AI can transform fragmented user research into virtual personas, offering consolidated, multi-perspective feedback from a single query.
A previous article explored how AI can streamline the creation of functional personas. That discussion focused on developing personas centered on user objectives, rather than demographic profiles which often have limited impact on design choices.
The greater challenge lies in delivering these insights to relevant individuals precisely when they are needed.
Daily, various teams within an organization make decisions impacting user experience. Product teams prioritize features, marketing teams develop campaigns, finance teams design invoicing, and customer support teams create response templates. Each of these choices influences the user’s interaction with a product or service.
Often, these decisions occur without direct user input.
The Problem With How We Share User Research
Research is conducted, personas are developed, reports are written, presentations are given, and even infographics are created. What typically follows?
The research often remains in a shared drive, unused. Personas might be mentioned in initial meetings but are subsequently overlooked. Reports are briefly reviewed and then rarely revisited.
When a product manager considers a new feature, they are unlikely to search through past research. Similarly, a finance team redesigning an invoice email will probably not consult user personas. Instead, they often rely on assumptions and proceed.
This observation is not a critique of these teams. They operate under time constraints and deadlines. Even if they wished to consult the research, locating it or interpreting it for their specific needs might be challenging.
Crucial knowledge often remains confined to the UX team, who cannot participate in every decision made throughout the organization.
What If Users Could Actually Speak?
Consider a scenario where, instead of static documents requiring discovery and interpretation, stakeholders could access all user personas simultaneously?
For instance, a marketing manager developing a new campaign could inquire: “If this email leads with a discount offer, what would users think?” rather than recalling persona messaging preferences.
The AI, utilizing all available research data and personas, could then provide a consolidated response. This would include each persona’s probable reaction, points of agreement and divergence, and collective recommendations. A single query could yield synthesized insights across the entire user base.
This concept is not speculative. AI enables the construction of such a system, transforming fragmented research (surveys, interviews, support tickets, analytics, and personas) into an interactive resource accessible for multi-perspective feedback.
Building the User Research Repository
This approach is founded on a centralized repository containing all user knowledge. This repository serves as a single source of truth for AI access and utilization.
Organizations conducting user research likely possess more data than realized, often dispersed across various tools and formats:
- Survey results sitting in your survey platform,
- Interview transcripts in Google Docs,
- Customer support tickets in your helpdesk system,
- Analytics data in various dashboards,
- Social media mentions and reviews,
- Old personas from previous projects,
- Usability test recordings and notes.
The initial step involves consolidating this information. Perfect organization is not essential, as AI is adept at processing unstructured data.
For those starting without extensive existing research, AI deep research tools can establish a baseline.
Online deep research using tools like Perplexity can be a valuable starting point for user research. (Large preview)
Such tools can scan the web for discussions related to a product category, competitor reviews, and frequently asked questions. This provides foundational data while primary research is developed.
Creating Interactive Personas
With a repository established, the next phase involves developing personas for AI consultation by stakeholders. This extends the functional persona approach previously discussed, with a crucial distinction: these personas function as lenses for AI analysis of questions, rather than mere reference documents.
The process unfolds as follows:
- Feed your research repository to an AI tool.
- Ask it to identify distinct user segments based on goals, tasks, and friction points.
- Have it generate detailed personas for each segment.
- Configure the AI to consult all personas when stakeholders ask questions, providing consolidated feedback.
This approach significantly differs from traditional personas. Since AI is the primary user of these persona documents, they do not require scannability or single-page formatting. Traditional personas are limited by human readability, necessitating distillation into bullet points and concise quotes for quick comprehension. AI, however, faces no such constraint.
Consequently, personas can be significantly more detailed. They can incorporate extensive behavioral observations, conflicting data, and subtle context that would typically be edited out of traditional persona documents. The AI can manage this complexity and utilize it when responding to queries.
It is also possible to develop distinct lenses or perspectives within each persona, customized for specific business functions. For example, a “Weekend Warrior” persona could have a marketing lens (covering messaging preferences, channel habits, campaign responses), a product lens (detailing feature priorities, usability patterns, upgrade triggers), and a support lens (addressing common questions, frustration points, resolution preferences). When a marketing manager poses a question, the AI accesses marketing-specific data. A product manager’s query would draw from the product lens. The same persona offers varying depths of information based on the inquirer.
Personas should still encompass previously discussed functional elements: goals and tasks, questions and objections, pain points, touchpoints, and service gaps. These elements now form the foundation for how AI evaluates questions from each persona’s viewpoint, synthesizing their perspectives into actionable recommendations.
Implementation Options
The Simple Approach
Most AI platforms currently provide project or workspace functionalities for uploading reference documents. ChatGPT refers to these as Projects, Claude offers a similar feature, and Copilot and Gemini use terms like Spaces or Gems.
To begin, establish a dedicated project and upload essential research documents and personas. Subsequently, provide explicit instructions for the AI to consult all personas when answering questions, such as:
You are helping stakeholders understand our users. When asked questions, consult all of the user personas in this project and provide: (1) a brief summary of how each persona would likely respond, (2) an overview highlighting where they agree and where they differ, and (3) recommendations based on their collective perspectives. Draw on all the research documents to inform your analysis. If the research does not fully cover a topic, search social platforms like Reddit, Twitter, and relevant forums to see how people matching these personas discuss similar issues. If you are still unsure about something, say so honestly and suggest what additional research might help.
This method has limitations, including upload file limits. Therefore, prioritizing crucial research or consolidating personas into a single document may be necessary.
The More Sophisticated Approach
For larger organizations or continuous use, a tool such as Notion provides benefits, capable of housing an entire research repository with integrated AI functionalities. Databases can be established for various research types, interconnected, and then queried by the AI across all data.
Notion serves as a robust tool for user research, featuring integrated AI capabilities that can reference all personas and the complete research repository. (Large preview)
The advantage here is the AI’s access to significantly more context. When a stakeholder poses a question, the AI can simultaneously draw upon surveys, support tickets, interview transcripts, and analytics data, leading to richer, more nuanced responses.
What This Does Not Replace
Virtual personas do not replace direct interaction with real users. Instead, they enhance the accessibility and actionability of existing research.
Primary research remains essential in several scenarios:
- When launching something genuinely new that your existing research does not cover;
- When you need to validate specific designs or prototypes;
- When your repository data is getting stale;
- When stakeholders need to hear directly from real humans to build empathy.
The AI can be configured to identify such situations. If a question exceeds the scope of available research, the AI can respond: “Insufficient information exists to confidently answer that. A brief user interview or survey might be beneficial for this question.”
New research conducted then integrates into the repository. Personas evolve as understanding deepens, a superior approach to traditional methods where personas are static and quickly become outdated.
The Organizational Shift
The role of the UX team transforms from gatekeepers of user knowledge to curators and maintainers of the research repository.
Rather than producing reports that may go unread, efforts are directed toward maintaining a current repository and ensuring the AI provides useful responses.
Research communication shifts from a push model (presentations, reports, emails) to a pull model (stakeholders seeking answers when needed). User-centered thinking becomes disseminated throughout the organization, rather than confined to a single team.
This shift does not diminish the value of UX researchers; it enhances it by broadening their work’s reach and impact. However, it does alter the nature of their responsibilities.
Getting Started
To implement this approach, begin modestly. For a foundational understanding of functional personas, a detailed guide is available. Select a single project or team and establish a basic implementation using ChatGPT Projects or a comparable tool. Collect existing research, even if incomplete, develop one or two personas, and observe stakeholder reactions.
Observe the questions posed by stakeholders. These inquiries will highlight research gaps and indicate what additional data would be most beneficial.
As the approach is refined, expansion to more teams and advanced tools is possible. However, the core principle remains: consolidate fragmented user knowledge and provide it with a voice accessible to anyone in the organization.
A previous article advocated for transitioning from demographic to functional personas, emphasizing user actions. The next progression involves moving from static personas to interactive ones that can actively contribute to decision-making discussions.
Daily, decisions are made across organizations that impact users. Users merit representation in these discussions, even if virtually.
Further Reading On SmashingMag
- “A Closer Look At Personas: What They Are And How They Work | 1”, Shlomo Goltz
- “How To Improve Your Design Process With Data-Based Personas”, Tim Noetzel
- “How To Make Your UX Research Hard To Ignore”, Vitaly Friedman
- “How To Build Strong Customer Relationships For User Research”, Renaissance Rachel
