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When Accenture announced plans to lay off 11,000 workers who it deemed could not be reskilled for AI, the tech consulting giant framed the decision as a training issue: some people simply cannot learn what they need to learn to thrive in the world of AI. But this narrative fundamentally misunderstandsand significantly underplaysthe deeper challenge. Doug McMillon, the CEO of Walmart, pointed to this bigger challenge recently when he said, AI is going to change literally every job. Now, if this turns out to be true, every role will have to be reimagined. And when every role changes, this is more than a change in each job or even a specific field. It implies a profound and systemic change in the nature and meaning of the work itself. For instance, when a customer service reps job changes from answering questions to managing AI escalations, they are no longer doing old-fashioned customer servicethey are doing AI supervision in a customer service context. Their supervisor isnt managing people anymore; they are orchestrating a hybrid intelligence system composed of humans and AI. And HR isnt evaluating communication skills; they are assessing humanAI collaboration capacity. The job titles remain the same, but the actual work has become something entirely different. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/creator-faisalhoque.png","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/faisal-hoque.png","eyebrow":"","headline":"Ready to thrive at the intersection of business, technology, and humanity?","dek":"Faisal Hoques books, podcast, and his companies give leaders the frameworks and platforms to align purpose, people, process, and techturning disruption into meaningful, lasting progress.","ctaText":"Learn More","ctaUrl":"https:\/\/faisalhoque.com","theme":{"bg":"#02263c","text":"#ffffff","eyebrow":"#9aa2aa","buttonBg":"#ffffff","buttonText":"#000000"},"imageDesktopId":91420512,"imageMobileId":91420514}} You cannot prepare people for this disruption by sending them to a three-day workshop on how to prompt more effectively. When the change is as systemic as this, the real question is not whether individuals can be separately reskilled. It is whether organizations can transform themselves at the scale and speed AI demands. Two types of transformation To understand the reskilling demands created by AI transformation, it helps to distinguish between bounded and unbounded transformations. Bounded transformations are organizational changes that follow a predictable path, starting from specific areas of operation with well-defined capabilities to develop. They unfold in distinct stages, allowing companies to master one phase before moving to the next. Unbounded transformations, on the other hand, are sweeping changes that affect all parts of an organization at the same time, with no single point of origin. Because they simultaneously alter job functions, competencies, processes, and performance measures in interconnected ways, they can’t be tackled piecemeal or rolled out sequentiallythey demand a holistic, coordinated strategy. The AI revolution is a paradigmatic example of an unbounded transformation, as it fundamentally reshapes how we think, work, and create value across every industry, function, and level of the organizationredefining not just individual tasks but the very nature of human contribution to work itself. And that means that it is not enough to simply reskill employees for AI. Instead, business leaders will need to transform the entire ecosystem of workthe infrastructure, the interconnected roles, and the culture that enables change. And they will often need to do all of this across the entire organization at oncenot sequentially, not department by department, but everywhere simultaneously. There are three key dimensions that organizations need to address if they are to successfully transform themselves and reskill their workers for the AI revolution. 1. Rebuilding the infrastructure of work Most reskilling budgets cover workshops and certifications. Almost none cover what actually determines success: rebuilding the systems people work within. For example, AI often now handles routine inquiries in contact centers while humans tackle complex cases. As McKinsey argues, successfully implementing this shift demands far more than teaching agents to use AI tools. Businesses must rethink operating models, workflows, and talent systemscreating escalation protocols that integrate with AI triage, metrics that measure human-AI collaboration rather than individual ticket counts, and training that builds the judgment needed to handle the ambiguous cases that AI cant decide. Career paths and team structures must evolve to support hybrid human-AI capacity. Very little of this work is training in any classical senserather, it is organizational architecture and system-building. And the organizations that do not undertake this work will find that their AI reskilling programs will inevitably fail. 2. The network effect: why roles must transform together Organizational roles do not exist in isolation. They are interconnected nodes in an organizational network. When AI transforms one role, it also transforms every other role it touches. For example, when AI chatbots handle routine customer inquiries, frontline agents typically shift to managing only complex situations, which may be more emotionally charged for the client. This immediately transforms the role of their trainers and coaches, who must now redesign their curriculum away from teaching efficient delivery of scripted informational responses toward teaching de-escalation techniques, empathy skills, and complex judgment calls. Further, team supervisors will now no longer be able to evaluate performance based on call handle times and throughputthey must instead develop new frameworks for assessing emotional intelligence and problem-solving under pressure. The result is that holistic and comprehensive role redesign is essential if employees are to be successfully reskilled for AI. AI transformation requires synchronized change across interconnected roleswhen one piece of the network shifts, every connected piece must shift with it. 3. Cultural transformation As Peter Drucker almost said, culture eats reskilling for breakfast. It is crucial for organizations to understand that cultural transformation is not a nice-to-have follow-on that comes after technical change. Rather, it is the prerequisite that determines whether technical change takes root at all. Without the right culture, training budgets become write-offs and transformtion initiatives become expensive failures. Consider a financial services firm training analysts on AI tools. If the culture punishes AI-assisted mistakes more harshly than human mistakes, adoption dies. If success metrics still reward heroic individual effort, collaboration with AI will be undermined. If executives do not visibly use AI and acknowledge their own learning struggles, teams will treat it as optional theater rather than strategic imperative. The culture that enables AI reskilling is one built on curiosity, not certainty. This culture prizes experimentation over perfection and treats failure as data, not disgrace. Indeed, because AI tools evolve so quickly, the defining capability of an AI-ready culture is not mastery but continuous learning. Relatedly, psychological safety becomes essential: people must feel free to test, question, and sometimes get it wrong in public. And the signal for all of this comes from the top. When leaders openly use AI, admit what they dont know, and share their own learning process, they make exploration permissible. When they do not, fear takes its place. In short, successful AI cultures dont celebrate competencethey celebrate learning. Conclusion AI reskilling is not a training challengeit is an organizational transformation imperative. Companies that recognize this will rebuild their infrastructure, redesign interconnected roles, and cultivate learning cultures. Those that dont will keep announcing layoffs and blaming workers for failures that were always about systems, not people. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/creator-faisalhoque.png","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/faisal-hoque.png","eyebrow":"","headline":"Ready to thrive at the intersection of business, technology, and humanity?","dek":"Faisal Hoques books, podcast, and his companies give leaders the frameworks and platforms to align purpose, people, process, and techturning disruption into meaningful, lasting progress.","ctaText":"Learn More","ctaUrl":"https:\/\/faisalhoque.com","theme":{"bg":"#02263c","text":"#ffffff","eyebrow":"#9aa2aa","buttonBg":"#ffffff","buttonText":"#000000"},"imageDesktopId":91420512,"imageMobileId":91420514}}
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E-Commerce
AI is often sold as the ultimate productivity hack. Just imagine: the report you dreaded writing, drafted in seconds. The spreadsheet you didnt want to touch, analyzed instantly. The code that once took you days, generated before lunch. For professionals who already struggle with overwhelm and the daily battle to manage their time, AI feels like salvation. At Lifehack Method, where we help clients master time management and build systems for living fulfilling, balanced lives, we see this every day. People are desperate for tools that promise to take the weight off their shoulders. AI seems like the next logical step in that search. Theres no denying the dopamine hit of a blank page suddenly filling with words or lines of code. AI gives the illusion of acceleration, and in the moment, that feels like productivity. Youre doing something, and the grind of starting from scratch is gone. But theres a problem: faster doesnt always mean more productive, and saved time doesnt always translate into better outcomes. The real test of productivity isnt how quickly you start, but whether you finish with work thats accurate, useful, and aligned with your goals. Thats where cracks begin to show. AI can make you feel productive without actually being productive A recent MIT study found that 95% of generative AI pilots in companies produced little to no measurable impact on profit and loss, despite $3040 billion in enterprise investment, because most GenAI systems do not retain feedback, adapt to context, or improve over time. In other words, the time people think theyre saving isnt translating into organizational productivity. A similar story shows up among software developers in a recent controlled study. After trying AI coding assistants, developers estimated they experienced 1030% productivity gains. But in actuality, experienced coders took 19% longer when using AI tools on codebases they knew well. They not only lost time in practicethey walked away convinced theyd saved it. Thats a dangerous mismatch. McKinseys research adds nuance: AI can indeed help with repetitive or shallow work tasks like painstakingly referencing large documents or analyzing invoices. But the productivity boost shrinks when tasks are complex or require deep, sustained attention. In other words, AI may help you clear the easy stuff off your plate, but its harder to get it to do the work that really moves the needle. Why is that? The 90% mirage Heres the paradox of AI: it often gets you 90% of the way there, which feels like a huge time savings. But that last 10%checking for errors, refining details, making sure it actually workscan eat up as much time as you saved. The most common mistake is assuming 90% is good enough and shipping it. Jeff Escalante is an engineering director at Clerk, puts it bluntly: Anything that you ask it to do, it will more than likely end up making one or more mistakes in what it puts out. Whether thats fabricating statistics, or making up things that are not real . . . or writing code that just doesnt work, he says. Its something that is really cool and really interesting to use, but also is something that you have to know you cant trust and cant rely on. It needs to be reviewed by an expert before you take what it puts out and deliver it, [especially if] its sensitive or important. His advice? Treat AI like an intern: great for low-level work, occasionally useful when given training, but absolutely not someone youd send into a client meeting unsupervised. And if youre hoping eventually itll be foolproof, think again. Jeff Smith, PhD is the founder of QuantumIOT and a serial technology entrepreneur. He says its important to think of the AI as an assistant because it still makes mistakes and it will make mistakes for a long time. Its probabilistic, not deterministic. If youre a domain expert, you can spot and fix that last 10%. If youre not, you risk handing off work that looks polished but is quietly broken. That means wasted time correcting mistakesor worse, reputational damage. Many ambitious employees eager to level up with AI end up doing the opposite: walking into client pitches with beautiful decks full of hallucinated insights and an action plan that doesnt match the Statement of Work. So should we throw AI out the window? Not exactly. But definitely stop treating it like a self-driving car and more like a stick shift: powerful, but only if you actually know how to drive. How to use AI without losing control of your time The most productive people dont hand over the keys to AI. They stay in the drivers seat. Here are a few rules emerging from early research and expert guidance: Be the subject matter expert. If you dont know what excellent looks like, AI can lead you astray. The time you save drafting could vanish in endless rounds of corrections. Use AI as a draft partner, not a finisher. The sweet spot is breaking inertiahelping you brainstorm, sketch a structure, or generate a starting point. Iterative prompting is the key to better AI outputs, but the final say will always belong to you. Automate the shallow, protect the deep. Let AI knock out routine, low-value worksummaries, boilerplate, admin, certain emails. Guard your deep-work hours for the kind of thinking that actually moves the needle. Real productivity isnt about speed; its about aligning time with your top priorities. Track actual outcomes. Dont confuse the feeling of speed with actual results. Measure it. Did the AI really shave an hour off your workflowor just generate more drafts to wade through? And keep some perspective: were still in the early-adopter stage. As Smith puts it, Itll be a bit of a rocky road [but] theres tons of great tools that are going to come your way. Productivity is still human business At its best, AI helps remove the drudge work that crowds our days, giving us more room to think, plan, and focus on what matters. At its worst, it tricks us into mistaking busywork for progress. AI wont manage your time for you. It wont choose your priorities or tell you which meetings to skip. That disciplineof mastering your schedule, focusing on high-leverage work, and knowing where your energy should gostill rests on human shoulders. Once that foundation is in place, AI can be a powerful ally. Without it, AI risks amplifying the chaos. AI is a fast, powerful, occasionally unreliable tool. But like any tool, it only works if you weld it with intention. Youre still the driver. AI can help you go faster, but only if you know where you want to go.
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E-Commerce
So long, nine-to-five. There’s a new work schedule that’s taking over. The grueling “996” schedulewhich stands for 9 a.m. to 9 p.m., six days a weekis gaining momentum across the U.S., especially in certain industries. If a 72-hour work week sounds all-consuming, that’s precisely the point. The 996 schedulewhich became popularized in China, eventually leading to protests and even claims that it led to a handful of worker deathsis meant to foster a eat-sleep-work lifestyle. Keith Spencer, a career expert at FlexJobs, told Fast Company that the trend is most commonly being seen across AI startups that “are embracing this approach to accelerate growth and remain competitive on a global scale.” While the intense work ethic sounds overwhelming, Spencer says that some young and hungry workers may actually be drawn to it. “Certain employees, especially younger workers, may even welcome this level of intense dedication, particularly when additional pay or incentives are offered,” he explains. That may be especially true as the rise in 996 culture has been touted by major tech leaders like Elon Musk, who have long promoted a work ethic that asks employees to make some major sacrifices. Musk opened up about the need for increased time commitments on X back in 2018 in a tweet promoting working for his companies as being revolutionary, but requiring immense dedication. “There are way easier places to work, but nobody ever changed the world on 40 hours a week,” Musk wrote. When a commenter asked the Tesla CEO what the right number of hours a week was, he replied that it “varies per person, but about 80 sustained, peaking above 100 at times. Pain level increases exponentially above 80. With that same hardcore work ethic in mind, companies embracing the lifestyle seem only to be interested in hiring employees who are “obsessive,” a word that appears on New York City-based AI startup Rilla’s career page to describe those who work there. Rilla explains on its applications that candidates who aren’t “excited” about working “70 hrs/week in person with some of the most ambitious people in NYC” should not apply. Will Gao, the companys head of growth, previously told Wired about the benefits of the schedule. There’s a really strong and growing subculture of people, especially in my generationGen Zwho grew up listening to stories of Steve Jobs and Bill Gates, entrepreneurs who dedicated their lives to building life-changing companies, Gao explained. Kobe Bryant dedicated all his waking hours to basketball, and I dont think there are a lot of people saying that Kobe Bryant shouldnt have worked as hard as he did. At Cognition, a San Francisco startup that is building an AI software engineer, the mansion workspace has living quarters for employees who don’t have time to go home. The company’s CEO Scott Wu explained what’s expected on X. “Cognition has an extreme performance culture, and were up-front about this in hiring so there are no surprises later,” Wu wrote. “We routinely are at the office through the weekend and do some of our best work late into the night. Many of us literally live where we work.” The 996 trend seems to be taking off in the U.S. at a time when burnout is already at an all-time high. A 2025 report from online marketplace Care.com found that burnout was more impactful than employers thought. Companies believed 45% of their workers were at risk of burnout. But a staggering 69% of employees said they were actually at moderate to high risk. For that reason, Spencer warns that companies should “exercise caution” when leaning into the 996 schedule. In addition to burnout and overwhelm, Spencer says that overworking can even trigger “a quarter-life career crisis” when employees feel disconnected with their career as a result of overworkingwhich isn’t great for the employee and doesn’t serve the company either. Winter Peng, founder and CEO of Silveroak Capital Academy, an elite career coaching and mentorship firm, agrees that the hustle culture can backfire. She tells Fast Company that it “destroys the creativity that drives real innovation.” Peng continues: “U.S. startups adopting 996 are trading innovation for compliance” and says that ultimately, “their best talent will simply leave” in favor of companies who believe in work-life balance.
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E-Commerce
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