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When a company with tens of thousands of software engineers found that uptake of a new AI-powered tool was lagging well below 50%, they wanted to know why. It turned out that the problem wasnt the technology itself. What was holding the company back was a mindset that saw AI use as akin to cheating. Those who used the tool were perceived as less skilled than their colleagues, even when their work output was identical. Not surprisingly, most of the engineers chose not to risk their reputations and carried on working in the traditional way. These kinds of self-defeating attitudes arent limited to one companythey are endemic across the business world. Organizations are being held back because they are importing negative ideas about AI from contexts where they make sense into corporate settings where they dont. The result is a toxic combination of stigma, unhelpful policies, and a fundamental misunderstanding of what actually matters in business. The path forward involves setting aside these confusions and embracing a simpler principle: Artificial intelligence should be treated like any other powerful business tool. This article shares what I have learned over the past six months while revising the AI use policies for my own companies, drawing on the research and insights of my internal working group (Paul Scade, Pranay Sanklecha, and Rian Hoque). {"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.","subhed":"","description":"","ctaText":"Learn More","ctaUrl":"https:\/\/faisalhoque.com","theme":{"bg":"#02263c","text":"#ffffff","eyebrow":"#9aa2aa","subhed":"#ffffff","buttonBg":"#ffffff","buttonHoverBg":"#3b3f46","buttonText":"#000000"},"imageDesktopId":91420512,"imageMobileId":91420514,"shareable":false,"slug":""}} Confusing Contexts In educational contexts, it is entirely appropriate to be suspicious about generative AI. School and college assessments exist for a specific purpose: to demonstrate that students have acquired the skills and the knowledge they are studying. Feeding a prompt into ChatGPT and then handing in the essay it generates undermines the reason for writing the essay in the first place. When it comes to artistic outputs, like works of fiction or paintings, there are legitimate philosophical debates about whether AI-generated work can ever possess creative authenticity and artistic value. And there are tough questions about where the line might lie when it comes to using AI tools for assistance. But issues like these are almost entirely irrelevant to business operations. In business, success is measured by results and results alone. Does your marketing copy persuade customers to buy? Yes or no? Does your report clarify complex issues for stakeholders? Does your presentation convince the board to approve your proposal? The only metrics that matter in these cases are accuracy, coherence, and effectivenessnot the contents origin story. When we import the principles that govern legitimate AI use in other areas into our discussion of its use in business, we undermine our ability to take full advantage of this powerful technology. The Disclosure Distraction Public discussions about AI often focus on the dangers that follow from allowing generative AI outputs into public spaces. From the dead internet theory to arguments about whether it should be a legal requirement to label AI outputs on social media, policymakers and commentators are rightly concerned about malicious AI use infiltrating and undermining the public discourse. Concerns like these have made rules about disclosure of AI use central to many corporate AI use policies. But theres a problem here. While these discussions and concerns are perfectly legitimate when it comes to AI agents shaping debates around social and political issues, importing these suspicions into business contexts can be damaging. Studies consistently show that disclosed AI use triggers negative bias within companies, even when that use is explicitly encouraged and when the output quality is identical to human-created content. The study mentioned at the start of this article found that internal reviewers assessed the same work output to be less competent when they were told that AI had been used in its production than when they were told it had not been, even when the AI tools in question were known to increase productivity and when their use was encouraged by the employer. Similarly, a meta-analysis of 13 experiments published this year identified a consistent loss of trust in those who disclose their AI use. Even respondents who felt positively about AI use themselves tended to feel higher distrust toward colleagues who used it. This kind of irrational prejudice creates a chilling effect on the innovative use of AI within businesses. Disclosure mandates for the use of AI tools reflect organizational immaturity and fear-based policymaking. They treat AI as a kind of contagion and create stigma around a tool that should be as uncontroversial as using spell-check or design templatesor having the communications team prepare a statement for the CEO to sign off on. Companies that focus on disclosure are missing the forest for the trees. They have become so worried about the process that theyre ignoring what actually matters: the quality of the output. The Ownership Imperative The solution to both context confusion and the distracting push for disclosure is simple: Treat AI like a perfectly normalalbeit powerfultechnological tool, and insist that the humans who use it take full ownership of whatever they produce. This shift in mindset cuts through the confused thinking that plagues current AI policies. When you stop treating AI as something exotic that requires special labels and start treating it as you would any other business tool, the path forward becomes clear. You wouldnt disclose that you used Excel to create a budget or used PowerPoint to design a presentation. What matters isnt the toolit is whether you stand behind the work. But heres the crucial part: Treating artificial intelligence as normal technology doesnt mean you can play fast and loose with it. Quite the opposite. Once we put aside concepts that are irrelevant in a business context, like creative authenticity and cheating, we are left with something more fundamental: accountability. When AI is just another tool in your tool kit, you own the output completely, whether you like it or not. Every mistake, every inadequacy, every breach of the rules belongs to the human who sends the content out into the world. If the AI plagiarizes and you use that text, youve plagiarized. If the AI gets facts wrong and you share them, they’re your factual errors. If the AI produces generic, weak, unconvincing language and you choose to use it, youve communicated poorly. No client, regulator, or stakeholder will accept the AI did it as an excuse. This reality demands rigorous verification, editing, and fact-checking as nonnegotiable components of the AI-use workflow. A large consulting company recently learned this lesson when it submitted an error-ridden AI-generated report to the Australian government. The mistakes slipped through because humans in the chain of responsibility treated AI output as finished work rather than as raw material requiring human oversight and ownership. The firm couldnt shift blame to the toolthey owned the embarrassment, the reputational damage, and the client relationship fallout entirely. Taking ownership isnt just about accepting responsibility for errors. It is also about recognizing that once you have reviewed, edited, and approved AI-assisted work, it ceases to be AI output and becomes your human output, produced with AI assistance. This is the mature approach that moves us past disclosure theater and toward genuine accountability. Making the Shift: Owning AI Use Here are four steps your business can take to move from confusion about contexts to the clarity of an ownership mindset. 1. Replace disclosure requirements with ownership confirmation. Stop asking Did you use AI? and start requiring clear accountability statements: I take full responsibility for this content and verify its accuracy. Every piece of work should have a human who explicitly stands behind it, regardless of how it was created. 2. Establish output-focused quality standards. Define success criteria that ignore creation method entirely: Is it accurate? Is it effective? Does it achieve its business objective? Create verification workflows and fact-checking protocols that apply equally to all content. When something fails these standards, the conversation should be about improving the output, not about which tools were used. 3. Normalize AI use through success stories, not policies. Share internal case studies of teams using AI to deliver exceptional results. Celebrate the business outcomesfaster delivery, higher quality, breakthrough insightswithout dwelling on the methodology. Make AI proficiency a valued skill on par with Excel expertise or presentation design, not something requiring special permission or disclosure. 4. Train for ownership, not just usage. Develop training that goes beyond prompting techniques to focus on verification, fact-checking, and quality assessment. Teach employees to treat AI output as raw material that requires their expertise to shape and validate, not as finished work. Include modules on identifying AI hallucinations, verifying claims, and maintaining brand voice. The companies that will thrive in the next year wont be those that unconsciously disincentivize the use of AI through the stigma of disclosure policies. They will be those that see AI for what it is: a powerful tool for achieving business results. While your competitors tie themselves in knots over process documentation and disclosure theater, you can leapfrog past them with a simple principle: Own your output, regardless of how you created it. The question that will separate winners from losers isn’t Did you use AI? but Is this excellent? If you’re still asking the first question, you are already falling behind. {"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? 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Design flaws caused a Tesla Model 3 to suddenly accelerate out of control before it crashed into a utility pole and burst into flames, killing a woman and severely injuring her husband, a lawsuit filed in federal court alleges.Another defect with the door handle design thwarted bystanders who were trying to rescue the driver, Jeff Dennis, and his wife, Wendy, from the car, according to the lawsuit filed Friday in U.S. District Court for the Western District of Washington.Wendy Dennis died in the Jan. 7, 2023, crash in Tacoma, Washington. Jeff Dennis suffered severe leg burns and other injuries, according to the lawsuit.Messages left Monday with plaintiffs’ attorneys and Tesla were not immediately returned.The lawsuit seeks punitive damages in California since the Dennis’ 2018 Model 3 was designed and manufactured there. Tesla also had its headquarters in California at the time before later moving to Texas.Among other financial claims, the lawsuit seeks wrongful death damages for both Jeff Dennis and his late wife’s estate. It asks for a jury trial.Tesla doors have been at the center of several crash cases because the battery powering the unlocking mechanism shuts off in case of a crash, and the manual releases that override that system are known for being difficult to find.Last month, the parents of two California college students killed in a Tesla crash sued the carmaker, saying the students were trapped in the vehicle as it burst into flames because of a design flaw that prevented them from opening the doors. In September, federal regulators opened an investigation into complaints by Tesla drivers of problems with stuck doors.Jeff and Wendy Dennis were running errands when the Tesla suddenly accelerated for at least five seconds. Jeff Dennis swerved to miss other vehicles before the car hit the utility pole and burst into flames, the lawsuit says.The automatic emergency braking system did not engage before hitting the pole, the lawsuit alleges, even though it is designed to apply the brakes when a frontal collision is considered unavoidable.Bystanders couldn’t open the doors because the handles do not work from the outside because they also rely on battery power to operate.. The doors also couldn’t be opened from inside because the battery had shut off because of the fire, and a manual override button is hard to find and use, the lawsuit alleges.The heat from the fire prevented bystanders from getting close enough to try to break out the windows.Defective battery chemistry and battery pack design unnecessarily increased the risk of a catastrophic fire after the impact with the pole, the lawsuit alleges. Thiessen reported from Anchorage, Alaska. Mark Thiessen, Associated Press
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E-Commerce
President Donald Trump is directing the federal government to combine efforts with tech companies and universities to convert government data into scientific discoveries, acting on his push to make artificial intelligence the engine of the nation’s economic future.Trump unveiled the “Genesis Mission” as part of an executive order he signed Monday that directs the Department of Energy and national labs to build a digital platform to concentrate the nation’s scientific data in one place.It solicits private sector and university partners to use their AI capability to help the government solve engineering, energy and national security problems, including streamlining the nation’s electric grid, according to White House officials who spoke to reporters on condition of anonymity to describe the order before it was signed. Officials made no specific mention of seeking medical advances as part of the project.“The Genesis Mission will bring together our Nation’s research and development resources combining the efforts of brilliant American scientists, including those at our national laboratories, with pioneering American businesses; world-renowned universities; and existing research infrastructure, data repositories, production plants, and national security sites to achieve dramatic acceleration in AI development and utilization,” the executive order says.The administration portrayed the effort as the government’s most ambitious marshaling of federal scientific resources since the Apollo space missions of the late 1960s and early 1970s, even as it had cut billions of dollars in federal funding for scientific research and thousands of scientists had lost their jobs and funding.Trump is increasingly counting on the tech sector and the development of AI to power the U.S. economy, made clear last week as he hosted Saudi Arabia’s Crown Prince Mohammed bin Salman. The monarch has committed to investing $1 trillion, largely from the Arab nation’s oil and natural gas reserves, to pivot his nation into becoming an AI data hub.For the U.S.’s part, funding was appropriated to the Energy Department as part of the massive tax-break and spending bill signed into law by Trump in July, White House officials said.As AI raises concerns that its heavy use of electricity may be contributing to higher utility rates in the nearer term, which is a political risk for Trump, administration officials argued that rates will come down as the technology develops. They said the increased demand will build capacity in existing transmission lines and bring down costs per unit of electricity.Data centers needed to fuel AI accounted for about 1.5% of the world’s electricity consumption last year, and those facilities’ energy consumption is predicted to more than double by 2030, according to the International Energy Agency. That increase could lead to burning more fossil fuels such as coal and natural gas, which release greenhouse gases that contribute to warming temperatures, sea level rise and extreme weather.The project will rely on national labs’ supercomputers but will also use supercomputing capacity being developed in the private sector. The project’s use of public data including national security information along with private sector supercomputers prompted officials to issue assurances that there would be controls to respect protected information. Thomas Beaumont, Associated Press
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