Building AI products: 5 lessons from our founders' workshop

Welcome back to another episode of The Edge by Cherry Ventures where we discuss new, edgy topics about the future and AI. Live from London, today’s episode is a recap from the previous workshop at Station F, where they discussed building products with AI, and their most common challenges and mistakes.Building AI products requires a different approach than traditional software development. Unlike deterministic software, AI projects necessitate experimentation to determine the relevance and effectiveness of the AI used. Listeners are cautioned against the superficial integration of AI, such as adding generative AI to products where it doesn't add value.AI fit analysis is a tool used to evaluate whether AI is suitable for a specific product or problem. The analysis focuses on the quality and precision of data, and highlighting the inherent bias of data. Deciding the extent to which data should be de-biased is important, since sometimes biased data can be useful for achieving specific outcomes. When it comes to AI-based decision making, it is crucial to understand how and if AI can enhance the processes. For instance, in customer care, simple rule-based systems may suffice for straightforward queries, while complex, non-linear problems, like legal tech applications, benefit from AI's ability to handle numerous variables and provide more sophisticated solutions.Next, the conversation highlights real world use cases of AI. It is particularly valuable in things like handling complex decision-making processes with many variables. Some legal tech companies are using AI to analyze contracts, compare cases, and guide users through complex decisions. AI's ability to process and analyze large amounts of data quickly can significantly enhance such applications. In cybersecurity, AI can support infrastructure decisions by recognizing patterns and guiding users through changing scenarios. Industries such as social media monitoring benefit from continuously evolving AI modelsScaling AI involves considering infrastructure costs, latency, and the overall benefits of scaling the system. The costs associated with using AI, such as query charges, must be justified by the benefits it provides, such as in healthcare applications where AI can enhance a doctor's efficiency and effectiveness. Open-source models like Lama reduce costs to hosting fees, making AI more accessible. However, achieving exponential scaling can serve as a competitive advantage.