Building products in the ever-evolving world of AI observability, I've identified a critical pain point that can make or break the user experience: the onboarding process. While application observability tools have their onboarding processes perfected, AI observability products encounter unique challenges that we're still learning to address.
The Challenges of AI Observability Onboarding
- Complex Model Data: Unlike application observability, which deals with relatively straightforward data, AI observability models involve intricate data structures. This complexity can lead to errors and inaccuracies during onboarding, making the process more challenging.
- Transition from IDEs: Data scientists and ML engineers are used to working in IDEs like PyCharm, Visual Studio Code, and Jupyter Notebook. These environments are tightly integrated with programming languages like Python and R, offering a seamless coding and debugging experience. Transitioning to a new interface that provides visual data representation can be daunting but offers significant potential. This shift can make the onboarding process clearer and more user-friendly, ultimately revolutionizing the experience.
- Limited Editability of Onboarded Models: In AI observability, insights come from ingested data, such as detecting drift and providing model explanations. Any inaccuracies in the onboarding process can make it difficult to:
- Less Bandwidth for Key Stakeholders: Data science and MLOps teams often spend 80% of their time on new model development and hypothesis testing, leaving limited bandwidth for other critical tasks. Delays in onboarding can hinder model deployment, leading to lost revenue and missed opportunities. Streamlining the onboarding process streamlines the full potential of AI observability, allowing users to quickly derive value from their models.
The Cost of Suboptimal Onboarding: Insights from an Experienced Product Manager
- User Frustration and Abandonment: A cumbersome onboarding process can frustrate users, causing them to abandon the product and resulting in lost revenue and unrealized potential.
- Disappointment and Loss of Trust: Poor onboarding can erode user trust, leading to negative reviews and damaging the product's reputation.
- Inefficiency and Ineffectiveness: A prolonged onboarding process can result in inefficient use of AI observability tools, leading to suboptimal insights and decision-making.
- Missed Business Opportunities and Revenue: Onboarding delays can mean missed opportunities and a competitive disadvantage. Efficient onboarding allows businesses to quickly realize the benefits of AI observability and maintain a competitive edge.
Leveraging my expertise and experience in this space, I've learned that reducing onboarding time is crucial in AI observability. By prioritizing flexibility, adaptability, and user experience, we can streamline the onboarding process, activate user value swiftly, and minimize frustration. This approach unlocks the full potential of AI observability, driving better insights and business outcomes
