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In theory, AI should have transformed manufacturing by now. From predictive maintenance and fatigue detection to real-time quality control, the promise has always been smarter, faster, and safer operations. But in practice, the factory floor is still a place where AI ambitions often run into real-world limitations. Thats a huge problem, especially because the size and weight of this industry are hard to ignore. U.S. manufacturing alone contributes $2.9 trillion to the economy, accounting for over 10% of total output and supporting nearly 13 million workers, according to the National Association of Manufacturers. Globally, manufacturing represents 16% of world GDP and a total market value well over $16 trillion, per a new report from Cargoson. Now, as AI advances even further and policymakers push for reindustrialization in the U.S.aiming to restore domestic production capacity, regain supply chain control, and modernize strategic infrastructurethe spotlight is back on factories. Theres momentum and money behind the movement, but without restructuring the fragmented digital systems that dominate most production floors, that momentum may stall. An estimate by MarketsandMarkets projects the global AI in manufacturing market would grow to $155 billion by 2030, up from $34 billion in 2025 — but that growth will remain theoretical unless companies solve the bottlenecks slowing down adoption. Outdated infrastructure According to a 2025 survey of more than 500 manufacturing leaders, 92% say outdated infrastructure is holding back GenAI progress. Another report on the state of AI infrastructure by A10 Networks found that 74% of global IT decision-makers believe their current infrastructure is not fully prepared to support AI workloads. For all the talk of digital transformation, many factories are still running on architecture that predates smartphones, most of which cannot support new AI capabilities. The hype around AI in manufacturing is real, but so are the technical barriers, Shahid Ahmed, EVP of New Ventures and Innovation at NTT DATA, tells Fast Company. Modern connectivity is unlocking the next wave of AI-driven innovation in manufacturing. Private 5G and next-gen Wi-Fi give manufacturers the speed and reliability to finally turn AI into a productivity engine. However, better connectivity is just one part of the big problem with getting AI to produce optimal results on the factory floor. Whats really stopping AI from working on the ground isnt just weak networks but also a mismatch between how factories run and how AI systems think. At aiOla, a conversational AI company that works with Fortune 500 manufacturers, Assaf Asbag sees a common pattern: data silos, fragmented systems, and little end-to-end accountability. Even when manufacturers bring in advanced models and top-tier talent, the results rarely scale. Even with expensive AI talent, teams cant generate value if they dont have clean, connected data, explains Asbag, aiOlas Chief Technology & Product Officer. You need aligned data, integrated workflows, and clear accountabilityotherwise pilots never scale. Thats because many manufacturing systems were never built to support AI in the first place. Legacy enterprise systemslike outdated ERP (enterprise resource planning) tools, old-school CRMs (customer relationship management platforms), and manual data entrystill dominate much of the landscape. When critical insights are buried across disconnected platformsor worse, written down in logbooksit becomes nearly impossible to feed AI models the context they need. Ahmed points to a recent deployment with materials manufacturer Celanese, where private 5G and edge AI were introduced to improve worker safety and equipment monitoring. They were able to identify fatigue risk factors and detect hazards in real time, he claims. It was only possible because the infrastructure was there to support that intelligence. For him, the key to successful AI deployments in manufacturing isnt just having data but also having the right data, in the right place, and at the right time. Without that, he warns, factories will keep seeing failed pilots, no matter how powerful the model. Not all use cases are built the same While the buzz often centers on predictive maintenance and visual inspection, those arent plug-and-play features. They require reliable data flow, ultra-low latency, and hardware compatibility that many plants simply dont have. In remote or offline environments, traditional cloud-based systems cant keep up. Use cases that demand real-time decision-makinglike voice-enabled workflows or autonomous quality checksare especially sensitive to network and system performance, Asbag notes. Thats why edge computing matters. It allows speech recognition or LLM-driven tasks to happen on-site, without depending on cloud access. Picture a factory line that shuts down every time it loses Wi-Fi. Without local processingmeaning the ability to run AI tasks on devices in the factory instead of sending them to the cloudeven a short loss of connectivity can stop production and make AI tools more of a problem than a help. For factories operating with limited or unreliable connectivity, edge AI offers a way forward. By processing data locally, companies can cut lag time, protect sensitive data, and reduce downtime. But again, these benefits only materialize if the surrounding infrastructurefrom sensors to routersis up to the task. Think of it like trying to run a modern electric vehicle on outdated roads, Ahmed says. No matter how powerful the engine, if the path is broken, youre not going anywhere fast. Getting real ROI One of the biggest traps in AI adoption is mistaking model accuracy for business success. Just because a model performs well during testing doesnt mean it will drive positive outcomes on the floor. The most successful AI initiatives begin with a clear visionimproving quality, boosting efficiency, or unlocking insights, says Ahmed. From there, quick wins build momentum. Asbag agrees with him. ROI in AI is not about proving that the modelworks or that accuracy improves on a benchmark. Those are technology goals, not business goals, he notes. Companies should avoid fluff by defining ROI in clear, specific business termsfaster processes, better decisions, or measurable savings. That means tracking metrics like how many more inspections a worker can perform with a voice assistant or how predictive maintenance reduced unexpected machine downtime. When AI is tied to concrete, operational KPIs, it becomes a tool for transformationnot just a tech experiment. And thats the big difference between the hype-induced claims of faster operations in the AI space and real measurable impact. Its one thing to say your model is 96% accurate in a test environment. Its another to show that it actually helped to cut defect rates by 12% in real production. While the first might get a nod from the technical team, the second gets leadership to sign off on a bigger rollout. The path forward Getting AI to work in manufacturing isnt about chasing the most advanced model. Its really about understanding the problem, cleaning up the data, modernizing the systems, and making sure every deployment serves a real business need. Too many companies fall into endless discussions, pilots, and meetings without ever delivering value, says Asbag. Success with AI comes from being precise about the problem, aligning with the business outcome, and giving teams the autonomy to execute. Ahmed puts it even more directly: AI without infrastructure is like trying to build a smart city with no roads. You need the foundation in place before you scale. Sateesh Seetharamiah, CEO of Edgeverve, also agrees. Without a defined set of use cases and outcomes, manufacturers will be stuck without a clear strategy to prioritize the right emerging tech capabilities for business success, he says. Conversations about building AI infrastructure in manufacturing often stall because leaders assume it means ripping everything out and starting from scratch. But meaningful progress rarely requires a full overhaul. Some of the biggest wins come from small, targeted changeslike installing local edge devices to reduce lag, connecting isolated systems, or clarifying who owns what data so teams can move faster. Manufacturing may be one of the toughest environments for AI, but its also one of the most rewarding. The factories that get it right wont just optimize how work gets done. Theyll also lead a new era of industrial work, while the ones that hesitate may fall behind. This isnt the time to sit on the fence, says Seetharamiah. Manufacturers who delay risk missing out on enormous opportunities to create digital experiences for their customers.
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E-Commerce
Across the internet, eagle-eyed sleuths are crying “AI slop” after Saturday Night Live aired segments with what looks like AI-generated imagery. The first instance, from Saturday’s cold open, shows an illustrated Christmas storybook. The images feature a hazy, yellow-ish hue and an image of streets that don’t connect. The next, in “Weekend Update” showed an image of a woman playing a slot machine in an otherwise empty casino while using an oxygen tank with tubes that weren’t connected. [Image: NBC Universal] While the images were on screen for a fraction of the episode, they have led to some very vocal backlash by fans, who are convinced they are AI-generated.On Reddit, viewers called them “gross” and “a shame” while a Bluesky user said simply, “Booooooo.” “That Week In SNL,” a podcast, was having none of it. [Image: NBC Universal] AI fatigue is real, and the accusations against Saturday’s episode landed amid a wider conversation about AI-generated media. McDonalds Netherlands pulled an AI-generated ad from its YouTube page last week following widespread negative comments. Meanwhile, the studio behind Coca-Cola’s widely criticized new AI-generated holiday ad admitted it wasn’t 100% ready. Merriam-Webster on Sunday named “slop” its 2025 word of the year. Slop in an ad is one thing. But slop on a show like SNL strikes a nerve considering how well known the long-running show is for its intricate human-made sets and costuming. This is a show made by hand, and the janky Photoshop jobs during Weekend Update are part of the joke. SNL has joked about AI in sketches this year, including one in January starring Timothée Chalamet and Bowen Yang that poked fun at AI’s proclivity for producing images of people with extra fingers. And in a sketch last month, Glen Powell played a grandpa pictured in old photos brought to life in an AI app gone wrong. NBC, which airs SNL, has not confirmed that the images are AI-generated, and the network did not respond to a request for comment. SNL‘s visual effects workers unionized in July, and their contract included AI protections that VFX artist Richard Lampasone said at the time was “a worker-centric AI policy that will help us keep doing our best work as our craft evolves.”
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E-Commerce
The holidays are the perfect time to show people that you appreciate their time, their effort, and the value they bring. But when it comes to giving gifts at work, most people are confused about what to do. Should you, or shouldn’t you, buy your boss a present? What about your coworkers or direct reports? How much should you spend for the office gift exchange? What about your office bestie? We asked the experts to weigh in, and here’s what they had to say. Is it acceptable to give holiday gifts at work? “To gift someone in the workplace is always acceptable, Alyse Dermer, founder of Mr. Considerate, a luxury gift concierge service, tells Fast Company. “Gifting can reinforce positive working relationships, strengthen team connection, and create moments that feel personal in a world that often feels transactional.” “People work hard,” Dermer adds. “You spend a lot of time with your coworkers, and they want to be seen. Its different from a company bonus. It doesnt need to be expensive, it just needs to be thoughtful. And thoughtfulness really lands.” Dermer says a good gift shows you appreciate people’s work and pay attention to their interests: “You work with these people everyday, you depend on them, they depend on you”and a gift should reflect that. Ask yourself: “Where are they in their life?” For example, is someone getting married? How about matching mugs or luggage tags? Or, does your coworker want to learn how to cook? You could get them a cookbook. Should I get my boss a gift? “If you feel compelled to gift your boss, it should be something modest,” national etiquette expert Diane Gottsman tells Fast Company. “Something they can use, such as an inexpensive office gadget, baked goods, or a box of fruit. Not wine, cologne, or a tie.” Choose a minimal price point to show you aren’t sucking up to the boss, or trying to get special treatment from a supervisor or a colleague. What about colleagues? “Many offices have a Secret Santa or White Elephant exchange. Always stay within the price range,” Gottsman advises. But what if you want to gift your office bestie or “work wife” something special? “Anything else should be given out of the office, if you are only going to gift a few people and not others,” she says. “It avoids hurt feelings.” What are some expert-approved gifts? Gottsman recommends a thermal tote bag, a multi-prong cell phone charger, a beautiful bottle of olive oil, or a warm scarf. “One thing I have been gifting is games,” Dermer says. “Chess, checkers, Rummikub, or a Majong set. Games are fun and they bring you together.” Some of Dermer’s favorite gifts include: Flamingo Estate olive oil and vinegar set Leatherology tech organizer Aura digital frame Rummikub set Backgammon set Coffee table book Blunt umbrella
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E-Commerce
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