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The most consequential shift in business right now isn't AI - it's the gap between…
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Jeremy Korst shared thisWhat an awesome week in NYC kicking off "AI for Business" at Columbia Business School! I'm partnering with Malek Ben Sliman to deliver four sections of this popular course to 300 MBA students. We plan to provide a broad foundation that explores everything from technical fundamentals to building/managing agents to organizational transformation strategy to ethical considerations...whew, that's a lot! And, of course we're going to BUILD things! In fact, all the students completed a personal web app during session 1 using VS Code and Codex/Claude Code. I look forward to the next six weeks and what we will learn and build together! 🚀 🤖 ...and, I'm already looking forward to the vacation that all these frequent flyer miles will fund :)
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Jeremy Korst reposted thisJeremy Korst reposted thisThanks to the Financial Times for their coverage of our recent AI adoption study. This is the "Who's in charge?" question 🤷♂️ for when AI goes off the rails. No consensus whatsoever on this issue, and agents that cut across roles will certainly not make this an easier question to answer. Wharton AI & Analytics Initiative GBK Collective Stefano Puntoni Jeremy Korst
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Jeremy Korst reposted thisJeremy Korst reposted thisOur recent webinar on the new Wharton-GBK AI adoption report is now online. Enterprise AI adoption is moving beyond experimentation, but the organizations seeing the strongest returns are doing more than adding new tools. They are redesigning workflows, establishing clear ownership, and aligning employees around how AI should be used. With Wharton AI & Analytics Initiative and GBK Collective With Prasanna "Sonny" Tambe and Jeremy Korst https://lnkd.in/eM-d-8u4AI Horizons: How the Organizations Seeing the Most AI Value Are Redesigning Work AI Horizons: How the Organizations Seeing the Most AI Value Are Redesigning WorkAI Horizons: How the Organizations Seeing the Most AI Value Are Redesigning Work AI Horizons: How the Organizations Seeing the Most AI Value Are Redesigning Work
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Jeremy Korst shared thisHave you heard of “cognitive surrender?” 🧠 🏳️ The Wharton School researchers found that when people consulted AI and it gave them the right answer, they went with it over 90% of the time. But, when the researchers manipulated the AI to give them the wrong answer, they still went with it 80% of the time. Steven Shaw (now at King’s College London) and Gideon Nave call this “cognitive surrender”: accepting AI’s answer without critically evaluating it yourself. They tested how people responded to reasoning questions when AI advice was randomly made correct or incorrect. Gidi and I collaborate at Wharton, so we’ve had the opportunity to discuss this more. One of their findings that stuck with me: people with access to AI were about 12 percentage points more confident in their answers than those without it. And, that confidence boost came even though the AI was programmed to give wrong answers roughly half the time. (My wife refers to this as being “confidently wrong” when I do it 😁 ) Fast forward to our 4th annual Wharton and GBK Collective study on AI adoption, which I co-author with Stefano Puntoni and Prasanna "Sonny" Tambe. In it, 82% of executives (VPs+) told us their focus has shifted from creating outputs to managing AI-generated ones. And 76% told us they trust that output enough to use it with little checking. Further, among the senior leaders who told us they use AI output with little checking, more than half also said they are concerned their own skills and thinking are declining. That overlap doesn’t necessarily mean that AI caused or will cause a decline. But it’s worth paying attention to. Nearly half the managers and executives we surveyed also say a human reviews and approves AI work before execution. Managing AI output in increasingly important work. So, Gidi and Steven’s experiments make me think: how do we ensure that review involves independent judgment? Here are three questions worth asking yourself and your team this week: - Where did reviewing AI content quietly replace making content, and did anyone intentionally decide that? - How do we know our review process actually catches AI mistakes? - Does it depend on one person staying sharp through the umpteenth similar review of the day? If reviewing AI output is what many professionals now do much of the day, then how well they do it is an increasingly important skill. And, it’s a skill worth building on purpose, rather than assuming it automatically came with past experience or the promotion. Mindspan Labs
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Jeremy Korst shared thisThe The New York Times ran a piece this weekend on how differently chief executives and their employees see the AI rollout. It's worth a coffee and 5 minute read. "Nearly three-fourths of leaders say A.I. has significantly changed the way work is done, compared with 58 percent of entry and midlevel employees, according to a report released this month from the Wharton School." That's from our 4th annual The Wharton School and GBK Collective study, which I co-author with Stefano Puntoni and Prasanna "Sonny" Tambe. It highlights just one of many disconnects we found between executives, managers and the workforce. Jordyn Holman also cites the Milken Institute and Harris Poll: 68% of business leaders expect to be better off financially in five years because of AI, against 27% of workers. Here's what I would add: The disconnect isn't only between the corner office and the floor. It runs inside the leadership ranks as well. In our earlier Harvard Business Review article, Stefano, Sonny and I reported that 45% of executives saw significantly positive returns on their AI investment. Among middle managers, 27%. A BCG study with Stephan Meier at Columbia Business School found the same shape: 76% of executives agreed employees feel enthusiastic about AI, against 51% of middle managers and 31% of individual contributors. None of this shows that executives are wrong and managers are right. Maybe senior leaders see more of the organization and are better informed. Maybe they sit far enough from execution to be optimistic. Maybe junior staff cannot see what gets formally tracked and understate it. Maybe time horizons are different. Our cross-sectional survey separates none of those. But, it does match what I see on the ground with many of our Mindspan Labs clients. And it does show that the layers are not on the same page, which is what a leader can act on. Ask one layer whether there's a shared AI vision tied to strategy, or how the rollout is going, and you get that layer's answers. That disagreement sits on the people who must redesign workflows and lead their teams through it, which is where transformation actually happens. That makes it a potentially costly fault line, one that can seriously hamper the move from AI ambition to AI value. It is where I watch programs stall. Three things worth doing: - Ask more than one level the same question, and treat the distance between their answers as data about your company's current readiness for adoption and transformation. - Reduce your managers' load before you add an AI mandate. - Make cautious assessments and failed pilots safe to report upward. And importantly, the managers in our survey aren't opposed to AI: more than half called themselves excited and 62% are optimistic. We argued in HBR that their caution reflects how close they sit to the integration work, and to output that must be right rather than fast. Worth hearing and understanding, not overriding.
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Jeremy Korst shared thisIt’s budget planning season, and I keep hearing leaders worry about next year’s AI costs. Our own Wharton + GBK study revealed that nearly half of leaders (47%) expect budgets to increase over 10%. But in the discussions I've been part of, leaders aren't only worried about how big the number could get, but are increasingly concerned about how hard it is to estimate at all. What’s a token, and how many such things will an employee or an agent use next year? We struggle to explain this in business school classrooms, never mind putting hard dollars behind it. So leaders are stuck on basic questions: who gets how much, and for what? While this rationing can drive some worthwhile prioritization, my concern is that this slows down the experimentation and adoption that both companies and the AI providers are counting on. Laura Bratton's latest piece in The Information captures one dimension of this: the price war among the labs as they fight for share. Some of that is healthy competition, and some of it is the providers getting a better handle on their own cost to serve. As our Mindspan Labs agent team continues to grow, I’m all for lower prices! But there’s another way the providers could compete, and it might lead to bigger AI budgets and even more revenue and margin for them: predictability. A CFO will often approve a larger number they can count on over a smaller one that could swing wildly. Will be interesting to watch this develop as adoption and competition continue to heat up! Curious: how are you handling estimating your AI costs for next year’s budget? The Wharton School | Wharton AI & Analytics Initiative | Mindspan Labs | GBK Collective
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Jeremy Korst shared thisWhat’s it going to take to justify the trillions being poured into AI? Hint: more slide decks, better dashboards and quicker email drafts are great, but productivity and efficiency gains alone won’t cut it. Bain & Company estimates AI needs $6T in revenue by 2031 to justify the investment. Roughly 70% of that, $4.2T, will have to come from innovation: new products, physical AI, autonomous everything. Our latest annual The Wharton School + GBK Collective AI adoption report, The Adaptive Advantage, shows the same pattern inside companies. Departments that track at least one growth KPI are nearly 3x more likely to report strongly positive ROI (35% vs. 13%). Yet when asked about AI’s primary role over the next 12–24 months, only 22% of managers pointed to growth. My co-author and colleague Stefano Puntoni has been banging this gong for months (his recent HBR article is linked in the comments), and it’s a major focus with our Mindspan Labs client partners. Want to learn more about transformation and innovation in the age of AI? Join us in Philly for an upcoming Wharton Executive Education program: - Business Model Innovation in the Age of AI (Nov 9–13) - Generative AI and Business Transformation (Nov 16–19) Links to the reports and programs are in the comments. Wharton AI & Analytics Initiative #Innovation #Growth #Transformation #Strategy
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Jeremy Korst reposted thisJeremy Korst reposted thisThis is my prediction for the next two years: What separates winners and losers will be whether they have the leadership and vision to turn AI into an engine for organic growth (vs. treating AI as a cost saving tool—that’s the majority of companies). Here’s the most interesting finding in our new AI adoption report (link in comment). 17% of departments that measure AI performance using productivity metrics (mostly, that means “time saved”) report strongly positive ROI. That number shoots up to 35% (!) for departments that track at least one growth measure. To be clear: there is nothing wrong in using AI for productivity. But I’m worried that the relentless focus on productivity is crowding out efforts towards the real promise of this technology: innovation and customer value creation. With Prasanna "Sonny" Tambe and Jeremy Korst. The Wharton School Wharton AI & Analytics Initiative GBK Collective
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Jeremy Korst reposted thisJeremy Korst reposted thisPaige Gross at CIO Dive covered this year's Wharton-GBK AI Adoption Report, and its headline is the reading Mindspan Labs founder and study co-author Jeremy Korst spent the interview providing context on. "The headline many people will take from this is 'hire a chief AI officer.' That isn't necessarily what the data or my experience says." The study shows that among managers and above, 58% report strongly positive returns when their organization has created a dedicated AI ownership role, against 24% when that responsibility was added to an executive who already had another job. "The title is spreading faster than the dedicated role." Dedicated ownership is the third of the report's five takeaways, and two sit ahead of it: finish the redesign, and rebuild how the work is managed rather than cutting headcount. The returns, he told CIO Dive, "come from redesigning how the work actually happens, and driving growth and innovation, which doesn't sit primarily in IT." Which is why his closing line is about the business rather than the org chart: "A chief AI officer without the business beside them — or a strong partnership with their CIO — has a title, not a success mandate." #AI #EnterpriseAI #AIAdoption
Jeremy Korst replied to a comment
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Andrew Nowak well put!
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Mercy Mukuria good approach! (For AI or any process that requires checking!)
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Jeremy Korst liked thisBelow is a list of 50 Global Case Authors based on sales. I'm excited to have been named #26 from The Case Centre, which highlighted the following three top selling cases. 1) Tractor Supply: Steering ESG Strategy as Consumer Beliefs Shift Published by: Darden Business Publishing Reference no. UVA-M-1063 2) Branding the Master Brander (A): Positioning Procter & Gamble's Employer Brand Published by: Darden Business Publishing Reference no. UVA-M-1004 3) Using AI to Assess Creative Concepts Published by: Darden Business Publishing Reference no. UVA-M-1055 These cases (and others) have been co-authored by dozens of students, academic colleagues, executives, and others and would not have been possible without the wonderful experts at Darden Business Publishing. Special thanks to Hunter Firebaugh Jeremy Grossman Giorgi Ben-Meir Juliet King Daum Serena Hagerty Christopher Fuller Lauren Foster David Hendrick Jay Hodgkins Jennifer Hasher Leslie Mullins Riley Steiner Steve MomperJeremy Korst liked this🔢 Want to dig a bit deeper into the #Top50CaseAuthors2026 numbers? Find out more about our no.1, highest new entry, movers & shakers, and the geographical spread of schools. 👇
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Jeremy Korst reacted on thisJeremy Korst reacted on thisWA spending has increased from $26 B to $80 B+ in 15 years, we have a spending problem and an accountability problem: Why I am urging voters to Vote Yes on 645 to repeal WA’s income tax‘We don’t have a revenue problem’: Seattle venture capitalist urges voters to repeal WA’s income tax - MyNorthwest.com‘We don’t have a revenue problem’: Seattle venture capitalist urges voters to repeal WA’s income tax - MyNorthwest.com
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Jeremy Korst reacted on thisJeremy Korst reacted on thisOver the past two days at the Gartner HR Symposium, I joined Sarah Moughty of Harvard Business Review, to discuss how culture empowers people to navigate change, learn new skills, and turn strategy into action. Then I also joined Jolen Anderson of BetterUp to discuss how leadership and human capability translate #AI into meaningful business impact. The common thread was clear: technology can accelerate change, but people determine whether it takes hold. And, a personal highlight from the conference was hearing Misty Copeland speak. Her commitment to continual learning and making a difference in the world was truly inspiring. A special thank you to Jana Olivier and Heidi Atwal for the tremendous partnership behind the scenes. And, to my fellow panelists: Momina Javaid, Ania Krasniewska Shahidi, Lakshmi Nair, and Ginnie Carlier - it was a pleasure to speak alongside you.
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Jeremy Korst reacted on thisJeremy Korst reacted on thisAre AI data centers driving up your electricity bill? Our new research suggests we may be blaming the wrong companies. It was a pleasure to present this work at a retirement conference for Professor Larry Wein at Stanford University Graduate School of Business. On a photo below, I am sharing one interesting result from our new paper, “The Cost of Intelligence: Impacts of AI Data Centers on US Electricity Prices.” Not all data centers are created equal! Our research finds a striking difference: 🔹 Data centers operated by Big Tech companies like Google, Microsoft, Amazon, and Meta have no statistically significant impact on residential electricity prices. 🔹 Third-party colocation providers, such as Equinix, QTS, and Digital Realty, are associated with statistically significant albeit small price increases. The policy implication is clear: Blanket restrictions on AI data centers risk targeting the wrong problem. Who builds them, where they are built, and how utilities manage electricity demand matter enormously. As the political debate over AI infrastructure heats up, we need evidence rather than headlines. Proud to share this research with my coauthors Vishrut Rana and Christian Kaps. Link to the paper is in the first comment.
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Jeremy Korst reacted on thisJeremy Korst reacted on thisLast week, the Puget Sound community came together to celebrate the public launch of Greater, We Ascend, our $150 million comprehensive campaign to shape the future of Puget Sound. Thank you to everyone who joined us for this exciting milestone and to the alumni, donors, and friends helping make our shared vision possible.
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Jeremy Korst liked thisIt was so great to have you as a guest lecture, BJ Allen - you're the OG of inclusive design :)Jeremy Korst liked thisHow can involving marginalized users – users at the edge of a target market whose needs or constraints fall outside mainstream solutions – lead to better innovation? I recently attended The Wharton School to present my research on inclusive design, which is a design approach where companies create with marginalized users. We discussed how customers who are often overlooked, such as users with disabilities, often have the most creative and innovative ideas. These people are the original life hackers because they live in a world that, for the most part, wasn’t created to meet their needs. As product managers observe these users’ modifications, their creative ideas often lead to innovations that benefit the entire customer base. I had a blast learning with Wharton’s b-school students. They were sharp and inquisitive. Thanks to Gideon Nave and all my new Wharton friends for listening! P.S. this is research done with Vanessa Patrick and Deepa Chandrasekaran, which was recently published in Journal of Marketing
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The Wharton School, University of Pennsylvania
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University of Pennsylvania Board of Trustees
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IT Services Marketing Association Board of Directors
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Seattle Metrocenter YMCA Board of Directors
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