Bright Data CEO on ecommerce, AI, robotics, and workforce

On the latest episode of the Harvard Business School Project on Managing the Future of Work podcast, William Kerr welcomes Or Lenchner, CEO of Bright Data, for a discussion of how large-scale web data collection is shaping generative AI, automation, and workforce strategy.   The same dataset can power competition, investment analysis, or entirely new products—raising hard questions about what’s truly public information and echoing broader debates over the commercialization of the web.   As Lenchner explains, “the same data point, which is the price of bleach, can be used for competition … or by a hedge fund analyzing an acquisition.”   Key points: 💡 Data as infrastructure: Real-time web data underpins the training and operation of large language models, finance, e-commerce, and cybersecurity. 💡 Policy and litigation in flux: Recent U.S. court cases (e.g., against Meta and X) have favored data collectors’ definitions of “public” information, but privacy, competition, and copyright concerns remain unsettled. 💡 Workforce impact: AI is raising demand for speed, scale, and specialized skills, yet hasn’t broadly replaced engineering talent. 💡 Future trajectory: Bright Data customer demand points to an impending robotics boom, with AI models evolving into physical systems that will require continuous streams of reliable data.   Find the episode at https://hbs.me/3t8d7kf6 #GenAI #Robotics #BigData #Workforce #Automation #DataCollection #Skills  

On the #ManagingTheFutureOfWork podcast, William Kerr hosts Bright Data's Or Lenchner for a discussion of the #datainfrastructure and #workforce aspects of #AI. Great insights on talent strategy in a leading-edge firm: AI adoption as a complement — raising the premium on specialized skills and adaptability. Listen to the conversation at https://hbs.me/3t8d7kf6 #GenAI #Robotics #BigData #Workforce #Automation #DataCollection #Skills

📊 Data is the oxygen of modern businesses. Just like we need oxygen to survive, organizations today need data to thrive. Think about it: In healthcare, data helps doctors predict patient risks before symptoms even appear. In finance, it safeguards millions by detecting fraud in real time. In retail, it personalizes your shopping journey, making sure the right product finds you at the right time. In transport & logistics, it optimizes routes, saving fuel, time, and cost. This image is a reminder that data roles are not isolated—they are deeply connected: 🔹 Data Analysts uncover what happened. 🔹 Machine Learning Engineers build systems to decide what should happen automatically. 🔹 Data Scientists combine vision, strategy, and algorithms to predict what will happen next and why. ✨ The real power of data lies in collaboration—turning raw numbers into insights, intelligence, and innovation that truly impact lives. In the end, data isn’t just about technology—it’s about people, decisions, and shaping the future. 🚀 #DataScience #MachineLearning #AI #DeepLearning #BigData #Analytics #CloudComputing #BusinessIntelligence #Innovation #DigitalTransformation #FutureOfWork

🌍 The Data Professional of Tomorrow What fascinates me most is how the future of data is no longer just about numbers, but about impact. The next generation of data professionals will sit at the intersection of: 🔹 Analytics – transforming raw data into actionable insights. 🔹 AI – scaling solutions, predicting outcomes, and driving innovation. 🔹 Sustainability – ensuring our work contributes not just to efficiency and growth, but to a better planet. Tomorrow’s data leader won’t simply build dashboards or train algorithms. They’ll help cities reduce emissions, guide companies toward circular economies, and design systems where technology and sustainability go hand in hand. For me, this convergence is not just a career path—it’s a calling. 💡 The question is: Are we preparing ourselves, our teams, and our organizations to embrace this shift? #Data #AI #Sustainability #Analytics #FutureOfWork #GreenTech #ESG #DataScience #TechForGood #DigitalTransformation

While most biotechs obsess over AI algorithms, the real battleground is proprietary data built on a robust data infrastructure. After years of building and scaling in this space, I've learned that your AI is only as good as the data pipes feeding it. The harsh reality? Most companies are sitting on data goldmines trapped in silos. We're seeing breakthrough molecules like Recursion's REC-3565 reach human trials in 15 months instead of the typical 4-5 years—but only for companies that cracked the infrastructure code first. Here's what separates winners from pretenders: integrated, real-time data pipelines that turn every experiment into institutional learning. When your multi-omics data flows seamlessly from lab bench to AI model to next design, you're not just accelerating discovery — you're building a "digital brain" that compounds competitive advantage with every iteration. The strategic insight hitting boardrooms now: data quality directly determines program velocity and accuracy. Companies, like Ordaos Bio, investing in sophisticated schemas, automated ingestion systems, and real-time integration aren't just improving operations—they're constructing defensible moats in an increasingly commoditized AI landscape. Yet fragmentation remains the industry's Achilles heel. How many promising AI initiatives are dying in data integration hell right now? What's your experience with data infrastructure as a competitive differentiator? Are we finally ready to treat data ops as seriously as R&D? #Innovation #Biotech #AI #Leadership #TechBio

𝗪𝗲’𝗿𝗲 𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗮𝘁 𝘁𝗵𝗲 𝗲𝗱𝗴𝗲 𝗼𝗳 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝘀𝗵𝗶𝗳𝘁𝘀 𝗶𝗻 𝗵𝘂𝗺𝗮𝗻 𝗵𝗶𝘀𝘁𝗼𝗿𝘆: T𝗵𝗲 𝗿𝗶𝘀𝗲 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗔𝗜. Everywhere we look from healthcare and education to finance, supply chains, and even how we communicate data and AI are no longer “future tools.” They’re shaping decisions today, and their influence is only going to grow stronger in the years ahead. What excites me the most is not just the technology itself, but the possibilities it opens up: ✔️ Smarter automation that can think instead of just do ✔️ Generative AI making insights accessible to more people ✔️ Decisions made in real-time, right where data is created ✔️ A stronger focus on ethics, governance, and responsibility ✔️ New, evolving roles and careers we can’t even fully imagine yet For me, the real scope of AI and Data Science lies in how they help us solve real-world problems like climate change, global health, sustainable growth, and beyond. I truly believe the professionals who adapt, keep learning, and stay curious will be the ones leading this new era. 💡 The future isn’t about replacing humans with AI, it’s about empowering humans through AI. #DataScience #ArtificialIntelligence #MachineLearning #AITrends #GenerativeAI #TechFuture #ResponsibleAI #Automation #DataDriven

Unlocking Data Science Success: What Actually Moves the Needle? The pace of innovation in data science, machine learning, and AI is relentless but impact isn’t just about flashy models. It’s about actionable frameworks, meaningful measurement, and real business outcomes. Drawing from leading practices and authoritative resources, here’s what I see shaping the future right now: Outcome-Driven Frameworks: Teams succeed when they focus on core metrics tied to business impact not just technical benchmarks. Robust Experimentation: Rapid, controlled testing accelerates learning and adoption. Think: AB tests, scenario simulation, and iterative deployment. Cross-Functional Collaboration: The era of data silos is over; integrating domain knowledge with technical expertise unlocks true value. Continuous Upskilling: Staying current with trends, tools, and ethical standards is essential AI literacy isn’t optional anymore Pro Tip: Translate every algorithm to a simple value proposition “How does this solve a real, costly business pain?” The highest-impact teams bridge the gap between data and decisions. How are you applying these principles or where do you see gaps? Do frameworks enhance or hinder your team’s outcomes? Drop your perspectives, examples, or predictions below! #datascience #machinelearning #ai #aitrends #analytics #dataproducts #leadership #mckinsey #gartner #innovation #Collaboration #DataDriven

🔍 Have you noticed how data is becoming the real language of business? Every company talks about being “data-driven,” but the real challenge isn’t collecting more data—it’s making sense of it fast enough to guide decisions. That’s why I’ve been fascinated lately by tools and approaches that bring planning, analytics, and AI together instead of keeping them in silos. As someone studying Computer Science, Mathematics, and Business Administration, I can’t help but wonder: ➡️ Will the future of decision-making belong more to engineers building models, or to business leaders using AI-powered insights directly? Either way, one thing seems clear: the companies that succeed will be those that make data usable and connected across every team. Curious to hear—how do you see the role of data + AI evolving in business over the next few years? #Technology #DataAnalytics #AI #BusinessIntelligence #FutureOfWork

3 warning signs we consistently see in struggling data science projects: Data science project failure usually happens in the planning room, not in the code editor. With only 48% of AI projects making it to production according to Gartner, our team has observed these three warning signs consistently across struggling projects, and they're all fixable if you catch them early. ❌ Sign #1: The business problem isn’t clear “Let’s see what insights we can find” isn’t a plan. Winning projects start with sharp problem statements: “Predict churn within 30 days” or “Reduce defects by 15%.” Without clarity, there’s no way to measure success, and projects get cancelled. ❌ Sign #2: Nobody has checked the data Too often, teams obsess over algorithms while data sits in silos with messy formats. Gartner reports 63% of organizations lack proper data management for AI. If weeks pass and no one has opened the files, you’re building on assumptions. ❌ Sign #3: No production plan Proof-of-concepts die when “we’ll figure that out later” becomes the strategy. Production requires infrastructure, integration, and monitoring from day one. Successful projects always start with the end in mind. ✅And at Austin AI, we do exactly that. So you never invest months of effort into a project that never makes it to production. Our approach validates the business problem, assesses data readiness, and maps the production path before development begins, giving you confidence that your investment will deliver measurable results. Remember, the best code can't save a poorly planned project. Are you ready to start your next data science project with a solid foundation? We help organizations plan for success from day one. You can learn more here: https://lnkd.in/e7_UX7sc #AITransformation #AIStrategy #AIRegulation #AIGovernance #ComplianceStrategy #BusinessInnovation #DataScience #AustinAI #AustinArtificialIntelligence #AI P.S. If you've successfully navigated these challenges, what did you do differently in the planning phase?