Podcasts about mckinsey

The following article of the Professional Services industry is: “The Power of Companies to Boost Productivity” by Marina Cigarini, Managing Partner, McKinsey & Company.

Join Chris Winquist, Co-Founder and CEO of Groundhawk, for a compelling look at the digitization of critical physical infrastructure. Infrastructure construction remains one of the world's least digitized sectors, where billions of dollars in underground assets—like fiber optic conduits and power lines—are still documented using paper maps, hand scribbles, or delayed post-backfill estimates. Drawing from his career spanning McKinsey, Nokia, radar satellite unicorn ICEYE, and Groundhawk, Chris breaks down how AI-powered spatial intelligence and smartphone 3D photogrammetry allow field crews to capture centimeter-accurate 3D as-built data while trenches are open, solving a decades-old mapping challenge before the dirt goes back in.

In this episode, I enjoy a captivating conversation with Justin Schreiber, CEO, and co-founder of Terret. We explore the revolutionary concept of asymmetric growth, boosting top-line sales while driving down costs, a seemingly utopian vision for sales organisations. With his extensive experience across multiple business functions, Justin highlights the transformative power of technology, particularly AI, in redefining the sales landscape. The discussion delves into Justin's rich career trajectory, starting from childhood inspiration drawn from a simple game of Mousetrap to strategic roles at major corporations like McKinsey, Siebel, and LinkedIn. Justin unveils the inherent challenges sales teams face with CRM systems and shares how Terret's innovative platform acts as a ‘McKinsey consultant’ and ‘F1 pit crew’ for sales, effectively balancing human interaction and technological precision. Moreover, listeners will gain insight into how Terret is redefining sales roles and processes in a show-me world, encouraging agility and precision for increased efficiency and customer satisfaction. To connect with Justin and to learn more about what he does, please go to: LinkedIn – https://www.linkedin.com/in/justinshriber/ Website – https://www.terret.ai/

In this episode, we speak with Julian Lighton, who reflects on a career driven by curiosity, from law and consulting to leadership, entrepreneurship and executive coaching. Drawing on his book Navigating Your Next, he shares how major career transitions shaped his perspective on decision-making, identity and success, why clarity starts with knowing what you truly want, and how leaders can cut through complexity. Julian discusses the importance of developing people-centric leadership through learning and failure, before sharing the advice he would give his younger self.Julian Lighton is a Silicon Valley-based executive coach, strategist and author. Over a career spanning law, consulting and senior leadership, he held strategy and executive roles at McKinsey, Cisco and Hitachi before becoming an executive coach. He is the author of Navigating Your Next, a practical guide to helping professionals navigate career transitions, define success on their own terms, make better career decisions and approach what's next with greater clarity.Links from the episode: Julian's websiteJulian's book on Amazon.ukJulian's book Navigating Your Next on WaterstonesJulian's LinkedIn ProfileThanks for listening!Visit our homepage at https://disrupt-your-career.comIf you like the podcast, please take a moment to rate it and leave a review in Apple Podcast

Competing in a Future World of Infinite Intelligence Navigation: Intro From Knowledge Workers to Judgment Workers The AI-Native Company: Org, Hiring, Culture The Human Element: Are We Underestimating It? Scenarios Our Take Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show:   Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Nuno Gonçalves Pedro Introduction Welcome to episode 79 of Tech DECIPHERED. Today, we take a leap into the big unknown. This is a thesis episode, not your classic analysis, in-depth sharing episode. The big idea for this episode is that we may be approaching the cognitive age, and how would one, or how would a company compete in a world of infinite intelligence? The big idea, again, is that intelligence, which has been mostly scarce and expensive for all of human history, might become abundant and cheap. If that happens, what happens to work, what happens to companies, what happens to society? This episode will be really framing a lot of these discussions. From knowledge workers to judgment workers, addressing the AI native company and how does that change, going into the human element and whether or not we’re underestimating it, and finally, ending up going into scenarios, feasible scenarios of a future where, well, intelligence is abundant. Intelligence is quasi-infinite or infinite itself.Bertrand Schmitt Yes. Big questions for this episode 79. From Knowledge Workers to Judgment Workers We can start with from knowledge workers to judgment workers. Let’s go back first to how came the knowledge worker. It’s a 20th-century invention from Peter Drucker in 1959. The idea here is that that category might be splitting. The production of knowledge itself is on its way to being commoditized by AI. However, our perspective is that judgment around production of knowledge is not disappearing and is staying for a bit control managed by humans. What’s your take on this, Nuno? Do you agree with this split?Nuno Gonçalves Pedro I think it’s a little bit more profound than that. It’s not just judgment. Definitely, human judgment will be needed. We’ve seen agents perform all sorts of funny things in the wrong way when left alone to their own devices. Even some very well-known AI researchers coming forward and saying, “Hey, I tried to use this myself, and actually I messed up some of my systems,” or “I messed some of my code. I messed up some of my flows for a period of time.” I think just having human-in-the-loop from a judgment standpoint will be needed for a significant amount of time. That is something you can’t just delegate into machines, into algorithms, et cetera. The second part is, ultimately, there needs to be contextualization, and that contextualization, I think, comes from two forms. One from actual data, where the machine, I think, at some point will catch up, or the machines will catch up. The algorithms, at some point, on the data analysis will get better and better and have probably the closest to the truth that you can get, minus all the biases that are in the data, just to be clear, because data has a ton of biases. We’ve looked at this in the past and discussed it at prior episodes. But maybe on that, I think the machine has a chance to catch up, or the machines have a chance to catch up, so there’s less of distinctiveness from the human standpoint. But then, on just the attributes, the ability when you’re judging some situation, you’re in the middle of the situation. You’re judging the person and how it’s acting, in some ways, a lot of the things that end up happening, end up happening because there’s human interaction. There’s someone on the other side. I see how they’re delivering the message, how they’re implicating. We’ll talk about it later in the context of the organization and what changes in companies. I don’t think it’s just judgment. I think there’s a little bit more than that. One of the reasons I went to the dark side of management early on in my career from being an engineer was Peter Drucker and this notion of the knowledge worker, which he later on reemphasized with the publishing of his book, which for me was seminal and defined a lot of my career in life, the post-capitalist society, which is this notion that information rich and information poor is going to be the key distinctiveness that will happen in the world. The two big camps, information rich, information poor, which links back to this invention of the term knowledge worker, that knowledge is going to be key in some ways. I think that’s what we’ve seen for the last decades. Again, I think judgment is not going anywhere, but I think it’s beyond judgment. There’s elements of humanity and involvement that won’t go away anytime soon, where human-in-the-loop are particularly critical. We’ll discuss later some scenarios, but for me, that’s my stick in the ground. I think human-in-the-loop is going to be critical for many decades to come.Bertrand Schmitt While we are talking about all of this, and we share some possible scenarios, there is always that question. This is moving so fast right now. If you think about AI 10 years ago, AI 5 years ago, AI with the launch of ChatGPT 3, and then AI the past 2 years, now we have agents that are running at scale. Things are moving very fast. I can tell you, me in 6 months, the change has been pretty dramatic in terms of what I can use AI for. There is always that question that whatever we are thinking about cannot just be connected to what we were able to do 6 months ago or even today, we have to think and project ourselves at least in the next 6–12 months. Of course, we can go beyond that, and we will do that with some future scenarios, but it’s a very fast-moving, and it’s not clear yet where are the limits.Nuno Gonçalves Pedro I think that’s a very fair point. Let me try to analyze things that I don’t think will change anytime soon for the next few years. Agreed with you that many things will change, and we’ll have a lot better tools, platforms out there. That will be difficult to predict what exactly won’t change. I think there’s elements of humanity, and some of them do relate to judgment, like having good or bad taste, having a view on it, on whether something looks good or bad. Obviously, all of this sometimes is subjective, but some of it may not be as subjective as people think it is. The elements of contextualization. I think a little bit going back to what we did at Chamaeleon ourselves, where we built this platform, Mantis, and the objective of building Mantis was not really to replace us, was that it was a core augmentation layer in some ways that we would use investment or investor judgment as humans in the loop to systematize pattern recognition and a variety of other things, but that Mantis would really elevate all that judgment, not just in terms of timing, us being more productive, but also in terms of the quality of the decisions we’re making. Think of it as a little bit like having our human judgment in the context of operating Chamaeleon at a higher altitude, where we are more aware of the things that are happening and how they actually happen. The ability to really get to the data pieces and then make decisions on top of that that generate the needed alpha in our case for investors. What I mean by this is I think there’s always going to be core elements of humanity that I do think are going to be difficult for the machines to replace. For example, the taste piece people are like, “I can figure out what’s the taste in the market.” Yeah, but that’s mainstream. That doesn’t identify what’s the next big thing, which normally doesn’t start from mainstream. It starts from something else. It could start from opinion leaders and influencers. It could start by someone having a different way of addressing a problem and having a solution that hasn’t been thought through. For example, elements of creativity, I think, in human judgment and in human operations is something that I feel the machine will still have difficulty to replace.Bertrand Schmitt Let’s not forget how today current algorithms are working by feeding them enormous quantity of data, actually as much data as we can find. Finding more data is becoming a limitation these days. What it means is that it’s very hard for AI to think beyond its training data. There is some level of logic that’s being added, but at the same time, take the launch of the iPhone. What was the opinion before launch? Is that no, it doesn’t make sense. Not enough battery life, no keyboard, no this, no that. If you just base your analysis on what’s written out there, what’s being sold out there, you would just say, “It’s going to fail.” AI might really follow that more generic advice and perspective because that’s what in the training data and that’s what they’re in volume. It’s, of course, raising a lot of questions of, how do you improve the quality of the training data? How do you separate the weed from the chaff? There are a lot of questions there, and obviously, it will get better over time. But it’s still a critical part of how it’s working today. It won’t be that easy to change. I really like your point regarding Mantis, and I will say in general, platforms that you build with AI or leveraging AI capacity. Because when we say knowledge production is going to disappear, but we’ll keep judgment, it will be a different type of judgment because the quantity and quality of knowledge we will have in front of us to build our judgment will be very different. If suddenly we have for free the work of 10 interns or 5 junior analysts or whatever, and you can run that on nearly anything you do in life or at work, it’s completely dramatic. Your judgment was not used to be exercised so often because often you were missing quality data to have a judgment. Before it was a lot of finger in the wind and trying to smell something, but you didn’t have enough to make a serious analysis. Except if you are working as a strategy consultant, as you used to do, Nuno. That part is actually quite interesting. That the judgment itself will be exercised much more often and hopefully on the base of much more in-depth analysis for a lot of things. We will work very differently.Nuno Gonçalves Pedro We will go in-depth, faster and more fact-based, more data-based along the way. The question some of you might have right now is, is there some judgment that’s going to go away? Is there some judgment? We seem to be defining that there’s this organization, we’ll talk about it later, that goes from doers more into deciders. I think there’s some nuances to that, so I’ll just hit pause on that. In terms of judgment, obviously, there’s judgment that has been hidden over the years under the pretense of being wisdom, but it’s actually not wisdom. It’s just repetitive tasking, and it’s rules-based for the most. There’s a lot of judgment done, in particular in the white-collar space, that you could say it’s just reps. People have been doing it all along like that, and so therefore to say, “I’ve done it before like this, so I’ll do it the same way.” There’s actually no best in class, no analysis, no nothing. It’s just, “I’ve done it like that before.” I think that type of judgment will disappear because, again, algorithms will be as good, if not much better at that. They’ll be better at figuring out, actually, this would be the better way to do this. That’s how you play it forward. Then the question is, if there are fundamental, wise people in the organization, people that can really take that more complex elements of judgment, how do you go from the world we have today, which is a world of apprenticeship, where people come out of college, they go and work, and they learn their way, and therefore, hopefully over time, some of them, not all of them, we know that, but some of them will develop that wisdom to be great decision makers 15, 20 years down the road? How do we do that in a world that now is saying, “I don’t need people out of college because I can do it myself, and I can do individual contributor, and I can have agents doing the work that would require some manifestation of management in the middle.” Basically, “I don’t need this stuff. I don’t need you.” It’s a little bit the story we’re in. How do you create then this apprenticeship? How do we create then wisdom? My two cents on that is that wisdom, because of what we were just discussing and what, for example, myself and Bertrand was just saying, because of more often interactions with more data-stressed information and insights, what will happen is people will get better through their own reps in whatever form they’re doing, in day-to-day life, in internships, et cetera. In some ways, that will create the accelerated growth. It’s a little bit the interactions with agents and the interactions with our beloved AI algorithms that will create that growth over time and maybe not as much with other people. That still leaves the question around social interactions, but that’s probably the way this gets sorted. Apprenticeship gets sorted through the machine and the human having more interactions in effect.Bertrand Schmitt I agree with you because when we talk about apprenticeship, in some ways a lot of time was wasted on stuff that were not that important. But in a way, that was the price you had to pay in order to be there when people make the big decision to try to get some wisdom from that one hour of interactions that’s really useful and make a difference out of your full week. But the rest of your full week was just basic stuff that you had to do like a machine in a way. Why not let a machine do that? That, for me, is a big question. You could argue there is a transition period where it could be hard. For instance, if you can work hand in hand with AI smartly while you are doing your 4, 5 years of universities, you could graduate with a very different knowledge, perspective, judgment, skill set than anyone who graduated 5 years ago. I think that part will require a question around, “How do you change education?” You see what I mean? If you keep education the same way, expecting that the output is someone that should go now into 5 years of apprenticeship, that’s not going to work because companies will be, “No apprenticeship anymore.” On the contrary, you have to come much more knowledgeable and ready to use the tools. The tools are so efficient that the bar pretty high. You need to come already very well-grounded. If the education is not doing their job, that will be trouble. That part for me, I think is often forgotten. In some ways, the new-found importance of universities as a place to, and not just universities, the trade to really deliver people who are ready for the workforce. If on the business side, the expectation can change, of course, you have to change the education on the other side. My worry probably right now is that it doesn’t look like universities are in touch with what businesses are looking for, businesses are working on. Of course, that’s very worrisome because the cost of university has increased very significantly. It’s not clear quality of education has improved at all. If anything, it could be the opposite. It’s pretty scary. Of course, it’s going to raise a lot of questions. How much is education worth in that type of situation? Maybe another point because we talk a lot about apprenticeship, how this stuff was useful, but at the same time, if we go back in time, not long ago in the ’50s, if you wanted to be a developer, for instance, ’50s, ’60s, the job was very different. There was barely any programmation language out there. You had to use punch cards. Your time truly spent doing the coding was very limited. Once you had your stuff working, then, the debugging was a total nightmare. My point is that no one is looking back to that time saying, “You know what? It was great. It was a great way to learn and to do an apprenticeship for 5 years. To do that crappy job of punching cards for the boss.” There was little value in this. Guess what? Everyone is happy it’s not being done anymore by anyone. I think we also have to see what AI is bringing in a similar way is that everyone’s job is going to become quite different. There are a lot of big parts of the job who are not going to look back with fondness. Just looking back as, “Wow, that was very machine-like type of job. I’m glad I’m done with it.” People will want to jump directly to the next step. You don’t need to go to the punch card phase to be able to be a good developer for the past 40 years. I guess it will be the same with AI.Nuno Gonçalves Pedro I think so. The difficulty we have as humans is to also visualize dramatically different scenarios and landscapes, professionally. It’s difficult for us to anticipate what are the jobs of the future. Jobs have changed a lot in the last few decades, not even the last century. What people do, the migration initially from the agricultural society to then the industrial society to then the services society, and in some ways, the shift within the services industry, and now we’re seeing another shift, so we can’t really anticipate what those jobs look like. Back to your point on education, because I think that’s a very important point. If you’re right now an undergraduate student or a postgraduate student, for that matter, and you’re not figuring out your own mechanisms of learning outside of your syllabus, outside of what your professors are telling you, et cetera, you’re going to face very difficult times. If you’re not right now using all these AI tools proficiently, all these cycles of vibe coding, co-working, et cetera, with agents in the mix, you’re going to have a really tough time. If you’re not at this point in time as proficient as someone like myself or Bertrand, and given that we’re nerds, we’re relatively proficient with a lot of these tools that are out there. On top of it, some of us have our own platforms in-house. If you’re not as proficient as we are with those tools, you’re going to have a very difficult time because then people like us won’t need you. I think that’s the sad truth. It’s like at some point, if you’re not needed, you’re not needed. Then again, you may find something else that’s more interesting for you to do. Start your own company, go join a new exciting job doing whatever it is that you need to do next, et cetera. But again, I think the bar is very high. If you’re in college right now, again, undergrad, postgraduate, this is the time of transition. This is the worst time. It’s not the best time, it’s the worst time. Because education and all these institutions haven’t adapted to it yet. You need to adapt. You need to adapt. You need to adapt. If you don’t, you’re going to pay for it, not just in the loans you need to repay, but also in terms of actually having difficulty finding your career path in those first few critical years.Bertrand Schmitt You need to be especially proactive when you’re facing this type of period where businesses are adapting as fast as they can because they all know it’s going to be survival of the fittest very quickly. Universities typically are working on a very different pace, and it’s pretty guaranteed they are not going to have adapted as fast as businesses. In time of big dramatic change, it will be trouble. It will be trouble. Yes, you will have not fun. Not saying it was part of the deal when you sign up for that loan and decided to go for university. But that’s life. There has been issues before. It’s not the first time. You have to do something about it. You talk about your perspective about, “Hey, why do we need you if you are not already fluent and very efficient with these tools and stuff?” The truth, in some ways, it’s even worse than that. Each time we spend with someone who is not efficient with all of this is less time we spend with the tools that are already providing magic for us.Nuno Gonçalves Pedro Exactly.Bertrand Schmitt It’s a very big choice of, “Hey, do I spend more time training this person?” Do I just… there is an opportunity cost. Or, do I spend more time staying at light speed? Why do I slow down to do something else in the hope that maybe I will get to return versus the light speed I’m already on? It’s a lot of tension. Again, it’s certainly new. But if we want to look back, I think you talk about the switch from agriculture and society, industrial society, and now the service industry. The reality is that, yes, we have made dramatic changes in the past before. 140 years ago, we were 90% agricultural society in Europe, in the US, 90% of us. Today, it’s what? 2%. So my point is that that’s a normal evolution. There is no progress without change. Sometimes the rate of change is soft, and sometimes you have a step function. Now it’s a step function, and it’s also a pretty fast step function. Before, it could take decades to get new stuff being put in place, to have electricity come up, this or that. Now we see that the rate of investment in AI is insane, way beyond anything we have seen before. Two, in a way, a lot of the architecture behind the scene was already there to support an even faster transition. What’s new might be the pace of the transition, how unnatural it might look. But at the same time, if you put yourself in the shoes of someone who lived 150 years ago, I mean, this was also a dramatic change for them. From horses to cars to planes to rockets, pretty big change, maybe even bigger change.Nuno Gonçalves Pedro Maybe the silver lining, just to bookend this section, is one, there will be new roles. There are a lot of things we can’t anticipate. There will be new roles, there will be new jobs being created, and new things that we can’t really quite grasp yet. The second part is that the rules are changing, and they’re changing, I would say, in general, for the better. If you are a decision-maker or an organization, and you still have your job, you’re probably making more important decisions with more data, with more tooling around you, with less red tape, hopefully over time. I know that will not hold true for all the big corporations out there that are listening to us, but it is starting to happen. Things are making an impact on how decision-making is made. There’s less and less red tape along the way in certain organizations. There are more and more fact-based discussions happening as we move along. The silver lining is better jobs, more jobs, different jobs in the future, hopefully as well. Secondly, the second part of the silver line is that the jobs that exist today, hopefully, will be more interesting, certainly on the knowledge space and on this judgment space that we’re now introducing as part of this episode. The AI-Native Company: Org, Hiring, Culture Switching gears, maybe to how does that shift? How does the company of the future look like? How does an AI native company look like? I feel there are a lot of discussions on, “Oh, you only need one person to run everything.” Let’s not go to that level. We’ve had a couple of episodes where we focused on AI as your co-founder and a couple of other elements that you guys can go back to. Let’s focus on a more evolutionary view of what’s happening to organizations, and maybe start with the org structure. In general, we should see more flat organizations where mid-level managers have to justify their pay in some ways because middle management are routers. They are normally routing tasks. It’s sometimes aggregating it, synthesizing it, and pulling it back up. Guess what? AI and agents in general are very good at that. The synthesis piece, et cetera, is not as well needed. One could say there are several elements of middle management that are valuable, like the coaching of people, the creation of apprentices, and the accountability that comes with some of middle management. But lo and behold, most of middle management is seen as a little bit of a thin line that doesn’t need to necessarily exist. I feel we’re moving into a world of smaller teams, more senior teams, where there’s more judgment at the top, where you’ll have people that both do a mix of what we used to call management in its new form, but also a lot of individual contribution. If you’re not used to that, if you’re not used anymore to be an individual in the future, again, and if you’re a very senior in an organization, maybe this is the right time to either reinvent yourself, find some other job that doesn’t require as much of that, which we’ll have plenty of those jobs for the next few decades, or maybe retire. I’ve actually, shockingly enough, seen people who have said, “You know what? This thing is changing too fast, too dramatically. My industry is changing quite aggressively right now. I’m about to retire in a couple of years. I’m just going to retire now.” I’ve literally met two people who have done that. Again, there’s nothing wrong about it. I think we’re, again, going through a step function and a huge shift, but figuring out where you fit in this new model of organizations, more senior at the top, smaller teams, more of a mix of individual contribution with management than ever was done before.Bertrand Schmitt I agree with you. In some ways, I’m not surprised that some people might say, “You know what? It’s now time to retire.” I feel a bit sad, maybe because it means you don’t like to keep reinventing yourself and changing your habits and thinking about new stuff. You were a creature of habits, I would say, if that’s your conclusion. But everyone is entitled to their own opinion, obviously, and a way of life. I guess that’s what happened, again, at regular times in the past in terms of big change. What I can see is that the rise of, you can call it the full-stack individual, someone who will have multiple roles inside the team. Before, you had to really separate the role. Especially in the US, there is such a clear separation between every role you can have in a company. Let’s take a tech company. You will have people doing design, people doing different types of designs, people doing front-end development, back-end development, and operations. You see step-by-step hyper-specialization. I have seen that, and it’s true that the level of complexity you had to deal with at some point requires some level of hyper-specialization because it will take you 6, 12 months in order to be really, really strong on a specific topic, a specific language. God forbid, trying to go deep into something that you had no real experience into. But I feel with AI, it’s a big change, actually. It’s the opportunity to go beyond that. It’s the opportunity to do more, to touch more. You can combine designing and shipping code, product managing and shipping code, being an analyst and deploying. Of course, we have to think how it works because putting a marketer shipping code to production, maybe that will get you into trouble. But I think that there must be some change. We see it changing dramatically, how fast we can get into something, something different from what we are used to. I think it would be crazy not to take that opportunity to dramatically change the scope of many positions and put an end to that hyper-specialization. I think for me, in some ways, hyper-specialization was bad. There is only so much you want to be a specialist in because a lot of things, a lot of opportunities are actually coming from the mixing of many different ideas, many different perspectives, and you lose if you go to hyper-specialization.Nuno Gonçalves Pedro I don’t think the age that is coming is the age of the generalist. I think it’s going to be the age of the multispecialist. We’re going to go into an age of multispecialization, which is a little bit, we’ve mentioned it as well in the past, what Amazon defines as an athlete or T-shaped or pie-shaped people, people that have on top an amazing ability to do general management, strategy, managing teams, et cetera, then have spikes. Spikes into business development, corporate development, product management, whatever it is. With AI and with agents, the development of those spikes, as we’ve been discussing in this episode, will actually be easier. It’s almost like a given. If you want to go deeper and deeper into a certain area, you can go much faster. I think that level of multispecialization is going to be really cool to observe. I’m not sure we’ve had an age of multispecialization over the years. Maybe people would point out, well, the Da Vinci example, people that are great across very different areas. Maybe that’s an example of multispecialization. But honestly, from my perspective, this is going to be an exciting time because of that, because you’ll have people who, instead of being just focused on this area of sales, and I only do that, they can actually and should actually do a lot of other things. So the work, as we were talking before, can be more interesting. More demanding as well, because the judgments you need to make are more complex. The context you need to actually gain needs to be gained much faster. At a level of magnitude, you haven’t been able to do it before. Talk about information overload. But actually, ultimately, the roles can be a lot more interesting, a lot more exciting, because I can jump around. If I’m an investor, in this case, we have two investors on this conversation. But if I’m an investor, one of the things that we start looking at is actually not just looking at a startup as, is this startup doing something in AI or not? Is it AI-enabled or not? Is it an AI platform or not? But actually, more fundamentally, is this an AI native startup? Meaning, organizationally, culturally, is this the company that’s already in the AI age? How is the team working? How are they defining things? It’s not just that they only have two or three people. It’s like, what are those two or three people doing? How are they doing it? What cadence are they doing it on? What tools are they using? How are they making decisions? I feel we’re still actually relatively early on that track. It’s very interesting because we’ve had all these companies raising mega rounds. First round out, we invested in one of them, but there have been many frontier labs out there raising a ton of money. But a lot of them don’t have a fundamentally different way of doing business. Of organizing themselves, of how they do the day-to-day. Although they’re working on cutting-edge stuff, with very notable exceptions, they’re actually not using it themselves. They’re not actually shifting how they do stuff themselves.Bertrand Schmitt For me, that’s very interesting because in the past, I used to be quite conservative on how you manage and run a company in the sense that if you’re already in tech, if you are already on the cutting edge of what technology can deliver, and this and that, don’t waste time trying to invent a new org structure. Just focus on delivering something great, amazing, and be great at technologies. That’s already your huge differentiator. At the time, there was no real reason to innovate on the team organization. I have seen so many teams that tried to innovate, and it was just catastrophic because there was not much to innovate on, because we had decades of optimization that we could leverage. There was no reason to invent. But here it’s very different. There is a dramatic shift in how you can organize differently a company. I don’t think there are any blueprints yet on what’s the best way to do it because it’s too new. But at the same time, I would feel very bad to invest or support a company that first is not focused on AI or AI-enabled, but at the same time is not trying to innovate on the team itself. Because if you don’t do that, you’re going to get killed by someone who is going to innovate better than you on not just the product, but on the org as well.Nuno Gonçalves Pedro Indeed. The shifts are pretty substantial. If you look, for example, just at hiring, what do you hire for? Certainly, there’s this element of the multispecialized orchestrator, which normally will be someone with quite a lot of wisdom and expertise. It doesn’t necessarily mean someone who’s old, but someone who has the ability to work with all the AI tooling and platforms out there and be an orchestrator of agents. Why do they make judgments, make decisions, move stuff forward really, really, really quickly? Again, those jobs are going to be the best jobs. The second part, I think that is very interesting, around hiring, is you’re going to skew towards the elements that are potentially either very aligned with the use of AI tooling and platform, AI expertise, or being AI native, or someone who’s used to using AI. That’s one side of the fence. On the other side, you’re going to actually be optimizing to hire people that have the characteristics that will be difficult for AI to replace immediately, like taste and the notion of fundamental accountability and notion of implications, the notion of how you affect change in organizations, how you affect change in individuals, the elements of coaching, and beyond coaching. You’ll be optimizing for those kinds of hires as well. Then, last but not least, for me, I feel that there is a momentum already happening. I think it will happen even more, which is the tendency to under-hire rather than over-hire. The moment of the good old days of blitz scaling, “Oh, let me go and hire 300 people to scale my go-to-market and just land grab market.” Now, that’s not how it’s going to work. People are going to try and first get the efficiencies in-house with top talent and see if there’s, at the end, the need to hire more people or not, rather than the other way around. I think the issue here is a little bit of what we alluded to before in this episode. There is a tax on individuals. If you hire more people, you’ll have to manage people, you’ll have to work with them, et cetera. If I don’t need to, I might as well work with the agents that the tools and platforms that I use give me access to. Because that’s a world that’s much more efficient, right?Bertrand Schmitt I’m in total agreement with you on this. It’s definitely raising way more questions than before because, again, on one side, you have the product, the technology used to build products that are completely different. At the same time, all of this is also enabling new ways to design organizations and to scale differently, especially in a world where, as we have seen in 3, 6, and 12 months, stuff that you thought were impossible are suddenly becoming possible. So you’re, “Hey, I’m going to scale and burn a shitload of money for 6 months before I know if there is any return.” Versus, “You know what? Maybe I just wait 6 months. The AI has improved enough so that we don’t need this new team. We don’t need these people to do stuff.” Because actually, if you just wait 6 months, we will have stuff coming for free from either new AI models or new AI tools or this or that. If you remember, we used to say that in mobile, things were going three times as fast as on the web in terms of pace of innovation and speed of development and stuff. I mean, with AI, it’s 5X mobile.Nuno Gonçalves Pedro Maybe even more. Yes, well.Bertrand Schmitt Maybe even more, maybe 10X. Every assumption around blitz scaling or scaling in general was based on past assumptions. It’s not based on how is the industry evolving today. Might make more sense for you to really grow your agents and spend more money on more tokens. I remember, of course, Jensen is selling his business interest, but he was saying, “Hey, for each one of my 450K engineers, he better spend 250K in tokens a year.” I’m not saying it’s the right way to say it, but I think there is some truth in it, and that would be something to think about. Have we maxed out the token usage per employee? I’m not talking in a stupid way because token maxing and wasting money has no value and is as stupid as it gets. But if you are truly getting a return on these tokens, can you use more? Can you generate more? Can you create more loops so that one engineer manages not just 10 agents, but 50 agents, but 200 agents? I think that’s the big question. We’re trying to add more people. More people means more management, more issues, more this, more that. That would be a fair question. Another piece of the puzzle is how do you build in a way your… I don’t know if it’s a digital twin, but more like the digital version of your companies represented by agents. How do you make sure that everything you do as a business is truly captured, is truly leveraged so that your agents are getting better and better? Not just because the model gets better, but because you are putting more data into it, because it has more opportunity to learn, and as a result, gets better at your specific business.Nuno Gonçalves Pedro The next big thing is culture. How does culture change? I think the biggest shift that I see is, why would you do meetings all the time?Bertrand Schmitt Yes.Nuno Gonçalves Pedro At least at Chamaeleon, we have a very small team, just by the way. We have a very small team at Chamaeleon. We’ve reduced by way more than 50% the time we spend on meetings between each other across the board, one-on-ones, partner meetings, et cetera. I think we’re really pushing to be more and more asynchronous. There’s stuff you can process via message. I was just asking one of my colleagues, “Can you just send me that prompt for that so I can just do that on CoWork?” Or “Can I just go on Mantis and do this? Can you tell me the cycle?” Or vice versa. Basically, it’s a little bit like you’re just going to do it. I don’t need to meet. I don’t need to meet all the time. There are some things where we still need to meet and interact, and we need to brainstorm at times, and we need to go to a different level of abstraction on the top end. Then on the lower end, there might be things that are a little bit more specific and governance-related and operational-related that we need to agree on that are more sticky. But otherwise, the culture is going to be biased towards build. “Go and do it,” rather than, “Let’s do a meeting.”Bertrand Schmitt Yes.Nuno Gonçalves Pedro Async is the thing. I’m more and more like we have a couple of interns this summer. “Can we async this?” They’re like, “What does that mean?” “Can we make this interaction asynchronous?” Because synchronous interactions for me are very expensive. Can you send me something that I can process, and then I can send it back to you? We don’t waste time on you giving me context and whatever. Then I’m not ready quite yet because I need to process it. Maybe I’m in between two meetings that I’m actually thinking about other things in my mind.” Again, I feel that shifts how stuff is done. One, build rather than meeting. Two, asynchronous versus synchronous. In some way, millennials had it right when they shifted a lot to messaging and stuff like that. Let’s do more asynchronous rather than synchronous, those two elements from just an operating model of the company are significant. Maybe this is a good time for me just to put one parenthesis because there’s this thing that’s bugging me as we’re talking here. Everyone who is listening to us at this point in time might be saying, “Cool, but I work for this large organization. We’re just now…” Everything we’re saying here is contextualized by time. We’re giving you extreme situations. We’re looking into the future. Some companies that we’re talking about might be doing this already as we speak. Some of them might be in the process of doing this and might in the next couple of months be doing it like we are describing it here. Some of them might take years to get there. Then again, some of the companies that might take years might actually be destroyed in between or meanwhile, and be disrupted. Some of them might not because they’re in very legacy businesses, and it’s fine, and it’s okay. Again, don’t take everything that Bertrand and I are saying today as this is gospel, and it’s going to happen tomorrow, and why the hell are we not doing it? We think that aspirationally, this is where you should be moving to as an organization, whatever size you’re at. Speed will matter, as we discussed before, but not everyone, obviously, is going to move as fast as we’re describing it here.Bertrand Schmitt Yes. Me, for instance, take inspiration often with what some of the AI labs, frontier AI labs, are doing, the way they are working, especially in OpenAI and Anthropic. They are clearly at the top of the spear in terms of what is it that you can do because they have access to models we don’t have access to, because they have unlimited tokens they can use for tasks. They hire people who are, of course, 100% on AI. They are the best example of what is achievable if you have the top minds, if you have the latest models, if you have unlimited tokens. From there, you can take that for our needs and for our situation, and others in industries that are not as advanced. Definitely, you have some time. But as you say, things are moving fast, things are changing. Wall Street is going to expect better returns because when we discuss all of this, the conclusion is that you should be able to do more with less. That’s as real as it gets at some point. By the way, that’s what you see. You see better performance, a better business performance right now. So even if you might not get disrupted, you’d better start there. For some, it might take more time, and they might still be fine.Nuno Gonçalves Pedro Maybe to bookend this section, clearly what we’re saying is organizations are going to change. Their MOs are going to change, the structures are going to change. There are elements of what we discussed before in terms of judgment that are fundamental to this. The ability that in some ways, one would say a lot of the technique of getting solutions out there, even in brainstorming or problem-solving, is going to get democratized. The algorithms are able to do that. On the other hand, having points of view and having wisdom is not necessarily democratized, necessarily by the machines. It can be facilitated, it can be more productive in achieving that level of wisdom, but wisdom still will matter at the end of the day. We’re not saying that’s out of the question. Actually, that’s going to be the asset. People who have fundamental wisdom that can come to the table and frame things. We see this even today in prompt engineering, on just creating prompts. The better your prompt is, the better the outcome is going to be, the result that you get from the algorithms. That’s not going to change, in my opinion, anytime soon. That UI interaction piece is not going to change anytime soon. Again, if you’re an organization thinking through organizational structure, culture, if you’re thinking through hiring, these are some of the elements that we think will give you an opportunity, but I would actually go one step further. On the positive side, I would say, they give you arbitrage. If you’re able to move faster than your competitors and really adapt your org faster, you’ll reap the benefits faster as well. That’s what many still say and relate to as the word innovation. That’s how innovation gets accelerated. I think there’s a huge opportunity right now for arbitrage. If you move fast, experiment, experiment on new org structures, experiment with talent, you’ll know that some of them will work well, some of them will fail miserably, so you can’t experiment on literally everything. On the other side, I think the doomsday scenario is if you don’t, if you’re on the other side and your competitor is outpacing you on trying these different organizational models, structure, hiring models, and operating models, they’ll potentially just disrupt you. They’ll do stuff that you thought you had the moat on, and lo and behold, you don’t anymore. Sometimes it comes just from org, just from injection of people with a different MRO, different operating model.Bertrand Schmitt The Human Element: Are We Underestimating It? Maybe we can move to our next section about the human elements. Are we underestimating it or are we overestimating it? The three things that are a big part of the human elements, emotion, creativity, and synthesis. Is it just soft skills, replaceable part? On the contrary, is it the durable part now that we have automated intelligence?Nuno Gonçalves Pedro I’ll start with emotion first because I think it’s probably the easiest of all the ones you’ve mentioned. Emotion is key. Many of you listening to us will know this. The way you deliver a certain message, the emotion that you have when you deliver it, just in and of itself, this could be a sentence, it’s something verbal, et cetera. Makes a difference between the person or the people on the other side actually adopting it or actually just resisting it. Emotion is critical. It’s what runs the world. Everyone talks about a bunch of things, but emotion is a currency that is still naturally human. It will be, I feel, difficult for these AI tools and platforms to recreate it fully until there’s some literally very high-definition manifestation of them as avatars or some physical manifestation of them as robots and all that stuff. It will take a while for that emotion to be manifested. Emotion, I think, is still something that we as humans have as a moat, and it’s critical. As you mentioned before, I was a strategy management consultant at McKinsey, and getting people to action is actually 80% about the delivery, communication, the emotion that you surround the project itself, more than sometimes the truth. It’s great to have the truth and to have something that is similar to the truth in terms of analysis, but in some ways, that’s not what really moves change. Change is moved by, I would argue, a significant amount of emotion and alignment on emotions.Bertrand Schmitt You could argue that’s something that most politicians have perfectly understood. If you look at most campaigns these days, everything on emotions, maybe the tagline might be one word. It’s interesting when you see from that perspective that actually it’s very little on facts, very little on all of this, but more about emotion. You could argue it’s the same for businesses in the future? That’s a fair question. I think creativity is another one that’s quite important. At the same time, it’s not so easy because I must say I’m quite amazed when I’m looking for creativity from AI, either to generate the image, to generate video, to generate audio, or to generate text. AI can be pretty creative. I still think you need to control its creativity; you need to understand what’s good, what’s bad, what’s quality, but at the same time, I can see even in creative tasks, AI can be a very strong partner. I’m talking about any creative task, like invent a name for a product, let’s brainstorm the mission for the company. AI can actually be doing a pretty impressive job. That’s the type of job where you will hire experts, where you will use some of the best people in your team to help you for days. We say, “You can do quite a lot.” It’s an interesting one because I think there is some unique human creativity, and at the same time, AI can be pretty strong at creative task as well.Nuno Gonçalves Pedro I agree. In particular, if it represents benchmarking, if it represents repetition, if it represents seeing the world and then coming up with something that presents itself as creative, to be honest, it can actually outpace humans. If it’s like genuine light bulb moments of creativity, angles that haven’t been tried before, certainly not in the same way, I think humans still have the advantage. To your point, I agree. This is not a humans-win situation. On the previous one, on emotion, still, part of it is because, also on emotion, there are exchanges. You and I might be looking at each other, and from the facial expressions and the reactions, where you judge that for AI to get there, it’s going to take a long time. There’s going to be a lot of very complex algorithmic stuff put into that for AI to be able to create synthetic emotional behaviors, but creativity, I agree with you. There are a lot more nuances to it today, where AI does have significant advantages at the end of the day. Synthesis depends. Synthesis, I feel, if we’re talking about holding a bunch of messy assumptions, contextualized inputs with different layers of data adjacent to them and then trying to create and form one coherent, fully accountable point of view that you stake something on, like a decision, a company, a business unit, whatever, I think humans have the advantage. Part of it is the complexity of what we have today with generative, pre-trained transformers, today with GPTs, where the hallucination comes through, where it’s really more statistical analysis. Over time, maybe synthesis will be a forte for AI. Right now, I think we still have that ability to really be the ultimate decision-makers and judge-makers and have that wisdom put at the table to make those decisions. Honestly, models are very good on balancing both sides, so ended up, as we say in Portuguese, neither fish nor meat. It’s to balance both sides’ answers. That’s not helpful in most cases. When you’re in a difficult position where, for example, the future of a company, company is almost dying, what do you do? I’m not sure your AI algorithms that are going to give you a great solution. Because it will give you a median or average solution, which likely will lead you to a median or average outcome, which in this case would be failure. Again, on synthesis, there are some areas of advantage for human beings. If you are looking for clearly synthesized perspectives on certain elements that are maybe less edge-focused, they’re more than the normal part of the normal distribution, then probably AI agents are brilliant at that. All the tools we have today are pretty good at that, and I think they’ll just get better over time. That’s how I see synthesis.Bertrand Schmitt I think a lot of improvements will come with a better fine-tuning of agents to what’s special about your company. Because if you just take a general agent, there is only so much. It can understand your industry, your company, and your way of working. I think that part of making sure your agents are finely trained, finely tuned on your own business, so that they can give you a really well-calibrated feedback, will have a lot of importance.Nuno Gonçalves Pedro I think that’s absolutely spot on. Maybe to end it, what is definitely different about humanity? Definitely, emotion, as we discussed, some pieces of synthesis. Creativity, maybe the light bulb creativity, not the more repeatable creativity, the one that you can put and encapsulate into processes in some ways. There are elements of us being physical, which robots can’t still recreate. That’s definitely an advantage. The embodied, we’re embodied. That’s obviously a huge advantage. With that also comes advantages because we have to interpret each other, and we have to see the complexities in physicality that land to it. Is human and the human element categorical difference? If we’re having a more philosophical discussion around this, I think it is. I think it will be for at least the foreseeable future and maybe decades to come, even in whatever scenarios we’ll discuss, which is our next section, scenarios.Bertrand Schmitt I would say projecting beyond 10 years is always pretty hard on this because, again, some of the improvements we are talking about we can imagine based on how it has evolved, but at the same time, there will be disruptions in AI. Stuff that we take for granted in terms of weakness, especially, might not be there in a few years from now. Either because it has been solved through brute force or because the field will have made significant change and improvements and discoveries, making some of our points moot. If we talk about embodiment, obviously, robots are coming. How fast, how cheap? That will be a big question. Right now, they’re not very smart. They’re usually very specialized. The more we move to a more general form factor, humanoid form factor, the more I think it will change. Also, another piece of the puzzle is that we have the assumption of agents having trouble to convince humans and stuff. At some point, we keep assuming that humans in the loop. If we’re talking about agents convincing another agent, not having embodiment might be even more efficient. That will be another perspective. Going forward, we will have not just agents we control who are doing a job and scanning the job, but agents truly interacting with other agents. You have agents controlled by one person, one team in your company, working either together or maybe not confrontationally, but trying to think and having different perspectives with another agent, controlled by other teams. I don’t think we have seen much of that now. We have seen mostly agents that are controlled by one team doing one job in one direction. Not multiple teams agents working together, or against or in parallel with another team agent. I think we will see some interesting things coming out of that.Nuno Gonçalves Pedro Scenarios Switching to scenarios, we love our two-by-twos. We haven’t done one in a while. This time it’s a two by two. We have four scenarios. I think on one axis, we would have potentially the capabilities of AI. One side would be more incremental. The other side would be the extreme full AGI. I’ll define it in a bit so that we can at least have a little bit of a definitional view on what the AGI is. Then the other axis would be how gains are distributed, concentrated versus broad. Obviously, if they’re very concentrated, it’s more unequal. It only goes to a few companies, a few people, a few individuals. If it’s broad, it’s much more dispersed through society, et cetera. AGI, just to try to define it, the formal definition of it is that it’s a hypothetical AI that matches or exceeds human capabilities across virtually all cognitive and practical tasks. In some ways, AGI can learn, reason, and adapt to novel situations across any domain. Then there are several mutations on this, but there’s one notion, or rather, there are three notions that normally are across a lot of these definitions. One is generalization, ability to seamlessly transfer knowledge from one domain to another without needing retraining, which is a very impressive skill that we humans still seemingly have. Autonomy in agency, the capacity to operate independently, set goals, plan and execute complex tasks. I think AI is their issue with agents to a lot of that extent. Then, last but not least, human parity, performing economically valuable work at or above the level of a typical human knowledge worker. If you listen to one of our last episodes, you’ll realize that Bertrand and I have slightly different views on AGI, and if it’s already here or not. I think, definitionally, maybe we have slightly different views on what the definition actually is. For me, maybe AGI is a little bit more what some would call superintelligence and generalized superintelligence. Strict to census, Bertrand is more connecting to AGI as in its prime definition. It behaves as well or better than a human thing. Maybe that’s what’s leading us to differences on whether AGI has arrived or not.Bertrand Schmitt Personally, I will have a different scale where I will put AGI, as you just said, in some ways, relatively similar in performance to your average human being. On top of it, it’s able to touch different domains that most humans are not able to do. Usually, there is some level of specializations where in AI, it can be more generic. I will put ASI, Artificial Superintelligence, as clearly the step beyond. Something that, on any dimension you pick, it’s able to beat a human expert. From my perspective, I think we already discussed that, but we are at AGI already. We have AI that can do way better, not just way better, but at least as well as humans on many topics, sometimes better. Yes, there are some topics that are not for AI yet. Embodiment, for instance, to flock with your humanoid robot in 2026. For me, we are partially there or fully there in AGI. If we take the stricter definition, ASI, we are definitely not there, but my guess is that it’s moving quite fast. We might be there in a few years from now. I don’t think we are talking about multi-decades. It’s 5 years, maybe 10. Of course, there are questions because people will say, for instance, “Hey, how do you become truly super-intelligent when all your training is based on human data?” That’s not an easy one because how do you train on that? To be way better, not just a bit better, but way better. Maybe I’m going on a tangent, but some are looking at AI learning from AI, AI being taught from AI, AI fighting with AI, AI challenging AI. The same way we saw this AlphaGo moment where AI was not trained anymore, like in chess with human moves, but has been trained to play against itself. That’s when it reached superintelligence in Go. It reached superintelligence by playing against itself and basically letting go of that human baggage, if you want, and going to the next level. What I found interesting in that, actually, first, that’s what happened, but two, there was some analysis that the average level of Go players and the top players went up after AlphaGo because AlphaGo, in a way, opened doors that humans didn’t believe were open in front of them, or they didn’t see them. They didn’t see these doors, so they didn’t bother to open them. AI opened new doors, but interestingly enough, humans improved after that, thanks to AI. You see what I mean? It was an interesting, okay, that self-learning from AI was the way to go beyond the current level of human knowledge and human expertise, but at the same time, humans were able to follow up. It was not like suddenly humans are totally useless crap. They improved. Did they still beat AI? Maybe not, but it was definitely also helpful.Nuno Gonçalves Pedro Back to our scenarios. We’re going to take the definitional extreme just for argument’s sake for scenarios. We’re going to talk about maybe what you were saying, ASI rather than full AGI, but like ASI. Again, artificial superintelligence as the extreme on the one hand. Let me talk about maybe the first scenario that would come to mind. Maybe we can call it the plateau scenario. All of this was great, but it was all smoke and mirrors. They were great at some cognition stuff. They’re a great tool. At some point, they’re going to hit a wall. Hallucinations are never going to be a thing of the past. We can’t fully trust them on really hardcore stuff. We’ll gain productivity enhancements. We’ll keep gaining those productivity enhancements, but at some point in time, we really won’t reach ASI. We really will be stuck with what we have. It’s a little bit like we get the next big thing, the next big spreadsheet, the next big internet, but it’s not going to change the whole world beyond just productivity, enhancements, and amazing tools that we have available to us that makes us much better. In that scenario, the winners will continue being fast adopters, probably small and medium businesses, because there won’t be a push for maximum speed either, so they’ll catch up at some point. Then AI native companies will be better companies than other companies, but not necessarily overall disruptors across the board. It’s not necessarily a new species of companies. It’s just companies that are a little bit better at doing stuff, which we also saw during the internet phenomenon and that first big push forward and then bubble, where we had some companies that were fundamentally different on how they operated. It took us another couple of decades for companies to be more and more digitally native along the way. Basically interesting, but it’s boring. It’s like, cool, we got tools, we got promised the world. What are the implications? All these companies that are worth trillions and trillions of dollars are not worth trillions and trillions of dollars. Because at some point we’ll face competition, commoditization. It will just be tools and platforms. They will not unlock that next stage. Therefore, this will have been a bubble, and likely it would be a hard landing to that bubble. That’s the implication.Bertrand Schmitt I would just say that, yes, I agree with you, but I would just say overall, even if it stopped today in terms of quality improvement, speed or stuff, or it barely improves, I still think we will have 10 years of madness just to leverage everything that we have today.Nuno Gonçalves Pedro Understood, Bertrand. This is a scenario. I understand, but maybe we’re going to hit a wall, and we’re going to hit that wall next year, or we’re going to hit that wall in 2 years or whatever.Bertrand Schmitt Possibly. I’m just saying we still have 10 years of goodness from that big push in AI we experienced the past few years.Nuno Gonçalves Pedro Absolutely. Agreed, but it’s boring.Bertrand Schmitt It’s boring. It’s a plateau.Nuno Gonçalves Pedro It’s a plateau. The second one is more of something that we have AI, but humans in the loop are going to be critical along the way. The judgment work that we described earlier in the episode is going to be critical to everything that happens. It’s, I would call it the augmentation scenario. The AI will be a great augmentation tool for humans, but humans will never really quite stop being in the loop. Some of the gains that AI has are broadly distributed in society and in the startup, big corporation and small medium business world. Everyone will have access to them. We humans, are still very important. We have all these augmentation things, and AI is mostly benign. There will be a couple of issues, but honestly, at the end of the day, we’re just better. We’re better, faster, more data-driven, more factually current. We’re doing stuff faster, but humans

A new survey reveals a widening gap between airline sentiment and the rest of the industry regarding a potential downturn. Listen in as editors are joined by consultants from McKinsey who share results from their research carried out in partnership with Aviation Week.  Download McKinsey's white paper in partnership with Aviation Week as a PDF here

What happens when a 16-year Google veteran (who also worked at Microsoft) decides to fix drug discovery with AI?You get Pauling AI, a next-generation molecular simulation platform built to discover the medicines of tomorrow.In this episode of Game of Pharma, Javier, CEO & Founder of Pauling AI and Ex-Technical Director at Google Healthcare & Life Sciences, shares his journey from leading Search, Ads, Supply Chain, and Cloud at Google (with prior experience at Microsoft) to building AI tools that could shape the future of pharmaceutical research.What you'll learn in this episode:→ Why Javier left Google after 16 years to bet on biotech AI→ What's actually working in AI drug discovery (vs the hype)→ Why biotech startups adopt AI faster than Big Pharma→ How Pauling AI is reimagining molecular simulation→ The next 5 years of AI in pharmaIf you're curious about where AI meets drug discovery, this conversation is unmissable.️ Listen on Spotify: https://open.spotify.com/show/4EDgDoe...️ Apple Podcasts: https://podcasts.apple.com/us/podcast...Subscribe to our newsletter: https://game-of-pharma.beehiiv.com---ABOUT JAVIER:Javier is a technology executive, and entrepreneur. Currently leading Pauling AI, a next-generation molecular simulation platform.Previously: 16 years at Google (Search, Ads, Supply Chain, Cloud). Prior experience at Microsoft and McKinsey.Education: Superior Engineer in Computer Science, Masters in Mathematics (Dropped out of Ph.D. in Mathematics to pursue building software.)ABOUT GAME OF PHARMA:World's leading pharma podcast with 25,000+ subscribers globally. Featuring conversations with the brightest minds in pharmaceuticals.Website: https://gameofpharma.com/LinkedIn:  / game-of-pharma-podcast  

What's going on in the Chinese economy? China reported GDP growth of just 4.3% last quarter. Consumption is sluggish, exports are booming, electronics manufacturers and chipmakers are reporting huge profits as car sales slump. Joe Ngai and Nick Leung dive into the Chinese economy in their latest book The Next China Is Still China: An Insider's Playbook for Winning in the New Era (Scribner, 2026), which came out earlier this year. The book is motivated by a question many executives asked amid new U.S. tariffs and COVID-driven supply shocks: Is there another economy that can replace China's massive consumer market and deep manufacturing base? The answer: No. Joe Ngai is a McKinsey senior partner and chairman of the firm's offices in Greater China. Frequently seen on media such as CNBC and Bloomberg, he has been named one of Forbes China's 100 Most Influential Chinese Leaders, and he has also appeared on Bloomberg Businessweek China's “Person of the Year 2025” list. Nick Leung is a McKinsey senior partner and member of McKinsey's global board of directors. He is also a lead-director of the McKinsey Global Institute, the firm's independent research arm, where he directs research on macroeconomics, global trade, and geopolitics. You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books. Follow on Twitter at @BookReviewsAsia. Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at @nickrigordon. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

What's going on in the Chinese economy? China reported GDP growth of just 4.3% last quarter. Consumption is sluggish, exports are booming, electronics manufacturers and chipmakers are reporting huge profits as car sales slump. Joe Ngai and Nick Leung dive into the Chinese economy in their latest book The Next China Is Still China: An Insider's Playbook for Winning in the New Era (Scribner, 2026), which came out earlier this year. The book is motivated by a question many executives asked amid new U.S. tariffs and COVID-driven supply shocks: Is there another economy that can replace China's massive consumer market and deep manufacturing base? The answer: No. Joe Ngai is a McKinsey senior partner and chairman of the firm's offices in Greater China. Frequently seen on media such as CNBC and Bloomberg, he has been named one of Forbes China's 100 Most Influential Chinese Leaders, and he has also appeared on Bloomberg Businessweek China's “Person of the Year 2025” list. Nick Leung is a McKinsey senior partner and member of McKinsey's global board of directors. He is also a lead-director of the McKinsey Global Institute, the firm's independent research arm, where he directs research on macroeconomics, global trade, and geopolitics. You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books. Follow on Twitter at @BookReviewsAsia. Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at @nickrigordon. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/east-asian-studies

Most companies think they're transforming with AI. They're not, and the gap between what they believe and what's actually happening on the ground is costing them far more than they realize. In this episode, Craig Smith sits down with Chris Blackburn, founder and CEO of Liatrio, a consultancy that has spent a decade embedding directly inside large enterprises to help them actually change how they work, not just what tools they use. The conversation opens with a striking data point: the average enterprise Blackburn works with operates at just 5 to 6% efficiency, meaning employees spend only three to three-and-a-half hours per week on work that genuinely creates value, compared to Toyota's benchmark of 70%. The core argument is that AI is being applied to the wrong part of the problem: individual productivity gains don't flow through to the bottom line if the organizational system around the individual - the approvals, handoffs, bureaucracy, and middle management layers - stays exactly the same. Blackburn introduces a concept he calls "strangling the enterprise": rather than trying to transform a 5,500-person organization all at once, build a small, low-bureaucracy unit inside it that operates with radical autonomy, proves the model works, and expands outward.  The episode closes with a frank conversation about what real transformation actually costs: roughly half of total compensation spend across the organization, sustained for two years, a number Blackburn describes as "absolutely insane" and one he believes most CFOs aren't yet prepared to confront. Key Topics Covered: ●     Why the average enterprise operates at 5-6% efficiency, and what Toyota's 70% benchmark reveals about the scale of the opportunity AI could unlock ●     The critical distinction between individual productivity gains and system-level improvement, and why saving an hour doesn't automatically improve the bottom line ●     "Strangle the enterprise": how to build a small, autonomous AI-native unit inside a large organization rather than trying to transform the whole thing at once ●     Why most CEOs are dangerously disconnected from the actual work being done, and what McKinsey says about how much time they should be spending on transformation ●     What AI transformation actually costs: roughly half of total compensation spend, sustained over two years, and why most CFOs aren't ready for that number ●     Why AI isn't just changing jobs but changing life - from shorter work weeks to longer health spans - and what the farming analogy reveals about how slowly societies absorb new productivity As enterprises pour money into AI tools while reporting little bottom-line impact, this conversation offers the most operationally honest account available of why that gap exists, and what organizations that actually want to close it need to be willing to do differently. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI. Craig Smith on X: https://x.com/craigss EYE On A.I. on X: https://x.com/EyeOn_AI   Connect with Chris Blackburn LinkedIn: https://www.linkedin.com/in/chrisblackburn 

What's going on in the Chinese economy? China reported GDP growth of just 4.3% last quarter. Consumption is sluggish, exports are booming, electronics manufacturers and chipmakers are reporting huge profits as car sales slump. Joe Ngai and Nick Leung dive into the Chinese economy in their latest book The Next China Is Still China: An Insider's Playbook for Winning in the New Era (Scribner, 2026), which came out earlier this year. The book is motivated by a question many executives asked amid new U.S. tariffs and COVID-driven supply shocks: Is there another economy that can replace China's massive consumer market and deep manufacturing base? The answer: No. Joe Ngai is a McKinsey senior partner and chairman of the firm's offices in Greater China. Frequently seen on media such as CNBC and Bloomberg, he has been named one of Forbes China's 100 Most Influential Chinese Leaders, and he has also appeared on Bloomberg Businessweek China's “Person of the Year 2025” list. Nick Leung is a McKinsey senior partner and member of McKinsey's global board of directors. He is also a lead-director of the McKinsey Global Institute, the firm's independent research arm, where he directs research on macroeconomics, global trade, and geopolitics. You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books. Follow on Twitter at @BookReviewsAsia. Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at @nickrigordon. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/chinese-studies

In our latest 3 Lessons from Breakthrough Leaders podcast episode, we're joined by Patrick Coveney, CEO of SSP Group plc.Patrick is a strategic, people-focused leader with deep roots in the global food industry, having previously spent 14 years as the CEO of Greencore Group. He was a Managing Partner at McKinsey & Company and served on various boards, including his current role as a non-executive director at OFI.In this episode, we explored 3 lessons with Patrick Coveney:1. The CEO Mindset2. Leading Through Global Shocks3. Finding Direction in a Moment of Choice

What's going on in the Chinese economy? China reported GDP growth of just 4.3% last quarter. Consumption is sluggish, exports are booming, electronics manufacturers and chipmakers are reporting huge profits as car sales slump. Joe Ngai and Nick Leung dive into the Chinese economy in their latest book The Next China Is Still China: An Insider's Playbook for Winning in the New Era (Scribner, 2026), which came out earlier this year. The book is motivated by a question many executives asked amid new U.S. tariffs and COVID-driven supply shocks: Is there another economy that can replace China's massive consumer market and deep manufacturing base? The answer: No. Joe Ngai is a McKinsey senior partner and chairman of the firm's offices in Greater China. Frequently seen on media such as CNBC and Bloomberg, he has been named one of Forbes China's 100 Most Influential Chinese Leaders, and he has also appeared on Bloomberg Businessweek China's “Person of the Year 2025” list. Nick Leung is a McKinsey senior partner and member of McKinsey's global board of directors. He is also a lead-director of the McKinsey Global Institute, the firm's independent research arm, where he directs research on macroeconomics, global trade, and geopolitics. You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books. Follow on Twitter at @BookReviewsAsia. Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at @nickrigordon. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/economics

What's going on in the Chinese economy? China reported GDP growth of just 4.3% last quarter. Consumption is sluggish, exports are booming, electronics manufacturers and chipmakers are reporting huge profits as car sales slump. Joe Ngai and Nick Leung dive into the Chinese economy in their latest book The Next China Is Still China: An Insider's Playbook for Winning in the New Era (Scribner, 2026), which came out earlier this year. The book is motivated by a question many executives asked amid new U.S. tariffs and COVID-driven supply shocks: Is there another economy that can replace China's massive consumer market and deep manufacturing base? The answer: No. Joe Ngai is a McKinsey senior partner and chairman of the firm's offices in Greater China. Frequently seen on media such as CNBC and Bloomberg, he has been named one of Forbes China's 100 Most Influential Chinese Leaders, and he has also appeared on Bloomberg Businessweek China's “Person of the Year 2025” list. Nick Leung is a McKinsey senior partner and member of McKinsey's global board of directors. He is also a lead-director of the McKinsey Global Institute, the firm's independent research arm, where he directs research on macroeconomics, global trade, and geopolitics. You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books. Follow on Twitter at @BookReviewsAsia. Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at @nickrigordon. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/asian-review

What's going on in the Chinese economy? China reported GDP growth of just 4.3% last quarter. Consumption is sluggish, exports are booming, electronics manufacturers and chipmakers are reporting huge profits as car sales slump. Joe Ngai and Nick Leung dive into the Chinese economy in their latest book The Next China Is Still China: An Insider's Playbook for Winning in the New Era (Scribner, 2026), which came out earlier this year. The book is motivated by a question many executives asked amid new U.S. tariffs and COVID-driven supply shocks: Is there another economy that can replace China's massive consumer market and deep manufacturing base? The answer: No. Joe Ngai is a McKinsey senior partner and chairman of the firm's offices in Greater China. Frequently seen on media such as CNBC and Bloomberg, he has been named one of Forbes China's 100 Most Influential Chinese Leaders, and he has also appeared on Bloomberg Businessweek China's “Person of the Year 2025” list. Nick Leung is a McKinsey senior partner and member of McKinsey's global board of directors. He is also a lead-director of the McKinsey Global Institute, the firm's independent research arm, where he directs research on macroeconomics, global trade, and geopolitics. You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books. Follow on Twitter at @BookReviewsAsia. Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at @nickrigordon. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/finance

Send us Fan MailYou can nail every case you practice and still get rejected.That's because firms aren't grading you on vibes. McKinsey scores you piece by piece – opening, structure, math, brainstorming, closing. Other firms use 3 buckets instead: structure, problem solving, communication.Either way, a 4 out of 4 in math won't save a 1 out of 4 in brainstorming. Firms want you solid across the board.In this episode, Namaan breaks down exactly what's being scored – and why doing 100 more practice cases won't fix a gap you haven't identified.Resources:Whether you're prepping for McKinsey's rubric or the 3-bucket system, Black Belt closes the gaps costing you the offerNew to case prep? Case Foundations is our free crash course on the basics – the starting point before scoring even mattersConsulting Deadlines:McKinsey, Bain, and BCG deadlines hit August 11 – 13 (and more are coming) – get interview-ready fast with Black BeltConnect With Management ConsultedCreate a free MC account or download the MC app (Apple, Android) to start your prep todaySchedule a free 15min consultation with the MC TeamWatch the video version of the podcast on YouTubeFollow us on LinkedIn, Instagram, and TikTokJoin an upcoming live event – case interviews demos, expert panels, and more

Lizzie Shilko launched Revi Gear, a Colorado ski helmet brand, in August 2025 — and spent her first season hand-delivering helmets out of her car trunk to shops across the state. She walks through the retail hustle that landed her first account, why customers make better sales reps than any sales force, and how a cold-email campaign — sent from a brand with no website, no funding, no logo — landed her a manufacturing partner.Lizzie also gets candid about the gap between her McKinsey background and founder life: no salary, no security, no manager to validate the work. She talks through the anxiety of founder-led social media, separating internal conviction from public comments, and why speed to market beat waiting to get it perfect.Approaching year one, she's shifting from sprint to endurance — fewer SKUs, more intentional growth. A tactical conversation for anyone building a hard goods brand from scratch.REGISTER for the KORE SummitEpisode HighlightsShow up in person — retail hustle, hand-delivering helmets, founder-as-sales-forcePull marketing beats push marketing — customers become unpaid sales repsFake it till you make it — sourcing manufacturers with zero brand infrastructureManufacturer trust — flying overseas, wiring life savings, building the relationshipFounder-led social media — growth, public criticism, internal vs. external validationSpeed to market — pre-orders as real product-market-fit signalSprint to endurance — shifting from launch speed to intentional, incremental growthEvolving "why" — from proving personal capability to building something others can be proud ofLinksRevi Gear's websiteRevi Gear on InstagramRevi Gear on Christy SportsSeniqHalf DaysWild RyeAway luggageBuilt BarsThe KORE Podcast is a production of the Kootenay Outdoor Recreation Enterprise. Learn more about KORE and the podcast: https://koreoutdoors.org/podcast

Adarsh Pandit (founder of Cylinder Digital, ex-Thoughtbot, ex-McKinsey) met Àlex 12 years ago in San Francisco when MarsBased was just getting off the ground. They catch up on a decade of building software agencies, navigating career shifts, and managing team dynamics.Adarsh shares the story behind firing a high-profile client who was verbally abusing his team, the panic of dropping a production database on Heroku, and how applying systems thinking changes how you look at code, sales, and business operations. They also touch on how AI and vibe coding are reshaping agency pricing, the return of fixed-bid contracts, and why human trust remains the most critical asset in tech.Support the show

Durante muito tempo, o mercado olhou para as operadoras como utilities: empresas previsíveis, donas da infraestrutura por onde passavam voz, dados e internet.A Vivo quer desmontar essa tese. O CEO Christian Gebara diz que o objetivo da Vivo é ser, a cada dia, mais uma empresa de tecnologia – e menos apenas a companhia que simplesmente conecta o cliente.“O mercado financeiro chegou a nos falar que a Vivo seria simplesmente o canudo por onde passaria todo o conteúdo e que não teríamos nenhuma outra função que não fosse conectar. Eu nunca acreditei nisso,” Gebara disse ao McKinsey Talks.No segundo episódio do videocast, Gebara e Heitor Martins, sócio sênior da McKinsey, discutem uma mudança que vai muito além das telecomunicações: a corrida para transformar empresas tradicionais em ecossistemas digitais. Hoje, a Vivo já vende cloud, cyber, serviços financeiros, entretenimento, saúde e soluções de IoT. Esses novos negócios já representam mais de 12% da receita da companhia, com potencial de crescer nos próximos anos, segundo o CEO. Na visão de Heitor, a tecnologia permite que empresas antes confinadas a um setor avancem sobre outros mercados – algo que tem sido feito por bancos, varejistas, empresas de saúde e telcos no Brasil e no mundo. Ouça ao segundo episódio do McKinsey Talks, uma parceria do Brazil Journal com a McKinsey.

"The price of anything is the amount of life you exchange for it." — Henry David ThoreauHave you ever tied your self-worth to your career success? In our fast-paced world, it's one of the most dangerous traps there is...Brooke Taylor built her career in the high-pressure hallways of Silicon Valley. Yet, despite the external validation, Brooke found herself trapped in a cycle of "manic ambition" and severe burnout. It was a profound personal crisis that forced her to dismantle her own definition of achievement. Today, Brooke is the secret weapon for elite executive women at the world's most influential organizations, including Goldman Sachs, McKinsey, and Uber. Her new book Healing the Success Wound has just been released. In this episode:• How to build a world-class career without destroying your life in the process.• Where your Success Wound comes from—and the exact process to heal it.• Why high achievers mistake success for self-worth.• How to maintain a high-performing career without turning your home life into a second job.Let's WIN THE DAY with Brooke Taylor!_

Episode #1408: Toyota assesses earthquake impacts in Japan, Ford jumps back into military vehicle development, and a new McKinsey study reveals affordability is squeezing buyers while technology—not brand loyalty—is increasingly driving purchase decisi...

Pierre Marin, 35 ans, est le CEO et co-fondateur de RockFi — la boîte qu'il a construite en connectant les dots de toute sa vie professionnelle.Pierre démarre dans la finance sous toutes ses formes : audit, M&A, private equity, et conseil en stratégie chez McKinsey. En parallèle, il entreprend dès l'école de commerce avec Prépa First, un réseau de soutien scolaire à 200 professeurs. Il rejoint ensuite Iziwork, startup baquée par Rocket Internet, où il monte un réseau de plusieurs centaines d'agents indépendants sur toute la France en quatre ans. Deux learnings fondateurs : l'impact d'une densité de talent, et le prix d'une culture négligée.En sortant d'Iziwork, il itère deux ans — un projet dans le foot avec des fan-tokens, qui n'aboutit pas. Puis vient RockFi, né d'une frustration très personnelle : pourquoi doit-on choisir entre un conseiller de confiance et une app mobile ? Il passe trois mois à appeler des banquiers privés, 10 à 15 par jour. Le constat est unanime : ils passent 80 % de leur temps en administratif, et aiment leur métier pour les 20 % restants.Le casting s'assemble pièce par pièce : Didier Valet comme caution et premier investisseur, Marie comme co-fondatrice opérations et produit, Maxime comme CTO — tous deux présentés par la même personne, une ancienne de McKinsey devenue chief of staff chez Conto. Pour convaincre Alexis et Paul de rejoindre l'aventure, Pierre organise un hackathon de deux jours à Lyon. Ils disent oui à l'issue du week-end.Son obsession quotidienne ? Le recrutement. 80 % de son temps. Sa première question à chaque nouvel arrivant : quelles sont les trois personnes les plus fortes que tu connais ?Startup Mafia, c'est une émission qui plonge au cœur des startups et scale-ups à travers les parcours de celles et ceux qui les font grandir. Chaque saison met en lumière une entreprise de référence dans la Tech et se construit autour de 8 épisodes de 30 à 45 minutes. Au fil des conversations, 8 execs, dirigeants et talents clés de l'entreprise partagent leurs trajectoires, leurs choix, leurs échecs et tous leurs apprentissages dans leur aventure. Un format pensé pour valoriser les équipes, inspirer la communauté et raconter l'aventure startup de l'intérieur. Animé par Kamel Zeroual et Paul Terrasson Duvernon.

Enterprise technology has long promised to unlock operational potential, but the trade-offs between monolithic ERP systems and fragmented best-of-breed stacks continue to slow organizations down in ways most leaders don't fully account for. In this episode of Enterprise Unleashed on Supply Chain Now, Scott W. Luton and Wiley Jones (Co-founder at DOSS) are joined by Jindra Zitek (Partner and Head of Scale at Stripes), a former McKinsey consultant and C-level operator whose career spans Chobani, HelloFresh, and a range of board and advisory roles across industry. Together, they unpack what it actually takes to build operations that are truly AI-ready, and why the answer starts with people long before it reaches technology. Jindra draws on firsthand experience leading through hypergrowth and crisis to make the case that both ERP consolidation and best-of-breed ecosystems carry a hidden coordination tax, one that only gets more expensive the longer organizations avoid naming it. He challenges leaders to stop framing AI as a job automation tool and start treating it as a lever for dignity and purpose, giving people the ability to focus on the work that actually requires human judgment. The conversation lands on what separates organizations that experiment successfully from those stuck in pilot purgatory: shared business objectives, cross-functional coalitions, and guardrails that free people to move fast without losing control. Wiley grounds the discussion in what Doss is seeing in practice, where the complexity of edge cases is finally collapsing, and where the real work of comprehension still cannot be skipped. Jump into the conversation: (00:00) Intro  (02:29) Jindra Zitek's background across McKinsey, Chobani, and HelloFresh (04:16) Lessons from family, farming, and leadership (06:09) Why people should come before systems (10:28) The trade-off between ERP and best-of-breed tools (15:24) The hidden cost of technology choices (17:09) How company DNA shapes technology strategy (20:57) Eliminating the human tax of broken systems (21:49) Automation should enable people, not replace them (25:57) The rise of agentic operations (28:57) Building trust and control with AI systems (31:34) Measuring real ROI from AI investments (35:17) Building cross-functional teams for AI success (37:30) Creating guardrails for enterprise AI adoption (40:40) The first workflow to transform with AI (42:10) Finding business problems AI can solve (47:56) How AI changes workflow design and implementation Additional Links & Resources: Connect with Jindra Zitek: https://www.linkedin.com/in/jindrazitek/ Connect with Wiley Jones: https://www.linkedin.com/in/wileycwjones/ Learn more about DOSS: https://www.doss.com/ Learn more about Stripes: https://www.stripes.co/ Learn more about Supply Chain Insights: http://www.supplychaininsights.com Learn more about our hosts: https://supplychainnow.com/about Learn more about Supply Chain Now: https://supplychainnow.com Watch and listen to more Supply Chain Now episodes here: https://supplychainnow.com/program/supply-chain-now Subscribe to Supply Chain Now on your favorite platform: https://supplychainnow.com/join Work with us! Download Supply Chain Now's NEW Media Kit: https://supplychainnow.com/media-kit/ WEBINAR- Peak Reality Check: What Shippers, Analysts, and AI Models Are Predicting for 2026: https://bit.ly/4aTlsRv WEBINAR- From Volume to Resilience: How Automotive Supply Chains Are Adapting to a New Market Reality: https://bit.ly/4f6SUGA WEBINAR- The Automotive Industry's Next Digital Breakthrough: https://bit.ly/4vhUwT4 WEBINAR- From Disruption to Stability: Building Resilient Logistics Solutions in a Rapidly Changing Global Market: https://bit.ly/3TguZMt This episode was hosted by Scott Luton and Wiley Jones, and produced by Trisha Cordes, Joshua Miranda, and Amanda Luton. For additional information, please visit our dedicated show page at: https://supplychainnow.com/beyond-erp-tradeoff-building-ai-ready-operations-1614 The content in this episode, including all audio, videos, visuals, and graphics, is the property of Supply Chain Now and is protected by copyright law. Unauthorized use, reproduction, distribution, modification, or re-uploading of this content in any form is strictly prohibited without explicit written permission from Supply Chain Now.For licensing inquiries or permissions, please contact us at production@supplychainnow.com© 2026 Supply Chain Now. All rights reserved. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, Brandon Schuh and Trey Shields break down the latest news and trends in the world of insurance, including the headline-making AI integration at Brown and Brown, McKenzie's latest insurance industry analysis, and the recent state of P&C insurance rates. They explore how technology is transforming the industry and what mid market agencies can do to stay competitive.00:00 Introduction and episode overview02:37 Brown and Brown's AI big reveal explained05:00 Brandon's critique of Brown and Brown's AI claims07:25 Support staff implications of AI at Brown and Brown12:01 McKinsey's industry analysis and critique15:53 Insurance rate trends and K-shape recovery23:45 Small business insurance challenges and statistics

The aviation industry is grappling with an array of challenges, from a backlog of planes, to safety concerns, and widespread labor shortages. WSJ aviation reporter Ben Katz joins host Luke Vargas to discuss how the sector is thinking about AI, plus the latest technology on display at the Farnborough International Airshow in the U.K. Further Reading Aerospace Startup JetZero to Start Building Futuristic Planes in North Carolina Airbus Comes for Boeing in Battle Over World's Biggest Jets Boeing Chooses Palantir to Boost AI Adoption in Defense, Space Unit Honeywell Aerospace CEO Says AI Works for Blueprints but Isn't Ready for the Cockpit Sixteen Airbus A380s to Undergo Inspection After Cracks Found on Airplane Wings Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hur går man från att investera i över 150 bolag, sitta i styrelserum och bygga företag – till att ta över ledarskapet för ett av Sveriges partier? I detta avsnitt gästar Centerpartiets partiledare Elisabeth Thand Ringqvist podden för ett samtal om makt, entreprenörskap, politik och Sveriges framtid.Vi pratar om de intensiva första månaderna som partiledare, varför hon medvetet valt att stänga ute sociala medier och hur erfarenheterna från startup-världen påverkar hennes sätt att leda ett politiskt parti. Elisabeth berättar också om sina år på McKinsey, lärdomarna som format hennes förmåga att lösa problem och varför hon alltid tror att det finns en väg framåt – även när andra säger att något är omöjligt.Samtalet går vidare till några av Sveriges största samhällsutmaningar: arbetslösheten, integrationen, bostadskrisen och den svaga tillväxten. Elisabeth förklarar varför hon anser att jobb är nyckeln till nästan alla samhällsproblem, varför dagens integrationspolitik har misslyckats och vilka reformer hon vill se för att få fler människor i arbete.Dessutom får hon svara på de stora politiska frågorna inför valet. Vilka partier kan Centerpartiet egentligen samarbeta med? Varför säger hon nej till både Sverigedemokraterna och Vänsterpartiet? Hur ser hon på Ryssland som säkerhetshot, Nato, migrationen och Sveriges framtida vägval? Ett öppet och djupgående samtal om politik, entreprenörskap och Sveriges framtid. Följ Elisabeth här Läs mer om Centerpartiet här Läs mer om Framgångsakademin här.Ta del av Framgångsakademins kurser.Beställ "Mitt Framgångsår".Följ Alexander Pärleros på Instagram.Följ Alexander Pärleros på Tiktok.Bästa tipsen från avsnittet i Nyhetsbrevet. Hosted on Acast. See acast.com/privacy for more information.

Hur går man från att investera i över 150 bolag, sitta i styrelserum och bygga företag – till att ta över ledarskapet för ett av Sveriges partier? I detta avsnitt gästar Centerpartiets partiledare Elisabeth Thand Ringqvist podden för ett samtal om makt, entreprenörskap, politik och Sveriges framtid.Vi pratar om de intensiva första månaderna som partiledare, varför hon medvetet valt att stänga ute sociala medier och hur erfarenheterna från startup-världen påverkar hennes sätt att leda ett politiskt parti. Elisabeth berättar också om sina år på McKinsey, lärdomarna som format hennes förmåga att lösa problem och varför hon alltid tror att det finns en väg framåt – även när andra säger att något är omöjligt.Samtalet går vidare till några av Sveriges största samhällsutmaningar: arbetslösheten, integrationen, bostadskrisen och den svaga tillväxten. Elisabeth förklarar varför hon anser att jobb är nyckeln till nästan alla samhällsproblem, varför dagens integrationspolitik har misslyckats och vilka reformer hon vill se för att få fler människor i arbete.Dessutom får hon svara på de stora politiska frågorna inför valet. Vilka partier kan Centerpartiet egentligen samarbeta med? Varför säger hon nej till både Sverigedemokraterna och Vänsterpartiet? Hur ser hon på Ryssland som säkerhetshot, Nato, migrationen och Sveriges framtida vägval? Ett öppet och djupgående samtal om politik, entreprenörskap och Sveriges framtid. Följ Elisabeth här Läs mer om Centerpartiet här Läs mer om Framgångsakademin här.Ta del av Framgångsakademins kurser.Beställ "Mitt Framgångsår".Följ Alexander Pärleros på Instagram.Följ Alexander Pärleros på Tiktok.Bästa tipsen från avsnittet i Nyhetsbrevet. Hosted on Acast. See acast.com/privacy for more information.

Why do most new products fail — even when they solve a real problem? In this episode of The Voice of Retail, presented by Konek – Canada's way to pay. Visit Konek.ca to learn more., host Michael LeBlanc sits down with Rob Snyder, founder and author of the new book The Power of Pull, to unpack why understanding customer demand is fundamentally different from pitching supply — and what that means for retailers, vendors, and startups trying to win business today. Snyder's path here started at McKinsey & Company, where he grew frustrated with consulting from the outside and moved into the startup world to learn how companies actually work. After Harvard Business School, he built his first startup selling into retail and restaurants — and spent two brutal years hitting a wall before finally cracking the code and scaling to $4 million in annual recurring revenue in under two years. That hard-won experience became the foundation for his PULL framework, and for a career spent studying, through Harvard Innovation Labs, startup advising, and angel investing, why some new ideas take off while most don't. The conversation digs into the core distinction at the heart of Snyder's book: demand is not the same thing as wanting a product. Demand is a person trying to accomplish something in their life; supply is simply the tool they pull in to get it done. Snyder revisits the famous (and possibly apocryphal) Henry Ford "faster horses" quote to explain why asking customers what they want often produces the wrong answer — and why the real skill is identifying what people are stuck trying to do with the options already in front of them. For retailers fielding constant vendor pitches, Snyder offers a practical filter: can this startup speak your language, cite specific relevant case studies, and explain exactly how they've unstuck a comparable business — or are they hiding behind buzzwords like "agentic AI"? He also tackles how AI is reshaping the competitive landscape for B2B startups, arguing that as AI closes gaps in the "limitations" side of the PULL framework, vendors need to find pull in places AI hasn't reached yet. The episode closes with Snyder's advice for retail leaders trying to uncover genuine, unfiltered demand: skip the customer interviews, and instead watch shoppers struggle in real time — in-store and online — to find the moments where existing options fall short. Rob Snyder's new book, The Power of Pull, is available now wherever books are sold. Connect with him on LinkedIn or at robsnyder.org.  This episode presented by Konek – Canada's way to pay. Visit Konek.ca to learn more. Michael LeBlanc is the president and founder of M.E. LeBlanc & Company Inc, a senior retail advisor, keynote speaker and now, media entrepreneur. He has been on the front lines of retail industry change for his entire career. Michael has delivered keynotes, hosted fire-side discussions and participated worldwide in thought leadership panels. He brings 25+ years of brand/retail/marketing & eCommerce leadership experience with Levi's, Black & Decker, Hudson's Bay, CanWest Media, Pandora Jewellery, The Shopping Channel and Retail Council of Canada to his advisory, speaking and media practice.Michael produces and hosts a network of leading retail trade podcasts, including the award-winning No.1 independent retail industry podcast in America, Remarkable Retail with his partner, Dallas-based best-selling author Steve Dennis; Canada's top retail industry podcast The Voice of Retail and Canada's top food industry and one of the top Canadian-produced management independent podcasts in the country, The Food Professor with Dr. Sylvain Charlebois from Dalhousie University in Halifax.Rethink Retail has recognized Michael as one of the top global retail experts for the fifth year in a row, the National Retail Federation has designated Michael as on their Top Retail Voices for 2025 and 2026. Thinkers 360 has named him on of the Top 50 global thought leaders in retail. If you are a BBQ fan, you can tune into Michael's cooking show, Last Request BBQ, on YouTube, Instagram, X and yes, TikTok.Michael is available for keynote presentations helping retailers, brands and retail industry insiders explaining the current state and future of the retail industry in North America and around the world.

NESTA EDIÇÃO. Ameaças às exportações de petróleo da Arábia Saudita levam preço do barril a voltar para a casa dos US$ 90. No Brasil, imposto de exportação de petróleo gerou R$ 9,6 bilhões adicionais em royalties e reduziu embarques para o exterior, diz IBP. Consumidores esperam desconto de pelo menos 15% para considerar a migração para o mercado livre de energia, aponta estudo da McKinsey. Novo primeiro-ministro do Reino Unido reduz impostos sobre contas de luz para aliviar custo de vida das famílias. *Locução gerada por IA

The Hiring Crisis, TikTok Training and Gen Z Perfectionism. PLUS How to Keep Quiet Teams Motivated Welcome back to Truth, Lies & Work, the award-winning workplace podcast where behavioural science meets workplace culture, brought to you by the HubSpot Podcast Network. In this episode of This Week in Work, business owner Al Elliott and Chartered Occupational Psychologist Leanne Elliott break down why fewer than half of recruitment processes actually succeed, why TikTok-style L&D might be sabotaging employee memory, and how economic pressure is driving an epidemic of Gen-Z perfectionism. Plus, in Truth or Lie, we test the psychological myth: Can you bullshit a bullshitter?

https://youtu.be/L1wE2Koy7eQ Tomas Milar, Founder and CEO of Eqvista, is passionate about helping businesses value what’s hard to value by making private company valuations more accurate, transparent, and actionable. Through a product-first mindset and relentless innovation, Tomas has built Eqvista into a leading equity management and private market valuation platform serving more than 25,000 companies, helping founders, investors, and employees make better decisions with real-time company valuations. We explore Tomas Milar’s PrivateCo Valuation Framework: Industry Data, Private Data, Public Comparables, Client Data, and Audit Defensibility. Tomas explains why accurate private company valuations require combining multiple data sources instead of relying on static reports, how proprietary datasets and public market benchmarks improve pricing precision, and why audit-defensible valuations build confidence for fundraising, compliance, and M&A transactions. He also shares how a product-first approach has fueled Eqvista’s growth while advancing real-time valuations and expanding shareholder liquidity through controlled tender offers. — Value What’s Hard to Value with Tom Milar  Good day, dear listeners. Steve Preda here with the Management Blueprint Podcast, and my guest today is Tomas Milar, the founder and CEO of Eqvista, a leading equity management and private market valuation platform serving more than 25,000 companies. Tom, welcome to the show.  Steve, thank you so much for having me. Thank you.  Well, you’ve got a very interesting background and business. We talked before the show that both of us are from Europe, studied in different countries, and come from a financial background. So it’s very interesting. Out of the 350-plus guests I’ve had, I’ve never had someone with such a similar background.  Interesting. Thank you. Yes. I took every opportunity to study anywhere. One semester I studied at three different universities at the same time. So yes, I was taking exams left and right at different universities. It would be April in Finland, May in Turkey, and later June back in the Czech Republic.  That’s great.  In one week, I would really make that whole circle. Crazy times.  I heard about professors teaching at multiple universities, but students attending multiple universities… Maybe two at the same time in the same country.  But I had three.  Three in different countries? That’s completely crazy. I guess if you wanted to fast-track your career, get to Silicon Valley young, start a company, and already reach a modicum of success, you had to do that, right?  You know what? Or you drop out of school and do zero schools. That’s also an option. Now let’s be serious for a bit. For me, it wasn’t really about the education. For me, it was whether I could make it. So it was more of a challenge than an academic achievement. Yeah, it was a fun time back then. I would have a Tuesday exam in Turkey, then fly north. I would have to change clothes in Prague because in Finland I was only about 50 miles from the Polar Circle. It would still be winter there, so I’d pack winter clothes and then take an exam in Oulu, which is almost at the Polar Circle. Yeah. So from Istanbul to the Polar Circle. It was a fun time back then.  I saw it on your LinkedIn page—North Ostrobothnia—and I couldn’t imagine where that place was. So I actually Googled it, and the pictures were all ski slopes. I figured it had to be a cold place.  It was very cold. Yes. A lot of saunas.  Yeah, that’s lovely. Let’s get into the topics. One question I always ask my guests is: What is your personal why, and how are you manifesting it in your business?  You know, I learned the hard way that you never ask “why.” At least for me, after living in China. Things just happen. They just happen. Some people don’t really know certain things are possible until they make them possible. So from impossibility to possibility. It’s always interesting to see things happen without a rational reason. I don’t think it’s really about a why. I think it’s really about being naïve when you do things, just doing them, and figuring things out along the way.  Yeah. Well, I agree with you about naïveté. I believe it’s a great entrepreneurial trait because it allows you not to kill good ideas..  Yes, yes. …before they have a chance to develop. And Steve, the more you know about certain things, the less likely you are to start a business in that particular industry, right? Doctors never start hospitals. Bankers never start banks or neobanks because they understand how difficult it is. So that naïveté—to be foolish and hungry—it really works. Yeah. Actually, that’s what works for me. Again, I never thought about business ideas in terms of why I should be doing them. Let me ask you this instead.  What energizes you about running Eqvista?  I think it comes in different phases. Right now, we’re going to launch a controlled tender offer. For those who don’t know what that is, when you issue stock to employees or investors, you can get it back. We don’t really like the term “buy it back,” but that’s essentially what happens. Once you issue options or stock, you can actually buy it back and redistribute those same shares to different investors or shareholders. That’s our new product. We have a very big challenge in front of us, and it’s how to price stock.  We perform valuations on over $8 billion of client assets every month. We’re one of the largest equity valuation platforms on the market. That’s actually what energizes me. Not really the number, but how hard it is to become number one and what drives innovation. Because if you want to be number one at anything, you need to innovate. What we've actually fixed is the report. When you issue stock, you need to have a stock price.Share on X  That stock price reflects the market, how the company is doing, and the different funding rounds. Usually, you find that stock price in a report. It’s a PDF—40 to 60 pages. By the time you receive the report, the valuation is already outdated. One month. Two months. Three, four, five, even six months. In 2026, startups move so fast that in six months some companies grow exponentially. Yeah.  I saw your LinkedIn post about SpaceX. There was an April 2026 valuation of $1.6 trillion. Then you posted again four days ago, showing it was over $2.6 trillion. That’s more than a 40% increase in just two months for one of the most valuable companies in the world. Yeah. How does that happen?  Particularly for SpaceX, there are multiple factors. There’s definitely hype, no doubt about it, because 95 times revenue—that’s ridiculous, right? For a publicly traded company, we usually see six or seven times revenue. That’s probably the best multiple. But 95 times revenue—that’s unheard of. How does that happen? There are multiple reasons, right? One of them is innovation. Another is that SpaceX is the only company actually launching spacecraft into orbit.  And yeah, obviously, there’s the Elon Musk factor. If you look at the space economy, whatever we’re doing on Earth, we’ll be doing the same things in space, right? So looking at SpaceX as a bridge between the dream of space and practically mirroring what we do here on Earth, I think that’s a great analogy.  For us, it's a really nice demonstration of how any company going public should have a real-time valuation.Share on X It doesn’t have to be publicly available. You can choose. Again, think about how Merrill Lynch—I think it was Merrill Lynch and Bank of America—helped with the roadshow and all the due diligence and documents. I can’t even understand how they modeled the valuation one time. They probably had to do that every three days.  But if they had a real-time valuation, they could clearly understand what was happening in the market. So, going back to your original question about what excites me, this is exciting because we built the largest valuation model on the market. Currently, without SpaceX—because SpaceX would take up half of the benchmark—we constantly value over $4 trillion in assets.  Okay. So let’s talk about this because, in my time at my investment banking firm, we had a valuation practice, and we offered six different types of valuations. But really, we just wanted to have an enchilada process where we would have EVA valuations, multiples, private and public comparables, DCF, and all that stuff. But I’m really wondering because, when you’re valuing small private companies, you’ve got all these liquidity discounts, marketability discounts, ownership discounts, all these discounts.  So it kind of boggles my mind how you guys can value all these startups, which may not even have revenue and may be very early-stage. Because this podcast is about frameworks, I’d like you to share with us—with me and the listeners—a framework that simplifies this whole valuation. What are the major elements? If you had to constrain yourself to five major elements of your valuation, what would they be?  Yeah, I can definitely help with that. Obviously, the first one is the industry, right? The industry. We have our models and datasets that we’ve acquired over the years. We have valued $400 billion in client assets manually. Okay. Manually. Even before AI, we were using AI, right? So we really tested the models. We really worked on what works and what doesn’t. That’s one element. It’s the dataset.  Obviously, public comparables. We have incredible models for choosing and picking the right companies, right? There are 4,000 publicly traded companies on the market. That’s a very important dataset. Then we have data taken directly from clients. That’s also very important. The data actually reflects the reality of the company. Obviously, you can estimate, but all these estimates…  There are platforms out there that give you a range. There’s actually a company now that tried to copy us, but they came up with such an unfortunate solution. They have a range. And Steve, who knows the range? Let’s say you have a $100 million company valuation. They came up with a valuation range of $30 million to $100 million. Two hundred?  Okay, great.  Thirty to one hundred. Thirty to one hundred. Okay. I’m like, “That’s not even a range.” It’s ridiculous. So, again, you can try to copy it, but it’s not going to work. You have to have analysts. We have an in-house team of close to 20 valuation analysts with double master’s degrees, CBAs, CFA Level III, and NACVA certifications.  So we can also issue certified reports. We can support all the defensibility. So, in case any company is undergoing M&A, we can help with additional questions from the Big Four, McKinsey, or PricewaterhouseCoopers when they challenge the valuation. We can defend the stock price. It doesn’t really happen when we issue the stock, but it happens down the road.  Yeah. That’s fantastic. So let me ask you this. What drives growth at Eqvista?  What drives growth at Eqvista? The product. It’s really as simple as that. The product and our company economics. We don't really waste time and energy on things that don't work. We focus heavily on what really works and only on what works.Share on X We haven’t raised much money, right? We bootstrapped the company. We raised half a million dollars. I always gave up on fundraising because I’m a product founder.  I don’t really do many roadshows or meetings. I actually hate meetings. I stay with my team. I’m product-centric, super focused on the product, and I think that’s one of the reasons we’re successful. I’ve always been fortunate to build amazing products and hire very talented people who can understand what we’ve built and sell it. So it’s the product. I’ve always believed in pull marketing rather than push marketing.  Obviously, outbound outreach is also extremely important. But pull marketing is where you start, especially in the early stage when you want to grow to, let’s say, half a million, one million, or two million dollars in revenue. Because direct sales are extremely hard.  So what’s one thing that you’re actively trying to figure out in your business?  It’s the price. It’s the stock price. How to effectively value companies. That’s what excites me, and that’s what we try to figure out on a practically daily basis. We have a team of specialists focusing on stock pricing and stock price discovery. Adoption is going to be extremely important. How successful we’ll be in distributing the stock price to different segments of the market.  I’m trying to understand this. You already have 25,000-plus companies whose valuations you manage. You’re able to defend these valuations in court or in M&A situations. So what do you mean exactly by trying to figure out how to effectively value companies? I mean, it sounds like you’ve figured it out. So to what degree have you not figured it out?  We have it, right? But for us, it’s about efficiency. Efficiency and how precisely we can price the stock. That’s probably the big question, even for the public market. Some companies are underpriced. How is it possible that a company with a strong balance sheet has a lower value than the cash in its accounts? These are the types of things.  It’s definitely a different approach to value a company with a couple hundred thousand dollars in revenue versus a company with $300 million in revenue and a $22 billion post-money valuation. These are crazy formulas and approaches. We’re still working on the precision because, at that scale, companies are extremely sensitive to any type of deviation. Even one percent can mean hundreds of millions of dollars.  So that’s very important. Actually, it’s also very important for startups to realize that, at the lower level, they might be undervalued by a few hundred thousand dollars or a few million dollars. That’s also something founders, CEOs, and CFOs should really be considering. That’s why real-time valuation is extremely important for all of us. We’re just trying to teach the market about real-time valuation.  Yeah. I can imagine. Even listed companies that have public data can move dramatically based on market sentiment. Something happens in Iran, and the stock can drop ten percent in a day. What could happen in a private company when you don’t have real-time data available?  Yes.  It’s going to be a really thorny issue.  It is.  So if you had a magic wand, Tom, and you could fix one thing in your company in the next twelve months, what would that be?  Adoption. Real-time valuation adoption. How we teach the market about real-time valuation. That would be the thing I would love to fix.Share on X We actually have a few ideas about how to do it. We’re becoming our own client. We’re becoming our own client, so we’re actually launching a controlled tender offer—a secondary marketplace, practically. Down the road, we could apply for an ATS, an Alternative Trading System. But that’s eight, nine, or ten months from now. At least that’s our vision, and that’s where we’d like to be headed.  So you’re going to do something like a relisting or a reverse IPO?  Not really a reverse IPO. No. Just to help shareholders liquidate their stock. It’ll practically be a platform where employees or investors can liquidate their stock. That’s something we’d like to focus on. We issue stock, manage it, reprice it, and we’d also like to help provide liquidity.  Wow. These are very exciting challenges.  Yes.  A lot of people have tried to overcome this, so if you do, you’ll definitely be pioneers in this area. Perhaps AI can help with that.  Perfect. Yeah. I have a list of people I know, how to use them, and when. So you’re always welcome.  All right. If people would like to learn more or contact you, where can they find you? Or where can they find Eqvista? How can they connect with you and learn more?  Yeah. I can definitely help with multiple subjects: company formation, bootstrapping a company, any product-related questions, and, obviously, equity management, stock pricing, valuation discovery, efficiency, and, most importantly, anything about Eqvista. You can find us at eqvista.com. E-Q-V-I-S-T-A dot com. The same goes for email. It’s tom@eqvista.com.  Okay, awesome. You can check Tom out on LinkedIn as well. That’s Tomas Milar. I think you’re listed with your longer first name. He posts regularly—very exciting articles, especially the recent one I saw on SpaceX and how the valuation shot up like a rocket. Definitely check it out.  If you enjoyed this conversation, make sure you subscribe, follow us on YouTube, give us a review on Apple Podcasts, and stay tuned because once or twice a week we bring you exciting entrepreneurs like Tom, who share their frameworks with you. So thanks for coming, Tom, and sharing your insights. And thanks for listening. Important Links: Tom's LinkedIn Tom's  website Tom's Email: tom@eqvista.com

Jordan breaks down three shifts reshaping AI operations for marketing and ad agencies — and the expensive mistake agency owners are making right now. Why paying a consultant to build your "company brain" is the wrong move (and what to do instead), the affiliate-model agency software wave that monetizes your token and ad spend, and the orchestration shift that turns agencies into McKinsey-style consulting firms where one strategist manages $2M+ in client revenue.Timestamps00:05 — Who this is for + 8 Figure Agency's track record (1,000+ agencies served since 2019)01:30 — The $2M EBITDA founder who spent two months on a "brain product" and has nothing to show02:19 — Update 1: What a context window actually is, and why hallucinations happen04:27 — The 2027 prediction: funded brain products from serious Silicon Valley founders (all still in beta)07:00 — What to do instead: GitHub, organized folders, skills — stay portable, don't overcommit08:26 — Update 2: The affiliate-model agency platforms coming for a cut of your token and ad spend (full interview drops next episode)10:12 — Update 3: Loops, human-in-the-loop QA, and the December 2025 agency already running creative autonomously11:54 — The new economics: pay a killer strategist $300K to manage triple the revenue12:40 — Why 8FA is going boutique mode13:52 — Work with Jordan + 8 Figure Exit LiveKey takeawaysDon't pay anyone to build your company brain in 2026. Host client context in organized GitHub folders and skills, stay portable, and migrate when a dominant brain product emerges.If you're still manually running reports, writing briefs, and checking PM tools — start there. Efficiency first, then capacity.The next software wave makes money on your token spend and ad spend, not the license. Understand the model before you commit your operations to it.Loops are already running agency delivery. When production automates, you get hired for taste and strategy — so start building skills and agents now, then learn to orchestrate them.

"BẠN KHÔNG THỂ VƯƠN TỚI SỰ VĨ ĐẠI BẰNG CÁCH THU MÌNH LẠI." - Tom PetersĐỪNG TỰ HÀO VÌ DOANH NGHIỆP CỦA BẠN CHI ÍTTui thấy nhiều chủ doanh nghiệp có một niềm tự hào:“Công ty anh vận hành gọn lắm.”“Bên anh không tốn nhiều chi phí.”“Anh tiết kiệm dữ lắm.”Nghe rất êm tai hén?Nhưng có một câu hỏi quan trọng hơn:Tiền anh tiết kiệm được… có giúp công ty kiếm thêm tiền không?Vì trên đời này, có những doanh nghiệp không chết vì xài sang.Mà chết vì… tiết kiệm lãng nhách.Tui ví dụ.Một anh chủ quán cà phê nghĩ:“Tuyển nhân viên part-time rẻ bèo thôi, đỡ tốn.”Ừ thì rẻ.Nhưng nhân viên yếu kém -> phục vụ chậm, mặt chằm dằm -> khách chê, không thèm quay lại.Mỗi tháng trên sổ sách anh bớt được 5 triệu tiền lương.Nhưng thực tế anh mất đứt 50 triệu doanh thu.Vậy là anh đang tiết kiệm, hay anh đang tự phá chén cơm của chính mình?Cái này trong kinh doanh có thuật ngữ gọi là Vòng Lặp Tử Thần (Death Spiral).Doanh thu tụt -> hoảng -> lại cắt bớt chi phí -> trải nghiệm giảm -> doanh thu rớt tiếp.Game over.Thiệt ra tui có đọc một cái nghiên cứu của Harvard Business Review (HBR).4.700 công ty qua 3 đợt suy thoái kinh tế:Những doanh nghiệp suốt ngày chăm chăm vô việc cắt giảm chi phí, tỷ lệ vực dậy chỉ có vỏn vẹn 11%.Trong khi những công ty biết tối ưu nhưng vẫn "bạo chi" đập tiền vào R&D, truyền thông và trải nghiệm khách hàng thì bứt phá tới 37%.Starbucks năm 2008:Kinh tế suy thoái, doanh số giảm mạnh.Ban quản trị gào thét đòi gọt chi phí nguyên liệu, bớt nhân sự.Nhưng sếp Howard Schultz làm gì?Chơi lớn.Ông ra lệnh đóng cửa cái rụp 7.100 cửa hàng ở Mỹ trong 3 tiếng rưỡi chỉ để dạy lại 135.000 nhân viên cách rót một ly Espresso đàng hoàng.Tốn kém bao nhiêu?Bay mớ doanh thu khủng ngay ngày hôm đó, cộng thêm 30 triệu đô để xốc lại tinh thần quân cán.Người ta chửi ổng điên.Nhưng kết quả?Chất lượng quay lại. Khách hàng quay lại. Starbucks sống và cổ phiếu tăng phi mã cả thập kỷ sau đó.Như một báo cáo của McKinsey từng chỉ ra:Những doanh nghiệp dẫn đầu không phải là doanh nghiệp có chi phí thấp nhất.Mà là doanh nghiệp phân bổ nguồn lực khôn ngoan nhất.Tui thì tui hay chia ra 2 loại chi phí vầy để dễ quyết: 1. Chi phí tiêu hao   -> Chi tiền nhưng không sinh ra thêm được miếng giá trị nào.Ví dụ: - Làm cái văn phòng cho sang chảnh rần rần mà khách chả thèm quan tâm - Chạy quảng cáo mù mờ không đo lường được - Quy trình rườm rà, lãng phí nguyên liệuCái này? Cắt. Cắt mạnh. 2. Chi phí đầu tư   -> Bỏ tiền ra hôm nay để mua năng lực ngày mai.Ví dụ: - Trả lương cao để hốt người giỏi hơn - Đào tạo đội ngũ - Xây hệ thống dữ liệu (Data) - Nâng cấp trải nghiệm khách hàngCái này không những không được cắt, mà đôi khi còn phải gồng lên mà chi.Nhiều chủ doanh nghiệp nhỏ hay mang cái tâm lý:“Đợi có tiền rồi mới đầu tư.”Mà nghịch lý ở chỗ…Nhiều khi sự thật là: Không đầu tư nên mãi không có tiền.Nên bài toán rà soát ngân sách tháng này, đừng hỏi:“Làm sao để mình chi ít nhất?”Mà hãy hỏi:“Mỗi 1 đồng tui chi ra, nó đang kéo về cho tui bao nhiêu đồng giá trị?”Vì doanh nghiệp nghèo không phải vì họ chi nhiều.Doanh nghiệp nghèo vì họ không biết chi ra sao cho không phí.Để trò chuyện sâu hơn, mời mọi người lắng nghe tập podcast của Tùng BT cùng cô giáo, “bà ngoại tài chính” Thuý Tạ. Bên dưới nghen

Stewart Alsop sits down with investor and entrepreneur Adnan Hassan on the Crazy Wisdom Podcast to explore his thesis on creating a small state asset class, using evolutionary insights from asteroids, dinosaurs, and mycelium as a framework for understanding resilient systems. Hassan brings his background in Silicon Valley technology, New York and Washington finance, and sovereign funds—including senior leadership roles at the World Bank—to explain why 162 of the world's 200 states are actually small states with populations under 12 million, and why these distributed, autonomous entities might be best positioned to survive the coming global shocks from AI, currency disruption, and the wobbling international order. For more information about Hassan's work, visit www.sac-holding.com (SAC stands for Small State Asset Class), where you can find two-minute videos explaining his approach to building this new financial architecture.Timestamps00:00 Introduction and the asteroid, dinosaur, mycelium thesis as a framework for understanding evolutionary survival patterns across billions of years05:00 Global order institutions are wobbling while currency systems evolve and AI emerges, creating simultaneous shocks that favor adaptable networked systems over large centralized structures10:00 Small states defined as populations under 12 million represent 162 of 200 global economies, contradicting assumptions that most nations are large centralized powers15:00 States behave as selfish entities seeking regulatory control while individuals seek autonomy, creating tension as the Westphalian system undergoes fundamental transformation20:00 Cooperation versus competition in human systems, examining how KYC requirements and state surveillance are expanding globally including in America and Argentina25:00 Small states are most interested in rule-based global order because they need protection from larger powers, unlike powerful nations that prefer unconstrained action30:00 Of the twenty richest countries by per capita GDP, seventeen are small states, yet no small state asset class exists in financial markets35:00 Uncorrelated assets provide diversification protection for investors, while small states offer geographic distribution across Caribbean, Africa, Europe, Gulf, and Pacific regions40:00 Cross-border family business collaboration between small states will increase, leading to knowledge sharing and a proposed Davos for small states event45:00 The individual sits at the core of this framework, with AI enabling creative minds in small places to access world-class resources previously impossible50:00 Demonstration of accessible technology costing only ten dollars shows how AI removes barriers, allowing creativity to become the distinguishing factor for entrepreneurs globallyKey Insights1. Adnan Hassan presents a thesis grounded in billions of years of evolutionary data, arguing that systems which survive major shocks share common characteristics: they are autonomous, networked, cooperative, resilient, and lack single points of failure. He uses the asteroid strike that killed the dinosaurs as his central metaphor, noting that while massive dinosaurs went extinct, smaller organisms like mycelium, ants, bees, and marsupials survived because of their distributed and adaptable nature. Hassan believes we are currently experiencing a similar asteroid-level shock to our global systems through the simultaneous disruption of the rule-based global order, currency systems, and artificial intelligence, all happening at once over the next three to five years.2. Hassan identifies 162 out of 200 global states and economies as small states, defined as having populations under 12 million people. This number surprises most people, including sophisticated observers who typically guess around 60 or 70. Even more striking, 17 of the 20 richest countries by per capita GDP are small states, representing 85 percent of the wealthiest nations. These small states have disproportionate resources to deploy internationally and the greatest interest in maintaining a rule-based global order since they have the most to lose from chaos and cannot rely on size or military power for protection.3. The current global institutional framework established after World War Two, including the UN, IMF, World Bank, and WTO, is fundamentally wobbling and reaching the end of an era. Hassan argues that states are inherently selfish creatures addicted to regulatory sovereignty and control, but the systems designed to give these states structure and credibility are now failing. This represents the first major restructuring of the global order since the post-World War Two period, which itself followed 400 years of colonial systems. The transition period will be characterized by significant chaos and convulsions throughout the global system.4. Hassan advocates for creating a new small states asset class in financial markets, which does not currently exist despite small states representing the majority of countries and the wealthiest per capita economies. This asset class would provide large institutional investors like pension funds and sovereign wealth funds with globally diversified, potentially uncorrelated assets while simultaneously supporting political and economic structures that embody the evolutionary principles of survival through distributed, autonomous, networked cooperation. The small states asset class represents both sound evolutionary strategy and pragmatic investment opportunity.5. Technology without philosophy is efficiency without purpose, a concern Hassan raised as early as 1994 when he helped prototype the first electronic trading market on the internet. He witnessed the naive optimism of Silicon Valley technologists who believed simply throwing tools over the wall would create a better world, but this approach made both good and bad activities more efficient. Social media demonstrated this danger by efficiently creating disruption and loss of trust in political systems. Hassan warns that the same mistake is being made with AI, where powerful tools are being deployed without adequate philosophical framework or consideration of consequences.6. Small states and their leading families will find more common language and shared understanding with each other across geographic boundaries than with larger neighboring states. A family business in Montevideo has an easier conversation with counterparts in Singapore or New Zealand than with businesses in Sao Paulo because small state actors recognize each other's unique realities and circumstances. Hassan plans to create a Davos for small states in 2027 to facilitate this knowledge sharing among the mycelium colony, allowing different nodes to exchange innovations and strategies across the distributed global network of small state actors.7. The optimistic future involves unleashing individual creativity globally by giving people access to AI-enabled tools that provide world-class legal, financial, and consulting advice in their language of choice. The solopreneur can now become a conglomerate, with individuals no longer constrained by lack of access to execution machinery. Hassan envisions young people in places like Gabon, Swaziland, or Uruguay having the same access to sophisticated business infrastructure as those in traditional power centers, with the distinguishing factor being creativity of mind rather than geographic or institutional privilege. Small states can pivot faster on regulatory frameworks, sometimes achieving in a dinner meeting what takes large states three years of legislative, executive, and judicial wrangling.

Fewer than 100 companies have scaled enterprise AI from pilots to production to capture great value. Alexander Sukharevsky, who leads QuantumBlack, McKinsey's AI practice, joins Michael Krigsman to lay out the repeatable recipe behind those results and why the winners earn roughly three dollars back for every dollar invested. The conversation covers what capturing AI value really requires, why the CEO and board must own the transformation, and how to lead a hybrid workforce where agents work as colleagues, not tools.======This episode brought to you by Gartner IT Symposium/Xpo™: https://cxo.news/KGg8XY======YOU'LL DISCOVER✅ Why fewer than 100 companies captured two-thirds of AI's value, and what they did differently✅ The repeatable recipe: focus a few domains, ready your data, rewire architecture, and fix the economics✅ Why AI transformation must be led by the CEO and board, not handed to the CTO or chief digital officer✅ How to treat AI agents as accountable colleagues, and who stays accountable for the outcomes✅ Why reinventing a domain beats bolting AI onto an existing process✅ How the winners pursue cost savings and top-line reinvention at the same time✅ Why governance and digital trust belong in from day one, with adults in the room on ethics✅ How expertise and judgment become more valuable as agents speed up the work⏱️ TIMESTAMPS0:00 The repeatable recipe for AI value8:27 Treat agents as colleagues, not tools13:42 Why the CEO must own the transformation18:05 From token maxing to value maxing22:01 Managing a hybrid team of agents23:30 A flexible architecture for changing models26:25 Governance and digital trust from day one30:59 Cost savings versus reinventing the top line34:46 Human focus, judgment, and accountability44:12 Redesign workflows instead of bolting on AI46:24 Careers and apprenticeship in an agent world50:47 What real CEO ownership looks like

In This Episode, You'll Learn: • Why meaningful experiences matter more than résumé-building • How students can develop leadership and problem-solving skills before college • What admissions officers value beyond GPA and test scores • Why purpose-driven projects create stronger college essays and interviews • How students gain mentorship from professionals at companies like Google, Bain, McKinsey, Goldman Sachs, and Deloitte • The future-ready skills every student needs in an AI-driven world • What parents can do to help their teen find direction without adding more pressure

Send us Fan MailUsing AI for case prep? For most candidates, it's costing them the offer.McKinsey and Bain are already grading how you use AI in interviews this cycle.Most candidates ask AI for answers, skip the hypothesis, and accept whatever it hands back. That skips the exact skill firms are testing: how you think through a problem, not just the answer you get to.In this episode, Namaan breaks down the 3 AI case prep mistakes, and the right way to use it like a directed junior analyst instead.Resources:If you want an MBB coach checking your process, not just your answers, Black Belt pairs you with oneWant the foundational skills before you loop AI in? Grab free access to Case Foundations and build the process AI should be pressure-testing, not replacingConnect With Management ConsultedCreate a free MC account or download the MC app (Apple, Android) to start your prep todaySchedule a free 15min consultation with the MC TeamWatch the video version of the podcast on YouTubeFollow us on LinkedIn, Instagram, and TikTokJoin an upcoming live event – case interviews demos, expert panels, and more

The McKinsey & Company senior partner describes what high-performing advisors must do now to defend fees, deepen client trust, and drive organic growth. Host: Steve Sanduski, CFP. Learn more about your ad choices. Visit megaphone.fm/adchoices

What if your best customers are already sold — before you ever say a word? That's the promise of the PULL framework, and in this episode, startup advisor and author Rob Snyder breaks down exactly why smart, well-resourced founders keep pushing brilliant products onto people who aren't ready to buy — and what to do instead. If you've run the demos, done the research, and still watched prospects disappear, this episode is your wake-up call. You'll discover: Why doing everything "right" can still leave you in the pain cave The four-part PULL framework (Project, Unavoidable, List of options, Limitations) that predicts who will buy before you even pitch How to describe your product in the fewest possible words — and why less is always more Why your sales call should be a "see if they try to buy" call, not a convince-them-to-buy call How a repeatable customer success story does more selling than any pitch deck ever will Rob Snyder is a Harvard MBA, former McKinsey consultant, startup founder, and author of The Power of Pull. He has advised hundreds of startups on how to find product-market fit faster by stopping the push and building for pull. Connect with Rob: Website: robsnyder.org LinkedIn: linkedin.com/in/robsnyder Get the book: The Power of Pull — available on Amazon Learn more about the Story Cycle System™: businessofstory.com #576: The Power of Pull: Find Customers Who Are Already Sold, With Rob Snyder

Show Notes: Shankar Ananth shares his background, mentioning his nine years of consulting experience, highlighting his experience at McKinsey and Bain, and his role  in building the new app, Micro-Casing.  Bridging the Gap in Case Interview Preparation Shankar explains the gap in case interview preparation methods, which led him to create Micro-Casing. He also mentions his eight years of experience as a case coach and the usefulness of the app for students. He discusses the target audience of the app, which includes consulting candidates and those looking to improve their consulting thinking. Micro-Casing - Your Case Partner Reimagined Shankar explains the homepage of the app, where users can log in through Google or Apple.  Quick start guide The app is designed as a decision-making tool with a multi-choice game-like interface to mimic case interviews.  Categories Users can choose from various categories like market entry, growth strategy, profitability, market size, or M&A.  Avoiding AI Hallucinations Shankar explains the gameplay, where users can pick a category, race the clock, and solve timed cases with two attempts. The app includes a feature where incorrect answers lead to rabbit holes, similar to real-life case interviews. The scoring system involves penalties for viewing hints and entering rabbit holes. Shankar highlights the app's manual case creation process to avoid AI hallucinations, ensuring the cases are accurate. Mock Case Interviews Demonstration The app's growth strategy category is described, showing the available cases and their status (completed or not started). Coaches Shankar explains the optional feature of selecting a sidekick or coach, which provides tips during the case.  Growth Dilemma They start a case called "ShopSphere's Growth Dilemma," where a case prompt is read and  the process of solving the problem begins.  Case Profitability The Summary Page Shankar explains the summary page, which shows the case details, difficulty, pass score, and performance metrics.  Case results and performance review The summary page includes a growth category readiness score and an overall readiness score, indicating progress and areas for improvement. He explores the view of all hints feature, which provides detailed feedback on each question. The Quality Control System Shankar discusses the process of creating and validating cases, involving AI-driven reviews and manual checks by experienced case coaches. The app currently has over 60 cases across various categories, ensuring rigorous quality control. Shankar mentions the potential for users to disagree with answers, which will be tracked for future improvements. The app aims to provide a comprehensive and accurate case preparation experience. The Leaderboard Feature Will and Shankar discuss the leaderboard feature, which allows users to compare their progress with others. The leaderboard is based on overall category readiness and overall readiness scores, calculated using various metrics. Shankar explains the option for colleges and consulting clubs to create their own leaderboards and case competitions within the app. The app supports private quizzing clubs, allowing organizations to upload and co-create cases for exclusive use. Shankar shares his experience as a case coach, starting from word-of-mouth referrals and eventually joining platforms to offer his services. Case Practice  for Consultants He highlights the importance of case practice for consulting candidates, noting that it can take months to prepare. Shankar offers listeners of the podcast two months of free access to the entire app, providing an opportunity to try it out. Timestamps: 03:07: Overview of Micro-Casing App 06:29: Gameplay and Scoring Mechanism  10:03: Detailed Walkthrough of the App 13:54: Evaluation and Feedback 16:59: Creation and Validation of Cases  20:14: Leaderboard and Customization Features Links: iOS: https://apps.apple.com/us/app/micro-casing/id6754453873 Android: https://play.google.com/store/apps/details?id=com.microcasing.dev&pcampaignid=web_share Special link / discount code for the general audience for a free 2 months:  iOS: https://apps.apple.com/redeem?ctx=offercodes&id=6754453873&code=2MOTS   Android:Use the code 'Umbrex' at checkout, not within the app  Special link / discount code for participants in the Umbrex Consulting Fellowship for a free 3 months:  iOS: https://apps.apple.com/redeem?ctx=offercodes&id=6754453873&code=UMB200  Android:Use the code 'UmbrexCore' at checkout, not within the app  This episode on Umbrex: https://umbrex.com/unleashed/episode-654-shankar-ananth-micro-casing-a-gamified-way-to-practice-case-interviews/ Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com. *AI generated timestamps and show notes.  

This week, Ivy Slater, host of Her Success Story, chats with her guest, Marja Fox. The two talk about the power of diverse backgrounds in management consulting, the realities of transitioning from corporate roles at McKinsey and Ecolab to entrepreneurship, and how Marja built her own consulting practice specializing in strategic facilitation for executive teams. In this episode, we discuss: How trial and error, plus finding a community of other entrepreneurs, helped Marja Fox learn to manage the cadence between delivery and infrastructure. What it was like for Marja to join McKinsey with a science background and how the company purposefully sought out people with alternative professional degrees to bring diverse perspectives to client problems. Why Marja believes you should recognize the value of your accrued experience, "you owe me for the years, not the hours", and how this affects pricing and self-worth as a woman leader. What strategies she uses to build a client pipeline and maintain discipline in business development, including mindset shifts from "selling" to helping former clients solve problems. How Marja discovered her "source of distinctiveness" at McKinsey, focusing on problem-solving and deep intuition about people.    Marja Fox STRATEGIC FACILITATOR | EX-McKINSEY | PhD Meet Marja Fox, owner of Marja Fox Consulting. Marja is a former Associate Principal at McKinsey & Company with broad strategy, business development, and commercial operations expertise across multiple industries. Before turning independent, she was a VP of Marketing and Strategy & Business Development at Ecolab, responsible for growth strategy, innovation, new product launches, sales support, and marketing communications of a$1B growth business. As an independent consultant, she focuses on strategy and facilitation. She has found that client service - helping them succeed - is where her heart is; it's what gets her up in the morning. Marja's education is in the sciences. She holds a BS in Chemistry from Harvey Mudd College and a PhD in Physical Chemistry from the University of Illinois at Urbana-Champaign. She brings her analytical background and ongoing drive to understand how things work to every project.   Social Media & Contact Email: marja@mfoxconsult.com  Website: www.marjafox.com   LinkedIn: linkedin.com/in/marjafox/   YouTube: youtube.com/@marjafox Based in Minneapolis, MN    

No one gets fired for doing what McKinsey says. That's not a joke, it's the actual product being sold in half of enterprise consulting: credibility a board can point to when a decision goes wrong. This week, Tim Lidman, founder of Clyde and formerly of a collaboration platform absorbed into Accenture in 2021, argues that AI won't replace that function — and says so without flinching, even though it means some prospective customers will balk.Tim also shares why he thinks “human in the loop” is such a dehumanizing turn of phrase.The conversation moves through the politics that quietly derail group decisions, the trap of building for experts at the expense of everyone else, and a real case where a district court judge canceled a trial because both sides had submitted AI-generated arguments neither side had bothered to check.And this episode ends somewhere unexpected: a founder building frontier AI for a living who gives his own children zero screen time, and why he thinks boredom is the skill nobody is protecting anymore.Mentioned: Deloitte Canada's GenAI snafu citing made up research• • Judge cancels trial after finding out both sides use AI

We’re driving right into this one: The world of driverless cars, and how they’re already being integrated into rental car companies -- and how they might some day be integrated into rental car fleets. Host Rebecca Tobin, editor at large Robert Silk and Vik Krishnan, a senior partner at McKinsey & Co., debate: Is the era of self-driving, or autonomous, vehicles closer than we think? In this episode: How rental car companies are servicing self-driving cars, why someone might rent an autonomous vehicle, the hurdles to adoption, timelines and more. This episode was recorded June 29 and has been edited for length and clarity. Episode sponsor This episode is sponsored by National Geographic Lindblad Expeditions https://www.expeditions.com Related links Driverless vehicles: Rental car companies get in the race https://www.travelweekly.com/Travel-News/Car-Rental-News/Driving-forceSee omnystudio.com/listener for privacy information.

Sign up for Robert Rose's exclusive July event here. https://www.seventhbear.com/bearing-table-july/ This week, Joe and Robert dig into what happens when marketing, publishing, and customer data all collide with AI. First, the guys break down McKinsey's vision for the future of marketing, where brands must move from campaigns to continuous growth systems and become trusted by both people and machines. Then, they look at a provocative publishing essay arguing that AI-created, ad-supported content may overwhelm much of the media business. If content becomes cheap and endless, what is still worth paying for? The conversation then turns to HubSpot, which reversed a planned customer data enrichment change after backlash from users. Full disclosure: HubSpot is a sponsor of This Old Marketing. But the story is bigger than one company. It raises the question every platform will face in the AI era: when does better data become a trust problem? In Marketing Winners and Losers, Joe gives his loser to Meta for rolling out another AI feature that feels like it asked for forgiveness before permission. Robert's winner is bookstores, because in a digital world full of synthetic content, physical discovery and trusted human curation suddenly feel more valuable. In Rants and Raves, Joe comments on Pangram's research about AI-generated content filling social feeds, especially LinkedIn. Robert raves about new Ramp research suggesting that companies investing heavily in AI are hiring more, not less, pushing back against the idea that AI automatically means fewer jobs. Subscribe and Follow: Follow Joe Pulizzi and Robert Rose on LinkedIn for insights, hot takes, and weekly updates from the world of content and marketing.  ------- This week's sponsor: Did you know that most businesses only use 20% of their data? That's like reading a book with most of the pages torn out. Point is, you miss a lot. Unless you use HubSpot. Their AEO and customer platform gives you access to the data you need to grow your business. The insights trapped in emails, call logs, and transcripts.  All that unstructured data that makes all the difference. Because when you know more, you grow more. Visit https://www.hubspot.com/ to hear how HubSpot can help you grow better. ------- Get all the show notes: https://www.thisoldmarketing.com/ Get Joe's new book, Burn the Playbook, at http://www.joepulizzi.com/books/burn-the-playbook/ Subscribe to Joe's Newsletter at https://www.joepulizzi.com/signup/. Get Robert Rose's new book, Valuable Friction, at https://robertrose.net/valuable-friction/  Subscribe to Robert's Newsletter at https://seventhbearlens.substack.com/ ------- This Old Marketing is part of the HubSpot Podcast Network: https://www.hubspot.com/podcastnetwork

In this episode of Molecule to Market, you'll go inside the outsourcing space of the global drug development sector with Evren Ozkaya is the Founder & CEO of Supply Chain Wizard and SCW.AI.   Your host, Raman Sehgal, discusses the pharmaceutical and biotechnology supply chain with Evren, covering:   The job offer that pulled him away from the McKinsey fast track and into the pharma industry Getting hands on experience at Sandoz, delivering real impact before spotting the opportunity to found his own business The moment that triggered the shift from consulting to technology, and the rapid build out of a software platform The decision to spin out the Digital Factory into a standalone company and raise $10m in Series A funding The importance of simplicity and creating a single source of truth on the factory floor Why the industry must move towards interconnected, systems thinking rather than isolated point to point solutions Why strong networks remain a hidden but powerful advantage in the life sciences sector   Evren Ozkaya, Ph.D. is the Founder & CEO of Supply Chain Wizard (a management consulting firm) and SCW.AI (a technology platform company), helping Pharma manufacturers establish and execute cost-effective and scalable digital transformation programs across manufacturing and supply chain domains by leveraging the state-of-the-art technology.   As a former management consultant at McKinsey & Company and a supply chain executive at Sandoz/Novartis, Dr. Ozkaya led various business transformation programs in over 100 companies in industries such as pharmaceuticals, consumer goods, industrial, logistics and private equity.   Evren Ozkaya received his Ph.D. in Industrial and Systems Engineering from Georgia Institute of Technology with his award-winning thesis on Demand Management in Global Supply Chains. Dr. Ozkaya currently serves as the Chair of the Advisory Board at the School of Industrial and Systems Engineering at Georgia Tech, besides his other advisory board roles at Rutgers Business School and Seton Hall University.   Molecule to Market is also sponsored by Bora Pharmaceuticals, and supported by Lead Candidate. Please subscribe, tell your industry colleagues and join us in celebrating and promoting the value and importance of the global life science outsourcing space. We'd also appreciate a positive rating!

Ramiro Roballos grew up in Buenos Aires, and 6 or 7 years ago, moved to Miami and now lives in Buffalo, NY. His path to entrepreneurship has been different, as he started out as a musician, and then an orchestra conductor for several years. He eventually got into building how companies, starting his own music school and his own orchestra. Eventually, he got his MBA, worked for McKinsey and some startups before doing his own. Outside of tech, he is married to a cellist, and keeps playing music for fun. He also enjoys Formula 1, and watches every change he gets.Ramiro went through the immigration process in the US, and was very disappointed in the quality of the service, given the importance of this process in determining a pillar life outcome. He felt there should be a better way, one that has excellent service and quality, and centralizes the expansive process into one platform.This is the creation story of Tukki.SponsorsUnblockedTECH DomainsMezmoBraingrid.aiLinkshttps://tukki.ai/https://www.linkedin.com/in/ramiro-roballos/Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://cash.app* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Reed Smith and Chris Boyer walk through why systems pursue scale, what the deal model captures well and the parts it tends to leave out, the patient experience, brand and digital continuity that rarely get their own line in the financial plan. McKinsey's early-2026 read has healthcare M&A shifting from buying new markets toward integrating what systems already own, with roughly 70 to 80% of provider deals now like-for-like consolidation, so the integration work is getting more attention than it used to. Then they are joined by their guest expert, Christine Albert. She has led marketing and experience through multiple integrations as a chief marketing and experience officer, and she calls this stretch the messy middle. She is practical about it. How to get marketing and digital into the deal conversation early. Why brand architecture is more than a logo lockup. Why the internal team should be the first audience you communicate with. And what the leaders who integrate well tend to do differently, starting sooner and treating the acquired team as an asset. In this episode, Chris, Reed and Christine cover: What a merger's financial model captures and what the integration work adds Where AI helps in an integration and where the work still runs on people How marketing, digital and experience earn a seat in the deal conversation early Why brand and internal communication shape how an integration lands What leaders who integrate well do differently from the first planning meeting If your system is in a deal, or headed toward one, this is a practical look at the work that shapes how it feels to patients and staff on the other side. touchpoint.health Mentions from the Show: Kaufman Hall, M&A Quarterly Activity Report Q1 2026 (22 transactions, strongest Q1 since 2020; Sutter and Allina 39-hospital, $26B): https://www.kaufmanhall.com/insights/research-report/ma-quarterly-activity-report-q1-2026 Kaufman Hall, 2025 Hospital and Health System M&A in Review (46 transactions, the quieter 2025 baseline): https://www.kaufmanhall.com/insights/research-report/hospital-and-health-system-2025-ma-review-uncertainty-transitions-continue McKinsey, US healthcare M&A: value through diversification, Feb 2026 (shift toward integration; 70 to 80% like-for-like): https://www.mckinsey.com/capabilities/m-and-a/our-insights/us-healthcare-companies-continue-to-create-value-through-diversification McKinsey, Healthcare private equity outlook, HPE Miami 2026 (operational execution ranked over financial engineering): https://www.mckinsey.com/industries/healthcare/our-insights/healthcare-blog/healthcare-private-equity-outlook-takeaways-from-hpe-miami-2026 PwC 2026 healthcare M&A outlook, via Fierce Healthcare, Dec 2025 (AI as a driver of margin and growth): https://www.fiercehealthcare.com/finance/key-trends-will-shape-healthcare-ma-activity-2026-pwc Christine Albert on LinkedIn: https://www.linkedin.com/in/christineyalbert/  Reed Smith on LinkedIn: https://www.linkedin.com/in/reedtsmith/ Chris Boyer on LinkedIn: https://www.linkedin.com/in/chrisboyer/ Chris Boyer website: http://www.christopherboyer.com/ Chris Boyer on BlueSky: https://bsky.app/profile/chrisboyer.bsky.social Reed Smith on BlueSky: https://bsky.app/profile/reedsmith.bsky.social Learn more about your ad choices. Visit megaphone.fm/adchoices

Rob Snyder is a serial startup founder, venture partner, and Fellow at Harvard Innovation Labs who has spent years studying why some startups gain traction while others never find product-market fit. A Harvard Business School graduate and former McKinsey consultant, Rob has founded multiple companies, helped early-stage founders scale their businesses, and developed a framework for identifying real market demand. In this episode, he shares why entrepreneurs often waste years solving the wrong problems—and how learning to sell before you build can dramatically increase your chances of success. On this episode we talk about: Rob's unconventional journey from volunteering in Tanzania to McKinsey and Harvard Business School Why startups fail when founders solve problems customers won't actually pay to fix The difference between customer "pain points" and true market demand How selling before building leads to faster product-market fit Why entrepreneurs should focus on what customers already want instead of trying to convince them they need something new Top 3 Takeaways Customers validate products with their wallets—not compliments. Positive feedback means very little until someone is willing to pay. Sell first, build second. The fastest way to discover product-market fit is by learning what customers are already trying to buy and validating demand before investing heavily in development. Don't try to change people's minds. The best businesses solve existing demands instead of convincing customers they should want something different. Notable Quotes "Customers validate products with their wallets." "Figure out what they want to buy—not what you think they need." "Why play an already hard game on hard mode?" Connect with Rob Snyder: Website: https://www.robsnyder.org/ Newsletter & Book: The Power of Pull  A Word from Our Sponsors: - Visit DrinkAG1.com/TMM to get a free AG1 Travel Case with 7 free AG1Travel Packs in your Welcome Kit with your first AG1 subscription order while supplieslast.  - Go to Leesa.com for 30% OFF select mattresses (through July 12, 2026) PLUS get an extra $50 off with promo code TMM, exclusive for my listeners  - To learn more about Mode Mobile and its investor community, go to https://invest.modemobile.com/travismakesmoney -Travis Makes Money is made possible by High Level – the All-In-One Sales & Marketing Platform built for agencies, by an agency.Capture leads, nurture them, and close more deals—all from one powerful platform.Get an extended free trial at gohighlevel.com/travis Learn more about your ad choices. Visit megaphone.fm/adchoices

How much of your marketing and CX investment is being undermined in the final mile by a sales conversation that goes off the rails?Agility requires our customer-facing teams do more than recite a script; they need the situational awareness to navigate complex conversations in real time. This capability isn't just a sales skill—it's a critical component of the overall customer experience.Today, we're talking about the gap between traditional sales training and real-world performance. Most reps forget the majority of what they learn, and classroom theory often fails to prepare them for the pressure and unpredictability of live buyer interactions. We'll explore how AI-powered simulation is moving beyond static role-play to create deliberate practice environments where teams can build the muscle memory needed for high-stakes conversations.To help me discuss this topic, I'd like to welcome, Sam Dorison, Co-founder and Chief Executive Officer at ReflexAI. About Sam DorisonSam Dorison is the Cofounder and CEO at ReflexAI. An expert in artificial intelligence strategy and product deployment, Dorison has been involved with AI-powered products in industries ranging from mental health to cybersecurity to smart cities. Before launching ReflexAI, he served as Chief Strategy & Innovation Officer at The Trevor Project, the world's largest suicide prevention and crisis intervention organization for LGBTQ young people. There, he published 5 peer-reviewed articles on mental health and oversaw teams including crisis services, training, research, technology, and finance. Dorison previously worked at McKinsey & Company and Harvard Kennedy School. He graduated summa cum laude and Phi Beta Kappa from Princeton University's School of Public and International Affairs. He also holds an LL.M. from Cambridge University and a master's degree from University College London, both as a Marshall Scholar. Beyond ReflexAI, Dorison is heavily involved in causes that support families impacted by cancer. Dorison lives in New York City with his husband. Sam Dorison on LinkedIn: https://www.linkedin.com/in/samdorison/ ---------- Resources ---------- ReflexAI: https://www.reflexai.com The Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://aglbrnd.co/r/2868abd8085a9703 We're proud to be a media partner for #MAICON26 - Oct. 13-15! Learn how AI can power your marketing and business and help you grow smarter. Use code AGILE150 to save! https://aglbrnd.co/r/7fe458ced0f04658Reach your customers with Reddit. Spend $500 in ad spend, get $500 back in ad credit! Learn more: https://advertalize.com/r/491818c79fb1873fChaser is the only Slack-native project management platform that helps teams turn messages into tracked tasks, automate follow-ups, and maintain team-wide visibility, without adopting another tool. Now integrated with Claude and other generative AI tools, Chaser is the only platform that brings AI-powered project management into Slack, where teams already work. Chaser is based in Toronto, Canada. Learn more at trychaser.com.The most influential minds in software, AI, and engineering leadership will be at WeAreDevelopers World Congress North America, September 23-25 in San Jose. Learn more: https://aglbrnd.co/r/60a7299222a7bcf1 Enjoyed the show? Tell us more at and give us a rating so others can find the show at: https://aglbrnd.co/r/faaed112fc9887f3 Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstromDon't miss a thing: get the latest episodes, sign up for our newsletter and more: https://aglbrnd.co/r/35ded3ccfb6716ba Check out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company Hosted on Acast. See acast.com/privacy for more information.

Today's guest is Rebecca Anderson, a Senior Fellow at the McKinsey Global Institute, McKinsey's business and economics research arm. She leads research on economic growth and the financial system, in the United States and globally. In today's episode, Rebecca shares her McKinsey report on what has powered America's economy for 250 years: natural endowments, a culture of entrepreneurship, and the institutions that harnessed them. She examines labor force dynamism in the age of AI, the $2 trillion cost of reindustrialization, and a global balance sheet stretched to record highs. (0:00) Starts (1:11) Rebecca explains American's natural advantages (9:11) US leadership in science, technology, and education (16:29) AI, workforce transitions, and manufacturing ramp-up (25:56) Infrastructure challenges and US-China comparisons (33:38) US policy recommendations (43:44) The global balance sheet (52:14) Cultural attitudes and geopolitics ----- Follow Meb on X, LinkedIn and YouTube For detailed show notes, click here To learn more about our funds and follow us, subscribe to our mailing list or visit us at cambriainvestments.com ----- Follow The Idea Farm: X | LinkedIn | Instagram | TikTok ----- Interested in sponsoring the show? Email us at Feedback@TheMebFaberShow.com ----- Past guests include Ed Thorp, Richard Thaler, Jeremy Grantham, Joel Greenblatt, Campbell Harvey, Ivy Zelman, Kathryn Kaminski, Jason Calacanis, Whitney Baker, Aswath Damodaran, Howard Marks, Tom Barton, and many more.  ----- Meb's invested in some awesome startups that have passed along discounts to our listeners. Check them out here!  ----- Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Learn more about your ad choices. Visit megaphone.fm/adchoices