0 to 1 Product pragmatist | AI digital transformations | Strategy | Technical | Commercial | Turn complex technologies into products customers love
Need something to fuel your AI ideas? I spent one hour watching and summarising the top 5 takeaways, so you don't have to. 1. Current Use Cases: - Large Language Models (LLMs) are finding success in automating dull, repetitive tasks like form-filling. 2. Challenges and Uncertainties: - There are still many AI "tarpit" ideas that seem promising but may not pan out. - Companies are unsure about how customers will ultimately use AI products like AI co-pilots. - Actual, proven use cases for AI are still lacking 3. Business Implications: - AI could have second-order effects 4. Technical Approaches: - Fine-tuning open-source models on private datasets is a popular but limited approach. - There's a need for purpose-trained, smaller, and localised AI models. - Great UX will be crucial for successful AI products, similar to SQL wrappers. 5. Success Factors and Pitfalls: - Failure often comes from being too general or abstract, lacking a specific use case. - Successful AI products will be tailored to customers' business logic and specific needs - AI voice receptionists could be a promising application. Save this to your watchlist: https://lnkd.in/ejWfM8Yf #AI #productmanagement
