23: The One Mistake Most AI Teams Make (with Nico Posner)

For more on building AI products and careers, along with early course announcement and special pricing, subscribe to the AI Career Boost mailing list at https://aicareerboost.com/interested THE GUESTNico Posner’s whose career has spanned some of the world’s most innovative companies, including LinkedIn, eBay, Clover, and Xero. Nico’s career is a masterclass in innovation with massive impact across industries, from startup growth to some of the world’s largest companies.  Nico has been working with AI since 2018, with incredible results - for example, as a former VP of Product for Xero, Nico led initiatives integrating cutting-edge AI and predictive analytics into products used by over 100,000 small businesses worldwide, doubling the company’s revenue for multiple product lines. From AI ovens to molecular beverage printers - Nico’s been on the forefront of industry transformation his whole career, and integrating AI along the way. He’s also an alumni of our AI Product Leader Blueprint program - and he’s currently looking for his next opportunity - find him on LInkedIn.THE SUMMARYAI in the Real World 🌍: Implementing AI effectively requires bridging the gap between innovative technologies and real-world user needs. Success comes from creating tools that address tangible problems, like cashflow forecasting or vision-based cooking systems.The Human Touch in AI 🤝: Human-in-the-loop systems are vital for improving AI outputs over time. Products that allow users to refine predictions or provide feedback create a balance between precision and usability, enhancing overall satisfaction.Learning from Errors 🔄: Embracing experimentation and allowing for suboptimal results during development can lead to valuable insights. Iterative improvements and a willingness to learn from failures are critical for building robust AI systems.Culture of Experimentation 🚀: A successful AI-driven organization fosters a culture that tolerates smart failures and embraces unpredictability. This requires adaptability, continuous learning, and a focus on evolving processes and tools.Non-Technical Backgrounds in AI 🛠️: Breaking into AI doesn’t require a technical degree. Strong critical thinking, user empathy, and problem-solving skills, combined with collaborative approaches, can drive success in technical fields.Advice for Aspiring AI Product Leaders 🌟: Continuous learning is essential. Building expertise in AI starts with self-education, hands-on experimentation, and seeking opportunities within existing roles to explore AI-driven projects.THE SHOWWeekly conversations with the AI’s top product leaders. Join Polly Allen as she discovers the paths to success in the world of AI.THE LINKSHave a question you want us to answer? Send it through to [email protected] PosnerLinkedIn: https://www.linkedin.com/in/nicoposner/ My linksLinkedIn: ⁠https://www.linkedin.com/in/pollymallen/⁠ AI Career Boost: ⁠https://www.aicareerboost.com/⁠ 

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