CREATING COMPLEX PROPOSALS ASSISTED BY AI TECHNOLOGIES
Working closely with the Technical Product Manager, I contributed to shaping early roadmaps and managing tickets in Jira, ensuring seamless collaboration between engineering and design. I spearheaded the development of several key features that integrated ML/AI capabilities, deepening my understanding of backend processes to better tailor front-end designs. Concurrently, I laid the groundwork for a scalable design system called Dash, which streamlined our design efforts as the product evolved. Additionally, I partnered with leadership to craft compelling visuals for our North Star vision, offering both our internal team and investors a clear roadmap of the product’s future potential.
To design a user-friendly workflow that allows proposal writers to upload an RFP, have the system analyze it, and generate a proposal draft that aligns with the specific structure and formatting requirements of the RFP.
Historically, creating RFP-based proposals has been challenging for software due to the wide variation in RFP formats, inconsistent instructions, and the need for precise alignment between the proposal content and the RFP requirements. Ensuring that AI could assist without sacrificing user control was a critical hurdle.
Cross-Functional Collaboration:
To kick off the project, I facilitated a cross-functional brainstorm with stakeholders from product, engineering, and customer support. This session helped uncover pain points proposal writers face, including the difficulty of parsing unclear formatting instructions and ensuring all RFP questions were addressed.
Competitive Research:
I conducted competitive research to analyze how existing tools approached similar problems. This analysis revealed gaps in their user flows, particularly around balancing AI automation with user customization.
Machine Learning Collaboration:
I worked closely with AI engineers to understand not only more about generative AI in general — but also, for this feature, the data and user inputs the system needed to generate high-quality drafts. Together, we iterated on a process that ensured writers could review, verify, and refine both the AI’s output and formatting requirements with minimal friction.
User-Focused Iterations:
The final solution included an AI-assisted workflow that was the sweet spot between users wanting full control and those seeking efficiency.
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