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Don’t be fooled—LLMs don’t reason

By Thore Graepel | MIT Technology Review | October 2, 2026

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According to the author, on an afternoon in Seoul in March 2016, he watched a program he helped build that put a stone on the fifth line of a Go board in what looked like a gift to its human opponent. Move 37 in game two of the five-game match looked so absurd that some commentators thought it was a programming glitch.  It wasn’t. AlphaGo won the game, ultimately triumphing 4-1 over Lee Sedol, one of the greatest professional Go players of all time. “I thought AlphaGo was based on probability calculation and that it was merely a machine,” Lee said afterwards. “But when I saw this move, I changed my mind. Surely, AlphaGo is creative.” 

That is why many accounts of AlphaGo’s match against Lee portray move 37 as a flash of pure machine intuition. But that is a misunderstanding. It was actually AlphaGo’s powers of reasoning that made this creative choice—and these are powers that today’s AI lacks. If we want future AI systems to produce trustworthy results and really novel insights in fields like science and medicine, we need to equip them with genuine reasoning capabilities of this kind.

A well-known theory in the behavioral sciences, popularized by Daniel Kahneman, distinguishes between two modes of human thought: System 1 is fast, gut-level, effortless; system 2, slow, step-by-step, and deliberative. AlphaGo offered a striking machine analogue of that split. Its networks supplied the hunches—this move looks promising, this position looks won—and its search supplied the deliberation, testing those hunches against the moves and countermoves that would follow. As in human cognition, neither half works alone. Intuition alone would never have opted for move 37, and brute-force search would have struggled to sieve through all the many possible moves.

This is strikingly different from the way today’s AI models work. A large language model picks the next token, over and over. That amounts to system 1 in action—fast, associative, and surprisingly good pattern completion across almost every subject people write about.

Not long after ChatGPT debuted, the field realized that language fluency alone falls short of true usefulness. The apparent solution was to make models that deliberate: Instead of answering immediately, they can now generate intermediate steps that decompose a problem, carry forward partial results, and influence subsequent reasoning—a process known as chain of thought.

The gains have proved real, above all in mathematics and coding. But unlike AlphaGo’s search, this does not introduce a genuinely separate reasoning mechanism: The intermediate reasoning is still produced by the same next-token prediction process, iterated for longer before the model commits to an answer.

Three shortcomings prevent what chatbots do from qualifying as reasoning (in a way that a scientist might recognize). First, these models typically maintain no explicit, persistent, and inspectable epistemic state. There is no open ledger that lays out the hypotheses a model is considering, the confidence it has in various explanations, the evidence it’s weighing, and the unresolved questions it’s holding onto—all things that should be systematically revised as new information arrives.  Second, they lack a clean separation between what the system knows and how it manipulates that knowledge. Knowledge and reasoning are inextricably interwoven in the weights of the neural network—there is no independent, explicitly represented set of beliefs. Third, while the chains of thought chatbots produce look like deliberation, research has demonstrated that the bots often concoct them after the fact, reaching an answer by one route but reporting another.

This is a problem because in the high-stakes applications we all care about, such as medicine, engineering, and scientific research, it matters not only what a system concludes but also how it arrives at its conclusion. When mistakes happen—for example, in medical diagnosis and treatment—we need to be able to pinpoint what went wrong: Was the system’s reasoning at fault, did it draw on invalid evidence, or did it make incorrect assumptions? 

According to the author he does not think we reach trustworthy machine intelligence by making system 1 bigger. Scale sharpens intuition, but it does not make intuition more deliberative. Move 37 mattered because a machine held a position, weighed the possible futures, and chose the move its artificial instincts would likely have rejected.

Society needs such creative moves in drug discovery, materials, climate, diagnosis—fields where the board looks nothing like a Go board and nobody hands us the rules. We will get such insights only from systems that reason—systems whose conclusions arise from an auditable sequence of evidence, inference, and belief revision rather than from a convincing story told after the fact. 

3 key takeaways from the article

1. According to the author, on an afternoon in Seoul in March 2016, he watched a program he helped build that put a stone on the fifth line of a Go board in what looked like a gift to its human opponent. Move 37 in game two of the five-game match looked so absurd that some commentators thought it was a programming glitch.  It wasn’t. AlphaGo won the game, ultimately triumphing 4-1 over Lee Sedol, one of the greatest professional Go players of all time. “I thought AlphaGo was based on probability calculation and that it was merely a machine,” Lee said afterwards. “But when I saw this move, I changed my mind. Surely, AlphaGo is creative.” 
2. That is why many accounts of AlphaGo’s match against Lee portray move 37 as a flash of pure machine intuition. But that is a misunderstanding. It was actually AlphaGo’s powers of reasoning that made this creative choice—and these are powers that today’s AI lacks. If we want future AI systems to produce trustworthy results and really novel insights in fields like science and medicine, we need to equip them with genuine reasoning capabilities of this kind.
3. Three shortcomings prevent what chatbots do from qualifying as reasoning (in a way that a scientist might recognize). First, these models typically maintain no explicit, persistent, and inspectable epistemic state. Second, they lack a clean separation between what the system knows and how it manipulates that knowledge. Third, while the chains of thought chatbots produce look like deliberation, research has demonstrated that the bots often concoct them after the fact, reaching an answer by one route but reporting another.

Full Article

(Copyright lies with the publisher)

Topics:  AI & Society

Strategy & Business Model Section

The cash hiding in plain sight

By Kevin Carmody and others | Mckinsey & Company | October 1, 2026

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Cash is king, as the saying goes. Yet CFOs pour enormous energy into managing EBITDA, the language of investors, lenders, and boards, while paying less attention to how much of these earnings ultimately becomes cash. Two areas in particular can absorb more cash than necessary.  Many companies have substantial amounts of cash unnecessarily tied up in working capital.  Companies also tend to commit more cash than necessary to fixed assets.

Addressing these two areas, however, is not as simple as the CFO setting cash targets. Many of the decisions that determine how much working capital a company needs happen beyond the finance function’s immediate reach. CFOs have to discover where excess working capital is building up, determine what needs to change, and get buy-in from leaders across the company to make these changes. Fixed-asset investment presents a different challenge: CFOs need to control unnecessary spending without depriving businesses of the investment they need to grow. The right level of investment varies by business and its role in the company’s strategy.

CFOs’ focus on cash conversion varies widely by country, depending on access to capital markets and the cost of capital.

Why managing cash conversion is harder than it looks.  CFOs who aspire to improve their companies’ cash conversion face two major challenges. First, finance often sees the consequences of decisions affecting working capital only after those decisions have been made elsewhere in the business.  The second and greater challenge is determining how much cash a business should commit in the first place. There is no single optimal level of cash conversion.

Four ways CFOs can improve cash conversion across the business. 

1. Designate a working capital coordinator but keep capital investment with senior leaders.  CFOs can designate a coordinator to work across sales, procurement, supply chain, manufacturing, and finance, helping those teams understand how their decisions affect working capital and making sure they are working toward the company’s targets. The coordinator should be someone with strong financial and analytical acumen who understands the business’s operations.  On the other hand, decisions about capital investment require trade-offs among businesses and need to reflect the company’s broader strategy. Responsibility for those decisions should therefore remain with the CFO and CEO.  A cash center of excellence—a cross-functional team that coordinates working capital decisions across areas such as sales, procurement, and supply chain—can also help companies manage cash across the business rather than function by function.
2. Use data to find trapped working capital.  Companies can use transaction-, invoice, and SKU-level data to identify where cash is getting stuck. AI tools available today can easily identify mismatches between contracted and applied payment terms, predict which invoices are likely to be paid late, and improve demand forecasts to reduce excess inventory and release cash.  Automated alerts, streamlined invoicing, and systems that provide real-time visibility of unbilled work can help companies collect cash sooner.  It can be a challenge for CFOs to reduce unnecessary working capital without creating problems elsewhere in the business.  To help strike the right balance, CFOs can benchmark receivables, payables, and inventory against appropriate industry and regional norms, use these benchmarks to set reasonable targets, and hold business leaders accountable for meeting them.
3. Optimize capital spending rather than simply cutting it.    To get more value from each dollar invested, companies can use a structured approach to capital expenditure management. This includes comparing proposed project costs with similar projects, using a cross-functional review board to challenge whether the proposed scope is necessary, designing projects around the value they need to deliver, and looking for opportunities to reuse existing assets. Cash released from working capital can then be directed toward the capital investments with the greatest potential to create value.
4. Build cash management into how the company operates. CFOs can use incentives to encourage people across the business to consider cash in their decisions. Some companies tie a portion of compensation to cash performance, while others reward cash collected rather than sales booked. Some companies put an internal “price” on capital—for example, 1 percent a month—to make the cost of using it more visible. This means putting an explicit internal cost on decisions that tie up cash, such as giving customers longer to pay or paying suppliers sooner, to discourage employees from making these choices.  Incentives need to be designed carefully. Too much emphasis on cash can encourage underinvestment in growth or behavior toward customers and suppliers that harms the underlying business. Companies can balance cash measures with growth and returns so that improving cash conversion supports the company’s relationships and goals.

3 key takeaways from the article

1. Cash is king, as the saying goes. Yet CFOs pour enormous energy into managing EBITDA, the language of investors, lenders, and boards, while paying less attention to how much of these earnings ultimately becomes cash. Two areas in particular can absorb more cash than necessary:  many companies have substantial amounts of cash unnecessarily tied up in working capital and companies also tend to commit more cash than necessary to fixed assets.  Addressing these two areas, however, is not as simple as the CFO setting cash targets.
2. CFOs who aspire to improve their companies’ cash conversion face two major challenges. First, finance often sees the consequences of decisions affecting working capital only after those decisions have been made elsewhere in the business.  The second and greater challenge is determining how much cash a business should commit in the first place. There is no single optimal level of cash conversion.
3. Four ways CFOs can improve cash conversion across the business.  CFOs can improve cash conversion by making it a consideration in decisions across the business and giving people the tools and incentives to manage it carefully. This requires putting the right people in charge, using detailed data to identify problems, making disciplined capital-investment decisions, and helping employees understand how their everyday choices affect cash.

Full Article

(Copyright lies with the publisher)

Topics:  Cash Management, CFOs, Strategy, Business Model

How CEOs Set the Tempo

By Nitin Nohria | Harvard Business Review Magazine | November-December 2026 Issue

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When Nvidia became the most valuable company in the world in 2024, many assumed its CEO must be operating at a breakneck pace that matched the frenetic speed of the AI revolution its chips were powering. The reality was almost the opposite. Jensen Huang had spent decades building a distinctive internal cadence characterized by deep immersion in product reviews, rigorous group discussions, and an unrelenting emphasis on long-term architecture over short-term reaction. Throughout Nvidia’s evolution, he continued moving at this deliberate pace. That discipline turned out to be its own form of competitive advantage.

Every organization’s tempo—the pace at which its employees make decisions, launch products, tackle problems, and pursue change—is set, intentionally or otherwise, by its CEO.

Tempo is a consequential lever for CEOs because knowing what to do is a necessary starting point—but knowing when and how fast to do it is the key to successful execution.  

A company rarely moves at a single, consistent tempo over a CEO’s tenure. Most CEOs vary their tempo, speeding up or slowing down in accordance with their company’s context, current needs, and planned changes.  CEOs must also attune their tempo to their organization’s response.

The Five Kinds of Tempos.  Drawing on his observations of several hundred CEOs across industries and eras, the author developed a typology of tempos. A memorable adage captures each one and reflects its distinct operating philosophy. Each tempo has its own logic—and its own failure mode.  These tempos are not one-size-fits-all; they’re context-dependent strategies, and choosing among them is a leader’s critical task. Think of them as a set of tools, each of which fits a different set of circumstances.

1. Experimental speed.  “Move fast and break things,” the motto famously associated with Mark Zuckerberg’s early leadership at Facebook, aptly sums up this tempo. CEOs operating at this pace prize speed and disruption above all. 
2. Relentless pressure.  “Stretch; reach to the point where it almost comes unglued” was a catchphrase of Jack Welch during his tenure as GE’s CEO from 1981 to 2001. He described his ideal tempo as one in which the organization always felt slightly on edge, as though it were on the verge of losing control. The result was a productive discomfort, a tenuously controlled speed still lively enough to teeter and thrill.
3. Disciplined urgency.  “Be quick, but don’t hurry,” the legendary UCLA basketball coach John Wooden’s famous mantra, has been embraced by leaders like GE’s Larry Culp. It suggests a different speed: insistently fast yet bridled. The goal is to act quickly and deliberately. With this tempo, CEOs try for a balance between swiftness and precision, operating at a pace that’s brisk but never frantic. Disciplined urgency is less a burst of speed than a steady operating philosophy. It sustains momentum without sacrificing coherence. 
4. Front-loaded deliberation.  The phrase “Slow, slow, quick, quick” is used by ballroom instructors to teach the foxtrot, a dance that originated in the early 20th century. CEOs who use it to describe their preferred pace: They start slowly, taking the time to understand, align, and prepare; then, once they’ve gathered information, they execute quickly and decisively.  
5. Patient compounding.  The adage “Slow and steady wins the race,” popularized by Aesop’s fable of the tortoise and the hare, captures this tempo. It’s often associated with the leadership of Warren Buffett. His signature patient pace defined his decades at Berkshire Hathaway as he focused not on delivering quarterly results but on building enduring value.  This tempo emphasizes consistency, restraint, and the power of steady progress. CEOs who adopt it don’t rush decisions. They avoid fads and build cultures that prioritize sustainability over spectacle.

Several elements including leader’s own temperament, the company’s life stage, The nature of the business challenge, the internal readiness, and external pressures are the critical elements inform the tempo that a company would adopt.

CEOs must learn to sense when the organization is exhausting itself or drifting into complacency, recognize when their demands energize or overwhelm, and shift gears without disorienting their teams. Important for CEO is: read the organization’s pulse, manage the transitions between tempos, and sustain a tempo.

3 key takeaways from the article

1. Every organization’s tempo—the pace at which its employees make decisions, launch products, tackle problems, and pursue change—is set, intentionally or otherwise, by its CEO.  Tempo is a consequential lever for CEOs because knowing what to do is a necessary starting point—but knowing when and how fast to do it is the key to successful execution.
2. Drawing on his observations of several hundred CEOs across industries and eras, the author developed a typology of tempos.  These are: A) Experimental speed – “Move fast and break things,” B) Relentless pressure – “Stretch; reach to the point where it almost comes unglued”, C) Disciplined urgency – “Be quick, but don’t hurry,” D) Front-loaded deliberation – “Slow, slow, quick, quick”, E)  Patient compounding – “Slow and steady wins the race.”
3. These tempos are not one-size-fits-all; they’re context-dependent strategies, and choosing among them is a leader’s critical task. Think of them as a set of tools, each of which fits a different set of circumstances.

Full Article

(Copyright lies with the publisher)

Topics:  Leadership, CEO’s Tempo, Strategy, Business Model

Personal Development, Leading & Managing Section

Why Every High-Performing Leader Needs a Pacer

By Jim Detert and Rahul Bhandari | MIT Sloan Management Review | October 14, 2026

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In sports, pacers reduce the cognitive load of the performer, help the performer better manage their energy, and prevent second-guessing and anxiety that can affect both short-term performance and long-term well-being.

Leaders, too, often create significant performance problems for themselves, their teams, and their organizations by running too fast or too slowly. Leaders may spend far too much time and cognitive energy monitoring one factor at the expense of others that are critical to success. And leaders, too, can suffer from the mental and physical effects of running at the wrong speed, such as losing confidence in their own timing-related decisions.

Nonetheless, many of us focus primarily on what leaders do without paying sufficient attention to the pace at which they do it — and rarely treat the latter as a distinct dimension of leadership effectiveness. As a result, leaders are seldom encouraged to examine whether their natural tempo fits the demands of the moment or to consider who or what could help them recalibrate their personal pace. 

The authors’ findings reveal that pacers help leaders:  Make better decisions and learn more from prior actions, stick to strategic goals and related metrics and timelines, and manage others’ energy levels, motivation, and commitment.

As obvious as the problems caused by fast movers and slow movers may be to the people around them, it’s often hard for a leader to connect the dots. If you want a more well-rounded view, seek input from a few people you believe are most likely to tell you the truth.  You can also take a look in the mirror and consider these signals that you’re running too fast:  Your team regularly asks for clarification on priorities you thought were obvious.  You’ve reversed a major decision within 90 days more than once in the past year.  Key people describe your organization as “exhausting” in engagement surveys or exit interviews.  You feel frustrated that others “can’t keep up.”  And you’ve launched initiatives that died from neglect because the next thing had already arrived.

And look for these signals that you’re running too slow:  Competitors often launch what you’re still “studying.”  Your default response to a new idea is to commission more analysis.  Your team uses phrases like “when leadership decides” or “still waiting for direction.”  You have more than three “pending” strategic decisions older than six months.  And those above you ask you what’s taking so long regarding new initiatives.

Choosing the Right Person.  If you believe that you might benefit from a pacer — or that you need a new one — ask yourself some questions about what you need from them, beyond the tempo adjustment itself.  Having decided what you need from a pacer, determine which qualities are nonnegotiable. High on your list should be evidence that they will be trustworthy — not just protecting information but speaking the truth, whether you want to hear it or not.  You must also have confidence in the pacer’s competence and credibility, or you’ll be unwilling to follow their suggested pace. The best pacer is not necessarily the most senior person. It is someone experienced enough to recognize the appropriate pace, independent enough to speak candidly, and familiar enough with your context to offer more than generic advice.

2 key takeaways from the article

1. In sports, pacers reduce the cognitive load of the performer, help the performer better manage their energy, and prevent second-guessing and anxiety that can affect both short-term performance and long-term well-being.   Many of us focus primarily on what leaders do without paying sufficient attention to the pace at which they do it — and rarely treat the latter as a distinct dimension of leadership effectiveness. As a result, leaders are seldom encouraged to examine whether their natural tempo fits the demands of the moment or to consider who or what could help them recalibrate their personal pace. 
2. The authors’ findings reveal that pacers help leaders:  make better decisions and learn more from prior actions, stick to strategic goals and related metrics and timelines, and manage others’ energy levels, motivation, and commitment.

Full Article

(Copyright lies with the publisher)

Topics:  Leadership, Pacer

Microsoft’s CEO is still haunted by staying silent in a meeting with Bill Gates. Years later, his boss Steve Ballmer taught him how to speak up

By Preston Fore | Fortune | October 15, 2026

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Microsoft CEO Satya Nadella still regrets staying silent in a meeting with Bill Gates around 2008–09—but says the experience shaped his approach to career success.

Satya Nadella spent more than two decades climbing the ladder at Microsoft before becoming CEO in 2014. But looking back, the billionaire recently admitted that he once failed to speak up when he believed the company was making the wrong call—and is still haunted by the memory today.

Nadella recalled a meeting around 2008–09, convened by then-chairman Bill Gates, to review a potential acquisition involving a team working on parallel data warehousing. Nadella, who was leading Microsoft’s search engine team at the time, thought the deal made little sense.

“I looked at it [and thought], ‘It is completely architecturally stupid in that time to go buy that,’” Nadella said on an episode of the Confession With Ali Partovi podcast released this week. But rather than challenge the proposal, he sat through the review without voicing his concerns.

“It haunts me to think that I may be in a review where there’s somebody in that meeting who sees the future a lot more clearly than I do and the other person presenting—but does not really advocate for it,” Nadella said. “And so that’s what I did.”

He left the meeting thinking it had been a “waste of time.” In hindsight, however, the experience became a reminder of how easy it can be to stay quiet rather than challenge a decision—even when you believe speaking up could lead to a better outcome.

Nadella admitted that voicing dissent can be difficult at any company, especially one as large as Microsoft. But a conversation with then–Microsoft CEO Steve Ballmer helped shape his view of leadership: Employees should think beyond their individual responsibilities and take an “owner mindset,” considering what’s best for the company as a whole.

“Life’s not lived by just being so transactional about everything,” Nadella said. “Because at the end of the day, we all perish, and nobody’s going to look at any balance sheet or income statement. It’s going to be like, ‘Hey, how do you live your life?’”

In other words, career success isn’t just about checking off boxes or avoiding conflict. It’s about having the courage to stand up for what you believe is right and helping the company make better decisions—even when that means challenging your colleagues. The key, Nadella said, is learning how to push for the right outcome without putting others on the defensive.

And now that he’s leading the $4 trillion giant, Nadella is building a culture where employees can consistently speak up and push for the best possible outcome.

“To create an environment in which somebody feels like, ‘Wow, I always want to go for the global maxima’—and to have that be a cultural norm is a tough thing to orchestrate.”

Reed Hastings and Ray Dalio say disagreeing at work isn’t disloyal—it’s essential for success

The ability to respectfully challenge a colleague or boss is a trait many business leaders value—but fostering that kind of candor takes deliberate effort.

Netflix cofounder Reed Hastings has acknowledged that disagreeing with a boss or coworker doesn’t come naturally to most people. At Netflix, however, managers are encouraged to “farm for dissent” among employees.

“To disagree silently is disloyal,” Hastings said in 2024. Bridgewater Associates founder Ray Dalio has a similar philosophy, boiling the principle down to a simple rule: “Speak up, own it, or get out.”

3 key takeaways from the article

1. Microsoft CEO Satya Nadella still regrets staying silent in a meeting with Bill Gates around 2008–09—but says the experience shaped his approach to career success.  Satya Nadella spent more than two decades climbing the ladder at Microsoft before becoming CEO in 2014. But looking back, the billionaire recently admitted that he once failed to speak up when he believed the company was making the wrong call—and is still haunted by the memory today.
2. Nadella admitted that voicing dissent can be difficult at any company, especially one as large as Microsoft. But a conversation with then–Microsoft CEO Steve Ballmer helped shape his view of leadership: Employees should think beyond their individual responsibilities and take an “owner mindset,” considering what’s best for the company as a whole.
3. Career success isn’t just about checking off boxes or avoiding conflict. It’s about having the courage to stand up for what you believe is right and helping the company make better decisions—even when that means challenging your colleagues. The key, Nadella said, is learning how to push for the right outcome without putting others on the defensive.  The ability to respectfully challenge a colleague or boss is a trait many business leaders value—but fostering that kind of candor takes deliberate effort.

Full Article

(Copyright lies with the publisher)

Topics:  Leadership, Culture, Communication, Conflict Resolution

Five Ways Higher Ed Leaders Can Center Human Work In AI Adoption

By Dr. Aviva Legatt | Forbes | October 15, 2026

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In the survey behind EDUCAUSE’s 2026 report, The Impact of AI on Work in Higher Education, 94% of 1,960 respondents reported using AI for work in the previous six months. Only 54% knew of policies or guidelines meant to guide that use.

For college leaders, the question is how to turn AI experimentation into a supported part of daily work.  Based on his work the author suggest five practical steps for leaders who want AI to strengthen teaching and student services.

Build A People Plan First.  Organizations prepare carefully for a technology launch while giving much less attention to the people expected to use it.  Before expanding an AI initiative, ask each participating unit to produce a one-page plan naming the work to improve, the people affected, the time available for practice and the person responsible for resolving problems. Decide which existing demands can be reduced while people learn.

Prepared Leaders, Responsible Decisions.  AI doption is a shared responsibility. IT handled the platform, security, access and vendor relationship. Faculty governance, professional learning and support for academic leaders were also essential.  Four questions leaders needed to answer: What can people use AI for? What data should never enter an AI system? Where can people get help? How will the institution know whether AI is improving learning, services or work?  Bring academic leaders, faculty representatives, IT and teaching-support staff together to develop a short decision guide. Test it against situations people are already encountering. Name a contact for unresolved questions and make updating the guide someone’s ongoing responsibility.

Turn Training Into Supported Work Sessions.  Faculty preparation deserves its own investment. In the 2026 report The AI Challenge, the American Association of Colleges and Universities and Elon University found that 68% of faculty respondents rated their institution as not very or not at all prepared to help faculty teach and mentor effectively with generative AI. The nonrepresentative survey points to a concern leaders should explore on their own campuses.  A practical advice could be: Schedule a working session during supported work time, with a specific outcome such as a reviewed rubric or set of teaching examples. Before participants leave, book a follow-up to discuss what they used, what needed revision and where they still need help.

Support Peer Networks And Invite Questions.  A shift could be achieved when faculty and staff began seeing AI practices discussed by their professional associations, disciplinary peers and colleagues at other institutions. That outside validation gave the work credibility. Hands-on sessions and recognition then helped people take part.  A practical advice could be: Give a small cross-departmental peer network protected time, a defined purpose and a route to people who control resources. Ask members to share unsuccessful experiments alongside useful ones. Recognize the work of helping colleagues, and include thoughtful skeptics in shaping the agenda.

Measure Changes And Act On The Feedback.  Tracking licenses, logins and aggregate usage are early indicators. They show whether people have access and are experimenting. They do not establish whether learning, services or decisions are improving.  Workload is one place to look beyond the promise of time savings.  A practical next step: Establish a baseline before a pilot and set a review date. Ask participants to bring an example of the work, an account of the time involved and any concerns that remain. Use that evidence to decide whether to continue, revise or stop the approach, and tell participants what changed because of their feedback.

3 key takeaways from the article

1. In the survey behind EDUCAUSE’s 2026 report, The Impact of AI on Work in Higher Education, 94% of 1,960 respondents reported using AI for work in the previous six months. Only 54% knew of policies or guidelines meant to guide that use.
2. For college leaders, the question is how to turn AI experimentation into a supported part of daily work.  Based on his work the author suggests five practical steps for leaders who want AI to strengthen teaching and student services:  Build A People Plan First.  Make AI doption as a shared responsibility.  Turn Training Into Supported Work Sessions.  Support Peer Networks And Invite Questions.  And Measure Changes And Act On The Feedback.
3. The human work of AI adoption is visible in these examples: A chair clarifying expectations, a colleague helping revise an assignment, a faculty member questioning an output and a leader making time for practice. An AI strategy should make that work easier to carry out and worth sustaining. Its value should become visible in what faculty, staff and students are able to do.

Full Artilce

(Copyright lies with the publisher)

Topics:  Leadership & AI, AI Adoption in Academics

Entrepreneurship Section

Why Founders Are Taking Their Marketing Offline in an AI-Saturated World

By Ash Kumra | Inc | October 15, 2026

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The best growth strategy in 2026 happens offline. During the 2026 SF Tech Week, thousands of founders and venture investors brought massive energy back to San Francisco across hundreds of decentralized events spanning AI, longevity, fundraising, media, and content creation.  Tech and media companies transformed lounges, warehouses, conference spaces, and local espresso bars into vibrant hubs for builders to connect and share ideas.  The investment paid off in a surprising way. 

Four ways:

1. Earn trust through original voices.  Search discovery is changing fast, and faceless corporate blogs are losing ground. As platforms filter out automated AI slop, audiences are moving toward recognizable human voices across newsletters and podcasts. People are following voices they trust across both established and new platforms, changing how influence is built and attention is earned.    For brands and communicators, that means being active readers, listeners, and participants in those communities, and understanding what people value before asking for their attention.  Stop treating your marketing channels like a one-way megaphone. Before you ask a community for its attention, show up as an active listener and put a real human voice behind your brand. 
2. Ditch the sales pitch and build your own stage.  Most founders treat live events like a trapdoor into a sales funnel. Customers can smell that desperation from across the room. If you have zero marketing budget, stop trying to outspend corporate giants on digital ads. Build a local room for your ideal customers, hand them the microphone, and judge your success by a single metric. 
3. Engineer unscripted offline connections .  Online content builds awareness. It rarely closes a major partnership by itself. In-person experiences build relationships. Even in an industry built online, partnerships still run on trust. That trust often comes from time spent together and conversations you didn’t plan.  Never use a live gathering to recreate what already happens on a screen. The real magic is the unplanned introduction sitting next to you or the dinner conversation that turns into a signed deal. 
4. Put people back at the center of your growth.  Software scales your reach. Only humans build loyalty.  Open your calendar for next month right now. Book a back room at a local restaurant for 15 of your best customers and introduce two builders who can solve each other’s hardest problems. You will build more trust over two hours of shared plates than you will in six months of digital ads. 

2 key takeaways from the article

1. The best growth strategy in 2026 happens offline. During the 2026 SF Tech Week, thousands of founders and venture investors brought massive energy back to San Francisco across hundreds of decentralized events spanning AI, longevity, fundraising, media, and content creation.  Tech and media companies transformed lounges, warehouses, conference spaces, and local espresso bars into vibrant hubs for builders to connect and share ideas.  The investment paid off in a surprising way. 
2. Four reasons why  founders are taking their marketing offline in an AI-saturated world:  A) Stop treating your marketing channels like a one-way megaphone. Before you ask a community for its attention, show up as an active listener and put a real human voice behind your brand. B)  Ditch the sales pitch and build your own stage.  Build a local room for your ideal customers, hand them the microphone, and judge your success by a single metric.  C)  Engineer unscripted offline connections .  Online content builds awareness. It rarely closes a major partnership by itself. In-person experiences build relationships. Even in an industry built online, partnerships still run on trust.  And D)  Put people back at the center of your growth.  Software scales your reach. Only humans build loyalty.

Full Article

(Copyright lies with the publisher)

Topics:  Entrepreneurship, Startups

The More You Automate, the More Your Business Needs Human Leadership. Here’s Why.

By Sheldon Yellen | Entrepreneur | October 14, 2026

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In 2026, automation is no longer something leaders are preparing for — it’s something they’re already managing.   Yet the human side of work is telling a more complicated story. While employees who use AI daily report gains in productivity, pay and job security in PwC’s 2025 Global Workforce Hopes & Fears Survey, many employees say they feel worried about how AI may be used in the workplace going forward. Efficiency is accelerating. Confidence and clarity are not.

This tension is the leadership challenge of the moment. As machines take on more tasks, the qualities that define effective leadership aren’t becoming obsolete — they’re becoming more visible. Judgment. Empathy. Trust. The ability to steady people when the environment is anything but steady.

Technology may be scaling faster than ever. Human leadership is scaling in importance right alongside it.

Why leadership matters more when systems scale.  Automation excels at process. Leadership exists for the moments when process breaks down. Entrepreneurs know this instinctively. Systems handle the predictable. People lead through uncertainty.  As AI absorbs more routine work, leaders are left with what has always been hardest to automate: making decisions with incomplete information, aligning teams during disruption and earning trust when outcomes aren’t guaranteed. Those moments don’t call for more dashboards or better prompts. They call for leaders who can think clearly, communicate calmly and keep people focused without losing sight of performance.

That’s where human leadership stops being a philosophy and becomes a competitive advantage.

Why leadership matters:

1. Automation replaces tasks — not trust.  AI can optimize workflows, analyze patterns and surface insights faster than any human ever could. What it cannot do is build trust. Teams still look to leaders for reassurance, direction and meaning when change accelerates.  Leaders who explain decisions, acknowledge concerns and stay visible during change create stability that technology alone cannot provide.
2. Empathy is becoming a performance multiplier.  Empathy is often misunderstood as emotional accommodation. In reality, it’s a necessary, strategic capability. Leaders who understand how their teams are experiencing change make better decisions — especially under pressure.
3. Companies that scale humanity outperform those that scale control.  As organizations grow, it’s tempting to rely on systems, metrics and automation to maintain order. Structure matters. But structure alone doesn’t preserve culture.  Some of the most resilient organizations invest heavily in psychological safety, structured communication and shared accountability. These elements allow teams to surface issues early, adapt quickly and recover faster when things go wrong.
4. In a fully automated world, values become the operating system.  As automation handles more decisions and processes, leaders must be clear about which decisions should never be automated. Values fill that gap. They guide judgment when data conflicts, shape behavior when incentives misalign and help teams navigate ambiguity without waiting for instructions.

3 key takeaways from the article

1. In 2026, automation is no longer something leaders are preparing for — it’s something they’re already managing.   Yet the human side of work is telling a more complicated story. While employees who use AI daily report gains in productivity, pay and job security in PwC’s 2025 Global Workforce Hopes & Fears Survey, many employees say they feel worried about how AI may be used in the workplace going forward. Efficiency is accelerating. Confidence and clarity are not.  This tension is the leadership challenge of the moment. As machines take on more tasks, the qualities that define effective leadership aren’t becoming obsolete — they’re becoming more visible. 
2. Automation excels at process. Leadership exists for the moments when process breaks down. Entrepreneurs know this instinctively. Systems handle the predictable. People lead through uncertainty.  As AI absorbs more routine work, leaders are left with what has always been hardest to automate: making decisions with incomplete information, aligning teams during disruption and earning trust when outcomes aren’t guaranteed. Those moments don’t call for more dashboards or better prompts. They call for leaders who can think clearly, communicate calmly and keep people focused without losing sight of performance.
3. Why leadership matters:  Automation replaces tasks — not trust, empathy is becoming a performance multiplier, companies that scale humanity outperform those that scale control, and in a fully automated world, values become the operating system.

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Topics:  Entrepreneurship, Leadership in the age of AI