Qualtrics introduced its AI-powered Strategy & Research suite at its summit, X4, and launched its “Strategic UX” product — officially entering the experience research space. The new product supports various UX research methods like video feedback, unmoderated usability testing, card sorting, and tree testing, and it leverages AI to generate insights and recommended actions. Adding UX research to its experience management solutions, Qualtrics aims to support organisations’ research efforts at scale with a single platform.
Qualtrics AI was the highlight of the event as it aims to help users get deeper insights through interactive dashboards, data analysis, and recommendations on the next steps. These are strong promises and other leading firms in the experience research space are also similarly improving their AI capabilities and expanding their research toolset. UserTesting, one of the leaders in experience research, recently added AI-powered surveys to its toolset to collect feedback at scale and introduced the Feedback Engine.
What do these AI integrations and the research platforms’ expansion of their toolset tell us about the future of experience research? The conversations I’ve had with our clients and attendees at X4 drive me to conclude that experience research will unlock AI’s true potential — as long as the research expertise plays a key role in shaping research strategy. That means:
Not a replacement
AI is not a replacement for research. AI is changing how we ideate, analyse, and synthesise research findings by creating efficiencies (e.g., lowering mundane tasks, summarising interviews, etc.), but interpreting those findings in the broader context of the user is on us.
Determining the right approach to the right problem is still what drives impact. Therefore, while AI accelerates the research workflow, companies still need a collaborative, rigorous research process to produce high-impact research that supports decision-making.
AI facilitates the democratisation of research — but it needs guardrails. As AI has the potential to accelerate the research workflow, it also brings new possibilities to democratise research. For instance, AI-generated tools (e.g., AI-generated interview templates) help novice researchers conduct research and improve their skills.
However, when executed poorly, democratisation can do more harm than good and can lead to the wrong approach or poor interpretations of data, which is even more important with AI because it can sometimes produce biased outputs. To democratise research responsibly, institute a process with guardrails.
For instance, one of the speakers at X4, who is working to democratise research in their product teams, mentioned that only people with a proven track of high-quality research get permission to contribute to the research repository, so they can keep its quality high use it to inform their AI.
Companies’ research needs vary. Consolidation of research tools benefits companies looking for all-in-one solutions, but companies with specialised research needs and limited budgets must explore alternatives. For instance, firms primarily conducting research with their community of users do not need research platforms with robust recruitment capabilities. Assess your current research needs and consider your future research plans (e.g., how your practice will grow) to pick a research platform that best suits your needs.
Originally posted on Forrester
