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Welcome to Pressing Questions, Fast Companys workplace advice column. Every week, deputy editor Kathleen Davis, host of The New Way We Work podcast, will answer the biggest and most pressing workplace questions. Q: How do I decide what to take off of my résumé? A: There is much debate about if your résumé really needs to be one page. But regardless of if its one page or two, there are some common elements you can cut to make your résumé easier to read and more effective. Here are a few: Work history thats more than 15 years old This one isnt cut and dry. You shouldnt just delete everything before 2010. But heres what to consider cutting or condensing. If you have been at your last (or current) position for fewer than five years, you can keep a few bullet points under each of your previous jobs dating back 1015 years.But if a role on your résumé is from more than 1015 years ago, your old responsibilities, tasks, and accomplishments are likely not very relevant anymore.The exception is if you are looking to make a career change and you have older relevant job experience. You can list your positions with just title and dates but add a couple of bullet points to the older related experience. Your cover letter (yes, you still should write one) is the best place to connect the dots for the hiring manager on how your older experience is related. Jargon, clichés, and hyperbole You should avoid unnecessary jargon, clichés, and hyperbole in all parts of your job search process, but your résumé is the most crucial place to trim it out. If your résumé is being read by AI, phrases like seasoned solutions-oriented team player wont pass the keyword screens. Those words are similarly meaningless (and might actively work against you) if your résumé is being read by a real person.The best résumés follow the basic rules of good writing. Think of the golden rule of prose: Show, dont tell. An easy way to do this is to replace adjectives with numbers and facts: Instead of dynamic sales professional with a distinguished track record, try Closed over $1 million in new partnerships each quarter. Your objective or summary and your mailing address I dont know who ever thought an objective on a résumé was necessary. Your objective is to get hired, so anything you write here is just throat-clearing wasted space. Some people replace objective with a summary, which, in theory, can be useful for recruiters or AI that’s doing a quick scan. Most often though summaries are also useless because they are filled with meaningless buzzwords (see above). Youre better off formatting your résumé in an easy-to-scan way where your skills and accomplishments are clear.Including your mailing address is a relic of a time when applications were sent via snail mail and is an easy place to streamline. If you’re applying to an in-person job, however, you may still want to include the city you live in. The header space of your résumé should also include your email, phone, website/portfolio link, and one or two professional social media profiles, like LinkedIn. Fancy formatting, your photo, basic skills If you are early in your career and feel like you dont have enough to fill the page, it can be tempting to pad your résumé by adding basic skills like proficient with Microsoft office suite. But skills like that are assumed and adding them can actually make you look less experienced. Dont try to fill the space with colorful large fonts, formatting tricks, or your headshot either. White space is better and a simple format is easier to read, by both humans and applicant tracking systems. If you feel like your résumé is too thin, you may need to expand your definition of experience. (Heres my advice on how to make up for lack of experience on your résumé.)Want more advice on what to cut from your résumé? Here you go: 5 ways to make your résumé more impressive without lying Many job applications are never read by a human. Heres how to make sure your résumé gets seen 5 things you should take off your résumé right now
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If AI lives up to its hype and we can outsource the thinking, planning, and strategy parts of our jobs, do we risk losing the skills that make us human? Research from the Center for Strategic Corporate Foresight and Sustainability found that there is “a significant negative correlation between frequent AI tool usage and critical thinking abilities, mediated by increased cognitive offloading. In other words, use AI too much, and your mental faculties take a nosedive. But theres another way to think about the issue. Could AI actually improve our cognition by freeing up our mental bandwidth for higher-value work? {"blockType":"creator-network-promo","data":{"mediaUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/04\/workbetter-logo.png","headline":"Work Better","description":"Thoughts on the future of work, career pivots, and why work shouldn't suck, by Anna Burgess Yang. To learn more visit workbetter.media.","substackDomain":"https:\/\/www.workbetter.media","colorTheme":"blue","redirectUrl":""}} Make time for strategic thinking Ive worked at jobs in the past where Ive begged my boss for the budget to purchase technology that would make work better and easier. If technology could do part of my job for me, I could spend more time on other things, things that typically fell to the bottom of the pile because they didnt have an instant, tangible result. Thinking strategically about improvements I could make in my department, for example. I suspect most knowledge workers can relate. We compile reports, attend status meetings, and follow processes with endless tedious tasks. Theres rarely time for higher-level thinking. While technology improvements may have previously been a no, the response to AI has been a resounding yes. Perhaps its the promise of 10x everything but CEOs are enamored with the potential of AI. AI as sparring partner For many people, this poses a threat. For others, it can create an opportunity. Farm out the redundant, tedious tasks to AI so we can focus on work that requires our unique expertise. Take coding, for example. Software can have millions of lines of code, which previously needed to be entered manually. Now, AI can handle much of the repetitive work. Human coders take on the role of orchestrators: the brains behind the operation, guiding AI agents to the correct result. Personally, Ive used AI to expand my existing skills. Im self-employed, so I dont have any colleagues to bounce ideas off of if Im stuck. I was working with an app recently, and couldnt get it to do what I wanted. I turned to ChatGPT and asked for help. ChatGPT gave me incorrect information, which I recognized right away, based on my knowledge of the app. I prodded ChatGPT again, explaining why the previous answer wouldnt work. ChatGPT replied, Youre right! Here are some additional steps you need to take. The instructions were, again, incorrect. However, the incorrect instructions were enough to spark an idea . . . and my idea worked. As a sparring partner, AI let me work through a problem that I otherwise wouldnt have been able to solve on my own (at least not without a significant amount of trial, error, and frustration). My skills havent atrophied because of AI. Quite the opposite: AI takes over some of the boring work, and lets me focus on more creative workthe type of work only a human can do. The right use cases Even if the research currently suggests that AI negatively affects critical thinking abilities, that doesnt have to be your experience. You can find the right use cases to remove the boring and tedious work from your day. Once you do that, use the additional time for impactful work that was always pushed to the back burner. Or spend the time learning something new that could help your career. The people who will experience skill atrophy are those who outsource everything to AIand cant recognize when work needs human oversight, decision-making, and experience. {"blockType":"creator-network-promo","data":{"mediaUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/04\/workbetter-logo.png","headline":"Work Better","description":"Thoughts on the future of work, career pivots, and why work shouldn't suck, by Anna Burgess Yang. To learn more visit workbetter.media.","substackDomain":"https:\/\/www.workbetter.media","colorTheme":"blue","redirectUrl":""}}
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The technology industry is in the midst of a skills shortageone that shows no signs of slowing. The U.S. Bureau of Labor Statistics projects that tech jobs will grow at twice the rate of Americas overall workforce, creating hiring shortfalls as organizations struggle to fill critical positions in IT, cybersecurity, and other vital areas. The emergence of AI has only exacerbated the issue, as organizations in nearly every industry are seeking employees who can help them better understand the technology and get the most out of their solutions. Even as AI becomes a part of everyday life, most organizations are still determining how best to utilize itand how to limit the risks it may pose. Interestingly, these challenges mirror another (relatively) recent innovation: the cloud. Before cloud computing became commonplace, businesses with any sort of digital footprint needed to buy rack space or manage their own on-premises servers. That was reasonable for businesses with a high degree of technical expertise, but building and maintaining a climate-controlled server room wasnt realistic for most companies. The advent of cloud computing democratized access to advanced computing capabilitiesand AI is already having a similar impact. As businesses wrestle with managing and securing their AI deployments, they can look to the cloud for lessons and guidance on how similar challenges were tackled in the recent past. The Evolution of Cloud Adoption and Security Consider the cost of on-premises computing. Server rooms are expensive, as are the servers themselves. They also require a substantial degree of technical expertise to maintain, and employees with experience in that area understandably command high compensation. The emergence of platforms like Amazon Web Services, Microsoft Azure, and Google Cloud changed all that, lowering the barrier to entry for advanced computing capabilities: businesses could eliminate the high initial investment associated with purchasing servers and building server rooms in exchange for a modest (in most cases) operating expense. Perhaps most importantly, it allowed businesses to work with reliable partners to get the most out of their cloud services, rather than relying on difficult-to-come-by in-house expertise. That said, securing the cloud still presents its own challenges. During the early days of the cloud, businesses often made the mistake of assuming that providers would handle any cybersecurity needsa misconception that left them dangerously exposed. Today, the most common rule of thumb is that the provider is responsible for the security of the cloud itself, while the customer is responsible for the security of the data inside it. Essentially, the provider ensures attackers cannot exploit their systems to get to your data, but if poor password management, device security, or other data hygiene practices allow attackers to compromise your accountsthats on you. This delineation has helped businesses better understand where their potential risk factors lie when it comes to cloud security and mitigate them appropriately. Applying Lessons from the Cloud to AI Its not hard to see the parallels between the cloud and AI. Like the cloud, AI has democratized access to resources that were previously difficult to come by for many organizations. The widespread availability of generative AI models like ChatGPT means organizations no longer need to hire costly AI engineers to create, manage, and fine-tune their own models. Instead, they can put an application layer on top of an existing model and deliver a compelling service to their customers at a relatively low cost of ownership. While this still requires a level of technical expertise, the barrier to entry is much lowerand organizations can move forward faster with smaller, more flexible engineering teams. The risks posed by this model mirror those posed by the cloud. When you upload data to the cloud, it is no longer under your direct control. The same is true of third-party AI modelswhen customers (or employees) input data into an AI-powered application, its important to know where the data is going, how it is being stored and protected, and how it is being used. With AI still in its relative infancy, the answers to those questions arent always clearwhich means businesses providing AI functionality need strong AI governance practices in place to establish trust with their customers. For some businesses, that might mean offering customers the ability to opt out of AI features. For others, it might mean putting clear safeguards in place to prevent AI tools from accessing sensitive or confidential information. By demonstrating the ways in which they are limiting AI risk, businesses free their customers to evaluate the benefits of AI. Over time, most businesses migrated to the cloud because the efficiency gains substantially outweighed the perceived risksand a similar pattern is already emerging when it comes to AI. In fact, its happening even more quickly this time. Since AI has use cases across nearly every business unit, the potential ROI is much easier to illustrate. While its true that every customer will have a different risk appetite, the trend is clear: eventually, nearly every business will decide that the rewards significantly outweigh the risks. By establishing strong governance practices and lowering the amount of risk associated with AI, businesses can help their customers reach that point more quickly. Freeing Customers to Embrace AI with Confidence While businesses and their customers are understandably concerned about AI risk, the history of the cloud provides a helpful road map for navigating those risks successfully. The risks associated with AI are not dramatically different from those associated with other technologyand businesses can mitigate them in much the same way. By establishing strong governance practices and implementing clear transparent policies regarding AI and its use, businesses can enable their employees and customers to embrace the potential of AI with confidence.
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