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The Fast Company Impact Council is an invitation-only membership community of leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual dues for access to peer learning, thought leadership opportunities, events and more. Every generation has its tinkerers. People who get their hands dirty not because they know exactly what they’re doing, but because theyre following a feeling. No formal training. No permission. Just curiosity, instinct, and a slightly obsessive need to mess with things until they do something interesting. Welcome to the age of vibe coding. The term itself surfaced just weeks agocoined by AI researcher Andrej Karpathy in February. In a now widely memed post, he described vibe coding as the act of programming through intuition rather than structure, trusting the feel of what youre building, not just its logic. The phrase exploded across dev forums, design threads, and TikTok sidebars. Merriam-Webster added it the following month under slang & trending, defining it as the practice of writing code, making web pages, or creating apps, by just telling an AI program what you want, and letting it create the product for you. Which is a long way of saying: winging it, brilliantly. Even Sir Demis Hassabis, founder/CEO of DeepMind, recently stated that the explosion of natural language coding will open up fields for creative people, tipping the balance away from and engineering mindset to an instinctive, creative one. But lets be honestthis isnt new. When instinct outpaces instruction Take early electronic music. The pioneers of modular synth werent conservatory-trained composers. They were sonic explorers, patching cables into buzzing machines and twisting knobs until emotion emerged. As Brian Eno famously observed: Whatever you now find weird, ugly, uncomfortable, and nasty about a new medium will surely become its signature. What is that, if not analog vibe coding? Or look at the rise of the indie game scene. Minecraft, Braid, Undertalenone of these were born from a major studio pipeline. They were built by people making weird, emotional things with code, trusting their gut over any formal game design doctrine. Same with the postwar hot rodders in California, or the drift racers in Japan. They werent automotive engineers. They were teenagers in garages, modding beat-up engines until they could tear through salt flats or carve hairpin turns sideways. Tuning by ear. Testing by feel. Rewriting what cars could be without ever asking how cars should be made. Sound familiar? Vibes have always been a feature, not a bug Vibe coders are the natural descendants of this lineage. Theyre working with AI the way early skate culture worked with architecturenot as passive users, but as instinctive reinterpreters. Theyre pushing limits not by following a manual, but by making one up as they go. The outputs might look a little glitchy. A little offbeat. But thats part of the point. The future rarely starts with polished perfection. It starts with side quests, zines, garages, and basement experiments. It starts with people making things that feel right, even if they cant yet explain why. Dont mistake chaos for lack of vision To the outside world, this kind of experimentation can look messy. But look closer, and youll see a different kind of intelligenceone that isnt defined by credentials, but by creative fluency. These are people who speak machine, even if they dont always write it perfectly. Theyre fluent in feeling. Fluent in remix. Fluent in future. And when the tools are this powerfulwhen a few prompts can conjure films, music, code, business plansfluency in vibes becomes a serious superpower. So before we rush to regulate or rationalize this new wave, maybe take a moment. Listen to the noise. Feel the current. Theres something big building here, and it isnt coming from the top down. Its coming from the garages again. From the kids with GPT in one tab and Ableton in the other. From the creators who dont need to ask permissionbecause they already have momentum. The takeaway? You dont need a roadmap to lead a movement. You just need a signal, a pulse, and a willingness to follow the vibes. Mark Eaves is founder of Gravity Road.
Category:
E-Commerce
The Fast Company Impact Council is an invitation-only membership community of leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual dues for access to peer learning, thought leadership opportunities, events and more. Building a resilient technology company is hard. Building one that can withstand constant policy change is another level of hard. Right now, companies across sectorsnot just fintechare staring down government and regulatory shifts happening faster than most orgs can process, let alone implement. For industries like financial technology, where regulatory changes directly impact how products work, how they’re priced, and how they’re sold, the stakes are existential. Adapting in real time isn’t just an edgeit’s the bare minimum to stay in the game. Thats why companies need to think beyond using AI as a tool. They need to rethink the entire way they build software, make decisions, and operationalize compliance. At april, we didnt bolt AI onto our dev team; we restructured how we work to make regulatory agility the foundation. Our approach uses AI to take human-written analysis and turn it directly into code. It means faster updates, fewer silos, and a dev cycle that actually moves at the speed of policy. When every state writes its own rules, you build for change The U.S. tax system isnt a single rulebookits a fragmented, constantly shifting web of federal and state-level regulations. Each year, we see hundreds of changes across jurisdictions: new credits, sunset clauses, redefinitions of income, filing thresholds, and form logic. And none of them arrive on a predictable timeline. A change that passes in October still needs to be implemented and tested before filing season begins in January. We knew we couldnt keep up with that kind of churn using the legacy software development model most incumbents rely onlong handoffs between policy, legal, and engineering teams, often stitched together manually. So we built something different. At april, our Tax-to-Code system lets policy experts write structured analysis, and generative AI turns that into functioning software, reviewed and refined by engineers before it ships. The AI doesnt replace experts; it extends them. It kills the back-and-forth and accelerates our response time from weeks to days. This is what regulatory agility looks like: Tax code changes go from policy to product without bottlenecks. Automation isnt the goalstrategic bandwidth is Theres a lot of noise about AI automating work. But in regulated environments, the real value isnt just speedits the space it frees up for experts to focus on strategy. AI helps us eliminate the repetitive, time-sucking tasks that bog down compliance work. That doesnt just cut costs; it gives our team the bandwidth to think several steps ahead. Whats the next policy change likely to be? What would it take to adapt? What needs to be built now to stay ahead? Thats what most companies are missing. Theyre spending all their energy reacting. AI infrastructure, done right, gives you the room to anticipate. AI cant function without the right architecture This only works if your infrastructure is designed to support it. We didnt start with generative AIwe started with the assumption that regulatory change is constant and unpredictable. From there, we built a system where: Domain experts define the logic. AI transforms it into code. Engineers validate and ship. The result? A feedback loop where tax and policy changes get implemented at pace, not after a six-month dev sprint. More importantly, its adaptable. This model isnt just for tax. Any company in a volatile regulatory spacehealth insurance, auto, logistics, energyneeds a system that can cascade policy changes through their tech stack fast, accurately, and with oversight. Lessons for leaders in regulated industries If youre leading a company where compliance is high stakes, heres what to prioritize: Structure your tech org for change, not stability. You cant assume next quarters rules will match this ones. Build collaboration between experts and AI. Dont let legal, ops, and engineering operate in silos. AI works best when it sits between human knowledge and execution. Focus on speed and oversight. AI without accountability is dangerous. Human-only systems are too slow. You need both. This is the new baseline In todays environment, adaptability is non-negotiable. Leaders cant rely on manual processes or slow engineering cycles to keep up with real-time policy shifts. And AI isnt some magic solution on its own; it needs the right infrastructure, the right workflows, and the right people in the loop. At april, weve built our company around that reality. Thats how we move fast without breaking thingsand how others in high-regulation industries can, too. Ben Borodach is the cofounder and CEO of april.
Category:
E-Commerce
The Fast Company Impact Council is an invitation-only membership community of leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual dues for access to peer learning, thought leadership opportunities, events and more. Many companies forget AI-powered enterprise applications are just business apps at the end of the day. The reality is, AI is simply another arrow in our quiver, albeit incredibly more powerful. But what IT has done since generative AI exploded on the scene is frantically rush to deploy any and all possible applications, causing massive confusion and huge resource wastes, without delivering much business value. The process of firing arrows at the target (increased business value) has stayed the same, just like the goal of hitting a bullseye. But many businesses miss the mark, trying to create significant and oftentimes unrealistic returns. How to generate more value with AI To be fair, the urgency is real, particularly as the next big target arises. Gartner predicts, By 2028, 33% of enterprise software application will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously. Everyone wants to fire with precision. Theyre just unsure where to aim. At our recent Insight Amplify technical conference, I sat down with a handful of fellow tech executives. We discussed our experiences rolling out AI apps, and we agreed that most successful use cases so far have been internal-facing as a proving ground to master the technology. Slowly but surely, the hope is it shows its mettle in customer-facing applications. Everyone agrees this is uncharted territory. I not-so-innocently asked a few follow-up questions, such as how they went about finding workloads. They each described the broad strokes, and as I suspected, their processes were remarkably similar. Theres much more nuance to it, but generally they followed these steps: Identify the business problem, ideally in partnership between the business line and IT team. Build a minimum viable product and, with a subset of capabilities, deploy the app to see if it is appropriate from functional and practical standpoints. Evaluate the return on investment to ensure it makes sense financially. Expand it for widespread use with the necessary cost and security controls and backlogged features. These are the same proven steps to build a traditional appyet all too often, adding the shiny AI component blinds us from the fundamentals. When I pointed out the similarities, it was like a light bulb flipped on. They hadnt thought about it like that. Unfortunately, many companies struggle to reach that Eureka moment in app development and make a few common missteps along the way. For example: Misstep #1: Developing AI apps yourself The first step is critical. Without a clear business problem, is there a point to pouring resources into an app? Even if a company has a solution, they need to step back and determine if they can deliver it. I often see clients trying to reinvent the wheel when theyre unequipped to do so. If a company’s business isnt writing software, they should focus on their core business instead. There’s a reason why auto manufacturers don’t make the lights that go into cars. They simply buy and put them into what they build. That’s the mentality businesses should have with AIrelying on partners with the specialized knowledge to guide them through this evolving landscape. This isn’t to say buying off the shelf is always the answer. Sometimes, the need is unique enough or rooted in specific business processes where developing custom AI-powered solutions makes sense. But first look for someone doing that as their business. If you cant find them, thenand only thencreate your own. Misstep #2: Improperly preparing your data I sometimes joke that clients have data swamps, not data lakes. Poor data quality is a significant gap in many organizations. It can be terribly organized and inadequately secured across different sources, costing companies 15-25% of their revenue, according to MIT Sloan Management Review. The key to unlocking datas immense value lies in organizing and normalizing it in one place, but most data is siloed across various locations based on its functions. While a small subset may seem manageable, this can mask underlying issues that arise once you deploy an app to a larger end-user community if your data isnt properly cleaned. This will be problematic whether you buy off the shelf or develop yourself. Data mastery is fundamental to driving any outcome. Misstep #3: Locking yourself in My colleagues are right: This is new territory. While basic app development steps remain the same, a rapidly evolving sector introduces countless variables to consider. Even without GenAI, changes would still occur at breakneck speeds. Welcome to IT. Among all the AI hype, what is just noise you can ignore? What are legitimate signs of the frenetic activity around us? I guide clients through these types of questions all the time. Developing AI, they might invest too much capital in on-premises solutions, lock themselves into a specific cloud provider, or partner with an independent software vendor thats a darling today but dead in six months. Given technologys rapid pace, its crucial to stay flexible. You’ll need to pivot eventually. Locking yourself into a category, location, vendor, or similar commitment is extremely risky. The stumbling block that so many struggle with is they dont yet have enough muscle memory working with AI to unlock its full potential. In the absence of certainty, what should be logical is to do whats familiarwhats worked before: Stick to your strengths as a business. Stick to proven app-development processes if that is, in fact, your business. If not: Stay the course with trusted partners who have that expertise. In other words, dont overthink things. Its AInot rocket science, unless thats the app you need. If youre unsure where to begin, work with a solutions provider with proven success delivering agentic, generative, and traditional AI applications. With a few reps under your belt, youll be locked in to hit future targets. Juan Orlandini is CTO, North America ofInsight Enterprises.
Category:
E-Commerce
The Fast Company Impact Council is an invitation-only membership community of leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual dues for access to peer learning, thought leadership opportunities, events and more. At the Exceptional Women Alliance (EWA), we enable high level women to mentor each other for personal and professional happiness through sisterhood. As the nonprofit organizations founder, chair, and CEO, I am honored to interview and share insights from some of the thought leaders who are part of EWA.This month I introduce to you Emily Moorhead, president of the Henry Ford Jackson Hospital. Q: Tell me how you are embracing change in the healthcare industry. Moorhead: We live in an age of remarkable medical innovation. Technology has advanced healthcare in ways we could have only imagined a decade ago. Artificial intelligence can help identify diseases in their earliest stages. Robotic-assisted surgery offers unprecedented precision. Patients can consult with a physician from the comfort of their living rooms. Yet, human connection is still the most powerful medicine we offer. At Henry Ford Jackson Hospital, we recognize that healing doesnt begin with a test result or a treatment planit begins with a conversation and a sense that someone truly cares. When people feel seen, heard, and valued, outcomes improve. Trust deepens. Teams thrive. And the experience of giving and receiving care becomes more meaningful. This belief isnt just rooted in philosophy, its revealed in practice. In every role across our hospital, we ask: How do we make space for connection? How do we create environments where peoplepatients, families, caregivers, and team membersfeel supported and respected? Human connection is not a soft skill, but a strategic imperative. We are working to hardwire it into every corner of our organization. Q: Why do you believe human connection still matters in a high-tech healthcare environment? Moorhead: Healthcare is fundamentally human. While we celebrate the role of data and devices in diagnosing and treating illness, what patients remember most is how we made them feel. Did we listen? Did we look them in the eye? Did we take time to explain whats next? Connection builds trust, and trust drives everythingfrom medication adherence to satisfaction scores to team morale. When we prioritize relationships, we dont just provide better care, we create a better experience. Q: Are there tangible outcomes linked to stronger provider-patient relationships? Moorhead: Absolutely. Numerous studies have shown that patients who feel connected to their care team are more likely to follow treatment plans, report higher satisfaction, and have better overall outcomes. Thats not a coincidence. Its the result of feeling respected, informed, and involved in decisions about their own health. Its not just about patients. Providers and team members who feel connected to their colleagues and their purpose experience lower rates of burnout and higher engagement. Its easy to focus solely on clinical excellence, but we cant overlook emotional well-being. When our people feel supported, theyre more present, compassionate, and effective in their roles. Healthcare is complex, high-stakes work. Connection can be the stabilizing force that keeps us aligned, grounded, and resilient. Q: How do you balance the demand for efficiency with the need for connection? Moorhead: Thats the tension so many leaders face. Healthcare is under pressure to do more with less, and every minute matters. But what weve found is that connection and efficiency arent in conflict; they reinforce each other. When patients feel understood, they ask fewer repeated questions. When teams communicate clearly and respectfully, workflows improve. Investing a few extra moments in meaningful interaction can prevent backtracking or miscommunication later. Its about being intentional in how we show up. Presence doesnt require an extra hour in your dayit requires a mindset. Even brief encounters can be deeply meaningful when approached with empathy and authenticity. Q: What role does leadership play in modeling this culture of connection? Moorhead: As a president, I make it a point to be visiblewalk the halls, join huddles, and engage in real conversations, because culture is contagious. If I want my team to prioritize people, I must demonstrate that myself. Every leader sets the tone, intentionally or not. When leaders make time to listen, offer encouragement, and show appreciation, it sends a powerful message about what we value. We equip our leaders with tools to celebrate effort and support physical and psychological safety. Creating a culture of connection starts at the top, but it grows when everyone sees its realwhen it becomes part of daily habits, not just organizational statements. Q: How can organizations outside of healthcare apply these lessons? Moorhead: Whether youre leading a hospital or a tech startup, people want to feel seen. They want to know their work matters. They want to trust the people around them. Organizations that foster those connections outperform those that dont. Every company should be asking: Are we designing our systems only around efficiency or around people? Human connection isnt a healthcare issueits a leadership issue. It affects everything from retention to innovation to long-term sustainability. Q: What gives you hope about the future of healthcare? Moorhead: Despite the challengesworkforce shortages, financial pressures, the emotional tollI see daily reminders of what makes healthcare extraordinary. Were surrounded by people who choose to show up every day not just to do a job, but to make a difference. Those moments may not make headlines, but theyre the heartbeat of healthcare. No matter how much technology evolves, the most powerful breakthroughs will always begin with human connection. Connection isnt an add onits the foundation. Larraine Segil is founder, chair, and CEO ofThe Exceptional Women Alliance.
Category:
E-Commerce
The Fast Company Impact Council is an invitation-only membership community of leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual dues for access to peer learning, thought leadership opportunities, events and more. While talent intelligence platforms (TIPs) serve an important purpose in identifying skills, they are inherently limited and never designed to address the fundamental question: How is work itself structured and how is it changing? AI has dramatically magnified and accelerated those pre-existing limitations. Its not just creating new skill gapsits redefining work at its core. Yet most organizations are still trying to track skills like its 2020. In the next few decades, AI is projected to transform up to 70% of all job tasks across industriesnot just by replacing work, but by fundamentally reshaping how work gets done, who does it, and what value it creates. Past technological shifts unfolded over decades; todays agentic AI is reshaping entire industries in a matter of months. Work intelligence is the next evolution: a strategic, systems-level approach that moves beyond skills to decode how work itself is being restructuredtask by task, role by role, organization by organization. Why TIPs were never the full solution Many companies have invested in TIPssystems that identify emerging skills by analyzing job postings and creating “living taxonomies” to inform talent practices. While valuable as a first-generation approach, TIPs suffer from a fundamental limitation: They’re inherently reactive. By analyzing existing job postings and making projections, TIPs create a perpetual time lag. By the time organizations identify, develop, and deploy these skills, the landscape has already shifted. With AI changing jobs faster than companies can update talent strategies, organizations need to look beyond current skills to understand how work itself is being reimagined. Skills are changing because work is changing The rapid evolution of skills isnt the root challengeits a symptom of a deeper issue. AI has simply brought this issue to the forefront: Work itself is changing. Skills development remains importantbut it must be anchored in a deeper understanding of how work is transforming. Organizations investing in skills programs haven’t wasted their efforts, but they need to evolve their approach to connect it with work redesign. Otherwise, even robust skills initiatives wont deliver lasting value in an AI-transformed landscape. Failing to grasp how work is transforming leads to: Blind workforce decisions Hiring for roles that wont exist Reskilling for skills that wont matter next year Ignoring AIs fundamental impact on work design Work intelligence is a smarter way to navigate AI disruption Work intelligence begins with a comprehensive understanding of the work itselfthe outcomes, tasks, processes, and roles that drive business value. Advanced work intelligence systems can analyze work across industries and create a universal language of work that integrates with existing organizational structures. This deep understanding enables business leaders to: Eliminate redundancies across roles: Consolidating overlapping responsibilities into fewer roles can reduce coordination costs while creating more meaningful work. Identify AI automation opportunities: Work intelligence can pinpoint exactly which tasks are prime for automation, which tools can accomplish this, and how to reallocate remaining human tasks. Optimize end-to-end process flows: By analyzing entire workflows, leaders can redesign processes to leverage AI and human capabilities. In customer service, automating initial contact while routing complex inquiries to specialists might reduce process steps by 30%. Focus talent development strategically: Work intelligence anticipates the roles and skills emerging from these transformations before implementation. This enables proactive talent development that runs parallel to work redesign efforts. Organizations can build learning paths aligned with their future work design, ensuring investment in capabilities that drive business value while preparing employees for meaningful roles in advance of changes. The future of work design This approach creates a fundamentally different talent ecosystem where roles, skills, and capabilities evolve naturally from optimized work processes. While competitors struggle with isolated AI initiatives or broad automation targets, leaders with work intelligence can make precise, strategic decisions about where to invest in technology and human capabilities. Redesign work for the AI era What organizations face isn’t merely a skills problemit’s a fundamental workforce capability challenge accelerated by AI transformation. The most successful organizationsthose reengineering work with the future in mindwill be able to answer these critical questions: Which work should be done by humans versus AI? How should we reorganize roles and processes around these new capabilities? What truly human capabilities should we develop in our workforce? How can we create systems that continuously evolve as technology advances? Transform your organization with work intelligence Don’t wait for AI to disrupt your workforce. The competitive advantage gap is already widening between organizations that proactively redesign work and those that merely react to change. Heres how to start your work intelligence journey: Assessment: Begin with a rapid, data-driven assessment of your current work design using work intelligence tools that quickly identify high-value transformation opportunities. Pilot project: Select a high-impact process to redesign using work intelligence principles. Strategic roadmap: Develop a phased approach to implementing work intelligence across your organization, aligned with your broader business strategy. Capability building: Equip your leaders with the tools and mindsets to lead transformation through a work intelligence lens. The market leaders of tomorrow aren’t just adapting to AI disruptionthey’re actively harnessing it to reshape work, drive value, and create meaningful roles that maximize human potential. Siobhan Savage is cofounder and CEO of Reejig. Amy Wilson is product strategy advisor at Reejig.
Category:
E-Commerce
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