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I was interviewing for a job as a customer service agent with Anna. She had a low, pleasant voice and shed nailed the pronunciation of my namesomething few people do. I wanted to make a good impression except I had no idea what Anna was thinking because Anna couldn’t think. Anna wasnt technically a person. She was AI. Not only is AI changing how we do our jobs, its also changing how we get jobs. This ranges from using AI to screen resumes, schedule interviews, even conduct them. According to a 2025 report, 20% of companies are using AI to interview candidates. Even so, nothing can replace human recruiters, the folks whove deployed Anna into the wild stressed to me. After I spoke with her, I quickly understood why. In this story, paid subscribers will learn: What its like to actually go through a job interview with an AI agentand how to speak to them Where companies should deploy AI interviewers that would benefit them and job seekers AI Anna clocks in Even though I wasnt really interviewing for a jobthis was all an exercise for this story, of courseI was still nervous. I asked the team behind Anna to provide a job description so I could prepare, but outside of this experiment, I was sadly lacking in actual customer service experience. I also didnt know how AI Anna was going to react to awkward silences, panicked misdirection, or if shed be able to tell if I was lying. These worries are bad enough with a human. How would a computer program react? I got on the phone and connected with Anna. She was pleasant, and frankly, sounded way more human than I was expecting. We exchanged greetings, and before long, I was in full-on job interview mode with an AI. First up, she asked me to describe a time when I had to explain something complex over the phone clearly. I blanked. Finally, I described how journalism involves explaining complex ideas because youre asking questions. It sounded weak even to my own ears. Sure enough, she was not impressed. Id like to explore a scenario thats more specific to the role were discussing, she replied firmly. Fair point. I managed to dredge something up from a high school job. Mercifully, AI Anna accepted the answer and moved on. Next, AI Anna wanted me to talk about a time when I had to problem solve for a customer. This, I could answer. I dove into my brief stint organizing a literary conference where writers paid to meet with agents. Occasionally agents went astray because they were hungover or running marathons and Id be left to find alternatives like rescheduling Anna cut me off. That sounds like a high-pressure situation. . . . Its great that you were able to come up with alternatives. Now Id like to switch topics for a moment. Yikes. I wasnt ready to switch topics, but AI Anna was, and I couldnt tell why. Was my example off topic? Was I taking too long to answer her question? Before I could ask, Anna had already swept on to background checks. I invented a criminal background and told AI Anna I had done some time in prison. She thanked me for being honest, and told me that she could not make any decisions. She said candidates with a criminal record would be considered on a case-by-case basis (something that would have to be verified by a human). Then I wanted to know if Id be required to work overtime. She let me know Id be required to do overtime the first six months, but only one or two times a month. Needed, accurate information that couldnt just be googledgreat. Honestly? While I found her transitions a bit jarring sometimes, she handled most questions with aplomb. How we got here AI Anna is the product of PSG Global Solutions, a staffing firm. Before deploying AI Anna in the market, the firm asked Brian Jabarian, a researcher at the University of Chicago Booth School of Business with doctorates in economics and philosophy, to study the AI Annas effectiveness. (Jabarian received no funding from PSG). In a study released in September, Jabarian conducted an experiment where 70,000 applicants for a customer service job were randomly assigned a human interviewer, an interview with AI Anna, or the ability to choose between the two. The results are surprising, and surprisingly promising for the candidates. AI interviews resulted in a 12% increase in job offers, and a 17% increase in 30-day retention on the job. Moreover, when offered a choice, 78% of applicants chose to be interviewed by AI. Jabarian theorized this was because the AI was easier to schedule with: job applicants who needed a job quickly could book a call immediately. Why the positive outcome data? Jabarian pointed out that, on average, an AI interviewer got through more required topics than human recruiters since they couldnt be distracted. (I mean, Anna did move at a brisk clip.) AI leads to a more consistent interview experience, he said. It lets the candidate talk more, and has a 50% chance of covering 10 of 14 required topics compared to 25% for human interviewers. AI Anna clocks out Afterwards, I debriefed the interview with David Koch, PSGs chief transformation and innovation officer. First, he showed me AI Annas backend: The platform had generated a recording of our conversation, a transcript, a summary of the call (including suggestions for next steps, like a follow up to discuss my criminal background), and an overall recommendation: AI Anna thought I was qualified (yay!) but merited human follow-up because of my criminal background. AI Anna also recommended a follow-up because shed cut me off when I was talking about the literary conference. Koch explained my speaking cadence is a touch slower than average, and AI Anna is programmed to respond after a certain amount of time or else the flow of conversation can become jerky. Koch noted that AI interviewing was better suited for some situations and not others. He recommended AI interviewing for high-volume hiring where theres a need to source candidates quickly for jobs that are seasonal and high turnover, like customer service agents or travel nurses. Koch also said AI interviewing is best suited for cases where theres less complexity, in which you dont need to sell a candidate on a role. From my standpoint as a lay person, the technology behind AI Anna struck me as marvelous. She corralled me into staying on topic, and was capable of social niceties. She provided detailed answers to all my questions. For recruiters, this could be life changing. Its not that AI Anna might replace them, per se (there were already things from the interview that a human colleague would have to address or follow up on). But recruiters could farm out tasks like screening calls to AI while they worked on more hih-level tasks. However, this made me worry. If AI Anna existed to save companies time, what happens to candidates who get flagged for follow-up, even for something as simple as speaking slowlylet alone a criminal background? If there are more than enough qualified candidates to fill roles, I imagine a harried hiring manager would make offers to people who dont require follow-up. Exception cases that require more time, like me, might fall to the wayside. The future: cold, but competent After my conversation with AI Anna, I felt hollow. Typically, if an interview goes well, I have the high of having connected with someone who might make me feel valued, desired, and possibly in the mix for a new job. If it doesnt go well, I spend the next couple of days wallowing in self-pity and dissecting potential red flags. AI Annas preprogrammed human-like intonation left me nothing to go on. Did she like me? Or was meh on me, but still think I was qualified? I couldnt tell probably because AI Anna does not have emotions and did not care about me. But how much does this matter? A Gallup study found that 44% of respondents said their interviews drove them to accept an offer or not. Ideally, candidates would be able to interview with their direct supervisor before getting a job in order to suss out personality matchbut for a screening interview, AI Annas value was undeniable. She raised the floor for interview quality: Shes personable and she offers a consistent experience. Theres no need to worry about the mysterious intangible of chemistry. Jabarian also pointed out that AI interviews reduce gender discrimination by half. Done right, AI interviewers could reduce bias and help qualified job candidates who may not perform well during interviews because they lack intangibles such as charisma. Still, I missed talking to a human.
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Generative AI is evolving along two distinct tracks: on one side, savvy users are building their own retrieval-augmented generation (RAG) pipelines, personal agents, or even small language models (SLMs) tailored to their contexts and data. On the other, the majority are content with LLMs out of the box: Open a page, type a query, copy the output, paste it elsewhere. That dividebetween builders and consumersis shaping not only how AI is used but also whether it delivers value at all. The difference is not just individual skill. Its also organizational. Companies are discovering that there are two categories of AI use: the administrative (summarize a report, draft a memo, produce boilerplate code) and the strategic (deploy agentic systems to automate functions, replace SaaS applications, and transform workflows). The first is incremental. The second is disruptive. But right now, the second is mostly failing. Why 95% of pilots fail The Massachusetts Institute of Technology recently found that 95% of corporate GenAI pilots fail. The reason? Most organizations are avoiding friction: They want drop-in replacements that work seamlessly, without confronting the hard questions of data governance, integration, and control. This pattern is consistent with the Gartner Hype Cycle: an initial frenzy of expectations followed by disillusionment as the technology proves more complex, messy, and political than promised. Why are so many projects failing? Because large language models from the big platforms are black boxes. Their training data is opaque, their biases unexplained, their outputs increasingly influenced by hidden incentives. Already, there are companies advertising SEO for GenAI algorithms or even Answer Engine Optimization, or AEO: optimizing content not for truth, but to game the invisible criteria of a models output. The natural endpoint is hallucinations and sponsored answers disguised as objectivity. How will you know if an LLM recommends a product because its correct, or because someone paid for it to be recommended? For organizations, that lack of transparency is fatal. You cannot build mission-critical processes on systems whose reasoning is unknowable and whose answers may be monetized without disclosure. From out of the box to personal assistant The trajectory for savvy users is clear. They are moving from using LLMs as is toward building personal assistants: systems that know their context, remember their preferences, and integrate with their tools. That shift introduces a corporate headache known as shadow AI: employees bringing their own models and agents into the workplace, outside of ITs control. I argued in a recent piece, BYOAI is a serious threat to your company, that shadow AI is the new shadow IT. What happens when a brilliant hire insists on working with her own model, fine-tuned to her workflow? Do you ban it (and risk losing talent) or do you integrate it (and lose control)? What happens when she leaves and takes her personal agent, trained on your companys data, with her? Who owns that knowledge? Corporate governance was designed for shared software and centralized systems. It was not designed for employees walking around with semiautonomous digital companions trained on proprietary data. SaaS under siege At the same time, companies are beginning to glimpse what comes next: agents that do not just sit alongside software as a service (SaaS); they replace it. With enterprise resource planning systems, you work for the software. With agents, the software works for you. Some companies are already testing the waters. Salesforce is reinventing itself through its Einstein 1 platform, effectively repositioning customer relationship management, or CRM, around agentic workflows. Klarna has announced it will shut down many SaaS providers and replace them with AI. Their first attempt may not succeed, but the direction is unmistakable: Agents are on a collision course with the subscription SaaS model. The key question is whether companies will build these platforms on black boxes they cannot control, or on open, auditable systems. Because the more strategic the use case, the higher the cost of opacity. Open source as the real answer This is why open source matters. If your future platform is an agent that automates workflows, manages sensitive data, and substitutes for your SaaS stack, can you really afford to outsource it to a system you cannot inspect? China provides a telling example. Despite being restricted from importing the most advanced chips, Chinese AI companies, under government pressure, have moved aggressively toward open-source models. The results are striking: They are catching up faster than many expected, precisely because the ecosystem is transparent, collaborative, and auditable. Open source has become their work-around for hardware limits, and also their engine of progress. For Western companies, the lesson is clear. Open source is not just about philosophy. Its about sovereignty, reliability, and trust. The role of hybrid clouds Of course, there is still the question of where the data lives. Are companies comfortable uploading their proprietary knowledge into someone elses black-box cloud? For many, the answer will increasingly be no. This is where hybrid cloud architectures become essential: They allow organizations to balance scale with governance, keeping sensitive workloads in environments they control while still accessing broader compute resources when needed. Hybrid approaches are not a panacea, but they are a pragmatic middle ground. They make it possible to experiment with agents, RAGs, and SLMs without surrendering your crown jewels to a black box. The way forward Generative AI is splitting in two directions. For the unsophisticated, it will remain a copy-and-paste tool: useful, incremental, but hardly transformative. For the sophisticated, its becoming a personal assistant. And for organizations, potentially, a full substitute for traditional software. But if companies want to make that leap from administrative uses to strategic ones, they must abandon the fantasy that black-box LLMs will carry them there. They wont. The future of corporate AI belongs to those who insist on transparency, auditability, and sovereignty, which means building on open-source, not proprietary, opacity. Anything else is just renting intelligence you dont control while your competitors are busy building agents that work for them, not for someone elses business model.
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Below, Scott Anthony shares five key insights from his new book, Epic Disruptions: 11 Innovations That Shaped Our Modern World. Scott is a clinical professor of strategy at the Tuck School of Business at Dartmouth College. His research and teaching focus on the adaptive challenges of disruptive change. Previously, he spent over 20 years at Innosight, a growth strategy consultancy founded by Harvard Business School professor (and father of the idea of disruptive innovation) Clayton Christensen. Whats the big idea? In 1620, Sir Francis Bacon wrote that there were three technologies for which it was possible to draw a clear line before and after: the printing press, the compass, and gunpowder. Those three technologies that changed the world stretched over 1,600 years. Today, it feels like theres a big disruptive development every 1,600 seconds. Autonomous vehicles . . . augmented reality . . . artificial intelligence . . . additive manufacturing. And those are just the ones that begin with A. How do we make sense of a world where change is truly the only constant? Understanding how disruptive innovation and epic change happens allows us to see the world more clearly. 1. Disruptive innovators transform the world. Florence Nightingale was a nurse. You might have a visual of The Lady with the Lamp, and thats part of Florences story, but there is so much more. Shocked by her experience in the Scutari hospital during the Crimean War, she developed a series of analyses, brilliantly visualized in polar area charts that showed the power of prevention and proper hygiene in hospitals. She wrote books explaining the essence of nursing that anyone could buy and read, and set up schools to train nurses. What she did was disruptive innovation. Nightingale enabled a broader population to improve health standards and living conditions, focusing on prevention rather than treatment. Many of the things that we take for granted today, such as modern sewage systems or having light and fresh air during recovery, trace back to Nightingales work. Disruptive innovators transform existing markets and create new ones by making the complicated simple and the expensive affordable. They open markets to broader populations that historically lacked wealth or specialized skills. They literally change the world. 2. Every story of disruptive innovation has heroes. In the year 1437, Johannes Gutenberg was working on something in Strasbourg. No, it was not the printing pressat least, not yet. He was part of a team working on a trinket: a mirror that could capture the essence of the Holy Spirit during a planned pilgrimage in 1439. Well, that pilgrimage was called off because of an outbreak of the Bubonic Plague. That was bad for many people, but good for the world, because Gutenberg and his team went in a different direction. They met someone named Conrad Saspatch, who had an innovative wooden press. In 1440, they combined that with a range of other things to create a working version of the printing press. If you have an idea that you think could be disruptive, you need to find people who will support you. To commercialize it, they needed customers, scale, and funding. They found a merchant named Johann Fust who gave them the capital to build their business. Fust ultimately sued them and took control of the technology, but thats not the primary point here. The point is that every story of disruption has a protagonist, but it is always accompanied by multiple people involved. Every story has heroes, and that word is plural. So, if you have an idea that you think could be disruptive, you need to find people who will support you. If youre in an organization thats seeking to have more disruptions, you need to make sure the environment supports those innovators who are going to do the work. 3. Disruptive innovation is predictably unpredictable. In 1947, a trio of researchers at Bell Labs developed a breakthrough that would change the world: the transistor. Their goal was to create a technology that would replace vacuum tubes in communications networks. That happened, but the path to get there was unexpected. The transistor was an imperfect product in its early days. It had the benefits of being small, rugged, and not giving off heat, but it was also unreliable. You would have to redesign a system if you were going to use it. It wasnt good enough to plug into communications networks. The first commercial market was in hearing aids. In 1952, the Sonotone 1010 featured a transistor. The fact that the transistor doesnt give off heat was a huge benefit for people wearing battery packs on their waists. The fact that its rugged was incredibly beneficial. The limitations just didnt matter. A couple of years later, 95 percent of hearing aids were powered by transistors, and the market had exploded. This is a very predictable pattern. You never know exactly where disruptive innovation is going to start. Generally, however, you know it will be in a place that values it despite its limitations. That place is typically on the fringe of an existing market or in a completely new setting. Around the same time that Sonotone was taking license to the transistor technology, chef Julia Child was dealing with a surprising setback. When we think of disruptive innovations, we dont think of chefs, but Child changed the world of cooking, making it much easier for people to cook great French dishes in their own homes. Pull back and watch the full movie to understand disruptive change. In 1951, the French chef failed her final exam at Le Cordon Bleu. That same year, she met Simca Beck and Louisette Berthold. The two were working on a book that would bring French recipes to an American audience. They asked Julia to join the team and bring her voice to the project. She agreed. Mastering the Art of French Cooking came out 10 years later. Success was not a straight line. There were three different publishers and one near-death experience in November 1959, in which, at the very last minute, publisher number two said this book cannot be published. This is predictable. Every story of disruptive innovation has twists and turns and fumbles and false steps and things that look and feel like failures. You cannot predict the specifics. You can, however, predict they will happen. What separates success from failure is not how good the original idea was. Its how the disruptive innovator deals with the journey. When youre trying to understand disruption, focus on patterns like this. Recognize that a single moment can deceive you. Pull back and watch the full movie to understand disruptive change. Julia Child ultimately passed her test at Le Cordon Bleu and, in my opinion, her chocolate mousse recipe is perfection. 4. Disruption casts a shadow. Disruption is very good for some, but it can be less good for others. Particularly in the middle of a disruptive change, there can be a lot of messiness. Back in the 1920s, Henry Ford was obsessed with his visionto create a car for the great multitude. In 1908, he rolled out the Model T. It cost $890, or about $30,000 in todays terms. By 1924, the assembly line and lower employee turnover, facilitated by better wages, allowed Ford to dramatically decrease the cost to $260, or approximately $5,000 in todays terms. Sales of automobiles took off. This was good for some, but less good for others. Cities were designed for people, not for cars. There were no traffic signals. There were no rules and norms governing who could do what, and sadly, people were getting hit, injured, and sometimes killed. Two sides broke out. The motorists said, The problem here are the pedestrians. Were going to brand them as jaywalkers. Jay being slang for a country bumpkin who wasnt very educated. They had Boy Scouts hand out cards in cities, telling people to cross at designated areas. This was good for some, but less good for others. The pedestrians fought back. They sought to brand the motorists as flivverboobs. Flivver was slang at the time for cars, and boob . . . well, thats still pretty universal. You know who won the battle. In 1924, a New York traffic warden said, We now know about 80 percent of incidents are caused by jaywalkers. By the late 1920s, the word flivverboob had basically disappeared. Disruption always casts a shadow. The middle can be very messy. You have to understand it, or it will swallow you. 5. Success with disruption requires patient perseverance. People talk about the accelerating pace of change, but we forget that when we see a big breakthrough, theres often been decades of work before it. For example, in 2022 OpenAI introduced ChatGPT. It became the fastest technology in history to get to 100 million users. But by some dimensions, that technology was 67 years old, tracing back to a 1956 conference at Dartmouth College where the term AI was coined. Around the same time as that conference, a chemist at Corning, Don Stookey, made a surprising discovery. He accidentally set his kiln to a temperature that was way too hot. He expected a gooey mess, but instead he discovered the first synthetic glass ceramic. Corning commercialized this in a line of kitchenware and, in parallel, launched Project Muscle to make the material clear. The result was something 14 times stronger than normal glass. But Corning couldnt make it thin. They thought a possible market could be automobile windshields, but tests with crash test dummies showed that the head would not survive a collision with the glass because it was that strong. In 1971, after $300 million investment in todays terms, Corning put the project on ice. In 2007, Steve Jobs was getting ready to launch the iPhone. He picked up the prototype, and its plastic screen just didnt appeal to his aesthetic sense. He wanted glass. He knew Corning had provided an innovative screen for Motorolas RAZR phone. Even though Corning shut down the project, they continued experimenting and exploring, and ultimately made the glass thinner. They called it Gorilla Glass. Steve Jobs came to Cornings headquarters, talked to CEO Wendell Weeks, and said, I want this, I want it at scale, and I want it fast. Weeks said, Great, but we cant do it at scale and we cant do it fast. Steve Jobs turned on his reality distortion field and, without blinking, said, Yes, you can. You can do it. And Corning did. By 2024, eight billion devices had screens with Gorilla Glass. When it comes to disruption, you must be comfortable being uncomfortable because it almost always takes a lot longer than you think. This article originally appeared in Next Big Idea Club magazine and is reprinted with permission.
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