amplified by itself.

[Podcast Interviewer]

I actually love that and we've heard it across industries that this is not about taking your process and sticking AI on it, it's about really reconsidering the entire process. And I guess for some systems it is a mind shift because they may have been doing things the same way for forever. And you have to go into that process and really question it, really look at it and ask where are the weak points. You want to be geared towards agility for volatility because volatility is a given and I don't think it's going to clear up soon. And paired with thin margins, like you said, it's at the seams where we will rip and just being able to have those what if scenarios really plan out what all the different tweaks would do across the enterprise but then break it down to the different departments so it's operational. It can't just be intelligence at the top level. It needs to be broken down and practical for each department department that's going to put you in a good position to whatever comes next that you are agile enough and ready to absorb what what's coming.

[Dr. Kopalindi Powell]

Yeah, that's absolutely correct. And don't think about the solution first. Right. So the very typical say don't go find a hammer looking for nails. Yes. Right. So really understand where the nails are and understand what hammer do you need to buy. So think about an AI on automation is a fantastic hammer. AI is such a great invention, honestly and it's not new. I mean AI existed for many many times and one of the funny stories I tell when I was a PhD student and I had few other fellow PhD students I was working on large data sets and automations and some of my colleagues and friends were working on neural network. In one summer, I remember 2006, we were sitting down and we are thinking boy, if our research ever see the light of the day in next 50 years it's going to be magical. Right? And then 15 years later here we are seeing all the work that we did is already being used in a hyperscale. My point is the 50 years we thought about is because that was the timescale before Industry 2.0 to 3.0. It took about 50 years to change that but now that cycle is squeezed. But that's just because it's available is just a good hammer. Right? But we have to really use that good hammer for the right set of nails to solve the problem. Don't just pick out an AI fantastic LLM model for a problem that may not even the problem that to solve for your organization. Just to keep some talking points, really identify the problem and then go after the right solutions. Right? Workflow automations it might be your solution might be as simple as like a linear regression model and that's fine, there's nothing wrong with it. Or your solution might be that you need a 7 billion parameter large language model to optimize your the sentiment of your customer and create a prediction model that creates an inventory modeling appropriately then do that instead of a very simple model but just find out what is the right problem first rather than okay, this is my solution and then I'm going to look for what is the right problem. I do say that a lot nowadays, especially with the cusp of so many fantastic AI models that we're going to use AI and then we're going to look for the problems. No, no, no, no. I advise you against it. I advise you to look for the problems first and then see what AI model really applies it really know your

[Podcast Interviewer]

business because that's the only way you'll find it. Dr. Pal, thank you so much. This has been great. Looking forward to more conversations.

[Dr. Kopalindi Powell]

Thank you for having me today. It has been a pleasure

[Podcast Interviewer]

Wrapping up

[Podcast Host - Intro/Outro]

today's episode, I think there are three key takeaways from our conversation with Dr. Paul. First, volatility exposes the gaps between forecasting, procurement and operations, and leaders need visibility across the entire system rather than team level. KPIs second, traditional scenario planning is no longer sufficient. Enterprises must be able to run hundreds of interconnected simulations to understand trade offs before disruptions hit. And finally, simplifying and stabilizing core processes is essential because automation and AI only create value when underlying operational foundation is strong enough to absorb change. For our solutions partners, position your brand alongside the Fortune 500 leader leaders defining the enterprise AI roadmap. For the opportunity to showcase your solution to the executives currently funding and scaling global initiatives, partner with Emerge. Secure your [e-mail] partner that's go.emerj.com partner for further executive level analysis and to join our network of leaders delivering workflow impact with AI, visit emerge.com on behalf of the team at Emerge. We'll see you on the next episode.