In this feature series, Fortune reporters and editors take you behind the scenes to explore how AI is being deployed across industries; how are companies using AI to stay ahead of the competition, and what will this inflection point mean for the future of business?

AI Features

Nearly half of all Americans wear a fitness tracker, up from just 13% a decade ago. But while early wearables just counted steps, connected fitness giants like Oura and Tonal are using artificial intelligence to predict your recovery before you even feel it. 

Fortune senior writer Allie Garfinkle goes inside the $2 trillion wellness economy to test the tech, analyze the multi-billion-dollar data moats, and ask a fundamental question: Is hyper-optimization making us peak performers, or just more anxious?

Inside the "Lifemaxxing" Economy: Has Fitness AI Optimization Gone Too Far?

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3 We're going all out. Welcome to the life maxing economy. We're capturing an additional 1000 points of data every second from our camera that's tracking 36 different points on your body. The global consumer wellness market is now worth $2 trillion and nearly half of American adults own at least one wearable of 33% points from a decade ago. Sherman and Allie here today. We are going to show you the new Order Ring 5 and our thoughts on it. When it says you're getting sick in 3 days' time, you will be sick, and it's the AI that's tuned to you. Now AI is. Transforming passive step counters into active coaches with hyper personalized insights designed to optimize every hour of your day, awake and asleep. So it's your AI spotter. Exactly. But some people are getting burnt out. Do you really need to be wearing 56 devices? No. Will all this information really make us healthier, or are we just becoming data obsessed? There's no reason to overoptimize. That 1% improvement is not going to change your life. Amazing. Later March. Hey Ali. Hi, Coach Woody, it's so nice to meet you. Super excited to have you here. I'm so excited to be here. Yeah, this is where the magic happens. The global smart fitness market is projected to grow to over $60 billion over the next decade, fueled by an increase in wearable devices and connected gym systems. Oh, it's helping me. Maybe you start to reach failure or fatigue. You cannot complete a rep. Burnout will kick in to lessen the load so that you can finish the wrap, so that you can finish the set. And fitness tracking isn't new. Basic pedometers go back decades, and more modern devices tracking calories, heart rate, and sleep surged in the late 2000s and early 2010s. AI is transforming the capabilities of these tools. Connected devices are no longer just collecting data. Now they're telling users how to use this data. AI really kind of underpins almost everything that we do, and we're becoming predictive in that. A world with generative AI allowing members to talk to the product in kind of human language. Tonal makes smart home gym equipment with AI-powered resistance weights that learn and adjust based on the user. They also offer AI coaching software and human-guided workout classes. Founded in 2015, Tonal has accumulated trillions of data points from everyday lifters to brand ambassadors like LeBron James and Serena. Williams Toal says its members have lifted more than 235 billion pounds, building the largest strength training data set in the world. What our members are telling us is that I want to run a marathon or I want to get my mobility back, you know, we're now offering through Tia, which is our total intelligent assistant, an ability to ask those questions, predict outcomes for individuals, and prescribe them workouts along the way. Pal is one of several connected fitness devices introducing conversational AI coaches into their products, but established large language models pose a significant threat. If chatbots can analyze the same fitness data for free, they risk reducing connected devices to what they once were pricey data collectors. Do you feel like you're competing with the large LLMs on that level? I think what they built is a phenomenal system where those systems fall short is in areas of specialization. What we bring to the table is a wealth of data, 8 years plus worth of workout experience for hundreds of thousands of people that allows us to augment what those companies do and specifically address. Strength training outcomes in a way that these companies just don't have systems trained to accomplish. What are we looking at here? We've talked about all this data. This is a system that we use that measures the force input for the motor. Every time you pull on a cable, go ahead and grab a cable. We're collecting data 50 times a second, thousands of data points every second that aggregate to give us a really, really, really highly specific force curve on what it is. That you're doing, how that complements your given move and how we build all of that into a system that personalizes your lifts. Companies like Tonal are positioning their proprietary data as the defense by matching trillions of data points with its own closed loop large language model. Tonal says they provide personalized analysis. This also adds a lucrative subscription revenue stream to their existing hardware sales. We didn't want to just be a wrapper on top of one of these big foundational models, which no harm or no foul to any company that wants to do that. What we wanted to do was take the time to really train our system on the data that we have and augment it with the expertise of our staff. To what extent do you feel you're competing with chat GBT or cloud? I mean we certainly have observed people taking medical information and uploading it to. Chat GPT or cloud and asking questions and we thought oh we can do this but we can do this with with the data not just as you prompting the LLM. Why don't we have that machine intelligence, the doctor in your pocket monitoring what's going on with you and then giving you an insight. Aura, the Smart ringing and health tracking platform that monitors sleep, physical activity, and other biometric signals, originally only sold physical devices. But in 2021, Aura introduced a subscription-based revenue model built on AI-powered personalized health insights. Under CEO Tom Hale, that software model became a revenue driver. I think AI and our particular take on it is going to be a pretty big mode. The second thing that is a competitive mode is the data set itself. The data is actually the scarce resource here. Our data set, 42 billion hours of data across a huge population, that data asset is what fuels the intelligence. The intelligence is the thing that you can't. Create unless you have the data. The company says it generated over $1 billion in revenue in 2025, doubling sales for the second consecutive year. About 20% of that comes from subscriptions. A rumored IPO candidate, or it could be valued at more than $16 billion if it goes public in 2026. Where we end is everyone's already got a supercomputer of some sort, and there'll be a device that you wear on your body. And that device will monitor your physiology, and those things are looking out for you and making you healthy. And by the way, that will bend the curve not just of your personal health outcome, but bend the curve on a societal health outcome. We spend 1 in $5 in this country of our GDP on healthcare. That's ridiculous compared to European countries where it's like 1 in 10 or less. We think that should change and we think this is part of that change. So this is my favorite drawer here. This is my wearables drawer. OK. We go ahead and open it up. Oh my gosh, there's so many. This is where people get the most shocks. It's probably like 30 different watches, smart rings, bands just in this drawer right here. Shervin Shaikh, known as Shervin Shares on YouTube, is a fitness influencer known for reviewing the latest products in the booming health and fitness technology industry. I've With every fitness tech equipment out there, and today I'm reviewing the tonal 2. He has done paid branded content for Tonal and Aura and earns commission on Aura rings sold through his affiliate link. And then what have we got in this corner over here? So this is the filming setup. What did we say? Sherman and Ali here. Sherman and Allie here today. We are going to show you the new Aura Ring 5 and our thoughts on it. So we're going to show you our little colors. What do you have on? What do you have on 2 Aura rings right now, OK. And then I have a Fitbit, the new Fitbit Air, and then the Garmin Circa. And then the whoop on this arm. So there's 3. There's an invisible one as well, exactly. And then just the Apple Watch on this side. When every choice becomes a data point from sleep to heart rate variability, some question whether all that information ultimately does more harm than good. There are also questions about the reliability of this data. Aura is currently facing a lawsuit accusing the company of providing misleading sleep tracking information. In a written response to Fortune, Aura said that they stand behind their claims of accuracy and are committed to communicating how members should use the data that Aura is measuring. I think a lot of people are kind of hitting a wall after years of over optimizing perhaps um I certainly have a complicated relationship with the data myself. How do you think about that, especially in the context of this massive data set you have? Our goal is to improve people from a strength outcome perspective and improve their health. What we're attempting to do is build a system that allows you to basically get that same value out of your training that you would get with a personal trainer. But importantly, without the cognitive load that would require you to even think about data. Has there ever been a time where you sat there and were like, oh, I have too much data? Always for me specifically, like, do you really need to be wearing 56 devices? No. And I think there was one point where I was wearing 2, like I was wearing an Apple Watch and a Whoop, and people were like, why are you wearing 2 watches? And now it seems like it's become more common where I see other people wearing 2 devices, and I guess I've become the guy who wears 6 devices. So many people think if I buy X, Y, Z, you know, this gadget, this fitness tech is going to make me fit. Buying anything is not going to change your behavior. If we use the data to influence how we feel, then we forget how we feel, and I think. How we feel is is the biggest metric for how that day is gonna go.

The secondhand clothing market is booming worldwide. Yet it took over a decade for The RealReal to reach profitability, and ThredUp is still chasing that goal, due in part to the hugely complex intake process that goes into receiving, authenticating, pricing, listing and delivering millions of unique items.

Today, AI is revolutionizing that process. Fortune senior writer Phil Wahba goes behind the scenes at The RealReal’s authentication center in New Jersey to see how the new AI-powered intake tools are transforming the secondhand clothing industry.

Inside The RealReal’s AI-Powered Authentication Center

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In the US alone, there's $200 billion trapped in people's closets of luxury goods, and then every year $80 billion gets added. Resale is booming, but just a few years ago, used apparel resellers like the Real Reel and Thread Up were hanged by a thread. Despite the US secondhand clothing market reaching $43 billion in 2023, shares in both companies plunged towards record lows. Single skew items, 50 million items sold, 200 attributes per item. When you get in there and you really start to understand the business, you realize it's much more complex than you think it is. That same year, ultra fast fashion giant Sheehan. Expanded its third party marketplace to the US, further threatening resale on speed, price, and variety. So how did digital secondhand sellers survive? Enter AI. Really what we're doing here is capturing more and more data. The decisions are happening at the speed of machines as opposed to humans. Can AI help resellers compete against the world's biggest, fastest retailers? This facility is about 500,000 square feet network wide across the east and west coast. We have about 1.3 million square feet of warehousing space. The real real is one of the largest luxury goods resellers on the planet. Yet the company operated at a loss for more than a decade, and the complexity and costs of processing secondhand goods remained a major challenge. If you think about real real, you're not sourcing from like other brands, right? You're sourcing from individuals. All of these items will be very, very unique. You could have a particular color or a particular style or a fabric. You have a significant supply side uncertainty, which makes this whole problem much, much more challenging. Unlike broader resale sites like eBay, the Real Real and ThreadUp are responsible for intaking, authenticating, pricing, and shipping millions of items flowing through their inventory. This puts them at a major disadvantage against other online retailers, including ultra fast fashion giants like Xi'an which can drop up to 10,000 new designs daily and ship products within weeks at deep discounts. Today, The Real Real believes AI can change that. So this is our receiving department and we have one of our merchandise operations specialists and he is taking some scanned images of tags, the designer tag, the fabric tag, and this is the start of feeding data into Athena, and that is our AI powered intake platform that helps us to automate the authentication and lifting process of these items. And it's essentially going to assign it a counterfeiting score, and that's going to help route it to the next phase in the warehouse. The Real Real has been incorporating AI tools across its operations since 2018, significantly cutting processing time through AI enhanced intake, authentication, and pricing. The strategy is starting to pay off. In 2025, for the first time, the real real logged four consecutive quarters with positive adjusted EBITDA. GMV rose 22% in the second quarter of 2026, and operating expense leverage improved by roughly 4.7% points. We really focused how we think about AI on our value props and our moat. Authentication is a big one for us. It's our pricing algorithms. We let the market set the price. We don't set the price, so it's all of that data, but it's also taking dollars out of the unit economics that it would take to process the item so we can take that money and invest it in. or invested in other parts of our business. Authentication has long been a challenge for the Real Reel. In 2018, Chanel sued the company for selling counterfeit Chanel products, and a few years later, the Real Reel settled a class action lawsuit accusing the company of using undertrained staff to help meet high authentication quotas. Today, the Real Real's AI powered intake system, Athena helps workers price and authenticate SKUs in seconds. The company plans for Athena to process half of all new items by the end of 2026, up from about 20% a year earlier. On the supply side, the decisions are happening at the speed of machines as opposed to humans. So typically if you think of like a supply chain, right, it has many, many subsystems, so AI can form that layer or a decision support system which can integrate all of this together and actually do scenario analysis of the speed of machines. So now you can have fewer experts or they can be used in a better way. And secondly, your kind of speed of decision making has significantly improved. We've actually built proprietary. for our gemologists, our watchmakers, our handbag experts to count the melee on every stone, the weight of our diamonds. It utilizes a highly magnified camera to help us take a high resolution picture, and it's able to come up with the carat weight in a moment, just 1 or 2 seconds. And so now actually our humans don't need to so much look at that. They're going to be focusing more on grading the actual diamond, which is where their expertise lies. Does this speed up the process? Does it make it more accurate, or both? It's both. It adds consistency, of course, and speeds up the process where we can process items much faster for the consigner so it gets on the site faster, but also the technology behind the scenes, making sure that each individual item is authentic. But also that the consigner is valid too. The global resale market is still booming. It's expected to reach $393 billion by 2030. And now secondhand resellers are integrating AI across buying and selling, ranging from one tap pricing to personalized agentic shopping experiences. Yet as supply, demand, and AI integration increases, critics still point to lasting industry issues around authentication, shipment delays, and low payouts. What are some of the pitfalls of AI where maybe companies lay too much on the back of AI? One of the big challenges that everybody is facing with AI right now is that fundamentally these are nondeterministic systems, what is known as the verification problem. They do tend to hallucinate. It's important for decision makers to understand, especially in the supply chain side. This is sort of mission critical. Decisions that you're making and if you make a wrong decision, it can potentially result in millions of dollars of losses. So when you integrate these things, it's super critical to figure out who is doing the verification, where is the human sitting in the loop. Somebody needs to be accountable for all of these decisions. We've invested millions of dollars over the years and continue to do so as fakes have gotten better. Over the years as things have changed, it requires us to continuously learn, continuously evolve our technology, but it is the art and science. Like at the end of the day you do need that gemologist to come in and look at the item or you need that watchmaker to come in and open the watch to make sure there's no aftermarket parts, and that's where the art and science is really important, but also makes. Us defensible to AI. Why does Gen Z like resale so much? You know, I think it's a few things. I think it's authenticity that's number one, you're right? Like in a world where everything's starting to look the same and it's algorithms after algorithm, it's the anti-fast fashion at the end of the day, but you know you're going to go somewhere and you're going to look authentic to who you are and you're going to look unique, be your own stylists, right?

What if computers could smell?

For decades, scent remained computing’s final analog frontier. Now, Osmo has accomplished what many thought impossible: digitizing smell. By training AI to decode the relationship between molecular structures and human perception, Osmo has built the world’s first olfactory intelligence platform—transforming scent into searchable, programmable data. While poised to reshape the $30 billion flavor and fragrance industry, its implications extend far beyond perfume, opening new possibilities across health, food, technology, and human-computer interaction.

Can AI smell you? This startup built a $130 million digital nose

Our mission is to give computers a sense of smell. For a century, we've been turning the world into data. Sight became pixels, sound became waveforms, but smell, the oldest sense no machine could digitize until now. For the first time ever, a teleported smell. With a single prompt, this startup says they can create a scent in just 48 hours. Completely reversing the model of how synth is designed and delivered a little bit fluffy, velvety. We see a lot of textures in perfumery too, but fragrance is just the beginning. The final frontier is using scent in the way that dogs can use scent to understand wellness or illness. This AI startup has raised $130 million to turn scent into data, perfume now, disease detection down the line. So what happens when you give a computer a nose? We're going to smell the whole world. For decades, 4 giants have dominated the $30 billion flavor and fragrance market. Now Ozmo's using AI to make it cheaper, faster, and more accessible. Neuroscientist Alex Wilschko created OSMO with a mission build the world's first olfactory intelligence, AI trained to predict what a molecule will smell like. What is OSO? Why did you create it? What we're trying to do is bring new, unique scents to more people in the world by removing the gates in the secrecy and frankly some of the arrogance that like, oh, only certain companies or only certain people can have a good scent. No, anybody can. We're democratizing scent. The idea of having shazam for smell, that you can walk into a room and just press a button and it just digitally can find the frequency of the music and match it to a correlated database, you should have that same feeling when you walk into a room and you smell a perfume or a cologne. You're like, what is that? Josh Wolf is a venture capitalist known for shaping cutting edge tech into real businesses. With a recently closed $1.5 billion fund, Lux Capital now commands more than $7 billion in assets. As one of the company's first outside investors, the firm helped raise $60 million for Ozmo's launch in 2023. How do you distinguish between what is scientifically possible and what is just hype? It's really sophisticated, hard. You need a PhD, and it comes down to three words. Does it work? If you're looking at something that is a hypothesis and you want to test it, then it's almost like a poker game, and you wanna ask how much capital accomplishes what in what period of time, and who will care. The bet is paying off. Osmo signed clients from indie brands to multinational CPGs. The company has authenticated sneakers by smell. All you have to do is get the shoebox close to the sensor, reduced animal testing, and received millions in funding from the Gates Foundation to support AI enabled insect control, altogether raising $130 million in just 4 years. How do you prove though that it's not just evocative, but it's also really a business? In our case right now, you just smell the results. I smell anxiety and 234 years of American capitalism. We took Osmo's tech to the floor of the New York Stock Exchange to see if a machine could actually capture the smell of ambition. To bottle a place like this, Osmo had to teach a machine to turn scent into numbers. What does it actually mean to collect data for scent? One is like you have to make or acquire an actual physical smell, and then you have to turn that physical smell into ones and zeros. The breakthrough came from an unlikely place, the AI built to discover new drugs. They figured out how to teach AI to deal with inputs that aren't shaped like a rectangle. So images are rectangles, they're grids of pixels. Text is a very long, thin rectangle, but molecules are atoms that click together in all kinds of different shapes. They're very much not a grid. And so there was a set of techniques developed to deal with that kind of input called graph neural networks. In 2024, Osmo captured the smell of a real plum, encoding it as data and rebuilding it across the lab. No human involved. We put that prompt of like give me the smell of the New York Stock Exchange into Osmo Studio, and Osmo Studio is able to take really anything like text and image and convert it into scent. What does the New York Stock Exchange smell like? So the first one is woody, floral, earthy. Well, it smells a lot better than the New York Stock Exchange, which is frankly a little bit of a sweatier place. But it does actually smell like the digital quality of it. Yeah, well, let me give you this direction and see what you think of this. That to me smells like the New York Stock Exchange. Did we get it? Yes, that to me smells like the New York Stock Exchange. This feels like a marble lobby where a musky warmth settles on polished stone. I can't stop smelling. At what point, if any, was there a human touch in this process? At the very beginning when you and I actually designed this, we're using a tool, we're using Osmo Studio to create scent, but like that's like using Canva or Photoshop or Figma. Like we were the designers. The brands behind most perfume, shampoo, and detergent don't formulate their own scents. They buy them from suppliers. That's the enterprise business Ozmo is disrupting, the platform layer behind scent. Now, to what extent is Osmo revenue generating? What parts are in research? For the first, I would say 2.5 years. Everything was R&D because what we were staring at had so much scientific risk and after we teleported to scent, we realized we've actually taken a lot of that scientific risk off the table and so we're a hybrid like we're highly technical, but the fastest growing part of our company is in manufacturing and in sales and we. finished fragrance to emerging brands that are growing super fast, but we also sell the software capabilities and access to our data for really large companies that also trade in smell. So we have a few different facets of our business, but they all are involved in making great fragrances for brands. The big suppliers often take months, with many requiring bulk orders. Ozmo, on the other hand, serves smaller brands, a market historically underserved. You could have done this as a B2C business, but you chose B2B specifically. Why? One thing I've noticed in B2C, you can be successful as a brand with an amazing story. And having amazing technology and science behind that can help, but it might not be the single reason why you win. And I wanted to set us up in a business where we're kind of dragged into the future where we keep developing our technology, keep developing our research to achieve even greater ambitions and getting better at the core science of scent and unraveling the mystery of scent to actually be good for the business. Who are your competitors? Who do you look at and say they're also marching towards the same mountain? I don't think there's anybody out there that's as weird as we are. I mean, there's companies that design and manufacture scent for sure. They've been around for hundreds of years. We're doing it very differently, right? So instead of it being a very gate kept process, we actually show our formulas to people. Traditionally, chemists test one molecule at a time. Osmo meanwhile says they use AI to screen billions at once, selecting only the best candidates to develop. The company says its success rates are 10 times higher and that it's 5 times cheaper compared to industry norms. Osmo also says it published more new fragrance ingredient patents than the rest of the industry combined in 2025. Where a traditional patent might cover one or two molecules, Osmo's patents can include up to 250, and they hope that scale means more profitability. What do you consider the moat for something like? Some of it is the data and then being able to be the only company that can predictably produce something with mathematical rigor every single time. We want it just like somebody was tuning a guitar. We want it to be a little bit more pungent or a little bit spikier. We want it to last for longer and Alex can basically say, OK, we're going to add this to this and we're going to be able to do it and we'll be able to do it every single time reproducibly and predictably. While AI expedites molecule discovery, humans are the artists who help craft them into a final scent. So this, this is a mini experimental robot. It can work day and night and can do a lot of samples because AI needs a lot of data, needs a lot of how do you make an apple, how do you make fresh, how do you make more. We have a lot of these, yes, because they're very helpful. Its name is Taylor Swift. That's it. Every robot here has a name. Christophe Laudemil, one of the few master perfumers in the world, leads Osmo's AI human collaboration. We know the AI is going to come and people are working on it, but what happens for us, I think as a perfumer to get into AI and now to steer it as well. It's not all AI, it's not all human. It's this beautiful blend of like creativity being unleashed in new ways that's enabled by the technology that we've created. But fragrance isn't the endgame. Ozma says it's building towards something bigger, a tool that could one day help detect disease. The final frontier is using scent in the way that dogs can use scent to understand wellness or illness. It's insanely hard. Dogs have been trained, who have sharper senses of smell than we do, to detect COVID, cancer, Parkinson's, Alzheimer's. And so every one of those diseases has a signature. For Osmo, fragrance is the commercial work paying for that longer term research. And the reason is we have to fund ourselves in order to do that, and the act of making fragrances actually generates data and understanding, which is strengthening our platform to make even more ambitious bets in the future. For Alex, that motivation is personal. His father died of brain cancer, a condition that some evidence suggests has a unique scent. What we need to uncover is perhaps those are also the same molecules in someone who has brain cancer like my father had. If you have a disease that's developing, there's going to be something that's going to change about the scents that are leaving your body, and nobody knows the details of what that is, but we know that dogs can detect it. And so what OSMO is is an organization, a group of people designed to unravel these mysteries kind of one by one. In a way that is responsible and resilient and designed to last over time.

Agentic AI has officially graduated from basic decision trees to unscripted autonomous agents, turning customer service into a massive $15 billion market. Startups like Decagon AI are pulling in a $4.5B valuation because of high enterprise demand. Hertz is already routing 50% of its phone interactions through AI voice, dropping its per-call costs by more than half. And the car rental giant is hardly alone. Companies are fast adopting AI customer service agents. However, about 80% of consumers still crave a human on the other line, and 40% of us would literally pay a premium just to bypass the bot. Fortune’s AI Editor Jeremy Kahn takes us behind the scenes to break down the delicate balance between instant resolutions and human connection.

Almost 80% of Americans Prefer Human Agents, but Why Is AI Growing in Customer Service?

AI is making customer service even more unbearable. Three second delay and it goes ha ha ha, and I couldn't talk to a representative. How often does someone come in and they're just like, 000 agent agent agent. In 2026, AI and autonomous agents have taken over customer service. A recent report says 88% of contact centers are now leveraging AI, with a quarter of them running on fully integrated automation. Most of the inquiries that happened today in five years, there's no question that AI. will be able to handle all of them, but there's a catch. Another study reveals that nearly 80% of Americans still prefer talking to a living, breathing human being. And the hidden costs of this automation, it's staggering. Research shows that simply disclosing a chatbot's identity before a chat even begins can plummet purchase conversion rates by 80%. There's always that little human touch that that we always need. Yet we're a generation obsessed with instant gratification. How do we square that? As the AI editor at Fortune, I focus on how AI is shaping business and the wider economy. With AI poised to transform customer experience, I wanted to talk with business leaders on the front lines of this shift to find out how they're adapting and what the future of business really looks like. Despite our collective craving for human connection, the AI customer service market has ballooned to over $15 billion in 2026. So agentic AI or AI systems that actually take actions in the world, whereas previously you had automated agents that had to follow a set decision tree. With the current systems, they actually are able to have much less structured flows of how they operate. One of the front runners leading the charge is Decagon AI. Founded in 2024, they've already hauled in $481 million in funding, commanding a $4.5 billion post-money valuation. That is a major indicator of just how eager enterprises are to adopt this tech. Decagon's mission building faster, smarter AI without damaging the customer experience, and it's working. They're maintaining a 93% customer satisfaction score while driving revenue from fully automated interactions with. Some individual contracts now clearing $1 million. What do you think that looks like going forward in, in 5 years? Most of the inquiries that happened today, I would say in 5 years there's no question that an AI agent will be able to handle all of them. In a lot of these mass, like huge volume use cases, the human touch doesn't really matter, right? It's, it's not like you need the human touch to book a new flight or to, you know, reorder a lost credit card. All you want in that situation is just like get it done. It's, it's a little bit more mundane. These are not canned responses. This is something way more personalized. It's very different from what you've tried before. You're kind of changing the customer's behavior. But get this, over 40% of consumers say they'd actually pay a premium just to guarantee they can speak to a real human representative. Around the time of the industrial revolution, people wanted to buy a Ford Model T because the presumption was the quality was higher because it was built in a factory. Now today, curated, handmade experiences. Seem to have more of a premium association with them. Look across the industry and you'll see giants leveraging AI in different ways. Take Chipotle, a behemoth with over 100,000 employees, while some, like Decagon, are adapting to customers' changing behavior, others are doubling down on the interpersonal interaction. What kind of customer experience do you really want people to have going forward? One of the things that I think sets us apart and makes us special is the human interaction. Hi, how are you? Those curated moments of human connection and service. Are always going to be a fundamental part of what the Chipotle experience is. Back in 2024, they automated their hiring process with an administrative AI system called Avocado. And what was the idea behind that AI system? The average restaurant manager was spending more than 10 hours a week on recruiting activity, 20% of their week just on that one task. It's a fraction of that now. And how has AI changed sort of what your daily routine looks like? It allows me to, you know, be in front with the guest, serve them, going over recipe cards, you know, tasting food, a lot of training, people development for Chipotle, you know, we always need that human touch, you know, we always need, uh, people tasting the food because that's what we sell at the end of the day. We definitely always gonna need that human aspect. I don't know what kind of day they might be having before they come here, you know, we give them a burrito, a bowl, you know, we serve them with gratitude, with a smile, you know. Thank you. That could make anybody's day. But to see the customer facing side of this revolution, I synced up with Hertz. As an international car rental giant, they handle massive call volumes. By deploying Decagon's tech since early 2025, they're now resolving 75% of customer inquiries autonomously. We want to take every single one of those calls that comes our way, and we want that to be as seamless for our customers as possible. To see this AI agent interaction, I tried giving Hertz's AI, Haley, a call. Hi, this is Haley. I see you have a rental scheduled for pickup. How can I help you today? Oh hey Hayley, I'd like to change the drop off location. Let me see, where would you like to drop off the car instead? Yeah, if I could drop it off at LaGuardia Airport, is that possible? Great, you're all set with the new drop off at LaGuardia. Yeah, that was pretty good. I mean, I think that's, you know, that's was smooth, and it definitely didn't have to wait, which is nice not to have to queue at all. That's the new expectation. They're going to expect faster. Response times, better experiences. When we look at the future of contact centers, we're gonna see more and more AI agents taking on this workload. Every business will have an AI agent that's kind of like the front door to me that actually isn't to say that the human agents will go away because there's gonna be a lot more things that can be handled. Like why are people doing more of the mundane conversations because they have to, um, like those are the bread and butter. But if the AI can take that away, it frees them up to do more conversations that are not being had today. In fact, Hertz told us that AI voice is now live across 50% of their total phone interactions. In 2026, the cost of a customer interaction dropped by more than half to under $1.50. That shift means companies like Hertz are now seeing roughly $3.5 back for every $1 they put into AI customer service. Yet, depending on the issue, Hertz still reroutes calls to human agents. Hi, this is Hayley. How can I help you today? Hi, Haley, I've got a Hertz rental car right now and I have a flat tire. I'll connect you to a live agent now. Thanks for your patience. Thank you. They've got a flat tire or maybe they're on the side of the road and they're at risk. They absolutely want to hear a human voice and have that level of connection where someone understands that they may be at risk and is doing everything they can to support them in that scenario. Earlier you mentioned that billing is an area that still reverts to the human agent. Why is that? Why, why is the billing so important to be handled by a human? Yes, we think that the billing conversation is one about trust and the customers looking to understand why maybe a charge is not what they believe. It should be. So that's one where we very much default to someone being able to explain those pieces to them, and we found customers much prefer that. But even with those undeniable margins, the trust gap persists. Studies show that only 13% of consumers completely trust AI. Generally speaking, people will say I would be a little bit nervous about that, or in some cases a lot nervous or being very careful to consider PII or other information that might be in those systems and make sure that the models are both protected and secure from that perspective. that were responsibly handling people's information. How do you make sure there aren't hallucinations, because there have been these cases, I mean this famous Air Canada case where the judge said Air Canada was sort of liable for that incorrect information that its AI system had provided as part of a customer support experience. The only way to prevent that in a nondeterministic system is to have all these checks around. We call them supervisor models, and you kind of think of it as like the main agent is executing and you have these like, you know, small models around it that are running in parallel. There's all these models around it to make sure it stays on track and stays within the guard rails. Data proves we want more human interaction and will actively penalize businesses that deprive us of it. But as agentic AI gets increasingly good, will that sentiment hold? Are we staring at a future where consumers are forced to choose between instant resolutions or the human touch? Is there anything you think AI agents? We never really be able to automate when it comes to the customer contact, honestly, no, I think at a certain point it's almost indistinguishable. You'll get into a situation where if you didn't tell, inform the user that you might not know, consumers will now learn the other side, which they're, they're now used to interacting with great AI agents that they'll be disappointed if they're waiting on hold. We have very high conviction that that's where, where the world is moving towards.

In 2026, AI sits on both sides of the hiring desk. Job seekers use it to perfect their resumes. Employers use it to screen those same resumes out. And caught in the middle, millions of people, especially first-time job seekers, are getting lost. Fortune Editorial Director Kristin Stoller goes inside the AI hiring arms race, talking to the experts, economists, and executives navigating it firsthand. Entry-level job postings are down 35% since 2023. AI is now interviewing candidates before a human ever does. So what does it actually take to get hired in 2026?

Robots Screening Robots: Inside the AI Hiring Arms Race

This has happened to me 4 times in the past month, interviewing again and again and again and getting rejected, to constantly be rejected, never hearing back, never knowing what's going on. 11,000 job applications hit LinkedIn every minute. Meanwhile, entry-level job postings are down 35% since 2023. I'll always see, say, on LinkedIn, that like thousands of people have already applied, and it's a pretty intimidating number. Candidates say AI both helps and hinders, allowing them to. resumes while screening tools are automatically filtering out candidates, making it harder to get past the AI bots. Employers saying, I don't hire entry level. In 3 to 5 years from now you're not going to have the people with 3 to 5 years of experience because you never hired them to begin with. As AI levels the playing field, it's also causing people to be left behind, and experts argue it's fundamentally changing the outlook for the US job market. We have AI interviewers interviewing AI trained candidates. It's mutually assured destruction. In the summer of 2022, we got a call from this guy Sam up in San Francisco, and Sam was calling to tell us about a brand new tool that was about to change the world forever. That Sam was Sam Altman calling about Chat GPT six months before launch. Jeremy Schieffling is a career coach who spent time inside LinkedIn, Khan Academy, and Apple. He now teaches job seekers at more than. 350 universities, how to get past the AI systems filtering them out. As an editorial director at Fortune, I focus on the trends shaping the future of work. With AI playing an increasingly outsized role, I wanted to speak to the people on the front lines of this change to learn how they're adapting and what this all means for today's job market. So pretend that I am a job seeker coming to you for advice. What are the top few things and tips you would tell me to use AI to succeed and land the job? So the first thing is, is actually to step away from the AI. Don't start by going to chat GPT, write me a cover letter. Instead, think about who you're writing to. That's a real human being that you need to understand. Where do you see the biggest mistakes that job seekers are making right now as they are using AI to maybe kind of game the system? What was once a 15% chance of getting interviewed for every online application is now a 2% chance. So what do you do? You say, I'm going to apply to more jobs than ever, especially with AI that makes it basically free to apply to as many jobs as you want. The reality is that every one of those jobs that you just applied for, you really have 0% chance of getting because you have the lowest common denominator application. Today is not about taking notes. It is about taking action. As I'm using it for writing things out, I notice it losing my voice in it. Interesting. So I think what I'm trying to do is. Fit into a formula that I'm trying to learn about specific job positions and resumes and cover letters, but at the same time trying to retain what distinguishes me. What have students been asking you the most today is what's the dark side of AI? It's taking this really unique differentiated thing which is. Talent and it's turning it into a commodity. The worst kind of product is a commodity because you have no advantage over anyone else in the marketplace. Job seeker confidence is down across the board, but among the most hard hit are first time job seekers. Entry level employment in AI exposed fields has dropped 13% since 2022. When both sides are using LLMs to do each part of the hiring process, what happens to the hiring process? What are you seeing right now? It's a much tougher environment in the macroeconomic sense, right, because you have, we've been dealing with a cooling labor market over the last 4 to 5 years at this point, and what that means is there are just fewer jobs to go around, so every job seeker out there has less variety to apply. To and they're feeling that too. So take those two things together and it's a much tougher environment, I think, for both parties in the marketplace. Are we facing a moment where in the future we're going to have a giant leadership crisis because the people who are supposed to be getting those jobs, working their way up right now, just can't? We're seeing less demand for entry level jobs. There's actually an increase in terms of wanting job seekers. that have 2 to 3 years or maybe 3 to 5 years of experience because you want them to be able to perhaps manage, use the AI tools to have a little bit of experience to be able to hop right in and hit the ground running. The problem, of course, is, to your point, it's, you're going to have a pipeline problem. One company that's famous for its leadership pipeline is the beverage giant PepsiCo. 12 PepsiCo alums currently lead Fortune 500 companies. For the people responsible for bringing this talent in on the ground floor, cutting through the AI noise and identifying potential is critical. Just because you've had a process that's been working for 40 years, why would we keep doing it that way? It can be different. What's stopping us from the future? We have a big project underway right now. It's called like GM General Manager of the Future, and we're looking at all the data we've collected for 20 years. What makes people successful? Did their data, their assessment, was it their lead indicator or actually was it the jobs we put them in? Was it the products they innovated? Was the problem they solved? And how do I reformulate that so that I can have a deep bench? A big conversation I think a lot of companies are having right now is around skills, and a lot of skills you can test for, you know, engineering ones, different process ones, but. There are a lot that you can't test for, especially ones that have to do with the human side of things, empathy, managerial skills. How do you identify how someone, their potential, what they're going to do for a manager later in their career? So PepsiCo has a rich history in doing this, and you probably see some of the media, oh, the CEO factory, that doesn't just happen over. Overnight, so we actually rely on a lot of employee data and employee profiles over, you know, like the last 50 years. What are indicators in their profile that made them successful? It's not generally their degree, although we find people who have an orientation towards business or engineering are more successful in our environment because we are a sales marketing. Culture, but we do find that there's a certain amount of hustle, ability to be open to new things, uh, critical thinking, and we use critical behavioral interviewing, right? We ask people to tell us a time when. How do we get you to tell us that story? To understand how we got here, we need to rewind to the 90s. Modern applicant tracking systems or ATS took off during the dot com era as hiring shifted from paper resumes to digital applications. The technology was built to manage the growing flood of resumes coming in from online job boards, digitizing what had long been a manual process. Just in the US alone, we averaged like almost 900,000 applicants. Nearly 99% of all Fortune 500 companies use ATS platforms on a regular basis. With AI-powered mass submissions, an oversaturated market, and an increased number of fake applications, relying on human review alone is no longer a. Realistic way to filter the flood. So it's clear that AI is here to stay. It's not going anywhere. Looking into the future, how do you foresee what this means for for the job hunt? So when AI first came out in 2022 and I got early access, I was like, wow, this is amazing for job seekers. Now they can apply to all these jobs and get all these opportunities. But here in 2026, AI is not just changing how we apply for jobs, it's changing the jobs that are available to us. These AI interviews are getting out of control. The latest. Evolution, AI interviews. Candidates are now interviewing with an AI bot before a human ever enters the loop. It's like the robots talking to the robots. But no, really, I think potentially an AI interview could be less biased than a human interview, right? It really depends on how it's been programmed. On the other hand, hiring is still inherently a very human process. You still need to have an employer and a job seeker come together and figure out, is this a good match or not. So you're entrusting the AI agent to do a lot of heavy work in that setting, and it could also not be a good outcome. How are you advising your job seekers to do these interviews or not do them at all? Play it by the rules of the game. Recognize that these tools are not as complex as human brains, and so they're looking for certain things. Do you mention the specific projects that align with the role? Do you seem excited and genuine about what you're talking about? All that stuff is fed into the algorithm. But then let's start to imagine this. Is there a way around the box, around that gatekeeper? And I think what we know to be true is that here in 2026, what was once a 10x advantage when you got a referral from someone on the inside is now a 20x advantage because what has AI done? It's destroyed trust in anything we see on the screen. Can you just paint a picture for me of the ideal AI job search in 2026? It would be three simple things. Number one, if you can start by understanding the skills you bring to bear, the passion that lights up those skills, and then connect that to what recruiters actually want in terms of specific job titles, you're already 10 steps ahead of the game. Number 2, focus on what works the best. If we know that referrals give you a 20x advantage, why are we wasting time with AI hacks when the human hacks are even more important than ever? And then number 3, once you get the job, don't stop the relational humanistic skills plus the AI skills on the job or what are going to help you land that next job after you're already in the building. When it's apparent that people lean into hard problems, it's super attractive for like any company. Tell us how you solved hard problems, how you resolve conflict, how you. Leaned into it. You didn't like wait for someone to do it for you. That is super attractive because we don't actually know what's coming. But we're definitely encouraging people to lean into it. Like, please use the tools. Tell us what you're learning. Uh, we need people to adopt these new ways of working. And as they adopt them, if they don't like it, I'm like, Well, don't like stew about it. Come back and tell us. Today, automated hiring can feel like a wall, but according to experts, it's also a doorway. AI is reshaping hiring, making it faster, easier, and more efficient. But as the process changes, so do the jobs themselves, making it tougher for many first-time job seekers to even get started. Still, one thing hasn't changed human connection matters. And in a job market increasingly shaped by AI, our human qualities may be what help us stand out the most. The way that I really want you using AI is AI is a compliment. A superpower that we did not have 3 years ago but now makes us even more effective and serving the world. I really do believe the American dream, the human dream period, is based on the idea that the next generation will have the chance to do more, do better than ever before. And if we can't make those connections in the workplace where people find that opportunity, we're not going to be planting that seed for those future generations.

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