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Humanizing Artificial Intelligence and Machine Learning Tools for Start-ups at Google

A tech-giant wanted to understand how they can improve their Cloud platform initiative for Start-ups.

How might we better support Start-ups as they implement AI/ML tools and platforms?

Held initial conversations with team members to understand their needs and how the insights might directly impact their workstream as I built the research plan. Conducted a quick literature review parallelly to identify knowledge gaps and anticipated impact.
Broke down the challenge into objectives to inform understanding of the current landscape: Understand the current user profile deeper - their goals, needs, pain points, and motivations as they implement various AI/ML tools in their business. Understand their current use and attitude toward the same and the top priorities that drive their vendor choices.
Launched a survey on the internal platform to gauge participation and interest. 7 participants were recruited for the in-depth user interview who represented 3 main variables: demographic location (across North America, Europe, and Asia), org size (small to medium), and their role (Founder, CTO, Data Engineer, PM, Enterprise Architect, Developer, etc.).
Conducted a 2-part remote user interview that included a Qualitative stack-ranking 'think-out-loud' exercise for factor prioritization of vendor choices.
Created a visual stack ranking map and thought bubbles during the analysis process to condense the data into consumable insights for stakeholders who needed the short version of the top focus areas.
Hosted a team read-out to present the top pain points and vendor choice drivers, then ranked them using an impact/feasibility matrix in real-time on Figma.

The team walked away with a deeper understanding of who the users are, what they currently use, what they want, their pain points, and the factors that drive their future vendor choices for AI/ML tools and platforms.
Established a core contextual understanding artifact that presented the current landscape, user persona, pain points, drivers, and needs that drove future product improvements and prioritization.
This study also opened the doors for more Developer advocacy and community support across users of varied levels of technical competence.

Mixed-Methods
Stakelholder interviews, Survey, User Interviews, Stack-ranking on Qualtrics, Heat-map analysis, Affinity diagramming
Deliverables: Research plan, interview guide, 1-pager analysis infographic, AI/ML Tools overview, Analysis deck, and a Detailed report with user quotes and relevant sources for related projects and past research.
Timeline: 3 Weeks

If you like what you see and want to chat more, reach out to me at uxrpooja@gmail.com