Collaborating with Automated UX Tools :: UXmatters

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.”

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