Sales reps wasted 3 hours per day analyzing documents
This inefficient workflow not only drained productivity but also delayed client engagement, prolonging the sales cycle and negatively impacting revenue.
Why this matters for the business
- 16%
reduction of the cost of the staff
- 13%
shorter in sales cycle
+ 22%
increase in adoption through increasing the client trust and data privacy
Quickly upload or access files for analysis with minimal clicks
AI highlight relevant information and suggests tailored follow-up questions
Organize and store past files and conversations for easy future access
Identifying initial demo flaws that limited usability and support for sales reps
I analyzed the usability and found several key flaws in the demo that diminished its functionality. These flaws resulted in the tool not being able to fully support sales reps in performing document analysis tasks.
Remapping the workflow based on the opportunity gap
With these gaps in mind, I developed a refined workflow to address sales reps' needs. The following user flow diagram maps how reps interact with the tool at each stage.
See the changes between Initial demo and final prototype
Adding file upload functionality
Problem: In the initial demo, there was no option to upload a file.
Solution: To address this gap, I integrated the file upload option directly onto the main page alongside the AI chat feature, reducing the need to switch between pages and saving time.
But...users were confused about where to start first
The UI had mixed calls to action, leading to a 30% incomplete task rate.
Addressing confusion in the document upload process by separating file upload/selection from AI chat actions
Iteration 1 highlighted user confusion around the initial steps in the upload process. To address this in Iteration 2, I refined the design by placing the primary action—file upload or selection—on its own dedicated page before transitioning into the AI chat interface.
Some users still struggled to distinguish between selecting a file and uploading a new one due to similar UI styles.
Check out final prototype again
Guided by insights from Iterations 1 and 2, the final prototype features key improvements, including a dedicated upload page, clear file selection options, and a smooth transition to the AI chat.
Final testing showed a 100% task completion rate, confirming that the design effectively addressed initial challenges and provided an intuitive experience for document analysis.
Final Prototype
Lessons Learned
Maintaining alignment through cross-functional communication
Working closely with the design, engineering, and business analytics teams, I realized that consistent communication was essential to prevent misalignment and ensure a unified product vision. To maintain alignment, I had weekly syncs with the engineering team to review the AI’s technical capabilities and ensure they matched user needs. By fostering an open feedback loop among all teams, we could address potential issues early, adapt based on real-world usage insights, and keep the project on track toward delivering a useful solution.
Prioritizing core functionality under constraints
One of the challenges we faced was time. The product roadmap initially included ambitious features, like highlight the content in the file based on user-specific data. These features had to be deprioritized to ensure we delivered the core functionality on time.
I focused on features that directly impacted the user’s daily workflow. If it didn’t save time or enhance trust, it was left for future iterations. This approach allowed us to deliver an MVP that met both user and stakeholder needs while staying within our deadlines.
Thank you for taking the time to read through my case study
If you’re interested in learning more about my design process or discussing how I can contribute to your team, I’d love to schedule a call. Please feel free to reach out, and I look forward to connecting soon!
