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Many news outlets have reported an increaseor surgein attention-deficit/hyperactivity disorder, or ADHD, diagnoses in both children and adults. At the same time, health care providers, teachers, and school systems have reported an uptick in requests for ADHD assessments. These reports have led some experts and parents to wonder whether ADHD is being overdiagnosed and overtreated. As researchers who have spent our careers studying neurodevelopmental disorders like ADHD, we are concerned that fears about widespread overdiagnosis are misplaced, perhaps based on a fundamental misunderstanding of the condition. Understanding ADHD as one end of a spectrum Discussions about the overdiagnosis of ADHD imply that you either have it or you dont. However, when epidemiologists ask people in the general population about their symptoms of ADHD, some have a few symptoms, some have a moderate level, and a few have lots of symptoms. But there is no clear dividing line between those who are diagnosed with ADHD and those who are not since ADHDmuch like blood pressureoccurs on a spectrum. Treating mild ADHD is similar to treating mild high blood pressureit depends on the situation. Care can be helpful when a doctor considers the details of a persons daily life and how much the symptoms are affecting them. Not only can ADHD symptoms be very different from person to person, but research shows that ADHD symptoms can change within an individual. For example, symptoms become more severe when the challenges of life increase. ADHD symptoms fluctuate depending on many factors, including whether the person is at school or home, whether they have had enough sleep, if they are under a great deal of stress, or if they are taking medications or other substances. Someone who has mild ADHD may not experience many symptoms while they are on vacation and well rested, for example, but they may have impairing symptoms if they have a demanding job or school schedule and have not gotten enough sleep. These people may need treatment for ADHD in certain situations, but may do just fine without treatment in other situations. This is similar to what is seen in conditions like high blood pressure, which can change from day to day or from month to month, depending on a persons diet, stress level, and many other factors. Can ADHD symptoms change over time? ADHD symptoms start in early childhood and typically are at their worst in mid-to late childhood. Thus, the average age of diagnosis is between 9 and 12 years old. This age is also the time when children are transitioning from elementary school to middle school and may also be experiencing changes in their environment that make their symptoms worse. Classes can be more challenging beginning around fifth grade than in earlier grades. In addition, the transition to middle school typically means that children move from having all their subjects taught by one teacher in a single classroom to having to change classrooms with a different teacher for each class. These changes can exacerbate symptoms that were previously well-controlled. Symptoms can also wax and wane throughout life. For most people, symptoms improvebut may not completely disappearafter age 25, which is also the time when the brain has typically finished developing. Psychiatric problems that often co-occur with ADHD, such as anxiety or depression, can worsen ADHD symptoms that are already present. These conditions can also mimic ADHD symptoms, making it difficult to know which to treat. High levels of stress leading to poorer sleep, and increased demands at work or school, can also exacerbate or cause ADHD-like symptoms. Finally, the use of some substances, such as marijuana or sedatives, can worsen, or even cause, ADHD symptoms. In addition to making symptoms worse in someone who already has an ADHD diagnosis, these factors can also push someone who has mild symptoms into full-blown ADHD, at least for a short time. The reverse is also true: Symptoms of ADHD can be minimized or reversed in people who do not meet full diagnostic criteria once the external cause is removed. Kids with ADHD often have overlapping symptoms with anxiety, depression, dyslexia, and more. How prevalence is determined Clinicians diagnose ADHD based on symptoms of inattention, hyperactivity, and impulsivity. To make an ADHD diagnosis in children, six or more symptoms in at least one of these three categories must be present. For adults, five or more symptoms are required, but they must begin in childhood. For all ages, the symptoms must cause serious problems in at least two areas of life, such as home, school, or work. Current estimates show that the strict prevalence of ADHD is about 5% in children. In young adults, the figure drops to 3%, and it is less than 1% after age 60. Researchers use the term strict prevalence to mean the percentage of people who meet all of the criteria for ADHD based on epidemiological studies. It is an important number because it provides clinicians and scientists with an estimate on how many people are expected to have ADHD in a given group of people. In contrast, the diagnosed prevalence is the percentage of people who have been diagnosed with ADHD based on real-world assessments by health care professionals. The diagnosed prevalence in the U.S. and Canada ranges from 7.5% to 11.1% in children under age 18. These rates are quite a bit higher than the strict prevalence of 5%. Some researchers claim that the difference between the diagnosed prevalence and the strict prevalence means that ADHD is overdiagnosed. We disagree. In clinical practice, the diagnostic rules allow a patient to be diagnosed with ADHD if they have most of the symptoms that cause distress, impairment, or both, even when they dont meet the full criteria. And much evidence shows that increases in the diagnostic prevalence can be attributed to diagnosing milder cases that may have been missed previously. The validity of these mild diagnoses is well-documented. Consider children who have five inattentive symptoms and five hyperactive-impulsive symptoms. These children would not meet strict diagnostic criteria for ADHD even though they clearly have a lot of ADHD symptoms. But in clinical practice, these children would be diagnosed with ADHD if they had marked distress, disability, or both because of their symptomsin other words, if the symptoms were interfering substantially with their everyday lives. So it makes sense that the diagnosed prevalence of ADHD is substantially higher than the strict prevalence. Implications for patients, parents, and clinicians People who are concerned about overdiagnosis commonly worry that people are taking medications they dont need or that they are diverting resources away from those who need it more. Other concerns are that people may experience side effects from the medications or that they may be stigmatized by a diagnosis. Those concerns are important. However, there is strong evidence that underdiagnosis and undertreatment of ADHD lead to serious negative outcomes in school, work, mental health, and quality of life. In other words, the risks of not treating ADHD are well-established. In contrast, the potential harms of overdiagnosis remain largely unproven. It is important to consider how to manage the growing number of milder cases, however. Research suggests that children and adults with less severe ADHD symptoms may benefit less from medication than those with more severe symptoms. This raises an important question: How much benefit is enough to justify treatment? These are decisions best made in conversations between clinicians, patients and caregivers. Because ADHD symptoms can shift with age, stress, environment, and other life circumstances, treatment needs to be flexible. For some, simple adjustments like classroom seating changes, better sleep, or reduced stress may be enough. For others, medication, behavior therapy, or a combination of these interventions may be necessary. The key is a personalized approach that adapts as patients needs evolve over time. Carol Mathews is a professor of psychiatry at the University of Florida. Stephen V. Faraone is a distinguished professor of psychiatry at SUNY Upstate Medical University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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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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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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