In what felt like overnight, artificial intelligence (AI) and machine learning (ML) exploded into our personal and professional lives. AI hit the mainstream with generative AI like ChatGPT, DALL-E, and Midjourney, allowing anyone to take advantage of the technology. 

The conversation shifted from “What can we do with AI?” to “What should we do with AI?” But what exactly does that mean for those designing and building customer experiences? Are we ready to rely on AI to tell us what end users want, what to make, how to create, and whether or not we’re delivering a meaningful experience? 

In recent conversations with UX teams, we’ve heard optimism for AI-enabled research, such as using it to analyze qualitative data at scale—allowing teams to action insights that improve their product experience and create more jobs because of its broad application. On the flip side, we’ve spent time with teams who caution against relying solely on AI, telling us that there’s no substitute for talking to customers and that AI isn’t ready to handle high-level tasks like generating test plans or running data analysis. Whatever your view, AI is transforming how we conduct experience research and our expectations around AI-enabled experiences. 

Here at UserTesting, we have been using ML models since 2019 to help organizations automate various stages of the research workflow and speed up time to insights. We’re excited to continue collaborating with our customers to explore ways AI can amplify UX teams and make it easier to deliver incredible experiences that their own customers fall in love with.  

How we envision AI and ML transforming experience research

We envision AI empowering teams, expanding the scope of what they can accomplish by providing them with the tools to scale and mature their research practice, the time to focus on strategic work they enjoy, and the evidence-backed insights to make a stronger case for customer-centric decision-making. Through thoughtful AI implementation, we see AI elevating the role of UX teams—making them an even more integral, strategic partner within their organizations. 

We see AI-human collaboration where AI evolves from automating functions behind the scenes to a trusted assistant with which resource-strapped UX teams can interact through a natural language dialogue to facilitate every stage of the research lifecycle and generate higher-level customer insights. AI will accelerate and scale research by suggesting what to test, when to test, how to test, and who to test with. It holds the ability to process large volumes of data across videos, audio, written, design, and behavioral data—data that would have otherwise gone unused—at an unprecedented speed. Processing data beyond human capacity will identify patterns, correlations, and anomalies to help teams make discoveries and access a new breed of high-value insights generated from multiple data streams.

By collaborating with the UX teams, the AI assistant will continue to learn from customer input, making more relevant, customized recommendations and becoming more reliable over time, just like real-life assistants. It will allow organizations to uncover new market opportunities and validate concepts more broadly to de-risk investments and protect engineering resources. It will help us create new revenue-generating experiences and make high-confidence, customer-forward decisions that give organizations a competitive advantage. 

In the future, the AI will uncover insights and summaries by processing data generated from the UserTesting platform and aggregate experience data with third-party data sources like usage analytics, social media, and customer support data. We see integrations not only with third-party data but with design and development workflows UX teams are already in, enabling them to launch tests from external platforms and collate the data in a central repository where AI can collectively analyze the data. AI will eventually return project-relevant insights on-demand by cross-referencing these multiple data streams, helping teams minimize redundant studies and giving data in existing insight repositories a longer shelf life, making them more valuable.  

AI, like any other emerging technology, is evolving at lightning speed. Our team at UserTesting will continue to evaluate the newly available technology while ensuring that we’re training commercially-ready ML models that free our customers from worries about data security and privacy. 

Continuing commitment to security and transparency

We care deeply about the privacy and security of confidential data, including customer and contributor data, and follow AI guiding principles focused on fairness, equity, transparency, explainability, and a human-first mindset. Through the use of proprietary data in our AI development, we’re also able to leverage anonymized demographic information to mitigate bias in the models.

As we advance our AI capabilities, we’ll ensure that the data we use to train our models are aggregated, anonymized, and comply with the contractual agreements we have in place and the rules and policies protecting Personal Identifiable Information. We are dedicated to enterprise-grade information security and protecting confidential data, including customer data, participant data, and video files that the participants opt-in to have our customers collect. 

Our customers’ data belongs to our customers. We’ll continue to explore emerging AI technologies to enhance our customers’ ability to deliver customer-first experiences while ensuring that data is always managed in a secure and compliant manner. 

In summary

We believe AI has a critical role in experience research, but that meaningful AI implementation is contingent on an organization’s ability to design solutions that amplify, not displace, human skills. UserTesting has and will continue to invest in AI and ML to help UX teams do their job faster and at scale, elevate their roles, and enable them to do more meaningful work with a deeper impact on their organization.   

AI enables us to process vast amounts of data to identify correlations and anomalies in human responses or behaviors with great speed and accuracy, while humans can apply lived experiences to generate new ideas and draw meaningful conclusions that lie outside of the model’s training. We feel it’s important to leverage both of their strengths. 

The UserTesting platform is all-encompassing. The fact that it covers the end-to-end research lifecycle gives us more surface area to apply AI in a way that enables us to re-envision the future of experience research and the UX teams that shape it.

This blog post is part of a series on UserTesting’s vision for artificial intelligence and machine learning in experience research.

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About the author(s)
Andy MacMillan

Andy brings 20 years of enterprise SaaS experience to UserTesting. As a former product executive at Oracle and Salesforce, he saw the critical role that customer centricity plays in creating great experiences. By helping companies become more customer-centric, he has grown multiple enterprise SaaS businesses to hundreds of millions of dollars.

Ranjitha Kumar

Ranjitha Kumar is the Chief Scientist at UserTesting, and an Associate Professor of Computer Science at the University of Illinois at Urbana-Champaign. Her research has won best paper awards/nominations at premier conferences in HCI, and is supported by grants from the NSF, Google, Amazon, and Adobe. She received her BS and PhD from the Computer Science Department at Stanford University, and co-founded Apropose, Inc., a data-driven design startup based on her dissertation work that was backed by Andreessen Horowitz and New Enterprise Associates.