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Artificial intelligence is everywhere. It fuels boardroom debates, guides priorities, defines access to information, and nudges consumer experiences. But while AI promises sharper insights and faster action, it also accelerates blind spots leaders already struggle with. The paradox is this: AI can widen vision, but if used without the right insight, it narrows it. And when those blind spots meet the speed of AI adoption, the consequences multiply. Ive seen this play out across industriesthrough my leadership roles at Google, Maersk, and Diageo, and in advising executives shaping some of the worlds largest organizations. The pattern is clear: technology does not pause at blind spots. Instead of alerting us, it often erases tracesuntil the competitive edge quietly slips into commoditization. Here are three ways AI makes blind spots bigger and how to shrink them. 1. Data Without Context is a False Comfort Every AI is shaped by what it has access to. Generative AI is guided by probability. Agentic AI acts on the data it is trained on. Both are only as useful as the context they can see. This is where the first blind spot appears: leaders mistake the outputs of AI for reality itself, forgetting that the system is bounded by its inputs. A dashboard may glow green, or an AI may return precise answersbut precision without context is a false comfort. This may feel like a familiar challenge, where reliance on fixed KPIs can make internal progress look convincing but fail to connect to real shifts in the market. I have seen hardworking teams pull in opposite directions: one rewarded for growing basket size through add-ons, another penalizing customers who adjusted orders, canceling each other out and driving customers away. AI applied to those metrics would only have reinforced the misalignment. If business rules are applied at too low a level in the organization or process, sub-optimization will occur. In an AI context, this compounds at scale, locking inefficiencies into every automated decision. All cases show the same trap: when data is cut off from context, leaders optimize for what can be measured instead of what matters. Availability is mistaken for reliability. How to address the blind spot: Shift from validating what you already track to exploring what you dont yet see. Treat data as a landscape to be tested, not a dashboard to be confirmed. Ask where contradictions appear, where signals conflict, and where the edges of the system reveal something different from the center. Blind spots shrink when leaders are curious enough to explore anomalies instead of explaining them away. 2. Outsourcing Judgment Dilutes Core Value Another growing blind spot comes when too much responsibility is placed on external systems or partners. AI is powerful, but it is not neutral. If leaders outsource judgment without feeding back their own expertise, they risk hollowing out the very value that makes their business distinctive. Think of it this way: you have personal knowledge, collective knowledge within a company or institution, and global knowledge. Businesses naturally try to connect and leverage collective intelligenceso why, when it comes to AI, do so many neglect the need to actively share, contextualize, and update knowledge to keep it valuable? I once debated a leading doctor responsible for defining a regions use of technology. He explained that he relied on his trusted X-ray machine and the same software he had used since the late 1990s. He did not log his evolving insights as structured inputs, nor did he feed edge cases back into the system, assuming vendor updates were enough. His judgment stayed in his head, while the softwareand the sectorfailed to learn from real-world experience. In a field where image recognition is advancing rapidly, that gap leaves value on the table and slows the diffusion of what works. The point is not to develop all AI in-house, but to be clear about what truly differentiates you and ensure that knowledge is not given away. Cost management through outsourcing call centers may deliver quantifiable savings, but it also shifts valuable customer insights outside the business. With AI, those insights compound quickly, and what begins as efficiency can end in commoditization where your uniqueness is absorbed into someone elses model if you are not conscious about how AI is deployed. How to address the blind spot: While AI is essential for efficiency and future operations, strategy must come first. Know your propositionthe value today and in the futureand build your AI approach on that, not the availability of pretrained software, partner rates, or the convenience of what others have packaged. Ask who gains value from the data you hold, and who has access to the data that could help you grow. In many industries, this will become the foundation for new revenue models and deeper partnershipsor the path to eliminate those without strategic clarity. 3. The Cognitive Trap Behind Algorithmic Comfort Even with broad and evolving data and strong strategic clarity, AI can still trap leaders in confirmation loops. Algorithms are designed to learn from patterns, but patterns are not the same as insights. By default they reinforce what is most represented, not what is most revealing. Some models can be tuned to flag anomalies, but in most business settings the gravitational pull is toward the familiar. Of course it isbecause so do we. The danger is that this collides with human blind spots. Neuroscience shows how the brain conserves energy by filtering out complexity, anchoring on what feels certain, and avoiding ambiguity. True neurogenesisthe creation of new thinkingrequires new contexts, yet most leaders default back to the familiar. Behavioral science confirms how leadersespecially experienced onesare prone to confirmation bias, mistaking familiarity for foresight. And the more changeable and unpredictable the world becomes, the harder it is to resist this pull. AI does not correct these tendencies; it magnifies them. It reflects back the certainty leaders crave, accelerating the speed at which untested assumptions harden into strategy. The result is a narrowing of visionmore convincing, faster moving, and harder to detect. Left unchecked, this is how organizations find themselves trapped in the comfort of familiar patterns while competitors redefine the market around them. How to address the blind spot: The way through is to stay grounded enough to notice when certainty becomes comfort rather than truth. That means questioning and stripping out assumptions that no longer serve and allowing the narrative to be retested against todays and tomorrows reality. Vulnerability is the entry pointnot weakness, but a signal of where assumptions have not been updated. Let these surface, acknowledge what it would take for you to change your mind, be curious about what could fit in, and explore new emerging directions to shape a new frame. Leaders who embody this stance expand their field of vision and prevent AI from hardening blind spots into strategy. AI Tests Leadership The thread across all three blind spots is the same: AI does not remove the limits of human judgment, it magnifies them. It amplifies whether a company is aligned or fragmented, insular or in tune, whether leaders are curious or complacent, wheter strategy is active or passive. The real test is not in the speed of adoption but in the awareness leaders bringwhether they can stay open enough to challenge what feels certain, while holding clear to what truly defines their value. That requires building a platform to connect, where diverse perspectives can feed into the systemconnecting both people and dataand ensuring a data access culture where exploration toward a common ambition is not just welcomed but expected. This paves the way not only for using AI, but for growing with it.
Category:
E-Commerce
When it comes to market segmentation, I dont see truly well-documented cases often. At a more simplistic level, we think of classic matrices such as BCG or McKinseys. But the real exercise of segmentation is far more complex. In certain contexts, it comes close to the behavior of a tensor: multiple dimensions, cross-dependencies, distinct weights, temporality, and contextual factors that shift the meaning of data depending on the axis being analyzed. Thinking like a tensor is practicing Model Thinking, which remains, above all, an analog discipline. It requires a brain, not a machine. The challenge is necessarily multidisciplinary, and this is exactly where executives suffer, spending enormous time compensating for immature teams. Even when business operators manage to bring quantitative data from ERP, CRM, or sector reports (which are often scarce or methodologically fragile), the information set must be normalized. This process demands an additional set of competencies: statistical knowledge, data-cleaning techniques, sampling concepts, dimensional modeling, and even systems logic to avoid collinearity and redundancy. When unstructured data is added, the challenge grows further. This includes everything from more sophisticated sentiment analysis to qualitative inputs from field teams, customer recordings, or information mined from third-party sources. In these cases, the problem is not confined to normalization: It involves interpreting, validating, reducing noise, and converting natural language into structures that can interface with transactional data. It is epistemological, not just technical. SERIOUS SEGMENTATION Serious segmentation is not a mere snapshot of the market. It plots and overlays multiple layers: data on strategic human resources (both internal and competitive), asset acquisition history, technological maturity, revenues and margins, pricing elasticity, media activity, public opinion, and ecosystem maps revealing the true position of players. Good segmentation uncovers unclaimed revenue, positioning errors, pricing failures, ignored clusters, asymmetries between capability and discourse, and even subtle competitor movements that go unnoticed at the tactical level. The entire process demands other equally essential competencies: dataset modeling, command of relational tables, use of manipulation languages such as SQL, Python, or R, basic and applied statistics, visualization techniques, clustering, similarity analysis, and, above all, the ability to formulate hypotheses. Without hypotheses, there is no segmentation. There is only table sorting. THE AGENT ERA In the so-called era of agents (some already speak of the decade of agents) a complementary arsenal emerges to support these processes. Agents capable of cleaning and normalizing data, agents for web scraping and data enrichment, agents that classify and label content using LLMs as annotators, statistical automation agents able to perform clustering, PCA, or churn analysis, reconciliation agents capable of resolving deduplication and probabilistic matching, and competitive-simulation agents designed to test elasticity scenarios, pricing movements, or anticipated reactions of market players. As a last resort, and not as the first option, as leaders outside tech hubs tend to believe, RAG enters the picture. This article could list agents available in the ecosystem for immediate use, but it is fundamentally about the capabilities that precede automation. Before any automation, there is foundational knowledge: truly understanding the discipline of segmentation, knowing principles of market behavior, and having clarity about the information models that generate strategic insights for guiding portfolio, productive capacity, and competitive advantage. No GPU, no matter how powerful, replaces this conceptual clarity. And this clarity is not necessarily the exclusive responsibility of IT, the CTO, or marketing teams (understanding marketing here, according to the American Marketing Associations definition). Segmentation belongs to multidimensional leaders capable of moving fluidly across strategy, operations, data, behavior, and finance. The provocative question remains: Do these leaders exist in the analog perspective, prior to automation? Many companies try to leap directly from subjective culture to algorithmic culture without building the intermediate methodological culture, and this is one of the silent sources of failure today. There is robust literature on segmentation and, it must be said, it requires intellectual musculature. I appreciate Malcolm McDonald and Ian Dunbar in Market Segmentation. Peter Fader, from the Wharton School, offers a more financial and pricing-oriented view in The Customer-Base Audit. Naturally, these two works only give a glimpse of the thinking underlying the structured idea. FINAL THOUGHTS Finally, two observations. First, what I have just written is not something that ChatGPTeven as a generative modelwould spontaneously produce. LLMs do not naturally form implicit assumptions across domains, nor do they articulate disciplinary layers whose connection depends on human repertoire and has not been previously mapped. They operate on existing corpora; they do not originate new paradigms on their own. Second, most business schools today, aside from a small group of highly specialized institutions, tend not to emphasize this mode of thinking. Not by fault, but by design. Their structure was built to serve the needs of upward-moving managers, not to cultivate the broader, integrative perspective required of executive-level decision makers. This gap in knowledge for top leadership has a structural explanation: The audience is relatively small, and therefore not the core economic engine of educational institutions. As a result, many executive leaders find themselves without ongoing renewal of their knowledge matrix, even in an era that promotes continuous learning. A paradox of our time. Rodrigo Magnago is researcher and director at RMagnago Critical Thinking.
Category:
E-Commerce
A recent New York Times headlineDid Women Ruin the Workplace?sparked a firestorm across social media. Alison Moore, CEO of Chief, the prestigious network for senior women executives, is pushing back on this notion with data and nuance. Drawing from an exclusive nationwide survey of women leaders, Moore unpacks how evolving career paths are being misread, the impact of market disruption, and why women-centered spaces remain vital. This is an abridged transcript of an interview from Rapid Response, hosted by the former editor-in-chief of Fast Company Bob Safian. From the team behind the Masters of Scalepodcast, Rapid Response features candid conversations with todays top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode. The environment that you’ve come into with the Trump White House is kind of heightened; discussions about diversity are heated. The term DEI has become kind of a negative. How do you frame what Chief is about in this kind of climate? There’s certainly more scrutiny on things today perhaps than there were in the past, but I think at the end of the day, there’s a desire for creating better, stronger leaders, better outcomes, better decision-making, more agile thinking. While there are different contexts being held and conversations being held around DEI and the nuances of that, the truth is when you cut through the big headlines, the realities remain the same, which is supporting leadership at a time of high velocity of change is always beneficial, and we happen to build that in a way that supports women leaders, and I think there’s a lot of support for that. Chief recently published a report in partnership with Harris surveying over a thousand senior level women, and it pointed to a sort of changing definition of maybe ambition and success, a shift away from playing it safe toward bolder career moves, which was in some ways more optimistic and more empowering than I’d expected. There had been a slew of articles that had come out focusing on changes in the workforce and finding the negative nugget in there to kind of put that on blast. And I’m not denying that there aren’t those factors at hand, but the she-cession is coming, women are being dumped out of the workforce. The return to office doesn’t work for all women in the polarity that we’re in today of big headlines being the only definition. We lose all nuance. And so for us here, I come in February and I’m looking at this incredibly energetic Chief membership and thinking, “This is what I’m seeing. I’m seeing people reacting and responding to change in ways that are innovative and curious and thought-provoking, this level of optimism that’s sitting in Chief, that they’re communicating together.” This is not just Chief telling women to be optimistic, this is the energy that you’re feeling from conversation. And so the genesis of this poll was how do we validate that? And so in the women that we surveyed, they’re citing that they’re more ambitious now than ever, and in fact they’re energized by the professional growth ahead because they feel like they can have more optionality and more of a hand on the wheel of their own career design than what they used to have. This is where that metaphor of the ladder and the stay rung to rung to rung and keep the course and stay the path and then something happens at the top. Everyone can see that’s not necessarily the case anymore. How much of the boldness do you think might be economy driven? I noticed that over 80% of the executives cited market disruption as a motivator. It’s almost like disruption is, I don’t know, forcing action? I think the economy is certainly always going to be a factor, but it’s also like, look at industry consolidation. You can look at what’s happening in the media business, you can look at what’s happening across retail. You can look at what’s happening in every vector and AI, which has a through line all the way across all of these industries, and a common thread of this duality of opportunity and threat of change. Is that rung as a metaphor again, for just career direction, not necessarily the corporate ladder as much as it is just the up a ladder? Is that the right path for me? Do I want other options? Do I want different kinds of flexibility? Do I have the sort of tools at my fingertips to actually do that? And that is what happens at Chief and that’s what’s been very interesting for me to see and come back actually from a founding membership in 2019 to where we are today. Women are building, scheming, meaning business is scheming, partnering, collaborating differently now. I think the lids off on a lot of that stuff for women. And so you do feel the optimism coming out in this survey, even though I think there’s a pragmatism and recognizing it is coming from disruption, but disruption does bring opportunity. So much of Chief is built around in-person connection and community, but in-person it’s so hard to scale. And I know when I was talking to your predecessor, Carolyn, she talked about the potential to have a LinkedIn-like product and a Masterclass-like product and a dating-app like product. How do you think about todays digital tools for community versus the IRL experiences? Where do you balance those things? What I think about Chief today, and I think that was a very accurate description that Carolyn gave when that started. I think we’ve morphed into this space where there are a couple of different components. There are intimate experiences. By that, I mean small space experiences where you talk and see and share about leadership challenges that can be in a virtual experience, very rewarding, but in certain places and markets, we can make that happen in real life. It doesn’t have to be either-or, coaching, one-to-one coaching, so that happens on a virtual piece sometimes, that can be very rewarding, but if I’m going to have a cocktail party around other senior marketers, that should be at the clubhouse in real life. I’m about to go to LA this week for my eighth and then San Francisco in two weeks for the ninth. Nine ChiefXs this year, and this is where nine locations across the country, we’ve had members come, and it’s in real life, and they’re like rallies, and I get so much energy out of these things. It’s unbelievable. We have some speakers, some programming, but it’s really about this kind of connecting energy between Chiefs with other Chiefs, big corporate people talking to founders of small things, folks who are in transition, all senior leaders in various parts of their journey, and these large rallies are definitely IRL and then peppered between that are the right size virtual experiences. My next tranche of focus is really on the digital experience, the Chief app, the Chief digital experience in terms of, how do we make that as bes of a member companion? The networking piece, the connections piece, whether that was the LinkedIn analogy that Carolyn meant, that’s where that really comes to life, but it comes to life in service of a lot of in real life experiences across multiple use types. Many of the things that a woman leader is interested in are things that any leader is interested in. It doesn’t have to connect to the fact that it’s a woman, and then there are a tranche of things that may be more applicable to women than to men, although you could argue they’re applicable to everyone. I always struggled with this when we mentioned you being in Fast Company. It was for a package we did on the most creative people in business and there were men and women on it, and we didn’t do a most powerful women in business like Fortune did. But there is something different when women get together without men there that maybe as a man I don’t quite appreciate or I want to, but I don’t really know. Listen, I think for women in leadership roles, there is a place and a time for being together and learning from each other that’s just different. It’s additive. The conversations are because a woman’s career journey is tied to that 360 view of who they are in life, it just makes those conversations different, but not necessarily better. I’ve been in multiple coworking environments where it’s male, female, that’s all great too. I’m a big believer in the bothI really am.
Category:
E-Commerce
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