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Sunday Confession: I Only Keep Friends Whose Household Income Matches Mine","\u002Fmedia\u002Fthumbnail_1_8f39e92c52.webp 245w, \u002Fmedia\u002Fsmall_1_8f39e92c52.webp 500w",{"pagination":201},{"limit":202,"start":203,"total":204},5,0,761,{"data":206,"meta":302},[207],{"author":208,"bylineDate":233,"bylineTimezone":234,"category":235,"createdAt":243,"documentId":244,"featuredImage":245,"id":267,"keywords":19,"publishedAt":268,"readerContent":269,"seo_desc":298,"seo_title":299,"slug":246,"title":300,"updatedAt":301},{"avatar":209,"bio":223,"createdAt":224,"documentId":225,"facebook":19,"id":226,"instagram":227,"linkedIn":228,"name":229,"publishedAt":230,"slug":231,"updatedAt":232},{"alternativeText":210,"caption":210,"ext":211,"focalPoint":19,"formats":212,"height":219,"mime":215,"name":220,"size":221,"url":222,"width":219},"the working gal author",".png",{"thumbnail":213},{"ext":211,"height":214,"mime":215,"name":216,"size":217,"url":218,"width":214},156,"image\u002Fpng","thumbnail_the working gal author-8b011626d0a8b20fab48a700e8ad81edab03fd226ef65d6763a5cbfc49873928.png",49.22,"\u002Fmedia\u002Fthumbnail_the_working_gal_author_8b011626d0a8b20fab48a700e8ad81edab03fd226ef65d6763a5cbfc49873928.png",250,"the working gal author-8b011626d0a8b20fab48a700e8ad81edab03fd226ef65d6763a5cbfc49873928.png",26.32,"\u002Fmedia\u002Fthe_working_gal_author_8b011626d0a8b20fab48a700e8ad81edab03fd226ef65d6763a5cbfc49873928.png","Specializing in cultural commentary and long-form retrospective profiles, Amalia resists easy optimism. At The Working Gal, she analyzes social architecture, historical women who challenged institutional norms, and the starker realities lurking behind contemporary empowerment rhetoric.","2026-09-06T10:31:20.371Z","1c262b4f3f6450eb207d1cb8",30950260948837,"https:\u002F\u002Fwww.instagram.com\u002Famalia.ka__\u002F","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Famalia-kakampakou-963945202\u002F","Amalia","2026-09-15T04:46:56.857Z","amalia","2026-09-15T04:46:56.808Z","2026-11-10","America\u002FLos_Angeles",{"createdAt":236,"documentId":237,"id":238,"name":239,"publishedAt":240,"slug":241,"updatedAt":242},"2026-09-06T10:31:16.599Z","da2176100119c15ae4d8e714",239837249536282,"Career & Money","2026-09-21T20:28:11.240Z","career-and-finance","2026-09-21T20:28:11.147Z","2026-11-08T23:39:08.810Z","537b20d7dda7b1efda32b572",{"alternativeText":246,"caption":19,"ext":20,"focalPoint":19,"formats":247,"height":47,"mime":24,"name":264,"size":265,"url":266,"width":51},"ai-skills-gap-training-gap-at-work",{"large":248,"medium":252,"small":256,"thumbnail":260},{"ext":20,"height":23,"mime":24,"name":249,"size":250,"url":251,"width":28},"large_ai-skills-gap-training-gap-at-work.webp",38.98,"\u002Fmedia\u002Flarge_ai_skills_gap_training_gap_at_work_cc161076a1.webp",{"ext":20,"height":30,"mime":24,"name":253,"size":254,"url":255,"width":34},"medium_ai-skills-gap-training-gap-at-work.webp",24.71,"\u002Fmedia\u002Fmedium_ai_skills_gap_training_gap_at_work_cc161076a1.webp",{"ext":20,"height":36,"mime":24,"name":257,"size":258,"url":259,"width":40},"small_ai-skills-gap-training-gap-at-work.webp",13.49,"\u002Fmedia\u002Fsmall_ai_skills_gap_training_gap_at_work_cc161076a1.webp",{"ext":20,"height":42,"mime":24,"name":261,"size":262,"url":263,"width":46},"thumbnail_ai-skills-gap-training-gap-at-work.webp",4.89,"\u002Fmedia\u002Fthumbnail_ai_skills_gap_training_gap_at_work_cc161076a1.webp","ai-skills-gap-training-gap-at-work.webp",132.24,"\u002Fmedia\u002Fai_skills_gap_training_gap_at_work_cc161076a1.webp",91788297100712,"2026-11-10T12:00:04.829Z",{"fragments":270,"html":294,"noticeHtml":295,"publicationId":296,"readingMinutes":297},[271,273,282,284,292],{"html":272},"\u003Ch2>What the gap actually is\u003C\u002Fh2>\n\u003Cp>Most conversations about the AI skills gap assume that workers lack the technical ability to use the tools, which is why the proposed fixes tend to be \u003Ca href=\"\u002Farticles\u002Fis-my-job-safe-from-ai\">prompt workshops and certificates\u003C\u002Fa>. The research points somewhere else, because the gap is made up of three layers that get blurred together: who uses AI at all, who has been properly taught to use it, and who can tell when the output is wrong. Companies measure the first layer because it is easy to count, and they buy solutions for the second, while the third, which is where the real damage happens, mostly goes unmeasured. A workforce can reach near-universal usage and still be badly exposed, because using a tool and using it well are separated by exactly the kind of training that most employers have been cutting back.\u003C\u002Fp>\n\u003Ch2>Usage is rising while teaching is shrinking\u003C\u002Fh2>\n\u003Cp>PwC’s 2026 survey of \u003Ca href=\"https:\u002F\u002Fwww.digit.fyi\u002Fai-benefits-concentrated-among-minority-of-workers-pwc-finds\u002F\">nearly 50,000 workers across 48 countries\u003C\u002Fa> found that 64% had used AI at work in the past year, ten points more than the year before, while the share of workers with access to learning and development resources dropped from 59% to 51%. Companies are therefore expecting people to \u003Ca href=\"\u002Farticles\u002Fbest-ai-productivity-tools\">adopt a tool\u003C\u002Fa> faster than they are willing to teach it, and the cost of that choice falls unevenly across the organization.\u003C\u002Fp>\n\u003Cp>PwC’s breakdown shows how uneven. About 14% of workers combine scarce skills with strong AI use, and nearly 80% of them have access to training, while the 56% who run most daily operations have access below 40% and use generative AI every day at roughly a fifth of the rate. Because the workers who already \u003Ca href=\"\u002Farticles\u002F5-free-coursera-courses-to-boost-your-career\">hold valuable skills\u003C\u002Fa> are also the ones receiving the help, the advantage compounds into higher pay, a stronger sense of job security, and more trust in leadership, and the majority fall further behind each year without anyone having made a deliberate decision to leave them there. That is what makes the problem hard to see from the inside, since each individual manager can point to a reasonable explanation, while the total effect is a company dividing into two workforces.\u003C\u002Fp>\n\u003Ch2>Women are hesitating at the start\u003C\u002Fh2>\n",{"callToAction":274,"controlKey":281},{"buttonLabel":275,"buttonUrl":19,"headline":276,"kind":277,"name":276,"pick":278,"stableKey":279,"text":280},"Get the email","The things people only say over drinks.","newsletter","override","cta_a62480a46c954af6ae35a5871cb19c3d","Salary numbers, office politics, skincare that actually changed our skin, and what to cook when you have fifteen minutes left in the day.","cta_9e627f82a4e04f31a8fa40c1989475e2",{"html":283},"\n\n\u003Cp>A meta-analysis led by researchers at Berkeley, Stanford and Harvard Business School pooled \u003Ca href=\"https:\u002F\u002Fwww.library.hbs.edu\u002Fworking-knowledge\u002Fwomen-are-avoiding-using-artificial-intelligence-can-that-hurt-their-careers\">18 studies covering more than 140,000 people\u003C\u002Fa> and found that women are roughly 20% less likely than men to use generative AI. Equal access didn't close the gap, since in an experiment with about 17,000 Kenyan entrepreneurs, women were still around 13% less likely to try the tool even when it was offered to them with instructions.\u003C\u002Fp>\n\u003Cp>The reasons the researchers identified are more interesting than the gap itself. Women reported \u003Ca href=\"\u002Farticles\u002Fai-anxiety-future-proof-career\">lower confidence\u003C\u002Fa> and familiarity, a tendency to stop sooner when the first answer disappointed, and a stronger sense that using AI amounts to cheating, while the study found no difference between men and women in how much they trust the output or how worried they are about its risks. That distinction matters, because it means women are not misjudging the technology, they are hesitating to build the habit, and a habit is much easier to change than a belief.\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"\u002Fmedia\u002Fai_skills_gap_training_gap_at_work_135810a154.webp\" alt=\"ai-skills-gap-training-gap-at-work\" \u002F>\u003C\u002Fp>\n\u003Cp>The consequences reach well beyond tool usage. In workplaces that reward visible output and visible initiative, a usage gap of that size turns into a slow pay and \u003Ca href=\"\u002Farticles\u002Ftoo-useful-to-promote-office-glue-trap\">promotion gap\u003C\u002Fa>, particularly when the men who are using the tools heavily are also the ones talking about it in meetings. Experienced women often hold the very thing that makes AI output safe to use, which is deep knowledge of their field, and yet they are the group most likely to talk themselves out of the tool before that knowledge can be applied.\u003C\u002Fp>\n\u003Ch2>The first danger: the entry-level ladder\u003C\u002Fh2>\n\u003Cp>Stanford’s Digital Economy Lab has tracked payroll data since 2025, and its \u003Ca href=\"https:\u002F\u002Fletsdatascience.com\u002Fnews\u002Fstanford-finds-no-broad-ai-job-displacement-2299c364\">August 2026 update\u003C\u002Fa> shows employment among 22-to-25-year-olds in the most AI-exposed occupations running about 19% below where it would otherwise be. The researchers attribute this mainly to reduced hiring rather than layoffs, they found no sign of broad job loss across the economy, and they are careful to say that the data fits an AI explanation without proving one.\u003C\u002Fp>\n\u003Cp>Even with those caveats, the pattern is worrying, because entry-level work has always been how people build judgment. A junior analyst who drafts a hundred reports, has them corrected, and gradually learns what a good one looks like is being trained to become the senior reviewer a company needs later. When the drafting is handed to a model and the junior role is quietly eliminated, the company saves money this year while shrinking the supply of experienced reviewers it will need in five years, at exactly the moment when reviewing AI output becomes one of the most valuable skills in the building.\u003C\u002Fp>\n\u003Ch2>The second danger: confident people with no supervision\u003C\u002Fh2>\n\u003Cp>The skills gap is not limited to people who use AI too little, since a Cybernews survey of US employees found that \u003Ca href=\"https:\u002F\u002Fwww.wicpa.org\u002Fnews\u002Farticles\u002F2858:survey-59-of-us-employees-use-unapproved-ai-tools-for-work\">59% use AI tools their employer has not approved, including 93% of executives and senior managers\u003C\u002Fa>. Three quarters of those users admitted to sharing sensitive information with the tools, which means the people with the most access to confidential material are frequently the ones moving it into systems the company cannot see.\u003C\u002Fp>\n\u003Cp>This is the judgment gap in its clearest form, because fluency without training produces a person who is quick and sure of themselves and unaware of what they are exposing. It also explains why simply encouraging more AI use is not a strategy, since a department that adopts the tool enthusiastically without guidance can create more risk than one that adopts it slowly, and the enthusiasm is easily mistaken for competence by leadership that is watching only the usage numbers.\u003C\u002Fp>\n\u003Ch2>The third danger: work that looks finished\u003C\u002Fh2>\n\u003Cp>The most subtle risk is that AI produces output that appears polished regardless of whether it is correct, which changes what it means to be good at a job. A manager who cannot distinguish a sound analysis from a plausible one will reward whoever delivers the cleanest slides, and over time the organization starts promoting presentation over accuracy. The person who spots the invented figure or the outdated policy is slowing the meeting down, while the person who presents without checking appears decisive, and so the incentives drift toward the behavior that creates the most exposure.\u003C\u002Fp>\n\u003Cp>This is why the gap is hard to close with technical training alone. The skill that protects a company is the ability to question a confident answer, and that ability comes from knowing a field well enough to sense when something is off, which is learned through experience and supported by a culture where checking is treated as part of the work instead of an obstacle to it.\u003C\u002Fp>\n\u003Ch2>Why it needs monitoring\u003C\u002Fh2>\n\u003Cp>A gap that is measured by how many people use AI will look healthy while the real problems grow, because usage says nothing about whether work is being checked, whether training reaches the people who need it, or whether junior employees are still getting the experience that builds judgment. The figures worth watching are the ones that rarely appear in company dashboards: who gets training broken down by seniority and gender, how often AI-assisted work is independently verified, how \u003Ca href=\"\u002Farticles\u002Fpromotion-case-building-before-review-season\">promotion rates\u003C\u002Fa> differ between heavy and light users, and how many entry-level positions exist compared with three years ago. Without those numbers, the gap will be managed by impressions, and impressions favor whoever sounds most certain in the meeting.\u003C\u002Fp>\n",{"callToAction":285,"controlKey":291},{"buttonLabel":286,"buttonUrl":19,"headline":287,"kind":277,"name":288,"pick":278,"stableKey":289,"text":290},"Join the List","Things we figured out the hard way.","Newsletter signup","cta_8e2824e5938b42399f8c437af1437e30","How to handle a passive-aggressive director, where to find trousers that fit, and what is genuinely worth paying full price for.","cta_df0d2bc4f1a74106897f49fbccff6ef5",{"html":293},"\n\n\u003Cp>Monitoring matters just as much at the individual level, because the market is rewarding AI skills faster than most people can tell whether they have them. Keeping track of \u003Ca href=\"\u002Farticles\u002Fthe-great-lock-in-trend\">your own progress\u003C\u002Fa>, including where the tool saves you time and where it has cost you time through errors, is the only reliable way to know if your confidence is earned.\u003C\u002Fp>\n\u003Ch2>How to be proactive\u003C\u002Fh2>\n\u003Cp>For an individual, the most useful habit is a verification log in which you record each time the tool gave you something wrong, what the correct answer was, and how you knew. In six months it becomes concrete evidence of the judgment employers say they struggle to assess, and it makes a stronger case for a raise than a claim that you use AI frequently. It also helps to ask for training in writing with a specific budget, since a refusal tells you something about where you stand, and to use the approved tool whenever one exists so that a data leak is never traced back to you. If you tend to stop after the first mediocre answer, it is worth practicing a second and third attempt, because persistence is one of the measurable differences between heavy and light users and it can be learned.\u003C\u002Fp>\n\u003Cp>For \u003Ca href=\"\u002Farticles\u002Fshift-away-from-management-pros-and-cons\">managers\u003C\u002Fa>, the priority is to check who is actually receiving training before looking at who is using the tools, to provide an approved option good enough that no one needs a secret alternative, and to evaluate AI-assisted work by how carefully it was verified instead of how quickly it arrived. Managers who care about their future bench should also give junior staff real review work on AI output, since checking is now the apprenticeship that drafting used to be.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"\u002Farticles\u002Fwhy-women-are-underrepresented-in-leadership-positions\">For leaders\u003C\u002Fa>, the practical conclusion is that the people holding the skills the company values most will leave if they are not rewarded, while the majority who are not being trained will stay but fall further behind, and both problems come from the same decision to treat training as optional. The companies that handle this well will be the ones that spend on teaching before they spend on tools, and that measure judgment as seriously as they measure adoption.\u003C\u002Fp>\n","\u003Ch2>What the gap actually is\u003C\u002Fh2>\n\u003Cp>Most conversations about the AI skills gap assume that workers lack the technical ability to use the tools, which is why the proposed fixes tend to be \u003Ca href=\"\u002Farticles\u002Fis-my-job-safe-from-ai\">prompt workshops and certificates\u003C\u002Fa>. The research points somewhere else, because the gap is made up of three layers that get blurred together: who uses AI at all, who has been properly taught to use it, and who can tell when the output is wrong. Companies measure the first layer because it is easy to count, and they buy solutions for the second, while the third, which is where the real damage happens, mostly goes unmeasured. A workforce can reach near-universal usage and still be badly exposed, because using a tool and using it well are separated by exactly the kind of training that most employers have been cutting back.\u003C\u002Fp>\n\u003Ch2>Usage is rising while teaching is shrinking\u003C\u002Fh2>\n\u003Cp>PwC’s 2026 survey of \u003Ca href=\"https:\u002F\u002Fwww.digit.fyi\u002Fai-benefits-concentrated-among-minority-of-workers-pwc-finds\u002F\">nearly 50,000 workers across 48 countries\u003C\u002Fa> found that 64% had used AI at work in the past year, ten points more than the year before, while the share of workers with access to learning and development resources dropped from 59% to 51%. Companies are therefore expecting people to \u003Ca href=\"\u002Farticles\u002Fbest-ai-productivity-tools\">adopt a tool\u003C\u002Fa> faster than they are willing to teach it, and the cost of that choice falls unevenly across the organization.\u003C\u002Fp>\n\u003Cp>PwC’s breakdown shows how uneven. About 14% of workers combine scarce skills with strong AI use, and nearly 80% of them have access to training, while the 56% who run most daily operations have access below 40% and use generative AI every day at roughly a fifth of the rate. Because the workers who already \u003Ca href=\"\u002Farticles\u002F5-free-coursera-courses-to-boost-your-career\">hold valuable skills\u003C\u002Fa> are also the ones receiving the help, the advantage compounds into higher pay, a stronger sense of job security, and more trust in leadership, and the majority fall further behind each year without anyone having made a deliberate decision to leave them there. That is what makes the problem hard to see from the inside, since each individual manager can point to a reasonable explanation, while the total effect is a company dividing into two workforces.\u003C\u002Fp>\n\u003Ch2>Women are hesitating at the start\u003C\u002Fh2>\n\n\n\u003Cp>A meta-analysis led by researchers at Berkeley, Stanford and Harvard Business School pooled \u003Ca href=\"https:\u002F\u002Fwww.library.hbs.edu\u002Fworking-knowledge\u002Fwomen-are-avoiding-using-artificial-intelligence-can-that-hurt-their-careers\">18 studies covering more than 140,000 people\u003C\u002Fa> and found that women are roughly 20% less likely than men to use generative AI. Equal access didn't close the gap, since in an experiment with about 17,000 Kenyan entrepreneurs, women were still around 13% less likely to try the tool even when it was offered to them with instructions.\u003C\u002Fp>\n\u003Cp>The reasons the researchers identified are more interesting than the gap itself. Women reported \u003Ca href=\"\u002Farticles\u002Fai-anxiety-future-proof-career\">lower confidence\u003C\u002Fa> and familiarity, a tendency to stop sooner when the first answer disappointed, and a stronger sense that using AI amounts to cheating, while the study found no difference between men and women in how much they trust the output or how worried they are about its risks. That distinction matters, because it means women are not misjudging the technology, they are hesitating to build the habit, and a habit is much easier to change than a belief.\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"\u002Fmedia\u002Fai_skills_gap_training_gap_at_work_135810a154.webp\" alt=\"ai-skills-gap-training-gap-at-work\" \u002F>\u003C\u002Fp>\n\u003Cp>The consequences reach well beyond tool usage. In workplaces that reward visible output and visible initiative, a usage gap of that size turns into a slow pay and \u003Ca href=\"\u002Farticles\u002Ftoo-useful-to-promote-office-glue-trap\">promotion gap\u003C\u002Fa>, particularly when the men who are using the tools heavily are also the ones talking about it in meetings. Experienced women often hold the very thing that makes AI output safe to use, which is deep knowledge of their field, and yet they are the group most likely to talk themselves out of the tool before that knowledge can be applied.\u003C\u002Fp>\n\u003Ch2>The first danger: the entry-level ladder\u003C\u002Fh2>\n\u003Cp>Stanford’s Digital Economy Lab has tracked payroll data since 2025, and its \u003Ca href=\"https:\u002F\u002Fletsdatascience.com\u002Fnews\u002Fstanford-finds-no-broad-ai-job-displacement-2299c364\">August 2026 update\u003C\u002Fa> shows employment among 22-to-25-year-olds in the most AI-exposed occupations running about 19% below where it would otherwise be. The researchers attribute this mainly to reduced hiring rather than layoffs, they found no sign of broad job loss across the economy, and they are careful to say that the data fits an AI explanation without proving one.\u003C\u002Fp>\n\u003Cp>Even with those caveats, the pattern is worrying, because entry-level work has always been how people build judgment. A junior analyst who drafts a hundred reports, has them corrected, and gradually learns what a good one looks like is being trained to become the senior reviewer a company needs later. When the drafting is handed to a model and the junior role is quietly eliminated, the company saves money this year while shrinking the supply of experienced reviewers it will need in five years, at exactly the moment when reviewing AI output becomes one of the most valuable skills in the building.\u003C\u002Fp>\n\u003Ch2>The second danger: confident people with no supervision\u003C\u002Fh2>\n\u003Cp>The skills gap is not limited to people who use AI too little, since a Cybernews survey of US employees found that \u003Ca href=\"https:\u002F\u002Fwww.wicpa.org\u002Fnews\u002Farticles\u002F2858:survey-59-of-us-employees-use-unapproved-ai-tools-for-work\">59% use AI tools their employer has not approved, including 93% of executives and senior managers\u003C\u002Fa>. Three quarters of those users admitted to sharing sensitive information with the tools, which means the people with the most access to confidential material are frequently the ones moving it into systems the company cannot see.\u003C\u002Fp>\n\u003Cp>This is the judgment gap in its clearest form, because fluency without training produces a person who is quick and sure of themselves and unaware of what they are exposing. It also explains why simply encouraging more AI use is not a strategy, since a department that adopts the tool enthusiastically without guidance can create more risk than one that adopts it slowly, and the enthusiasm is easily mistaken for competence by leadership that is watching only the usage numbers.\u003C\u002Fp>\n\u003Ch2>The third danger: work that looks finished\u003C\u002Fh2>\n\u003Cp>The most subtle risk is that AI produces output that appears polished regardless of whether it is correct, which changes what it means to be good at a job. A manager who cannot distinguish a sound analysis from a plausible one will reward whoever delivers the cleanest slides, and over time the organization starts promoting presentation over accuracy. The person who spots the invented figure or the outdated policy is slowing the meeting down, while the person who presents without checking appears decisive, and so the incentives drift toward the behavior that creates the most exposure.\u003C\u002Fp>\n\u003Cp>This is why the gap is hard to close with technical training alone. The skill that protects a company is the ability to question a confident answer, and that ability comes from knowing a field well enough to sense when something is off, which is learned through experience and supported by a culture where checking is treated as part of the work instead of an obstacle to it.\u003C\u002Fp>\n\u003Ch2>Why it needs monitoring\u003C\u002Fh2>\n\u003Cp>A gap that is measured by how many people use AI will look healthy while the real problems grow, because usage says nothing about whether work is being checked, whether training reaches the people who need it, or whether junior employees are still getting the experience that builds judgment. The figures worth watching are the ones that rarely appear in company dashboards: who gets training broken down by seniority and gender, how often AI-assisted work is independently verified, how \u003Ca href=\"\u002Farticles\u002Fpromotion-case-building-before-review-season\">promotion rates\u003C\u002Fa> differ between heavy and light users, and how many entry-level positions exist compared with three years ago. Without those numbers, the gap will be managed by impressions, and impressions favor whoever sounds most certain in the meeting.\u003C\u002Fp>\n\n\n\u003Cp>Monitoring matters just as much at the individual level, because the market is rewarding AI skills faster than most people can tell whether they have them. Keeping track of \u003Ca href=\"\u002Farticles\u002Fthe-great-lock-in-trend\">your own progress\u003C\u002Fa>, including where the tool saves you time and where it has cost you time through errors, is the only reliable way to know if your confidence is earned.\u003C\u002Fp>\n\u003Ch2>How to be proactive\u003C\u002Fh2>\n\u003Cp>For an individual, the most useful habit is a verification log in which you record each time the tool gave you something wrong, what the correct answer was, and how you knew. In six months it becomes concrete evidence of the judgment employers say they struggle to assess, and it makes a stronger case for a raise than a claim that you use AI frequently. It also helps to ask for training in writing with a specific budget, since a refusal tells you something about where you stand, and to use the approved tool whenever one exists so that a data leak is never traced back to you. If you tend to stop after the first mediocre answer, it is worth practicing a second and third attempt, because persistence is one of the measurable differences between heavy and light users and it can be learned.\u003C\u002Fp>\n\u003Cp>For \u003Ca href=\"\u002Farticles\u002Fshift-away-from-management-pros-and-cons\">managers\u003C\u002Fa>, the priority is to check who is actually receiving training before looking at who is using the tools, to provide an approved option good enough that no one needs a secret alternative, and to evaluate AI-assisted work by how carefully it was verified instead of how quickly it arrived. Managers who care about their future bench should also give junior staff real review work on AI output, since checking is now the apprenticeship that drafting used to be.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"\u002Farticles\u002Fwhy-women-are-underrepresented-in-leadership-positions\">For leaders\u003C\u002Fa>, the practical conclusion is that the people holding the skills the company values most will leave if they are not rewarded, while the majority who are not being trained will stay but fall further behind, and both problems come from the same decision to treat training as optional. The companies that handle this well will be the ones that spend on teaching before they spend on tools, and that measure judgment as seriously as they measure adoption.\u003C\u002Fp>\n","","5be1493b89891f88faa72b2fa8e18a7d4d1e65e3e4c2d306b70ff89311385c10",6,"Workers use AI more while employers train them less. What the AI skills gap really is, who it hits hardest, and how to get ahead of it.","AI Skills Gap at Work: Why Training Is the Real Problem","The AI Skills Gap Is a Training Gap, and Companies Created It","2026-11-10T12:00:04.705Z",{"pagination":303},{"page":304,"pageCount":304,"pageSize":305,"total":304},1,25,{"data":307,"meta":319},[308,309,310,311,312,313,318],{"documentId":12,"id":13,"name":14,"slug":15},{"documentId":100,"id":101,"name":102,"slug":103},{"documentId":170,"id":171,"name":172,"slug":173},{"documentId":137,"id":138,"name":139,"slug":140},{"documentId":237,"id":238,"name":239,"slug":241},{"documentId":314,"id":315,"name":316,"slug":317},"e95e6a3d50f0ccafea5813e1",256591718600945,"Nourish","food",{"documentId":63,"id":64,"name":65,"slug":66},{"pagination":320},{"page":304,"pageCount":304,"pageSize":321,"total":322},100,7,{"data":324},{"entriesPerColumn":325,"quickLinks":326,"socialLinks":360},3,[327,333,339,345,351,357],{"text":328,"page":329},"About",{"documentId":330,"slug":331,"publishedAt":332},"7f4d4595e9177e154c335213","about","2026-11-03T05:38:31.678Z",{"text":334,"page":335},"Privacy Policy",{"documentId":336,"slug":337,"publishedAt":338},"9f1a7e05a1d30714d9efb308","privacy-policy","2026-11-03T05:41:19.035Z",{"text":340,"page":341},"Affiliate Disclosure Policy",{"documentId":342,"slug":343,"publishedAt":344},"3c7ed0f8caa2cb5e70fb6f03","affiliate-disclosure-policy","2026-11-03T05:39:17.863Z",{"text":346,"page":347},"Editorial Policy",{"documentId":348,"slug":349,"publishedAt":350},"b255d79c59b6a3c0833ba8d6","editorial-policy","2026-11-03T05:40:10.867Z",{"text":352,"page":353},"Terms of Service",{"documentId":354,"slug":355,"publishedAt":356},"bddd32b77b4a749034598248","terms-of-service","2026-11-03T05:42:33.593Z",{"text":358,"url":359},"Pitch a Story","https:\u002F\u002Ftally.so\u002Fr\u002F2EzyjD",[361,364,367,370,373],{"text":362,"url":363},"Instagram","https:\u002F\u002Fwww.instagram.com\u002Fthe_working_gal",{"text":365,"url":366},"Pinterest","https:\u002F\u002Ffr.pinterest.com\u002Fthe_working_gal\u002F",{"text":368,"url":369},"Threads","https:\u002F\u002Fwww.threads.com\u002F@the_working_gal",{"text":371,"url":372},"Facebook","https:\u002F\u002Fwww.facebook.com\u002Ftheworkinggal",{"text":374,"url":375},"LinkedIn","https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fthe-working-gal",{"data":377},{"addedBodyCentered":378,"addedBodyHtml":379,"addedHeading":380,"askBodyCentered":378,"askBodyHtml":381,"askHeading":382,"buttonLabel":286},true,"\u003Cp>Keep an eye on your inbox.\u003C\u002Fp>","You're on the list.","\u003Cp>Drop your email. We’ll send you the things you actually want to read about your money, your routine, and your downtime.\u003C\u002Fp>","Let's make it official.",{"data":384,"meta":387},{"documentId":385,"id":386,"metaDescription":19,"metaTitle":19},"265b266ac1744f5593eff1bf",42172928409973,{},{"data":389,"meta":390},{"documentId":385,"id":386},{},1767225600000]