Summary:
Operating in a hybrid role as a Product Design Lead & Product Manager, I spearheaded the creation of an AI-powered product discovery tool aimed at streamlining research processes for internal and external use. By collaborating with cross-functional teams, we reduced research time from 2–4 weeks to 1 week, validated the tool internally, and pivoted based on user feedback to position it as a scalable solution for product teams.
Primary role:
Product Design Lead/Product Manager (focusing on product discovery and validation)
Skills & Capabilities:
Product Discovery
Product Management
Concept Development
Product Design
Team Leadership
Timeframe:
2024
Key Outcomes:
Research Time Reduced
Achieved a 75% reduction in research time.
Persona Creation Efficiency
Created personas in under 5 minutes vs. 4–5 hours manually.
The Challenge
Conducting research for client discoveries was a time-intensive process, often taking 2–4 weeks and requiring significant manual effort. This included:
Generating interview questions and finding interviewees.
Conducting market and competitor research.
Synthesizing research into actionable outputs (e.g., personas, user flows, customer needs).
This inefficiency slowed project timelines and added significant overhead for the design team. We hypothesized that an AI-powered research tool could reduce research time to 1 week while maintaining or improving the quality of outputs.
My Leadership Approach
1. Vision and Strategy:
Collaborated with the executive team to define the tool’s purpose and align it with business goals.
Mapped internal workflows to identify inefficiencies and opportunities for AI integration.
2. Team Collaboration and Design Oversight:
Partnered with the engineering manager to prioritize features based on impact and feasibility.
Provided design direction and mentorship to the product designer, ensuring wireframes and UI met functional and user-centric standards.
Worked closely with the engineering team to align on feature prioritization, ensuring smooth handoffs between design and development.
3. Product Discovery:
Planned and conducted workshops to define the problem space, user needs, and potential solutions.
Led iterative testing cycles, ensuring the product met both internal and external needs.
4. Process Innovation:
Introduced scalable workflows for wireframing, prototyping, and internal testing to accelerate design iterations.
The Process
Phase 1: Discovery and Research
Conducted industry research to validate the appetite for AI tools among product teams.
Explored existing workflows to identify where AI could reduce time and manual effort.
Phase 2: Proof of Concept (PoC) Development
Prioritized MVP features to test feasibility, focusing on AI synthesis of user personas.
Designed and tested wireframes using a design library to expedite iterations.
Collaborated with the engineering manager and development team to ensure feasibility and alignment with product goals.
Phase 3: Validation and Pivot
Tested the PoC with internal teams, measuring efficiency (time savings) and accuracy compared to manual methods.
Conducted customer discovery interviews with product managers, designers, and researchers, leading to a refined product vision.
Pivoted the tool’s focus to become an AI-powered Product Discovery Tool, targeting external product teams.-
The Outcome
Impact on Internal Teams:
Time Savings: Reduced research time from 2–4 weeks to 1 week.
Efficiency Gains: Generated user personas in under 5 minutes compared to 4–5 hours manually.
Impact on Product Vision:
Positioned the tool as a scalable, AI-powered product for external markets.
Expanded features to address real-world challenges, including problem space exploration, competitor research, and insight generation.
Impact on Business and UX Maturity:
Enhanced internal UX maturity by introducing structured workflows and scalable research processes.
Improved alignment across design, engineering, and executive teams, fostering better collaboration.
Key Metrics:
Research Time Reduced
Achieved a 75% reduction in research time.
Persona Creation Efficiency
Created personas in under 5 minutes vs. 4–5 hours manually.
Customer Validation
Confirmed product-market fit with external stakeholders during interviews.
Reflections and Lessons Learned
Leadership Reflections:
Driving Alignment: How I balanced executive vision with team capabilities and market needs.
Empowering Teams: Fostering collaboration and ownership while providing clear guidance.
Iterative Success: Validating assumptions and pivoting based on real-world feedback.
Challenges Overcome:
Navigating skepticism around AI tools by focusing on measurable outcomes and transparency.
Ensuring the PoC was robust enough for validation yet lean enough for quick iterations.