AMA: Salesforce Senior Director of Product, Generative AI Platform (Einstein GPT), Deepak Mukunthu on Generative AI

Both AI product managers and traditional product managers play crucial roles in guiding the development and success of products, but there are certain qualities and skills that may make someone better suited for an AI product management role: Technical Background: AI product managers often deal with complex algorithms, machine learning models, and data-driven decision-making. A background in computer science, data science, or a related field can provide a deeper understanding of the technology b ...Read More

Transitioning from a software engineering background to an AI product management role is achievable with the right approach and preparation. Here are steps you can take to make this career change: Understand the Role: Research and understand the responsibilities and skills required for an AI product management role. Gain clarity on the differences between traditional product management and AI product management. Acquire AI Knowledge & Build a Portfolio: Since you already have a strong softwa ...Read More

With the emergence of ChatGPT and generative AI wave, most AI Product Managers and teams are closely monitoring advancements in the industry including trends around large and small LLMs, open source LLMs, fine-tuning approaches, different RAG approaches and effectiveness of those. Given the subjective nature of LLMs, measuring quality/trust and ability to explain results in another area to pay close attention to. Last but not least, cost implications and ROI on generative investments are critica ...Read More

Integrating generative AI into a SaaS offering can significantly enhance its value proposition by providing users with innovative and personalized experiences. Here's a framework outlining ways generative AI can be integrated into a SaaS offering to make the product more valuable to customers: Personalization and Customization: Use generative AI to personalize user experiences by generating tailored recommendations, content, or product suggestions based on user preferences, behavior, and histori ...Read More

Testing and validating AI components of a product is crucial to ensure accuracy, reliability, and effectiveness. In addition to regular software testing, here are some strategies commonly used for testing and validating AI components: Data Quality Assessment: Assess the quality, completeness, and relevance of training data used to train AI models. Verify that the data is representative of the real-world scenario and free from biases or inaccuracies that could affect model performance. Cross-Vali ...Read More

Assessing the impact of AI on overall product performance and user engagement involves analyzing various metrics and KPIs to understand how AI-driven features and functionalities contribute to the product's success. Here are steps to assess the impact of AI: Define Success Metrics: Start by defining clear success metrics and KPIs that align with the goals of the product and the objectives of integrating AI. These metrics may include user engagement metrics (e.g., active users, session duration, ...Read More

Generative AI has gained traction across various industries and use cases, but some applications are particularly resonating with customers due to their innovative and engaging nature. Here are top generative AI use cases that are seeing significant customer traction: Chatbots and Conversational Agents: Generative AI powers chatbots and conversational agents that can engage in natural language conversations with users. Customers use these chatbots for customer support, virtual assistants, and in ...Read More