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Most workplace frustration doesnt come from a lack of effort or commitment. It comes from expectations that werent metnot because people failed to try, but because those expectations were never clearly stated or truly understood. In our organizational research over the past 30 years, weve seen this pattern repeatedly: when expectations are unclear, trust in leadership and collaboration begins to drop. When this happens, the frustration that follows is real. But the deeper cost is often invisibletrust begins to erode. This dynamic is increasingly common. Roles evolve, priorities shift, and teams are asked to move faster with less certainty. People continue to work in good faith, investing energy and time into what they believe is needed. They solve problems based on experience and what has worked before. When theyre later told the outcome fell short, the issue is more than disappointment. Its disorientation. People begin to question their judgment and whether they can reliably meet expectations going forward. Over time, that uncertainty weakens collaboration and trustthe sense that people are truly working with one another toward a shared outcome. Consider a common scenario. A leader asks a team member to move this forward quickly. The work gets done on time, but when its delivered, the leader is disappointed. What they needed wasnt just speed, but alignment with a broader strategyor more collaboration with another team before finalizing decisions. The expectation wasnt ignored; it was incomplete. The leader never named the strategy, nor the need. In the absence of clarity, effort went in one direction while expectations lived in another. Over time, moments like this teach people to hesitate, over-check, or disengage because trust in their understanding has been shaken. Heres how to break that cycle. Set expectations explicitly This means being clear not just about tasks or deadlines, but about what success looks like, along with what constraints or tradeoffs are in play. It also means being realisticconsidering current priorities and what support may be required to do the work well. Rather than assuming clarity, make it visible. Instead of saying, Can you move this forward? try something more specific: Id like to review my expectations with you for clarity. What Im trying to accomplish is [outcome], and what matters most here is [speed, quality, alignment, or collaboration]. I need this delivered by [timeframe], and I want to make sure thats realistic given everything else youre managing. Setting expectations this way signals partnership, not control. It shows consideration for others and consistency in how expectations are applied. It also opens the door to an essential question: What do you need from me? Asking that upfront helps leaders provide the right support and ensure people are set up to succeed. Confirm understanding before work begins Shared history and good intentions can create the illusion of alignment. Leaders may believe expectations are obvious, that others understand what matters most, or that capable people will speak up if something is unclear. In effect, clarity is assumedand theres often an unspoken expectation that people will initiate their own understanding. In reality, many people hesitate to ask clarifying questions, especially in environments shaped by urgency or rapid change. They dont want to slow things down, appear uninformed, or challenge direction. Trust is strengthened when leaders treat clarity as something to be created together, not something to be inferred. Rather than assuming alignment, invite it. That might mean asking someone to reflect back what they heard or encouraging them to surface concerns. For example, instead of asking, Any questions?which often shuts conversation downtry something more specific: Before you get started, Id like to make sure were aligned. What are you hearing matters most here? or What concerns or constraints do you see? And if youre the person receiving the instruction, this is a moment to step into ownership. Asking a clarifying question doesnt signal uncertainty: it signals engagement. Questions like, Can I confirm my understanding of what success looks like? or What would be most helpful from you as I work on this? both clarify expectations and demonstrate initiative. Managers notice this. It builds confidence on both sides and reduces the risk of misalignment later. Renegotiate expectations when reality changes Because it always does. Expectations can grow larger than anticipated, take longer than expected, or become more complex as work unfolds. New priorities emerge. Constraints surface. Resources shift. When these changes go unaddressed, people continue operating on outdated assumptions, drifting further out of alignment. Renegotiation isnt a failure of planning; its a leadership and partnership responsibility. If youre receiving an expectation and recognize that something has changed, bring it up immediately. Share what youre seeing, explain whats different, and be explicit about the support that would help you succeed. That might sound like:As Ive been working on this, Im realizing the scope is larger than expected because [reason]. Im concerned I wont be able to meet the original expectation as defined. Id like to talk about what supportor what adjustment to scope or timingwould help me complete this successfully. Asking for support isnt a sign of weakness; its a sign of ownership. If youre the one who set the expectation, make support visible. Ask questions like: Are you running into any challenges? Is there anything I need to be aware of thats creating a barrier to progress? or What support would help you get back on track? These questions normalize course correction and reinforce that success is shared. Renegotiation replaces disappointment with dialogue. It keeps people aligned to what matters now, not what mattered when the expectation was first set. And it reinforces a critical truth: trust isnt built by pushing through in silence, but by adapting together when reality changes. Managing expectations is one of the most overlooked ways trust is built at work. When managers make expectations visible, confirm understanding, and adapt together as needs change, they create more than alignmentthey create confidence. People know whats expected, why it matters, and where to ask for support when reality shifts. In a world defined by constant change, that kind of partnership isnt a luxury. Its a management responsibility.
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E-Commerce
When social psychologist Jonathan Haidt published The Anxious Generation in March 2024, his core proposalthat children should be kept off social media until at least age 16, with tech companies bearing the burden of enforcementwas treated by many as aspirational, even quixotic. The tech industry dismissed it. Libertarian critics called it paternalistic overreach. Skeptics questioned the evidence base. That was then. In barely two years, Haidt’s “radical” idea has become something close to a global consensusa textbook example of what political scientists call the “Overton Windowone that’s shifted at extraordinary speed. The Overton Window describes the range of ideas that are considered politically acceptable at any given time, ranging from unthinkable to popular and eventually to policy. Ideas outside the windowno matter how sensibleget dismissed as too extreme, too impractical, or too politically risky to touch. But when conditions change, the window can move, sometimes gradually and sometimes with startling speed, pulling yesterday’s fringe idea into today’s mainstream. That is exactly what has happened with children and social media. Politicians everywhere are now racing to get on the right side of a window that has moved decisively. The Floodgates Have Opened Consider what has happened just since late 2025. Australia led the charge, enacting an outright ban on social media for children under 16 that took effect in December 2025, with monetary penalties falling squarely on the platformsnot on parents or kids. France has passed a bill banning social media for children under 15. Denmark secured cross-party support for a similar ban, expected to become law by mid-2026. Spain, Germany, Malaysia, Slovenia, Italy, and Greece are all moving in the same direction. In the United States, where bipartisan agreement on anything feels miraculous, the Kids Off Social Media Act has attracted co-sponsors from both partiesSen. Brian Schatz (D-HI) alongside Sen. Ted Cruz (R-TX), and Chris Murphy (D-CT) alongside Katie Britt (R-AL). Virginia enacted a law effective January 2026 limiting under-16 social media use to one hour per day unless parents opt in. Over 45 states have pending legislation. And in the U.K., a January 2026 government consultation is explicitly considering a social media ban for children, after the House of Lords defeated the government to insert an under-16 ban into the Children’s Wellbeing and Schools Bill. This is no longer a debate about whether to act. It’s a debate about the details. Why the Window Moved So Fast Several forces converged to make this shift possible. First, mounting evidence. Haidt marshaled data showing that since the early 2010sprecisely when smartphones and social media became ubiquitous among teensrates of anxiety, depression, self-harm, and suicide among young people have surged across the developed world. The patterns are strikingly consistent across countries and cultures. As Haidt puts it: We “over-protected children in the real world and under-protected them online.” Second, personal stories that broke through the noise. Australia’s ban originated partly from a mother’s letter to Prime Minister Anthony Albanese about her 12-year-old daughter’s suicide following social media bullying. At the U.N. General Assembly in September 2025, a mother’s speech about her daughter’s “death by bullying, enabled by social media” won support from world leaders across continents. Data persuades policymakers; stories move publics. Third, the collective action problem became too painful to ignore. Haidt nailed this insight: Individual parents feel powerless against platforms engineered by billions of dollars of design expertise to maximize engagement. No single family can opt out without socially isolating their child. This is precisely why governments need to shift the responsibility to the platforms. When enforcement becomes the tech companies’ problemnot the parents’ problemthe collective action trap breaks. Fourth, early results from related interventions are encouraging. Arkansas’ phone-free-school pilot program showed a 51% drop in drug-related offenses and a 57% decline in verbal and physical aggression among students within the first year. Results like these give politicians the cover they need to act boldly. The Strategic Lesson For those of us who study how change happens, this is a master class. An idea that seemed politically impossible in early 2024 has become politically inevitable by early 2026. That’s the speed at which Overton Windows can move when lived experience, accumulating evidence, moral urgency, and a clear articulation of the problem all align. Note, too, where the burden of proof has shifted. Two years ago, advocates for restricting children’s social media access had to justify intervention. Today, it is the tech companies and their defenders who must explain why children should continue to have unrestricted access to platforms designed to be addictive. That reversalthe shift in who must justify whatis the surest signal that an Overton Window has decisively moved. It is further set against the backdrop of the first set of legal challenges to the platforms business models, arguing that their designers have deliberately designed their products to be harmful to maximize their profits. What Comes Next Haidt, a professor of ethical leadership at New York University, didn’t create this movement alonemillions of anxious parents, grieving families, and alarmed educators did. But he gave it a framework, a language, and a set of actionable proposals. And now, politicians everywhee are scrambling to catch up with what parents already knew in their bones: that we handed our children’s attention, self-worth, and mental health to companies that optimize for engagement, not well-beingand that better guardrails, uniformly enforced, are essential.
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E-Commerce
For decades, weve been told that the smartest organizations are data-driven. The phrase carries moral weight. To be guided by data is to be serious, rational, modern. If youre not, youre seen as ideological or sentimental. In the workplace, quantification has become synonymous with credibility and competence. And yet, the more data we accumulate, the less certain we seem to be that we are making better decisions. Theres a paradox. Organizations are drowning in dashboards, KPIs, performance metrics, behavioral traces, biometric indicators, predictive scores, engagement rates, and AI-generated forecasts. We have more data than we know what to do with. We pretend that the mere presence of data guarantees clarity. It does not. Thats data hubristhe arrogant belief that because something can be measured, it can be mastered. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2026\/01\/PhotoLVitaud-169.jpg","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2026\/01\/PhotoLVitaud-11.jpg","eyebrow":"","headline":"\u003Cstrong\u003ESubscribe to Laetitia@Work\u003C\/strong\u003E","dek":"Women power the worlds productivity its time we talked more about it. Explore a woman-centered take on work, from hidden discrimination to cultural myths about aging and care. Dont miss the next issue subscribe to Laetitia@Work.","subhed":"","description":"","ctaText":"Learn More","ctaUrl":"http:\/\/laetitiaatwork.substack.com","theme":{"bg":"#2b2d30","text":"#ffffff","eyebrow":"#9aa2aa","subhed":"#ffffff","buttonBg":"#3b3f46","buttonHoverBg":"#3b3f46","buttonText":"#ffffff"},"imageDesktopId":91472264,"imageMobileId":91472265,"shareable":false,"slug":""}} The Illusion of Objectivity In executive meetings, a slide filled with graphs and percentages signals authority. Numbers appear to silence dissent and create the impression of neutrality. But behind every dataset lies a series of human decisions: what to measure, how to measure it, what to ignore, and how to interpret it. Metrics are never neutral; they are constructed within particular frameworks, assumptions, and interests. Too often, data is used not to inform decisions but to justify them after the fact. It lends post-hoc legitimacy to strategies already chosen, wrapping subjective choices in the language of objectivity. Take creative industries, for example, where algorithms supposedly predict success. Netflix built part of its reputation on data sophistication, claiming to understand viewers better than traditional studios ever could. Yet insiders have described how metrics shift, interpretations vary, and executives selectively highlight numbers that support their preferred projects. The result can be content engineered to be watchable but forgettableoptimized for fragmented attention rather than lasting cultural impact. Also, the problem is that data reflects the past. It captures what has already worked, not what might resonate tomorrow. It struggles to grasp the emerging mood of a societythe intangible zeitgeist that makes a story, product, or idea feel timely. Focusing on backward-looking indicators institutionalizes mediocrity. When Data Confirms What We Already Know The same pattern appears in corporate HR, where the rise of people analytics promised revolutionary insight into engagement and performance. Sensors track badge swipes, algorithms map collaboration networks, and predictive models estimate attrition risk. After enormous investment, companies often discover that good managers matter, that employees dislike micromanagement, and that people leave when they feel undervalued. These findings are hardly revolutionary. Some of the most celebrated data-driven insights simply confirm what experienced people already suspected. There is a widening gap between the sophistication of measurement tools and the banality of many of the conclusions they generate. In open, messy environments, organizations often produce vast quantities of noise and mistake it for knowledge. Healthcare offers another revealing example. Radiology once seemed perfectly suited for AI transformation: millions of standardized images and clear diagnostic categories. Early systems performed impressively on routine cases. However, real-world practice quickly exposed limitations. Radiology reports are filled with cautious phrasescannot rule out, clinical correlation recommendedthe product of decades of medico-legal prudence. Algorithms struggle with this ambiguity and may flag excessive urgencies because they cannot distinguish legal caution from genuine clinical risk. More fundamentally, medicine is defined by exceptions. AI may handle 90% of common cases effectively, but it is the rare and atypical cases that truly test expertise. A seasoned radiologist can reason through an unprecedented situation; an algorithm remains confined to its training data. Abundant historical data does not eliminate the variability of reality. The Blind Spots of Overconfidence One of the most dangerous effects of data hubris is overconfidence. When decisions are backed by numbers, leaders may lose caution. Digital traces capture clicks and transactions but not informal conversations. Not everything meaningful leaves a digital record, and dashboards rarely display their own blind spots. We face what we don’t know we don’t know. In his work on uncertainty, Vaughn Tan distinguishes between riskwhere probabilities are calculableand deeper forms of not-knowing where probabilities themselves are unknown. Treating all uncertainty as if it were calculable risk is a category error. Mathematics cannot resolve questions about emerging values and unprecedented events. The COVID-19 crisis illustrated this confusion vividly. Some leaders relied heavily on models built from previous diseases, assuming that all unknowns were simply risk variables awaiting calculation. In reality, many were genuine uncertainties that required experimentation, humility, and adaptive learning. From Data Mastery to Uncertainty Literacy Data hubris can also extend into one’s personal life through the quantified self movement. Wearables measure sleep cycles, heart rate variability, step counts, and glucose levels, promising unprecedented self-knowledge. But more information does not always mean better well-being. In medicine, excessive testing increases the risk of false positives, detecting anomalies that may never cause harm but may trigger anxiety and invasive follow-ups. Constant self-tracking can fuel obsession. Instead of asking whether we feel rested or hungry, we defer to numerical indicators, thus ignoring more intuitive signals (feeling hungry, rested . . .). None of this means we should reject data. Of course not. Data is invaluable. But it must sit within a broader understanding of how knowledge is actually producedthrough field observations, expert judgment, and lived experience. Data demands interpretation. It requires humility nd open conversations. What is missing here? What assumptions shaped these metrics? Who decided what to measureand why? In genuinely uncertain environments, small, reversible experiments often outperform grand predictive models. Instead of pretending to know, organizations can probe, learn, and adapt. Intuitionfar from being irrationalrepresents compressed experience accumulated over time. Above all, leaders must remain humble in the face of unknown unknowns. The most sophisticated analytics cannot absolve decision-makers of responsibility. As sensors multiply and AI systems proliferate, the temptation to equate measurement with mastery will only intensify. Beware of data hubris. Knowing that we do not fully know is the foundation of sound judgment in a world that remains irreducibly complex. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2026\/01\/PhotoLVitaud-169.jpg","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2026\/01\/PhotoLVitaud-11.jpg","eyebrow":"","headline":"\u003Cstrong\u003ESubscribe to Laetitia@Work\u003C\/strong\u003E","dek":"Women power the worlds productivity its time we talked more about it. Explore a woman-centered take on work, from hidden discrimination to cultural myths about aging and care. Dont miss the next issue subscribe to Laetitia@Work.","subhed":"","description":"","ctaText":"Learn More","ctaUrl":"http:\/\/laetitiaatwork.substack.com","theme":{"bg":"#2b2d30","text":"#ffffff","eyebrow":"#9aa2aa","subhed":"#ffffff","buttonBg":"#3b3f46","buttonHoverBg":"#3b3f46","buttonText":"#ffffff"},"imageDesktopId":91472264,"imageMobileId":91472265,"shareable":false,"slug":""}}
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