Ayush Jain, Senior Director of product management at Microsoft, discusses the power of generative AI and how to incorporate it effectively into products. Topics include leveraging gen AI, execution with gen AI, risks and ethics of working with gen AI.
Generative AI simplifies tasks, automates tedious processes, and enables users to accomplish previously unknown jobs.
To ensure a seamless and intuitive user experience, clear guidance, user-friendly prompts, and feedback loops are crucial when incorporating generative AI into products.
Collaboration with stakeholders, leveraging existing expertise, focusing on production quality, and employing iterative feedback loops are key strategies for successful execution of generative AI projects.
Deep dives
Identifying the Right Problems: Leveraging Generative AI
To identify the right problems to solve for users through generative AI, it is important to consider how this technology can be applied to existing product scaffolding and simplify tasks that require multiple steps. It is also crucial to still follow fundamental product management principles such as understanding the user's job to be done, identifying pain points, and exploring different ways to solve them. Generative AI excels in automating tedious tasks, democratizing complex processes, and enabling users to accomplish jobs they didn't even know were possible.
Creating Natural Interactions: Designing a Seamless Experience
When incorporating generative AI into a product, it is essential to make the interaction feel natural and intuitive for users. This can be achieved by providing clear guidance on how to use the feature and ensuring that the AI model can understand the user's prompts. Feedback loops play a critical role in building trust, with the AI model explaining why it did not understand certain prompts and guiding the user to better articulate their needs. By making the interaction feel seamless, product managers can foster trust and encourage users to embrace the benefits of generative AI.
Collaboration and Execution: Bridging the Gap
When working on generative AI projects, product managers must collaborate with various stakeholders, including data scientists, engineers, and designers. To bridge the understanding gap between these stakeholders, effective strategies involve leveraging existing expertise within the team or seeking guidance from legal professionals. Encouraging a growth mindset and shifting the focus from lab quality to production quality can also help teams overcome organizational challenges and improve execution. Providing tools for prompt engineering and employing iterative feedback loops empower product managers to take control of the feature's outcomes and ensure continuous improvement.
Measuring Success: Metrics for Gen.A.I. Features
To measure the success of generative AI features, it is important to consider usage metrics, usage models, satisfaction, and the impact on desired outcomes. Tracking usage and engagement metrics provides insights into adoption rates and engagement with the feature. Usage models help understand user struggles and refine the feature accordingly. Satisfaction can be measured through in-app signals and feedback, while the impact on outcomes, such as email open rates or click-through rates, evaluates the effectiveness of generative AI in achieving desired results. By considering these metrics, product managers can gauge the success of their generative AI features.
Building Trust: Enhancing User Confidence in Gen.A.I.
Building trust with users when implementing generative AI involves multiple layers. Before usage, product managers should welcome users with clear guidance and explain that generative AI is a tool that requires user input. During usage, trust is fostered by providing feedback, guidance, and a real-time feedback loop. After usage, proactive interactions and personalized suggestions help maintain trust by continuously delivering value and addressing user needs. By considering trust at every stage of the user journey, product managers can ensure that users feel confident in using generative AI and reap its benefits.
Ayush Jain is a Senior Director of product management at Microsoft, leading generative AI and Customer Data Platform teams within the Dynamics 365 portfolio of products. Ayush has built an incredible career in the Enterprise SaaS space, leading the team that’s launched Real-time Customer Journey Orchestration from 0 to 1 and growing the business 3x the business every year for the last 2 years. In this conversation, Ayush talks about the power of generative AI and how to effectively incorporate it into products to drive a valuable user experience.
We cover:
Leveraging gen AI to build products for your users
What execution looks like when gen AI is involved
Risks, ethics and considerations of working with gen AI