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Since our return from Davos, Switzerland, earlier this year, we have been dissecting the World Economic Forums Future of Jobs Report 2025. The WEF surveyed more than 1,000 companies from 22 different industries across 55 countries to attempt to predict and paint a picture of what work will look like in 2030. The encouraging news is that there are projected to be 170 million new jobs globally by 2030; however, 92 million jobs are expected to be eliminated due to AI automation. That is a net gain of 78 million jobs by 2030. To get a true understanding of why and how this shift will occur, here is a look at the story beneath the story. 4 factors reshaping the global workforce Four disruptive forces driving the next chapter of work stood out in the WEF report: AI and automation will continue to permeate the globe. By 2030, 86% of companies anticipate that artificial intelligence will have a significant impact on their operations. The scale and speed of this shift are unprecedented. Economic pressure is growing. Half of employers say rising costs are forcing them to rework their business models by looking at ways to cut costs while increasing revenue and production. How? AI and automation are the fastest and surest ways. The green transition is accelerating. As companies pursue their own climate goals, demand will surge for renewable energy engineers, sustainability consultants, and carbon auditors. Demographics are shifting. Developed countries are aging at an accelerated rate, while developing nations are sending their younger populations into the workforce. This shift is creating opportunities in several regions worldwide. AI creates new business models, opportunities, and industries Every technological revolutionfrom electricity to the internetsparked fears of obsolescence. But each wave created more jobs than it destroyed. Take the internet: In the 1990s, critics predicted a massive increase in unemployment and economic destruction. Today? The commercial internet alone supports 28.4 million jobs and drives 18% of the United States GDP. It created new career paths that didnt exist before, like social media managers and DevOps engineers. Whole industries were created: software-as-a-service (SaaS) companies, cloud computing, e-commerce platforms, and the creator economy. Every technological advancement in history has led to a net increase in employment. AI is following the same pattern, and were at the edge of the job creation phase. Consider the rise of ChatGPT. Behind every AI tool is a multidisciplinary team of humans. Machine learning engineers designed and implemented the algorithms. Data scientists and data engineers cleaned, curated, and labeled massive datasets. AI ethics experts are working to ensure responsible and fair AI use. Infrastructure architects built the cloud systems that scale these models globally. Every AI tool you use required dozens of specialists to make it work. The more AI gets deployed, the more humans we need to build, maintain, and improve these systems. Its continuous work that requires human expertise. Jobs that are growing versus those disappearing As organizations navigate these times of change, there are multiple areas in which they should focus. To best prepare for the future and set a company up for growth, they must start building a deep team that relies heavily on big data specialists, AI and machine learning engineers, fintech developers, software engineers, and environmental and renewable energy specialists. Conversely, some roles will decrease significantly in number or disappear altogether. If there are people at a company who hold these roles, they need to be reskilled or upskilled if there is a desire to save on recruiting and training costs. Administrative assistants can be almost completely replaced via AI for handling scheduling and initial call response. Accountants and auditors at the entry to mid-managerial level can be replaced by software and AI. Additionally, graphic designers who create emails, internal and external company documentation, and presentations will also be phased out. Upskilling or reskilling is crucial for individuals currently in those roles. Irreplaceable skills In the World Economic Forum report, employers anticipate that 39% of workers’ core skills will change by 2030. Essentially, the tasks you were hired to do, and the skills you possess that qualified you for and secured your job, may become obsolete in five years. So what skills are rising in demand? Think of technical skills like proficiency in AI and big data, as well as cybersecurity. No matter the industry, the future will favor those who can think critically, collaborate effectively, and learn continuously to solve problems. AI will continue to automate repetitive and mundane tasks, so AI and automation will almost exclusively handle frontline traditional grunt work. In these times of generational workforce shifts, the talent and skills of resilience and adaptability will become increasingly necessary to maintain relevance and demand for your services. The jobs of tomorrow To envision what new jobs will be created, you need to think about the problems and challenges that will be created by increased reliance on AI. AI will create more than it replaces, albeit in different roles and skill sets. As digital transformation continues to accelerate, companies will begin to staff up in roles such as software developers and UX designers, focusing on individuals who possess the skills to bridge the gap between technology and the businesses’ needs. As medical science advances, the life expectancy of the population continues to increase, which is expected to drive demand for careers in healthcare, particularly in eldercare. As the population ages, the demand for doctors, nurses, and in-home caregivers will increase. This is the way of things. Environmental, social, and governance (ESG) compliance; experience design; and supply chain resilience were not recognized as distinct job categories 10 years ago. What new jobs will exist and be in high demand over the next five to 10 years? The age of endless preparation and flexibility Unless you plan to retire within the next 18 months, you cannot rest on your current skills. AI and automation will likey displace some jobs, possibly including yours. But massive opportunities will open for people who are willing to adapt. Be a student of your industry and develop an understanding of how it is evolving. That will put you in a position to increase your skills and remain a vital part of your field. Individuals’ futures are in their own hands. We all have to ask ourselves: Will I let the future happen to meor will I create it?
Category:
E-Commerce
If you listen to the CEOs of elite AI companies or take even a passing glance at the U.S. economy, its abundantly obvious that AI excitement is everywhere. Americas biggest tech companies have spent over $100 billion on AI so far this year, and Deutsche Bank reports that AI spending is the only thing keeping the United States out of a recession. Yet if you look at the average non-tech company, AI is nowhere to be found. Goldman Sachs reports that only 14% of large companies have deployed AI in a meaningful way. What gives? If AI is really such a big deal, why is there a multi-billion-dollar mismatch between excitement over AI and the techs actual boots-on-the-ground impact? A new study from Stanford University provides a clear answer. The study reveals that theres a right and wrong way to use AI at work. And a distressing number of companies are doing it all wrong. What can AI do for you? The study, conducted by Stanfords Institute for Human-Centered AI and Digital Economy Lab and currently available as a pre-print, looks at the daily habits of 1,500 American workers across 104 different professions. Specifically, it analyzes the individual things that workers actually spend their time doing. The study is surprisingly comprehensive, looking at jobs ranging from computer engineers to cafeteria cooks. The researchers essentially asked workers what tasks theyd like AI to take off their plates, and which ones theyd rather do themselves. Simultaneously, the researchers analyzed which tasks AI can actually do, and which remain out of the technologys reach. With these two datasets, the researchers then created a ranking system. They labeled tasks as Green Light Zone if workers wanted them automated and AI was up to the job, Red Light Zone if AI could do the work but people would rather do it themselves, and Yellow Light (technically R&D Opportunity Zone, but Im calling it Yellow Light because the metaphor deserves extending) if people wanted the task automated but AI isnt there yet. They also created whats essentially a No Light zone for tasks that AI is bad at, and that people dont want it to do anyway. The boring bits The results are striking. Workers overwhelmingly want AI to automate away the boring bits of their jobs. Stanfords study finds that 69.4% of workers want AI to free up time for higher value work and 46.6% would like it to take over repetitive tasks. Checking records for errors, making appointments with clients, and doing data entry were some of the tasks workers considered most ripe for AIs help. Importantly, most workers say they wanted to collaborate with AI, not have it fully automate their work. While 45.2% want an equal partnership between workers and AI, a further 35.6% want AI to work primarily on its own, but still seek human oversight at critical junctures. Basically, workers want AI to take away the boring bits of their jobs, while leaving the interesting or compelling tasks to them. A chef, for example, would probably love for AI to help with coordinating deliveries from their suppliers or messaging diners to remind them of an upcoming reservation. When it comes to actually cooking food, though, theyd want to be the one pounding the piccata or piping the pastry cream. The wrong way So far, nothing about the studys conclusions feel especially surprising. Of course workers would like a computer to do their drudge work for them! The studys most interesting conclusion, though, isnt about workers preferencesits about how companies are actually meeting (or more accurately, failing to meet) those preferences today. Armed with their zones and information on how workers want to use AI, the researchers set about analyzing the AI-powered tools that emerging companies are bringing to market today, using a dataset from Y Combinator, a storied Silicon Valley tech accelerator. In essence, they found that AI companies are using AI all wrong. Fully 41% of AI tools, the researchers found, focus on either Red Light or No Light zone tasksthe ones that workers want to do themselves, or simply dont care much about in the first place. Lots more tools try to solve problems in the Yellow Light Zonethings like preparing departmental budgets or prototyping new product designsthat workers would like to hand off to AI, but that AI still sucks at doing. Only a small minority of todays AI products fall into the coveted Green Light zonetasks that AI is good at doing and that workers actually want done. And while many of todays leading AI companies are focused on removing humans from the equation, most humans would rather stay at least somewhat involved in their daily toil. AI companies, in other words, are focusing on the wrong things. Theyre either solving problems no one wants solved, or using AI for tasks that it cant yet do. Its no wonder, then, that AI adoption at big companies is so low. The tools available to them are whizzy and neat. But they dont solve the actual problems their workers face. How to use AI well For both workers and business leaders, Stanfords study holds several important lessons about the right way to use AI at work. Firstly, AI works best when you use it to automate the dull, repetitive, mind-numbing parts of your job. Sometimes doing this requires a totally new tool. But in many cases, it just requires an attitude shift. A recent episode of NPRs Planet Money podcast references a study where two groups of paralegals were given access to the same AI tool. The first group was asked to use the tool tobecome more productive, while the second group was asked to use it to do the parts of your job that you hate. The first group barely adopted the AI tool at all. The second group of paralegals, though, flourished. They became dramatically more productive, even taking on work that would previously have required a law degree. In other words, when it comes to adopting AI, instructions and intentions matter. If you try to use AI to replace your entire job, youll probably fail. But if you instead focus specifically on using AI to automate away the parts of your job that you hate (basically, the Green Light tasks in the Stanford researchers rubric), youll thrive and find yourself using AI for way more things. In the same vein, the Stanford study reveals that most workers would rather collaborate with an AI than hand off work entirely. Thats telling. Lots of todays AI startups are focusing on agents that perform work autonomously. The Stanford research suggests that this may be the wrong approach. Rather than trying to achieve full autonomy, the researchers suggest we should focus on partnering with AI and using it to enhance our work, perhaps accepting that a human will always need to be in the loop. In many ways, thats freeing. AI is already good enough to perform many complex tasks with human oversight. If we accept that humans will need to stay involved, we can start using AI for complex things today, rather than waiting for artificial general intelligence (AGI) or some imagined, perfect future technology to arrive. Finally, the study suggests that there are huge opportunities for AI companies to solve real-world problems and make a fortune doing it, provided that they focus on the right problems. Diagnosing medical conditions with AI, for example, is cool. Building a tool to do this will probably get you heaps of VC money. But doctors may not wantand more pointedly, may never usean AI that performs diagnostic work. Instead, Stanfords study suggests theyd be more likely to use AI that does mundane thingstranscribing their patient notes, summarizing medical records, checking their prescriptions for medicine interactions, scheduling followup visits, and the like. Automate the boring stuff is hardly a compelling rallying cry for todays elite AI startups. But its the approach thats most likely to make them boatloads of money in the long term. Overall, then, the Stanford study is extremely encouraging. On the one hand, the mismatch between AI investment and AI adoption is disheartening. Is it all just hype? Are we in the middle of the mother of all bubbles? Stanfords study suggests the answer is no. The lack of AI adoption is an opportunity, not a structural flaw of the tech. AI indeed has massive potential to genuinely improve the quality of work, turbocharge productivity, and make workers happier. Its not that the tech is overhypedweve just been using it wrong.
Category:
E-Commerce
Everlane made waves for being among the first brands to eradicate virgin plastic from its supply chain. In 2019, after years of work, the startup managed to switch out new plastic with recycled plastic, largely made from discarded water bottles. It began incorporating them into jackets and fleece sweaters and bodysuits. It was a big step towards sustainability, since recycled polyester has a lower carbon footprint and diverts plastic from landfills. In the years that followed, many other brands on the marketfrom H&M to Pradafollowed in Everlane’s footsteps, but it didn’t solve the bigger problem of what happens when people decide to get rid of their recycled garment. But unless a recycled polyester jacket is further recycled at the end of its life, it will end up in a landfill. Since plastic does not biodegrade, but break into microplastics that will end up in our waterways and food chain. “We challenged ourselves to think, What does leading sustainability look like today?” says Alfred Chang, Everlane’s CEO, who joined a year ago. “We need to think beyond recycled fabric, to trying to make clothes that are, as much as possible, fully recyclable afterwards.” Today, Everlane launches a puffer jacket called Everpuff that can be recycled back into a garment at the end of its life. It is designed to be durable and easy to mend, but also easily taken apart so all of its component partsfrom the polyester exterior to the inner down fill to the hardwarecan be recycled. It’s part of Everlane’s broader vision to make the rest of its product line fully circular. The Challenge of Fabric-to-Fabric Recycling For decades, environmental activists have urged societies to transition away from a linear economy, where natural resources are used to make products that will eventually be discarded at the end of their life. Instead, they argued we should move towards a circular system, where products are kept in the economy for as long as possible (through mending and resale) then recycled back into new products. This process would vastly reduce humans’ reliance on natural resources, and cut down on the greenhouse gas emissions used to extract those materials from the earth. The fashion industry has talked about circularity for a long time, but until now, brands have tended to focus on extending the life of the garment by repairing them and creating secondhand sites. Recycling old garments into new ones proved a challenge because the technology to do so was still in its infancy. But Chang says that over the last few years, Everlane has been tracking how the recycling industry has been evolving. “We’re trying to understand where opportunities like when it comes to the infrastructure and investments in recycling,” he says. Fabric-to-fabric recycling is much more sophisticated than it was just a few years ago. Over the last few years, Everlane has focused on sourcing recycled fibers. For instance, it has partnered with Circ, which transforms fabrics made from polyester and cotton blends into recycled polyester and lyocell fabrics. It also partners with Manteco, an Italian mill which recovers wool fibers and turn them back into wool fabric. With Everpuff, the company take back the jacket at the end of its life and transform it into a new garment. It has partnered with Debrand, a company that can disassemble the garment, separate out the polyester, down, and hardware, then send each of these components to be recycled. Katina Boutis, Everlane’s senior director of sustainability and sourcing, says that the company’s designers partnered closely with Debrand to create a jacket that would be as easy to recycle. For one thing, they made it from as few materials as possible. The exterior is made entirely of recycled polyester while the interior is made entirely of recycled down; since these are mono-materials, they are far easier to transform back into polyester and down. They also designed the jacket without any complex stitching, so it is easy to take it apart with Debrand’s machinery. “We’re working with real innovators in this space, getting feedback about what challenges them in their operations, so we can create a product that isn’t just good for the customer but considers the entire lifetime of this product,” she says. Designing for Durability While this puffer can be turned back into a new puffer, Change points out that it is still important for the customer to wear it as long as possible. After all, it still takes a lot of resources to manufacture and recycle a product. “To be truly sustainable, we need to be thinking about how long a garment will be in circulation,” he says. “We want to offer guarantees and repairs to ensure the product can be kept for a long time. We’re also thinking about how it can be resold or passed down to another wearer.” The Everpuff is the first Everlane product to come with a lifetime warranty that will allow the customer to receive a free repair (or replacement of the jacket if the damage is beyond repair). Customers can also pay for additional repairs. To create this program, Everlane partnered with Tersus Solutions, which has expertise in repairs. Everlane also partners with Poshmark on a resale program called Re:Everlane, which allows you to more easily resell an Everlane item. The system automatically adds the style name, fabric content and original price, lower reducing the burden on the reseller. “A lot of circularity comes down to education,” says Boutis. “We want to create a ecosystem that allows our products to have a second or third life, before taking it back when it’s truly time for it to be retired. We’re trying to tell this story in a fun, creative way to keep people engaged.” The big question now is whether consumers really care about sustainability. At a time when politics and the economy are volatile, eco-friendly consumerism may not be a priority. Chang acknowledges that there are many other things consumers are worried about right now, but he says Everlane is trying to make the case the sustainable clothing offers immediate benefits to the customer, such as durability and the absence of toxic chemicals. “A lot of investments we put into sustainability equates to a better-made product,” he says. And ultimately, Change believes the pendulum swings back and forth. Eventually a time may come when consumers do care about the state of the planet, and when that happens, Everlane will have a clear edge. “We’re trying to have a sharp position in the marketplace, to show that this brand matters,” he says.
Category:
E-Commerce
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