New business pursuits can divert crucial resources from billable client work, and the timeframe in which we have to deliver these artifacts prevents us from conducting thorough user research. In order to demonstrate our process and our research methods without committing full-time to non-billable work, I piloted using AI to conduct synthetic user research. Using existing user data we're able to model user responses to interview questions and surveys. We used this to generate proto-personas and journey maps. We can even use this information to create product requirements which we then use to prototype concepts. This saves significant time and resources on pursuit requests, allows us to demonstrate user experience methods in initial conversations, highlighting our perspective and approach towards experience design.
Research Driven Business Pursuits
In one of our first attempts at research-driven pursuits, I conducted real user research with a combination of synthetic user research to create a thorough analysis of value-segment telecom consumers. This included an analysis of AEO summaries, competitive audits, and in-person retail observations. We developed personas with key pain points and experience goals, we mapped an ideal state user journey, and prototyped key interaction points.
This research is still being used within telecom clients to demonstrate our experience design and research methods while highlighting opportunities to improve customer experience.
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Synthetic User Research
To socialize how I used synthetic research in our research-driven pursuits, I documented the process from research to conception. By comparing traditional methods with AI-driven methods, I identified areas where we could reallocate time to more impactful, strategic activities. Throughout this process, I've made sure to reiterate that AI is not the autopilot. AI is a co-pilot that supports experts in delivering better work.
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AI Prototyping
With so many AI development and prototyping tools available, I decided to document the Verizon CX Innovation Studio's process for using tools like Lovable to build live prototypes, ensuring the design meets user-centered best practices, maintaining control over the output, and ensuring that concepts built with AI can be refined by design teams for additional rounds of iteration and development handoff.
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