Collaborating with Automated UX Tools

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A column by Janet M. Six
December 17, 2018

This month, the Ask UXmatters expert panel discusses how UX professionals can best collaborate with automated UX tools, considering the different, but complementary strengths of AI systems and human beings. Our experts also consider the deeper question of whether artificial-intelligence (AI) tools will replace UX researchers and designers and what the future holds for UX professionals working in an artificially intelligent environment.

Our experts also describe the respective roles of automated UX tools and UX researchers. Plus, they discuss the merits of automated versus moderated usability testing.

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Every month, in my monthly column Ask UXmatters, our panel of UX experts answers our readers’ questions about a broad range of user experience matters. To get answers to your own questions about UX strategy, design, user research, or any other topic of interest to UX professionals in an upcoming edition of Ask UXmatters, please send your questions to: [email protected].

The following experts have contributed answers to this month’s edition of Ask UXmatters:

  • Carol Barnum—Director of User Research and Founding Partner at UX Firm; author of Usability Testing Essentials: Ready, Set … Test!
  • Pabini Gabriel-Petit—Principal Consultant at Strategic UX; Publisher and Editor in Chief, UXmatters; Founding Director of Interaction Design Association (IxDA); UXmatters columnist
  • Cory Lebson—Principal Consultant at Lebsontech; Past President, User Experience Professionals’ Association (UXPA); author of The UX Careers Handbook
  • Gavin Lew—Managing Director at Bold Insight

Q: How can we best collaborate with automated UX tools? Is there a role for real UX researchers to play when companies are employing one or more of these tools to address their UX research needs? Will such tools eventually replace UX designers?—from a UXmatters reader

The Future of User Experience in an Artificially Intelligent Environment

“Rather than purchasing automated UX tools to replace UX professionals, savvy business leaders should employ such tools to augment and amplify the skills of human UX researchers, strategists, and designers,” advises Pabini. “AI tools and human beings have different, but complementary strengths that businesses can leverage to achieve optimal outcomes. For example, an AI system could more efficiently perform many repetitive tasks that UX professionals find boring, allowing UX professionals to focus on more interesting work that requires human empathy and judgment.

“The strengths of AI systems include speed, consistency, accuracy, scalability, and data organization and presentation. In addition to their machine-learning components, other AI components include predictive systems, knowledge representation, computer vision, audio processing, speech to text, and natural language processing. AI excels at data-intensive tasks—especially those that require synthesizing structured and unstructured data from many different sources—for example, the findings of user-research studies, analytics data from Web sites and mobile apps, survey results, videos from usability-testing sessions, data from voice of the customer (VOC) systems, data about specific issues from support databases, and data from published studies on the Internet. This is the sort of task that is challenging and time-consuming for people, but is a piece of cake for a machine-learning system. Nevertheless, people play a role in supporting supervised learning and modeling behavior for these systems.

“Human UX professionals excel at tasks that require

  • empathy—for example, conducting user research to understand what people really need and why. Plus, humans can model empathy for advanced AI systems with natural language processing (NLP) capabilities such as chatbots.
  • social skills—for example, modeling the appropriate behavior of a voice-user interface (VUI) or chatbot in specific situations
  • creativity—for example, coming up with creative insights, defining design problems, designing optimal solutions, and reimagining work processes
  • improvisation—for example, rapidly coming up with solutions for ill-defined problems
  • human judgment—for example, analyzing the outputs of an automated UX research tool to determine an optimal product strategy
  • leadership—for example, defining the vision for a company, product, or design

“An AI system could augment a human UX designer or researcher’s intuition by rapidly prototyping and testing many possible solutions to a well-defined design problem. But humans are better at defining such design problems, as well as the appropriate testing protocols for the AI to use. Thus, human UX professionals and AI systems can work in collaboration with one another to more rapidly deliver high-quality user experiences.”

“In their recent book Human + Machine: Reimagining Work in the Age of AI, Paul R. Daugherty and H. James Wilson wrote:

‘As bots become critical components of the customer-service infrastructure, … their personalities will need to be designed, updated, and managed. Experts in unexpected disciplines such as human conversation, dialogue, humor, poetry, and empathy will need to lead the charge. Moreover, in the new world of augmented and automated work, user interface and experience designers will have utmost importance, as the interface between people—whether an organization’s customers or its employees—[has] a disproportionate impact on whether an AI-based product or service survives and thrives, or if it fails.’

“I concur. UX professionals have key roles to play in the design, testing, and training of AI systems, products, and services. Among the existing disciplines within product-development organizations, UX professionals are best prepared to take full responsibility for those ‘unexpected disciplines’.”

“It seems likely that AI could eventually become so sophisticated that automated UX tools could use existing data, gather additional data behind the scenes, fully understand users, and creatively meet their needs in an effective, efficient, and satisfying way,” responds Cory. “But achieving the level of AI sophistication necessary to pull this off and truly design for the intended users means User Experience jobs are likely to be some of the later jobs to become automated. By then, we’ll be having much bigger discussions about what it means for people to be truly productive in society. For now, neither UX designers nor user researchers should have any fear of being replaced by automated tools.” For more information, Cory refers us to his article “Your UX Career Is Well-Positioned for an AI Future.”

The Roles of Automated UX Tools Versus UX Researchers

“Just as robots have been squeezing some manufacturing workers out of their jobs, automated UX tools are now squeezing UX researchers out of jobs,” answers Carol. “Having humans conduct UX research can be expensive and slow. Automated UX tools can provide qualitative and quantitative findings within hours and, generally, at a lower cost than employing a UX researcher to do the same work.

“So, what are we, as UX researchers, to do? Promote collaboration. It doesn’t have to be an us-against-them scenario if we make the case that these tools have a role to play and so do human researchers. Working with automated UX tools provides fast responses to research questions, which humans can then use to conduct analyses and make recommendations.

“For example, let’s say a development team has a question about a feature and needs to know how users would respond to it. Using one of the automated UX tools gets feedback quickly, but usually stops short of providing the whys and wherefores. Human UX researchers can step in at this point to review the research outputs—whether videos or spreadsheets—and interpret the findings in light of the research goals. While the results may provide a clear understanding of the user experience, they’re more likely to bring up some issues that need further exploration. Following up a large unmoderated study with a small moderated study can tease out missing insights and help a team to zero in on specific unanswered questions.

“If a move is afoot to subscribe to an automated tools platform or this has already taken place, be quick to act and show how best to leverage these tools. Many of the most popular UX tools platforms such User Testing and UserZoom have expanded over time from offering basic unmoderated research to offering many of the same services that human researchers provide, depending on the subscription license. If you don’t want automated UX research tools to replace you, make your value known.

“If your company is thinking of going the automated-tools route as a replacement for human UX researchers, step up to support the use of these tools to enhance the work you do instead,” advises Carol. “If necessary, make your case by actually doing a study using automated UX tools. Then highlight the questions that result, interpret the findings, and use them to make design recommendations.”

Automated Usability Testing

“Unmoderated, remote usability testing is fast, so ideal when research needs are to address simple hypotheses—where the questions can be answered without a need for moderated probes,” replies Gavin. “This can work for discovery or affordances of controls or single pages. But classic, moderated usability testing is more appropriate for a full experience.

“What we usually find is that even automated testing requires UX researchers to help shore up the prototype and tasks. With automated research, precision is more critical than ever! Having UX researchers integrate with business analysts, prototype developers, and designers is almost a full-time job.

“While some might think that automated UX research tools could replace human researchers, I don’t think the use of automated tools alone can enable teams to understood complex experiences.” 

Further Reading on Related Topics on UXmatters

Product Manager at Tom Sawyer Software

Dallas/Fort Worth, Texas, USA

Janet M. SixDr. Janet M. Six helps companies design easier-to-use products within their financial, time, and technical constraints. For her research in information visualization, Janet was awarded the University of Texas at Dallas Jonsson School of Engineering Computer Science Dissertation of the Year Award. She was also awarded the prestigious IEEE Dallas Section 2003 Outstanding Young Engineer Award. Her work has appeared in the Journal of Graph Algorithms and Applications and the Kluwer International Series in Engineering and Computer Science. The proceedings of conferences on Graph Drawing, Information Visualization, and Algorithm Engineering and Experiments have also included the results of her research.  Read More

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