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How will AI affect American workers? There are two major narratives floating around. The techno-optimist view is that AI will free humans from boring tasks and create new jobs, while the techno-pessimist view is that AI will lead to widespread unemployment. As a sociologist who studies job insecurity, Im among the pessimists. And thats not just because of AI itself. Its about something deeperwhat scholars call American exceptionalism. While people commonly use this phrase to refer to anything that makes the U.S. unique, I use it narrowly to refer to the countrys approach to work and social welfare, which is quite different from the systems in other rich countries. I suspect AI will turbocharge American exceptionalism in ways that make workers more afraid of losing their jobs. When fused with organizations adoption of new types of AI, workers fears may soon become reality, if they havent already. An “exceptional” system for American workers Lets start with what makes the U.S. different, especially from other rich countries. The U.S. has relatively low levels of unionization, an at-will employment system, a modest welfare state, and a two-party system that lacks a social democratic tradition. Many wealthy countries boast higher unionization rates, stricter protections against being fired, andparticularly in Europemore robust welfare states. In other words, even before AI came into the picture, American workers were facing a system stacked against them. This tendency grew more pronounced starting in the late 1970s, with Democrats and Republicans alike pursuing reforms such as stripping regulations and rolling back the welfare state. Between 1983 and 2022, unionization rates fell by more than 50%; they remain low today. In the 1990s, President Bill Clinton pledged to end welfare as we know it and followed with a law slashing support programs. Meanwhile, U.S. workers inflation-adjusted wages have stagnated, and income inequality has risen since the 1970s. The current Trump administration has taken these exceptional traits even further. From firing the head of the National Labor Relations Board to his executive order undermining federal employee unions, Trump has usurped the power of regulatory agencies and workers themselves. And more cuts to the welfare state are coming now that Trumps domestic policy bill has been signed into law, including reductions in food aid and health insurance. One telling example is the Trump administrations mass firing of federal workers. While they are making their way through the courts, these efforts are notable for targeting government positions that have long been thought to be the most secure jobs. As a sociologist, I think its fair to say that the U.S. is even more exceptional than it was just a year ago. This trend lays the foundation for U.S. workers to fear losing their jobs, for employers to cut workers loose, and for people to struggle making ends meet. American exceptionalism, meet AI To understand whats happening, it helps to look at the different kinds of artificial intelligence, which generally refers to machines such as computers that can perform tasks comparably to humans. One type is predictive AI, which is what powers your streaming and social media recommendations. The second type is generative AI, which is used to create seemingly novel content. ChatGPT and other large language models fall in this category. The third type, agentic AI, cannot only predict and plan outcomes but also can act autonomously to achieve those outcomes. Self-driving cars are perhaps the most well-known example. Companies are increasingly using generative AI to boost productivity. According to Stanford Universitys 2025 AI index report, generative AI has already surpassed human performance on a range of tasks, including visual reasoning and answering competition-level math and PhD-level science questions. The U.S. Bureau of Labor Statistics has warned that generative AI will affect jobs across a range of industries. I think agentic AI will have dramatic implications for the workforce. Companies are already beginning to use it for customer service in industries from finance to travel. As if on cue, OpenAI recently released ChatGPT Agent, which it says can handle complex tasks from start to finish. When you combine technological advancementssuch as the current transition to generative AI and the likely broader agentic AI transitionwith turbocharged American exceptionalism, you get a formula for job insecurity and displacement. How AI might affect the future of job security Interestingly, despite having fewer protections from firing and a more threadbare unemployment system, American workes are no more afraid of losing their jobs than workers in other rich countries, research shows. These perceptions are generally constant over time, but they spike as a result of certain economic reforms and during recessions. Among the findings in my own research on free-market-driven economic reforms in Europe, people were most worried about losing their jobs in countries that had ratcheted up such policies within the past five years. That trend has important implications for the United States. Recent polling shows that about a third of U.S. workers believe AI will hurt their jobs or job opportunities. Business leaders say they expect job losses in the service industries, supply chain management, and human resources over the next three years. Theres no shortage of predictions about AI-driven job gains and losses, but solid data is hard to come byand dont even bother asking most companies about AI-related layoffs. On one hand, business leaders surveyed by McKinsey in 2024 reported high demand for new jobs such as AI compliance specialist and AI ethics specialist, which are the kinds of new roles that techno-optimists say will be created by AI. On the other hand, its no small irony that AI was reportedly used to facilitate the mass firing of federal workers and may soon replace some Department of Education workers jobs. Americas fusion of limited labor protections and aggressive AI adoption could create the perfect storm for widespread job insecurity. While unions have organized for AI-related job protections, and states are attempting to regulate AI, the U.S.s path realistically depends less on workers and local politicians actions than on what companies do. And I think companies increasing integration of AI will likely hurt American workers more than it helps. If nothing changes, job insecurity may become the new normal. Jeffrey C. Dixon is a professor of sociology at College of the Holy Cross. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Nelson Repenning is a professor at the MIT Sloan School of Management and director of MITs Leadership Center. Donald Kieffer is a senior lecturer at MIT Sloan School of Management, founder of ShiftGear Work Design, and was previously vice president of operational excellence for Harley-Davidson. Whats the big idea? Managers and business leaders often find themselves confronted with a lot of employees earnestly trying to get projects done, meanwhile not seeing much get accomplished. A lot of companies struggle with workflow design challenges that stand in the way of getting real work done. Fortunately, for these similar obstacles, there exist solutions that apply across industries. Below, co-authors Nelson and Don share five key insights from their new book, Theres Got to Be a Better Way: How to Deliver Results and Get Rid of the Stuff That Gets in the Way of Real Work. Listen to the audio versionread by Nelson and Donin the Next Big Idea App. 1. Start small. The typical way managers think about major changes is in terms of big programs, armies of consultants, coffee mugs, banners, and weeks of training from fat binders in expensive hotels. We take a much different approach: Start small. This went against everything I had learned when I was a manager, but there was a particular day when the lights went on for me. I was working with a guy from Toyota, Scott Borg, who was helping me in the engine plant at Harley-Davidson. Scott was having a hard time getting a point through to me, and I was getting frustrated. Finally, he said, Lets just take a walk. He took me out to the center of the plant, a 300,000-square-foot area with 800 people at the two main aisles. We called those main aisles Hollywood and Vine. There were people moving everywhere. It was busy. He made me stand there and asked me, Don, tell me what you see. I noted that its a busy place. He repeated, Just tell me what you see. I took a breath and described the scene: I see a guy driving a fork truck. He stopped over there, talking to the guy thats making cylinders, and he has got a pallet full of finished cylinders on his fork truck. I assume hes going to take them to the assembly line. Next, Scott asked, Whats the design of the work? Whats he supposed to be doing right now at this time of the day? Suddenly, I saw his point. There was a design, but it was a terrible one. Give the fork truck driver a radio and let people call him to tell him when they want to move stuff around. We were building 350 engines a day with 800 people on the same schedule. Yet everyone was working to their own drumbeat. The first thing I wanted to do was organize that guys day in terms of a schedule for delivering parts to the assembly line. If we fixed his job, then the other four truck drivers and operators would quickly say, Hey, thats way better for us. Help us fix our work that way. And then work will get done faster because the new way gets rid of all the junk people, bureaucratic mess, confusion, and frustration, so people can focus on the work theyre supposed to do. They love it. 2. We have too many emails and meetings. Chances are, we use email when we should have a meeting, and we have meetings when we probably should send an email. Individuals have two ways of moving work from one person to the next: handoff or huddle. A handoff is exactly what it sounds like. The work is handed from one person to another with little or no communication. Handoffs work well for transferring many types of work. For example, when I get my paycheck every month, theres no negotiation required. In contrast, huddles should be used when the successful transfer of work from one person to the next requires two or more people to talk about it. It could be a problem that needs to be solved, a decision that needs to be made, or some recent change that needs to be clarified. Either way, the relevant parties need to come together and discuss it. Chances are, we use email when we should have a meeting, and we have meetings when we probably should send an email. The trouble comes when we use a huddle at a time when we need a handoff, or vice versa. When we use handoffs to transfer complex information, the result is typically lots of expensive back and forthwhat we sometimes call ineffective iteration. How many of you have been on long email chains where five to 10 people email back and forth endlessly trying to resolve a problem that probably could be solved in five or 10 minutes if they just got in a room together? Conversely, sometimes we use huddles when all we need is a handoff. When this happens, we get a long, boring meeting. How many of you have sat through a five-page PowerPoint presentation or project update that reported no issues and needed no discussion? In those cases, please just email me the status report, and Ill go through it if I need to. If you make the wrong call between huddle and handoff, the result is lots of emails that are being attended to either early in the morning or at night because the bulk of your day is spent in long, boring meetings that dont get real work done. 3. You are not in control when targets are missed. Adding more meetings, rules, and status reports gives a false sense of security that you are in control. But you are not in control. The traditional approachof setting targets and adding punishment and oversight when theyre missednot only doesnt help, but it also leaves you on the sidelines instead of in the game. It causes people to do more of what wasnt working and to make more private workarounds. In our approach, we moved from just getting work done to making better and innovative ways an integral part of the job. There are four elements that make this change: Clear targets with an intent or a why that everyone understands. This allows people to make little decisions on the fly that deliver what was intended rather than strictly what was said. Metrics that support the main target to ensure that the desired outcome is delivered. For example, please reduce the cost by 10 percent while still delivering on time, and ensure that quality does not slip. Otherwise, people are prone to cutting corners and gaming the system just to get the lower cost. Make the activities in the plan explicit and visible to all so that everyone can see how the activities tie to the results. Real-time feedback to see if those activities are delivering as predicted. If they are, how can we do more of them or do them faster and better? If not, why not? And what can we learn from this? This transformed the definition of success from just carrying out the plan to a series of intentional experiments, where learning became an integral part of the job, day-to-day, and we met targets while innovating along the way. This is the dynamic part of dynamic work design. Every problem encountered and lesson learned is a doorway into better and more innovative work. 4. Putting less work into the system will help get more done. Research shows that most systems re overloaded with too much work. R&D systems are notoriously overloaded, often having three to five times as many projects in them as they have capacity to execute. The workflow for most organizations looks like a highway leading out of the city to the beach on a Friday afternoon. Its just a huge traffic jam. Most organizations are staffed by smart, competent people who have lots of good ideas that they really want to get done, but that traffic jam is more expensive than you think. First, it takes a lot longer for a project to navigate its way through the system when theres a lot of other work in the way. Second, when everything is moving slowly, managers constantly shift priorities in the hopes of getting the most important tasks done. Constantly shifting priorities can kill productivity because people must drop what theyre working on to switch to another project and reset their focus. Technology is not as helpful as people think. A recent study suggests that we switch between apps on our computers approximately 1,200 times a day and spend nearly four hours a week simply moving from one application to the next. We wouldnt interrupt a pilot while they are landing a plane or ask a doctor performing heart surgery to stop and go work on something else, but we essentially ask knowledge workers to interrupt their important tasks over and over again. Each day, this comes at a huge cost in terms of productivity. The trick to good work design is allowing people to focus on one task without interruption until its completed, and then have them move on to the next. One solution is for senior leaders to identify the top 10 projects they want to complete and then rank them in order of importance. Then, leaders can create a culture whereby if youre on the critical path for project number one, you dont work on project number six. When you can get that kind of discipline with the appropriate loading, the work will flow, and youll begin getting things done much more efficiently. 5. Fancy workflow management hides underlying problems. Imagine if air traffic controllers had to do their work using spreadsheets and instant messaging. Even if they had real-time updates on plane locations, they would be in the form of numerical coordinates, and it would require a significant amount of cognitive work to determine which planes required the most attention and when they were getting too close to each other. We often ask managers to work this type of way. Fortunately, air traffic controllers dont actually work this way. They have a radar screen that gives a visual representation of the location of the planes in their area, making it far easier to spot trouble and take action. When managers use similar visual representations, their work often becomes similarly effective. You can do something similar for all knowledge work in the office by representing activities with sticky notes on the wall and mapping them against targets. The value of visualization became clear to Don when he had to temporarily substitute for a vacationing production supervisor at Harley-Davidson. The manufacturing areas worked hard to make enough parts to feed the main assembly line. At the daily production meeting, each supervisor would tell a story about how they were running close to the edge, but they would be okay. Of course, they all had problems, but they were hoping that their colleagues had bigger ones and that their colleagues would shut down the line first, giving them time to catch up without a lot of managerial help and oversight. It was a big game of chicken where people increasingly accepted the underlying problems as business as usual. When he became plant manager, Don changed all this by moving the stores of finished parts from the manufacturing areas to the main aisle in specially marked, timestamped areas on the shop floor. Now, a quick walk down the main aisle showed exactly where everyone stood relative to the schedule, and problems became obvious in real time. You can do something similar for all knowledge work in the office by representing activities with sticky notes on the wall and mapping them against targets. This allows the team to stand together and see the state of the work and whats getting in the way of getting it done. Meetings that were previously full of stories quickly become high-powered collaboration sessions where everyone works to figure out how to keep the work moving as efficiently as possible. Having a common radar screen that shows the state of important work and any underlying issues brings clarity and accountability. Teamwork follows naturally. This article originally appeared in Next Big Idea Club magazine and is reprinted with permission.
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We set out to push ourselves, improve, and grow. But when things get tough or dont go to plan? Its all too easy to pull the plug. Research from Headwaythe leading book summary appshows that 11% of people have already abandoned their 2025 goals, and 33% are close to giving up. For many, the fear of failing that stands in the way of self-improvement. In my work as a productivity coach, Ive come to see why. Were hardwired to link failure with finality, which fuels self-doubt and causes motivation to fizzle out. Yet, failure is just another stepping stone on the path to success. So if you hit a bump, dont take it as a sign to give up; take it as an opportunity to learn, adjust, and go again. 1. Challenge yourself If youre succeeding 100% of the time, youre not pushing yourself. As a productivity coach, it’s something I see clients do regularly, but I constantly remind them that you dont grow by playing it safeyou grow by stretching, stumbling, and staying with it. That means taking on challenges that stretch you to your limits, and sometimes beyond them. Sure, you wont always hit the mark, but each miss offers valuable insight into what works, what doesnt, and where you need to improve. In fact, studies show that failing 15% of the time is optimal for learning. Enroll in a class you know youll struggle with, ask for constructive criticism, and put yourself in situations that make you feel a little uneasy. This is where the magic starts. When I work with high-achieving women, we often create a discomfort challengeone small stretch per week. Why? Because the goal isnt to master everything overnight, but to get comfortable feeling uncomfortable. Over time, these lessons make you more confident and capable. With each failure, your limit increases and you take a step toward achieving your full potential. 2. Dont give up after a single failure Its natural to feel discouraged when things dont go to plan, but one setback doesnt put success out of reach. Every failure gives you a clearer sense of what works and what doesnt, making you better prepared for your next attempt. Bill Gatess first venture, Traf-O-Data, failed. And Steve Jobs isnt remembered for the Apple Lisaa costly flopbut as the creator of the revolutionary iPhone. Failure doesnt mean its over; it means youre in it. As I often remind my clients, Its not failing that stops youIts quitting too soon. Stick with the hard part. Its usually the bridge to your breakthrough. And when you finally succeed? Well, it tastes even sweeter when youve fought for it, gained the battle scars, and refused to let failure define you. 3. Dont start from scratch So often, my clients want to wipe the slate clean after a tough outcome. But hitting reset is rarely the answer. Instead, run a postmortem: Where exactly did it go wrong? Comb through the experience, note what worked, and use that as a launching pad. Often, you will find that the problem is small and easier to overcome than you initially thought. At the very least, there will be positiveswhether lessons, strategies, or resourcesthat you can reuse in your next attempt. Theres always treasure among the rubble if you take the time to look. Take Traf-O-Data, for example. The company didnt survive, but it gave Gates and Allen invaluable practice in writing software, building hardware, and pitching to customers. Those lessons directly shaped their approach to the Altair 8800 projectthe launchpad that eventually became Microsoft. Picking yourself up and trying again is never easy, but having an existing foundation in place makes it far easier to motivate yourself. 4. Keep a failure file Failure is only a negative if you learn nothing from it, so document every flop and failure, and note exactly what each one taught you and where it went wrong. This doesnt serve as a list of your losses, but as a blueprint for making progress. Youre essentially turning your setbacks into a data source, and you will quickly begin to see patterns emerging. Do you typically lose motivation midway through a project? Do you frequently fail to plan and inevitably run into problems you didnt foresee? Or do you lack a skill thats constantly preventing you from moving forward? With this insight, you can not only correct individual mistakes but also question the underlying assumptions, habits, and behaviors that consistently hold you backa concept known as double-loop learning, which is linked to sharper thinking, superior decision-making, and innovative problem-solving. I also encourage every client to keep a lessons learned docnot to track tasks but transformation, and not to dwell on mistakes but to honor growth. It serves as a powerful reminder of how far youve come and how many times youve already gotten up, brushed off, and overcome a challenge.
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