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: 1. 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 behind AI products. 2. Data & AI/ML Knowledge: AI products rely heavily on data. A strong understanding of data analytics, statistical methods, and data visualization is essential for analyzing user behavior, training models, and deriving insights from data. Familiarity with artificial intelligence and machine learning concepts is crucial for AI product managers. They need to understand how AI algorithms work, their limitations, and potential biases to make informed decisions about AI-powered features and functionalities. 3. Ethical Considerations: AI product managers must consider ethical implications related to data privacy, fairness, transparency, and bias in AI algorithms. A strong sense of ethics and a commitment to responsible AI development are essential in this role.

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: 1. 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. 2. Acquire AI Knowledge & Build a Portfolio: Since you already have a strong software engineering background, focus on gaining knowledge in artificial intelligence and machine learning. Take online courses, enroll in workshops, or pursue certifications in AI-related topics. Familiarize yourself with common AI algorithms, tools, and technologies. Showcase your interest and skills in AI product management by working on relevant projects. Develop AI-powered applications, conduct experiments with AI models, or contribute to open-source AI projects. Document your work and share it through a portfolio website or on platforms like GitHub. 3. Network with AI Professionals & Seek Mentorship: Connect with professionals working in AI product management roles through networking events, industry conferences, and online communities. Seek informational interviews to learn more about their career paths and gather insights into the field. Find mentors who have experience in AI product management or related roles. They can provide guidance, advice, and support as you navigate your career transition. Look for mentorship opportunities through professional associations, alumni networks, or online platforms. 4. Gain Experience: Look for opportunities to gain practical experience in AI product management. This could involve internships, freelance projects, or volunteering for AI-related initiatives within your current organization. Consider joining AI-focused startups or teams within larger companies.

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: 1. 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. 2. Cross-Validation: Use techniques like k-fold cross-validation to evaluate the generalization performance of AI models. Split the dataset into multiple subsets, train the model on different subsets, and evaluate its performance on unseen data. Cross-validation helps detect overfitting and provides more robust performance estimates. 3. Hyperparameter Tuning: Experiment with different hyperparameters, such as learning rate, regularization strength, or network architecture, to optimize the performance of AI models. Use techniques like grid search or random search to systematically explore the hyperparameter space and identify the best configuration. 4. A/B Testing: Conduct A/B tests to compare the performance of AI-driven features or algorithms against alternative versions or baseline models. Randomly assign users to different groups and measure key metrics to determine which version yields better results in terms of user engagement, conversion rates, or other KPIs. 5. User Feedback and Evaluation: Gather feedback from users through surveys, interviews, or usability tests to understand their perception of AI-driven features and functionalities. Incorporate user feedback into the iterative development process to improve the user experience and address any issues or concerns. 6. Monitoring and Maintenance: Implement monitoring systems to continuously monitor the performance of AI models in production. Track key metrics, such as accuracy, precision, recall, or F1 score, and set up alerts for any deviations or anomalies. Regularly retrain and update AI models as new data becomes available or the underlying environment changes. 7. Ethical and Fairness Assessment: Assess the ethical implications and fairness of AI algorithms, especially in sensitive domains like healthcare or finance. Evaluate potential biases in the data or model predictions and take steps to mitigate them to ensure fairness and prevent unintended consequences.

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: 1. 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, retention rate), conversion metrics (e.g., conversion rate, revenue), user satisfaction (e.g., Net Promoter Score), or specific AI-related metrics (e.g., accuracy, precision, recall). 2. Baseline Measurement: Establish a baseline measurement of the selected metrics before implementing AI-driven features or enhancements. This provides a point of comparison to evaluate the impact of AI on product performance and user engagement. 3. A/B Testing: Conduct A/B tests to compare the performance of AI-driven features or algorithms against alternative versions or baseline models. Randomly assign users to different groups and measure key metrics to determine which version yields better results. 4. User Feedback and Surveys: Gather qualitative feedback from users through surveys, interviews, or usability tests to understand their perception of AI-driven features and functionalities. Ask specific questions about the usefulness, relevance, and satisfaction with AI-powered enhancements. 5. Longitudinal Studies: Conduct longitudinal studies to track changes in user behavior and product performance over time after implementing AI-driven features. Monitor trends in key metrics and KPIs to assess the sustained impact of AI on product success. 6. Iterative Optimization: Continuously iterate and optimize AI-driven features based on insights from performance analysis and user feedback. Experiment with different AI models, algorithms, or parameters to maximize the impact on product performance and user engagement. 7. Benchmarking: Compare the performance of AI-driven features against industry benchmarks or competitors to assess the product's relative position and identify areas for improvement.

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 critical to have a close eye on, especially as you mature your investments and start putting the technology in production.

Yes, encountering challenges related to user understanding or acceptance of AI is quite common. Some of the challenges I've encountered include: 1. Lack of Trust 2. Misconceptions and Myths 3. Fear of Job Displacement 4. Bias and Fairness Concerns 5. Privacy and Data Security 6. Cultural and Ethical Considerations

If I were to offer one piece of advice to an AI product manager, it would be to cultivate a deep understanding of both AI technologies and the broader business context in which they operate. This dual expertise allows AI product managers to bridge the gap between technical capabilities and business objectives, effectively translating AI innovations into tangible value for users and stakeholders.

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: 1. Personalization and Customization: Use generative AI to personalize user experiences by generating tailored recommendations, content, or product suggestions based on user preferences, behavior, and historical data. 2. Content Generation and Automation: Integrate generative AI capabilities to automate content creation tasks, such as writing articles, generating social media posts, or composing marketing copy, to save time and resources for users. 3. Enhanced Communication and Interaction: Incorporate generative AI chatbots or conversational agents to provide personalized and responsive customer support, virtual assistants, or interactive storytelling experiences. 4. Creative Tools and Design Assistance: Provide generative AI tools for creative professionals, such as designers, artists, or filmmakers, to assist in ideation, inspiration, and content creation processes. 5. AI-Powered Analytics and Insights: Leverage generative AI for advanced data analysis and visualization, enabling users to uncover hidden patterns, trends, and insights in their data, leading to better decision-making and business outcomes.

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: 1. 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 interactive storytelling, among other applications, to provide personalized and responsive interactions. 2. Content Creation and Automation: Generative AI is being used to automate content creation tasks such as writing articles, generating product descriptions, or composing marketing copy. Customers benefit from increased efficiency and productivity, enabling them to scale content creation efforts while maintaining quality and relevance.