Highspot: Scaling AI through a native text refinement pattern
Summary
- Goal: Building a foundational AI refinement and generation pattern for the Highspot platform
- Role: Principal Product Designer
- Other team members: Principal product manager, principal front end engineer, and product designer
- Contributions: Platform-level AI patterns, interaction model architecture, and AI-assisted prototyping
- Timeline: 1 month
- Outcomes: Established a unified "AI text refine" primitive adopted as a platform-level pattern across multiple crews
The challenge
Highspot’s exploration into AI text generation had remained in an alpha state while the platform underwent a major transition to a new text editor. Once the migration was complete, we faced a market where Generative AI was commonplace, yet our internal authoring experience remained manual and fragmented.
We observed several critical friction points:
- The "blank page" hurdle: Creators struggled to initiate content, making authoring feel like a high-effort chore
- Workflow fragmentation: Users were "alt-tabbing" to external tools like ChatGPT to polish copy, losing the "deep context" of Highspot’s CRM data in the process
- Pattern gaps: Multiple crews were building isolated AI prompts, leading to a fragmented experience without a unified primitive for text refinement
Strategic framing
This project was a "Big Bet" for our product strategy. My role was to ensure the crew didn't just solve this for SmartPages, but built a common pattern that could be extended across the entire platform.
We focused on:
- Unified patterns: Designing a refinement component other crews could adopt without reinventing the solution.
- Strategic sequencing: Prioritizing high-volume, one-click refinements (Shorten, Elaborate, Professional) to build habits before moving to full generation.
- Scalability: Building a foundation to eventually layer on Highspot-specific insights like CRM data.
Principles for project
We ensured our design decisions met each of these criterias:
Low friction
Refinements should feel fast and lightweight
User control
Users should feel comfortable experimenting and easily reverting changes
In-context
Refinements should feel fast and lightweight
Scalable foundation
Whatever we design now should leave room to evolve toward richer experiences later
Synthesizing complexity
We began by auditing how industry leaders like Notion, Canva, and Google handle the transition from text generation to refinement.
I facilitated a whiteboard jam to align on several core hypotheses:
- Speed over robustness: For an early version, staying in context and building confidence may matter more than offering the most creative output
- Winning on refinement: Highspot may not compete with general-purpose tools on full content generation, but we win by helping users polish existing content directly where it lives
Refined internally
To ensure the first milestone of this project met our high standards for usability, we gathered critical feedback from the Crew to stress-test our initial patterns before moving into beta.
The key insights we prioritized included:
- Building trust through interaction: There was a strong preference for inline, "preview-before-insert" patterns to minimize distraction and create a more conversational, low-friction experience
- Architecting for platform scale: The crew wanted to ensure the refinement primitive was flexible enough to adapt across all surfaces, from the Email Composer to Item Properties
- Native editor alignment: To maintain the user’s flow, it was essential to focus on a seamless integration with the new text editing experience
This feedback acted as a vital "pulse check" for our first iteration, ensuring our foundational architecture was robust enough to support future agentic implementations.
These components and states were further refined by Abhijeet Saraf and took this project to completion.
Navigating tradeoffs
We approached the solution by investigating how to balance the power of GenAI with the speed required for a professional environment. We focused on building a native authoring primitive that reduced cognitive load and kept users in their flow.
Decisions we made:
- Inline over toolbar: While toolbar actions are familiar, they require a "travel tax" where users move focus away from their work. We chose an inline action that appears upon selection, meeting users exactly where they work
- Floating widget over modals: Modals provide space but obscure the page. We landed on a floating widget to keep the experience in-context, allowing users to see changes applied in real-time
- Refinement over generation: We made a deliberate bet to prioritize one-click refinements. We recognized that Highspot wins not by competing with ChatGPT on broad creativity, but by helping users polish existing copy directly where it lives
Click the image to view the conceptual prototype.
Innovation through AI-assisted prototyping
As an AI-first organization, we wanted to embrace AI tools within our own design workflows. Once initial concepts were established, I used Cursor to handle the heavy lifting of building a functional prototype.
This allowed us to:
Move from static designs to interactive environments much faster than traditional methods
Feel the actual streaming of text and nuanced transitions between states—details often lost in static mocks
Experience the responsiveness of the refinement, validating our hypothesis that confidence and context mattered more than robust AI output
