Problem Statement
How might we enhance Notion's user onboarding and workspace setup experience using AI to reduce user frustration and increase adoption rates among new and intermediate users?
Problem Background
Notion has grown into a versatile tool for personal and professional productivity, but its steep learning curve remains a significant barrier, especially for new users. Unlike simpler tools like Trello or Asana, Notion’s flexible “blank slate” approach can feel overwhelming, creating a problem of "too many choices" that can intimidate new users. While flexibility is a strength, it can also lead to confusion, making it difficult for users to know where to start.
The platform already offers templates and guides, but they are not personalized, limiting their usefulness for users with specific needs or workflows. New users are often overwhelmed by the open-ended nature of the tool, while intermediate users may find it challenging to transition to more advanced features. This project aims to pinpoint key onboarding and workspace setup pain points and explore AI-driven solutions that leverage Notion’s flexibility while addressing its complexity and making it more user-friendly for both new and intermediate users.
Research Insights
User Pain Points
Supporting Data
How would you describe your experience level with Notion?
Primary Use Case for Notion
Resource Usage During Onboarding:
Users identified gaps in available resources during onboarding, particularly in areas requiring more detailed guidance and customization options. Addressing these gaps could enhance the user experience and reduce confusion.
AI-Driven Solutions: User Expectations:
Proposed Solution
Leverage AI enhancement to create a personalized, guided onboarding experience and smart automation tools that assist users in setting up their workspace and managing their tasks efficiently.
Key aspects include:
- AI-driven recommendations for templates, workflows, and integrations.
- Interactive walkthroughs based on user-specific needs.
- Task prioritization and automation powered by AI.
Explanation of Solution
Explanation of Solution
To enhance Notion AI’s task prioritization and automation, the solution leverages AI-driven recommendations for templates, workflows, and integrations while ensuring user control, transparency, and privacy.
AI-Driven Recommendations for Personalized Workflows
Instead of requiring users to manually search for templates or configure workflows, AI analyzes their needs and provides tailored recommendations based on:
- User Role & Industry: A project manager may receive agile sprint templates, while a student might be suggested a study planner.
- Past Behavior & Preferences: AI tracks user interactions with Notion to refine and improve recommendations.
- Team Collaboration Patterns: AI suggests structures based on how a team interacts and shares information.
- Third-Party Integrations: The system recommends tools such as Slack, Jira, and Google Drive based on the user’s existing ecosystem.
Balancing AI Assistance with User Control
- AI-generated templates serve as customizable starting points, allowing users to modify structures to suit their needs.
- A human-in-the-loop approach ensures AI suggests optimizations without enforcing rigid automation, keeping decision-making in the user’s hands.
- User-adjustable settings allow customization of AI’s level of automation, ensuring adaptability to different workflows.
Transparency and Explainability
- Every AI-generated recommendation is accompanied by a clear rationale explaining why it was suggested.
- Users have access to explanatory resources to understand how Notion AI processes their inputs.
Privacy and Consent Considerations
- AI personalization is opt-in, giving users full control over what data is used for recommendations.
- Clear data policies ensure users are informed about how AI processes and stores their information.
Continuous User Feedback and Ethical AI Practices
- Integrated feedback loops allow users to refine AI suggestions over time.
- Regular bias audits ensure fair and equitable AI-driven recommendations.
- The system prioritizes assistance over over-automation, ensuring AI enhances user workflows without taking away their control.
Impact:
- Saves time by reducing the effort needed to explore templates and integrations.
- Enhanced user satisfaction and trust, with a focus on transparency and control over AI-driven decisions.
- Higher adoption rates of Notion AI by integrating privacy-first AI solutions, ensuring compliance with user expectations.
- Interactive Walkthroughs Based on User-Specific Needs
Explanation:
Not all users learn the same way, and a one-size-fits-all onboarding process doesn’t work. Instead, AI-powered walkthroughs will be:
- Adaptive: The system will adjust tutorials based on a user’s background (beginner vs. advanced).
- Contextual: Instead of generic tours, users will see real-time tooltips, checklists, and step-by-step guides based on their actual usage patterns.
- Conversational & AI-Guided: A chatbot or interactive assistant will answer questions and guide users based on their actions.
- Adaptive: The system will adjust tutorials based on a user’s background (beginner vs. advanced).
Impact:
- Reduces frustration by providing just-in-time learning instead of information overload.
- Increases engagement by making onboarding interactive and hands-on.
- Helps users feel more confident and productive sooner.
- Task Prioritization and Automation Powered by AI
Explanation:
Users often struggle with organizing tasks and prioritizing what to work on. AI can assist by:
- Smart Task Sorting: Automatically categorizing and prioritizing tasks based on deadlines, urgency, and past user behavior.
- Automated Reminders & Follow-Ups: AI will remind users of upcoming deadlines, overdue tasks, and pending approvals.
- AI-Assisted Task Creation: Users can input simple prompts like “Prepare a launch plan,” and AI will generate a structured to-do list with relevant subtasks.
- Recurring Task Optimization: AI will detect repetitive tasks and suggest automation (e.g., “You’ve created a weekly meeting note 3 times. Would you like to automate it?”).
- Smart Task Sorting: Automatically categorizing and prioritizing tasks based on deadlines, urgency, and past user behavior.
Impact:
- Improves productivity by reducing manual effort in task management.
- Helps users stay on top of priorities with minimal input.
- Ensures workspaces remain structured and efficient over time.
User Flows/Mockups
Future Steps
As we continue refining the AI-enhanced onboarding and automation features, we must focus on user feedback and iterative improvements. Below is a detailed roadmap for the next steps, along with examples of potential solutions.
Measuring Success Metrics for AI-Enhanced Notion Onboarding
To evaluate the success of AI-driven onboarding and personalization, the following methods will be used:
1. Reduction in Onboarding Time (Target: 30% decrease in setup time)
How to Measure:
- Baseline Measurement: Track the average time new users take to complete the initial onboarding process before AI integration.
- Post-AI Implementation: Measure the time users spend setting up their workspaces with AI assistance.
- Data Points:
- Time from account creation to first meaningful interaction (e.g., creating a project, editing a template).
- Time spent in onboarding flows (guided tours, interactive tutorials, etc.).
- Drop-off rates at different onboarding steps.
Measurement Tools:
Product analytics (Amplitude, Mixpanel, Google Analytics)
2. Increase in User Activation Rates within the First 7 Days
How to Measure:
- User Activation Definition: A user is considered “activated” when they complete key actions, such as:
- Creating a new page
- Using an AI-recommended template
- Inviting a collaborator
- Completing an onboarding task
- Pre-AI vs. Post-AI Comparison: Track the percentage of users who complete activation tasks within their first 7 days before and after implementing AI onboarding.
Measurement Tools:
Cohort analysis in analytics platforms ( Amplitude)
Event tracking in Notion’s database
3. Higher Engagement with AI-Driven Features
How to Measure:
- Feature Adoption Rate: Measure how often AI-driven features (e.g., AI recommendations, automated workspace setup) are used compared to manual setup.
- Usage Frequency: Track how often users engage with AI-driven elements (e.g., number of AI-powered suggestions accepted or modified).
- Retention Impact: Compare retention rates of users who engage with AI features vs. those who don’t.
Measurement Tools:
Feature flagging and tracking ( Optimizely)
Event tracking in analytics tools
Additional Considerations for Feedback Loops
- Human-in-the-Loop Validation: Periodic audits of AI suggestions to ensure relevance and accuracy.
- A/B Testing: Running controlled experiments to compare AI-driven onboarding vs. manual onboarding.
- Sentiment Analysis: Analyzing user feedback, NPS (Net Promoter Score), and qualitative responses from surveys.
Learnings
Product Manager Learnings:
Nisha Rani
Working on this project was a huge learning curve for me, especially as I was relatively new to AI. It helped me gain hands-on experience with AI-powered product management, deepening my understanding of how AI can enhance user experiences.
1. AI Concepts & Technologies
- I learned about LLMs (Large Language Models) and how they can be leveraged for personalization, automation, and contextual recommendations.
- Prompt engineering was a key takeaway—I explored how structuring prompts effectively improves AI outputs and user experience.
- Understanding Retrieval-Augmented Generation (RAG) was fascinating, as it showed how AI can pull from external knowledge bases to improve responses.
- I also gained exposure to vector databases, embeddings, and how AI-driven recommendations work under the hood.
2. AI in Product Management & UX
- This project reinforced how AI can solve real user problems, especially in onboarding and workflow automation.
- User-centric AI design is crucial—AI should enhance the experience, not add complexity.
- Balancing automation vs. user control is key. Too much automation can feel restrictive, while too little can make AI feel redundant.
- I saw firsthand how adaptive AI walkthroughs improve onboarding by tailoring guidance based on user actions.
3. UX & Prototyping with No-Code/Low-Code Tools
- I used Figma and FigJam to create user flows, wireframes, and interactive UX designs.
- Bolt helped me build prototypes with a no-code/low-code approach, making it faster to test AI-powered onboarding flows.
- Understanding how design and AI interact was critical, AI should enhance usability, not overwhelm users.
This project pushed me to deep-dive into AI, UX, and product-led AI thinking, making it a game-changer for my learning journey! 🚀
Designer Learnings:
Designer Learnings:
Jo Sturdivant
- Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
- Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
- Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.
Developer Learnings:
Developer Learnings:
Vanady Beard
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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.
Developer Learnings:
Stephen Asiedu
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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.
Developer Learnings:
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Developer Learnings:
Maurquise Williams
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- Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
- Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
- Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.
Developer Learnings:
Jeremiah Williams
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All in all this experience was very awesome I learned that in coding with others being transparent is key
Developers Learnings:
Justin Farley
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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.