{"paper_id":"2a63d0cd-db98-4b74-a3b3-d01df5cae4f9","body_text":"The \"Augmented\" Researcher: UX Researchers' experiences with incorporating gen AI into their work | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The \"Augmented\" Researcher: UX Researchers' experiences with incorporating gen AI into their work Tamara Aillali Reyes Ponce de León This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6550600/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study aimed to explore the use of generative Artificial Intelligence (AI) by User Experience Researchers (UX Researchers) in the context of the technology company IBM. Using a qualitative approach, it involved two rounds of in-depth interviews with 14 participants from five countries within the IBM Software organization. The goal was to examine their practices, experiences, and challenges in adopting generative AI. The research delved into how generative AI transforms their work in at least five dimensions, including (1) tasks, processes, and work intensity, (2) skills and autonomy, (3) interpersonal relationships, (4) relationship with AI, and (5) job satisfaction and motivation. In terms of productivity (the organization’s primary concern), generative AI has not met UX researchers’ expectations of efficiency and quality. Its adoption is heavily influenced by exaggerated expectations that do not fully reflect its capabilities. In addition, other challenges in its adoption arose, such as the lack of trust in generative AI and the quality of its results, and navigating the learning curve in the effective use of AI tools available within the enterprise. Regarding the potential threats of AI to employment, researchers view AI as an auxiliary tool rather than a replacement. Finally, to enhance UX Research practices and achieve greater productivity and efficiency through generative AI, it is recommended to invest in training, foster collaborative work, establish clear goals, and explore new AI tools to address pain points such as participant and stakeholder management. Generative Artificial Intelligence Future of Work UX Research Human-AI collaboration Augmentation Figures Figure 1 1. Introduction The present study stems from the current context of the Fourth Industrial Revolution, driven by technological advances such as cloud computing, advanced analytics, machine learning, and Artificial Intelligence (AI). Within this landscape, IBM (International Business Machines) stands out as one of the most prominent U.S. multinational companies with over a century of history, pioneering transformative technological developments, including the introduction and advancement of AI. Operating in more than 170 countries and led by CEO Arvind Krishna since 2020, IBM is currently at the forefront of promoting generative AI both inside and for its clients. As companies around the globe seek to embrace this new trend and take advantage of its potential benefits, one of the significant questions that arise is how generative AI will transform the labor market and workers’ experiences. Research claims that this innovation will transform not only the landscape for “manual” workers (commonly affected by automation) but even more so for “knowledge workers” such as data scientists, software developers, and UX researchers, who have more education and more specialized skills. In this scenario, the availability of jobs is at stake, as is their quality. In this setting, introducing and promoting AI across IBM presents challenges and opportunities for its employees. This research focuses on UX Researchers at IBM Software, who play an essential role in the design and development cycle of IBM's digital products and services. This function faces a dual challenge with generative AI: They are responsible for researching its use in IBM’s products that integrate the technology and learn to use it in their day-to-day work. Through a qualitative approach, this study explored what transformations generative AI produces in UX Researchers’ work, how these changes redefine their role, and what it means to be a researcher in the industry. Understanding the interaction between UX Researchers and generative AI is necessary because the company can incorporate their perspectives into designing and developing AI tools and recognize ways to collaborate effectively with them, yielding learnings that can be leveraged throughout the organization (Dell’Acqua et al. 2023 ). In addition to broadening the understanding of how this technology impacts contemporary labor markets (Frank et al. 2019 ), especially the technology industry. This document is structured as follows: Section 2 presents the background and context of the research. Section 3 discusses the methods used to achieve the research objectives. Section 4 describes the research’s main findings, and section 5 concludes the study with the discussion and recommendations. The references are included in section 6. 2. Research background This section reviews the study’s main background, which concerns technological advances in generative Artificial Intelligence and their impact on the labor market and job quality. 2.1 Artificial Intelligence The European Union (EU) defines Artificial Intelligence in its “EU AI Act” as: “a machine-based system that is designed to operate with varying levels of autonomy and that (...) infers how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.” (Article 3(1) Regulation (EU) 2024/1689). Within this broader category, generative Artificial Intelligence (gen AI), is the one which “can create original content—such as text, images, video, audio or software code—in response to a user’s prompt or request.” (Stryker & Scapicchio 2024 ). Generative AI is trending in scientific circles, industry, and the general public. One of the milestones that made this a reality was the launch of ChatGPT in 2022. This tool brought natural language generation capabilities to a personal-use application that can generate everything from a marketing plan to commenting on aviation history. ChatGPT is the fastest-growing application in history, reaching over 1 million users in just 5 days (Gordon 2023 ). Some of the common benefits of generative AI include increasing productivity and creativity, speeding up decision-making, and contributing to hyper-personalization. However, it also poses risks, such as “hallucinations” or the creation of fictitious/false but plausible-sounding content, the possibility of transmitting and perpetuating biases present in the data used in training, and the lack of explainability of its results (Stryker & Scapicchio 2024 ). 2.2 Generative Artificial Intelligence and the future of work There is extensive literature on how automation impacts job displacement and job creation (Berg & Gmyrek 2023 ; Acemoglu & Restrepo 2020 ; Carbonero et al. 2018 ). Studies on the impact of automation on jobs have generally focused on examining the case of low-skilled, low to medium-wage workers whose tasks have a very high potential for automation - thus, their roles are more likely to be displaced by technology (Fornino & Manera 2022 ; Acemoglu & Restrepo 2020 ). Generative AI, on the other hand, introduces a less explored phenomenon: It is expected to have a more significant impact on roles that typically offer better working conditions and require higher levels of education, commonly referred to as “knowledge workers.” Different reports (Gmyrek et al. 2023 ; Berg & Gmyrek 2023 ; Sears 2023 ; Goldstein et al. 2023 ) claim that rather than displacing knowledge workers by fully automating their tasks, AI will transform the way they work, modifying some tasks and creating new ones. “Augmentation” or “technological complementarity” refers to integrating technologies to enhance and complement human capabilities rather than replace them entirely. The promise of AI companies to workers and employers is clear: instead of doing routine and repetitive activities, “people can be left with more interesting work that requires creativity, problem-solving, and collaborating with others” (McKinsey Global Institute 2023 ). Despite the AI hype, as of 2024, the employment impact of generative AI has been little researched, and its adoption by companies remains limited. On this last point, a study by MIT Technology Review and Telstra found that: \"while about 75% of respondents experimented with generative AI in 2023, only 9% said they had adopted the technology widely.” (Butts 2024 ). This finding underscores the relevance of analyzing the perceptions of those who use this technology in different sectors of the economy to detect good practices and common challenges. 2.3 Generative AI for UX Research One of the many areas in the technology industry where the application of generative AI is being tested is User Experience (UX). According to ISO 9241 − 210:2019, UX refers to the perceptions, beliefs, emotions, and behaviors that result from using a product. This experience occurs during, before, and after use (International Organization for Standardization 2019 ). The UX Research (UXR) specialization has emerged in the intersection of Human-Computer Interaction (HCI) with social and market research to understand how people interpret and use a product, such as an application, website, or other interactive systems. UX Researchers (UXRs) conduct studies to reduce uncertainty in the product development lifecycle for design and development teams through usability testing, user interviews, and participant observation. Generative AI has caused great interest for UX Researchers, and its possible uses and implications are part of the AI debate. Some researchers anticipate that “we will be dealing with analysis that are a hybrid between human reflection and computer based suggestions” (Barros et al. 2023 p. 601). IBM strongly believes in the potential of AI to find new ways to increase teams’ productivity. For this reason, several organizational initiatives aim to develop strategic skills in employees (IBM AI Ethics Board 2024 ), such as using generative AI in daily work, mainly through the IBM watsonx™ platform. Within its AI application, watsonx.ai—which supports various LLMs for information analysis—is the “Prompt Lab”, a tool similar to ChatGPT that helps users create and refine prompts to interact with generative AI. 2.4 Job quality The ILO (International Labour Office) argues that the potential consequences of AI on the quality of work may be greater than the quantitative impacts, both in the creation of new roles and in “the potential effects on work intensity and autonomy when the technology is integrated into the workplace.” (Gmyrek et al. 2023 p.1). The latter is alarming due to negative consequences derived from algorithmic management , understood as the supervision of workers’ activities through algorithms (Van Zoonen et al. 2024 ), where organizations increase power inequalities in the workspace through surveillance and control practices (Jarrahi et al. 2021 ). Nurski and Hoffmann ( 2022 ) define a good job quality as one that contributes to workers’ well-being: “subjectively, in terms of engagement, commitment and meaningfulness; and objectively, in terms of material welfare and physical and mental health.” (p.2). These components, “objective” and “subjective”, characterize job quality as a multidimensional concept (Eurofound 2012 ). In Fig. 1 , the authors illustrate the model’s comprehensive view. Institutional elements or “antecedents” shape the “job dimensions”; which in turn impact the well-being of workers. Subjective components explore employees’ lived experiences, while objective ones refer to the work context that shapes those perceptions. This study excludes “physical working conditions,” as AI does not alter the physical work environment of UX Researchers, and “contractual conditions” since legal aspects fall outside the study’s scope. Discussing the quality of jobs is important because, firstly, it allows us to recognize that in addition to the wage level, “there are other employment attributes that shape workers’ experience, opportunities, and employment outcomes.” (Equitable Growth 2022 ). Secondly, job quality affects workers' well-being, general health, and satisfaction with their lives (Eurofound 2019 ). Thirdly, in the interest of organizations, more satisfied employees are more productive (Bellet 2023). For IBM, understanding the experiences of UX Researchers is valuable for integrating their insights early in designing AI solutions, helping to establish standards that enhance user experience. This study also sheds light on how workers perceive the digitization of employment, offering a fuller view of AI’s impact on knowledge work and contributing to a rapidly expanding field of research. On this basis, the present study has the following goals: 2.5 General objective Explore how UX Researchers experience the transformations in their work with the incorporation of generative Artificial Intelligence tools. 2.6 Specific objectives To identify what transformations UX Researchers have perceived in their tasks, work methodology, and work intensity by incorporating generative AI tools. To understand the transformations that UX Researchers perceive in their skills, satisfaction, and motivation after incorporating generative AI tools. To collect the expectations of UX Researchers regarding the use of generative AI tools in their work. 3. Methods A qualitative approach explored how generative AI transforms UX Researchers’ experiences, practices, interactions, and relationships with technology. This strategy offered flexibility to study an emerging phenomenon and revealed unexpected themes through participant conversations (Vasilachis de Gialdino (coord.) 2006). 3.1 Information production techniques Given the rapidly evolving AI landscape, a phased research design was adopted to explore how UX Researchers experience the introduction of gen AI and what changes they perceive in their work practices over three months (August to October 2024). Semi-structured in-depth interviews were conducted in two moments with the same people, one at the beginning and one at the end of the research, through the virtual conferences platform Microsoft Teams. A second observation period helped participants reflect on their incremental use of gen AI. The sample type was intentional and composed of 14 UX Researchers from the IBM Software organization (see Table 1 ). Table 1 Participants’ recruitment table, own elaboration. UX Research seniority World Region Gender - Female Gender - Male Total Junior (0–3 years) USA/Canada 1 1 6 Europe 0 1 India 2 1 Semi-senior (4–9 years) USA/Canada 0 1 5 Europe 2 2 India 0 0 Senior (10 + years) USA/Canada 1 0 3 Europe 1 1 India 0 0 Total 7 7 14 3.2 Analysis techniques Qualitative content analysis, which broadly consists of an inductive exploration of the data to discover recurring themes, patterns, or concepts and subsequently describe and interpret these categories (Nassaji 2015 ), was used to analyze the information produced from the interviews. It was an appropriate technique as it allowed for organizing and making sense of the data by identifying and interpreting recurrent and meaningful codes. The analysis began checking the automatic interview transcripts from Teams, followed by an in-depth review using an initial coding framework based on the operationalization. This framework was tested, refined, and applied across the dataset to develop codes, categories, and subcategories (Schreier in Flick 2014). In interpreting the material, the goal was not to create a definitive portrait of the research subject but to acknowledge that “these analytical processes (...) can help to reveal different facets of the data” (Coffey & Atkinson 2003 p.18). Striking that balance was one of the key challenges of the analysis exercise. 3.3 Positionality reflection In the qualitative approach, the researchers’ subjectivity is an inherent part of the construction of knowledge, so it is essential to consider the researcher's position as an IBM employee. This setting presented challenges in balancing the roles of researcher and designer within her daily work. Research within a private company presents challenges when entering the field, since “Research is always an intervention in a social system” and “is a disturbing factor for the system, (...) to which it reacts defensively.” (Wolff 2004 in Flick 2007 p. 71). Consequently, it was vital to achieve “rapport”, or a relationship of trust with the participants so that they could express themselves freely in a safe and respectful space. The study took place in the workplace with colleagues from different business units, requiring recognition of the power imbalances between the researcher and the participants (Creswell & Creswell 2018 ), especially since participants described work experiences that might include sensitive and personal issues. In addition, the interviews included diverse people from different world regions, so the interpretation had to consider cultural, ethnic, and gender aspects. The process began by acknowledging cultural differences, aiming to represent participants’ perspectives and mitigating personal bias. 4. Results This section presents the study’s findings, starting with a description of the UX Researchers. It then explores their experiences and expectations using watsonx.ai and highlights the changes in their work and barriers to gen AI’s smooth adoption. IBM Software UX Researchers work on different products that belong to IBM’s offerings in Automation, Data and Artificial Intelligence, Cybersecurity, and Sustainability. As generative AI features are being integrated into various IBM products, UX Researchers play a dual role with this technology, as they are both researchers and users. The sample includes UX Researchers with an average of 5.2 years in the industry and 2.8 years at IBM, ranging from recent hires to employees with over 15 years at the company, spanning junior to senior roles. The first round of interviews included seven women and seven men; the second included seven women and six men from North America (the United States, Canada), India, and Europe (Germany, Ireland). They had multiple backgrounds, including Social Sciences, Computer and Information Sciences, Design, and Communications. Most (9) had postgraduate studies, either a Master’s or PhD, with six beginning their academic careers before moving into the business world. The varied disciplines bring enriching perspectives to the research practice. UX Researchers’ relationships can be separated into the UX Research and the Product areas. Their peers can be anywhere worldwide, meaning they must be interconnected and aligned in different latitudes. UXRs in Product work within “product teams,” collaborating mainly with Product Managers, UX Designers, and Developers. Strong relationships with these stakeholders are key to conducting timely, business-relevant research that supports the product roadmap. UXRs are driven by curiosity and a passion for learning, discovery, and solving complex problems. Working at the intersection between human behavior and technology allows them to lead research on how emerging tools can benefit people. In that line, research is a mobilizer and agent of change: “It’s our job to find things that need to change and need to be improved” (Robert, Europe, senior). 4.1 Previous gen AI training Only four participants (two men and two women, all semi-senior and from Europe) had prior AI-related education before joining IBM. The others learned about AI upon joining, highlighting IBM’s commitment to enabling its employees. Regarding the “AI for Product Teams: Generative AI Fundamentals” training, which covers definitions, ethics, and prompt design, nine UXRs completed it; two did not, and three could not remember. The usefulness of this resource varied based on participants’ prior knowledge and experience with it. Newcomers shared an optimistic view, while those with more experience viewed it as less useful, which is understandable given its introductory nature. 4.2 Gen AI professional use Participants were asked about using watsonx.ai’s “Prompt Lab”; 11 out of 14 had used it at work. The three who had not—senior and semi-senior women from North America and Europe—cited either limited time at IBM or discomfort with the technology, which persisted in both rounds of interviews. These findings are insufficient to declare that there is a gender gap concerning generative AI use. Regarding the participants’ expectations before using the platform, some held an almost “magical” view of what AI can achieve: “I thought it would like read my mind and give me exactly what I want.” (Neha, US/CA, senior). Their previous experiences with ChatGPT, a tool all participants use personally, might have shaped this feeling. Nevertheless, most participants were enthusiastic about experimenting with watsonx.ai and exploring its capabilities. In summary, four general uses of generative AI emerged from the interviews: AI as an accelerator : This is the most commonly mentioned use and refers to the expectation that AI will speed up the work process and make it more efficient. An example of this is to speed up the search for user quotes. AI as a starting point : It intends to overcome the blank page or start from scratch. For example, it facilitates writing interview guidelines by providing an initial structure. AI as inspector : This refers to using AI as another “pair of eyes” with a critical lens and an awareness of detail. Researchers already have advanced work and use AI to give them a different perspective. AI as a guide : In these cases, AI is used as a guide to request explanations, recommendations, or advice on how to proceed in specific situations. The following table summarizes the specific uses mentioned in the interviews: Table 2 “What are UX Researchers using gen AI for?”, own elaboration. Gen AI uses round 1 Gen AI uses round 2 Doing thematic analysis, generating categories, coding interviews, finding patterns in the data Idem Get participant’s quotes Idem Paraphrasing, evaluating writing, and grammar Idem Code generation (Python or R) Create research plan Perform sentiment analysis Create an interview protocol or guide Summarizing data, transcripts, meetings Formatting insights the IBM way Chat with documents Simplify or explain technical concepts, acronyms, etc. - Ranking insights During the second round of interviews, the participants extended and diversified the uses declared in the first round, showing a proactive and creative attitude toward the technology. 4.3 Transformations in UX Researchers’ Work The role of UX Researchers transforms as they incorporate generative AI tools. This leads to performing different steps or processes or considering aspects that were not relevant before. Below are five dimensions of job quality in which AI is transforming the work of UXRs. The first four involve “objective” job quality characteristics, and the last is “subjective.” 4.3.1 Transformations in tasks, processes, and work intensity One of the main questions when talking about processes and tasks is how AI affects the productivity and efficiency of UX Researchers. The first way in which generative AI transforms the work of UXRs is in terms of time. Different participants indicated that using generative AI took longer than it would have taken them to do a task themselves: ...it's probably more efficient for me to just do it manually, because I spent like at least an hour trying to put in different prompts to get it to do what I wanted it to and it just wasn't doing it… So that's a waste of time, right? (Neha, US/CA, senior). In another similar case, a researcher sought to generate a research plan for which he detailed the context, objectives, and expected results and realized that the AI was merely repeating his input in the output : “And so if I'm already coming up with that text, I might as well just put it into a Word document, and that's my research plan.” (Serge, US/CA, junior). Both examples illustrate how AI is not always the most efficient or straightforward. It involves going back and forth, adjusting prompts, trying different models, and editing inputs so AI can better process them. Researchers face a trade-off between using gen AI or their current methods, which impacts their consideration of task time allocation, as illustrated by the following quote: …my big question for myself is how much do I rely on this? How much time should I spend doing my traditional synthesis process separate from this? …should I use this as a shortcut and just go from here? Or should I actually start from the beginning again? (Peter, Europe, semi-senior). The doubts regarding the concrete benefits of AI mean that not all participants are convinced to use generative AI for their work. In addition, the process of trial and error with prompts is something that not all researchers are willing to do, and for them, the value of using generative AI is questionable. From the experiences analyzed, the efficiency they aspire to is still out of reach. This fact undermines the promise of productivity that generative AI claims, putting researchers’ expectations in check. The following participant eloquently describes the researchers’ frustrations: I think many people have kind of given up quite quickly when it's not really returning what they expected, and I think a lot of people kind of come into it thinking I can just dump stuff in here, ask it to do something and it'll do it for me, where it doesn't really work like that. There's a lot of backwards and forwards and kind of negotiating with the LLM… (Frank, Europe, semi-senior). The next point where generative AI could contribute to UX Researchers’ performance is in the research quality. However, Peter (Europe, semi-senior) and Diya (India, junior) found the AI results unhelpful or unspecific, providing general summaries rather than relevant insights. The variability of results characteristic of generative AI becomes relevant, which makes the data-checking process practically mandatory. The last is a good example of how AI transforms or causes a recomposition of UXR tasks. We find that new tasks emerge and take center stage, such as preparing and cleaning data for the machine, running prompts, and reviewing AI outputs. As the next participant describes, uncertainty regarding the quality and thoroughness of the results, added to having to perform revision tasks manually, increases the intensity of the researchers’ work: I think once we get a hang of it, it would actually help us in streamlining the process a lot because right now we are doing a lot of extra work and spending a lot of time in it, like going through the transcripts, discovering the findings, connecting the transcripts. (Aisha, India, junior). To omit this new task risks delivering untrue findings, which could damage the researchers’ credibility. To avoid this, some even advise that they “don't get it (AI) to do things you couldn't do by yourself.” (Christian, Europe, junior). The third point related to the UXR work process leads to the question: “Is researching a product with Generative AI features different from one without?” For some, generative AI implies a significant change in UX Research. Why? Now, there is not only the experience with software or digital products but also the experience with the AI model embedded in them, which involves issues related to ethics, trust, transparency, output quality, data protection, and more. All these factors make the work of UX Researchers more complex. 4.3.2 Transformations in skills and autonomy Incorporating generative AI into the UX Researchers’ work means acquiring new knowledge and skills. The most critical competency mentioned by all participants is “Prompt Engineering”, i.e., the art and science of creating “prompts” or effectively communicating to generative AI to obtain desired results. Understanding how AI models work and their limitations (for example, token limit) is also relevant. Another related skill is familiarity with programming. On this point, some researchers perceive that watsonx.ai and other platforms are created by and for people with technical backgrounds, which makes them difficult to approach: I mean, it definitely showed me as like I came from a qualitative background (...) It's immediately obvious that this is made for people who know coding and do more quant because, like quant analysis, you know it's all about the pre structured setup... (Iris, Europe, semi-senior). The topic of adapting to new technology is compelling. More than background or experience, the key differences lie in researchers’ openness toward AI and the training they receive. Proper training helps them understand how AI works, which leads to more realistic expectations about its capabilities. The last point on skills refers to the ethical dimension. Researchers say they should consider the contexts of use and the risks and harms that AI can cause and ask questions such as: What data does the model use, how is it trained, and what biases can it bring? They should also think critically and be wary of the information they receive from AI. Researchers hold differing views on integrating generative AI with existing UXR tools. Some see value in incorporating it into their workflows, while others are concerned about potential disruptions and prefer to use it as a separate tool. In either case, it is essential to preserve researchers’ autonomy in deciding when and how to use generative AI and ensure they retain control over their results. 4.3.3 Transformations in interpersonal relationships Collaboration between UX researchers, product teams, and other stakeholders is crucial to implementing research results, and support networks are essential to sharing challenges and knowledge. A couple of researchers recounted their participation in a collaborative activity organized by other UXRs to work with generative AI, which positively affected building confidence in using AI: ...But before the working group had their meeting, I really didn’t feel comfortable doing this independently. I felt like I didn’t have enough direction and it was gonna actually take way too much time to figure out how to use the AI by myself. So it did really help give me some confidence to get started. (Peter, Europe, semi-senior). Cases like this one show that incorporating generative AI demands more interaction and collaboration between UX Researchers, where those with more experience can encourage their peers to participate and try AI for themselves. While today the use of generative AI is optional, researchers believe that may change depending on business decisions, as the following quote indicates: “I think as more and more managers start becoming more familiar with the tools then they probably (will increase) the encouragement to actually use them across their teams”. (Frank, Europe, semi-senior). This case also shows that relationships with others can be a catalyst for exploring AI. 4.3.4 Transformations in the relationship with generative AI Generative AI implies a new paradigm of interaction (Nielsen 2023 ) where the user’s “intent” or instruction is the raw material for the technology to work. In navigating how researchers conceive of generative AI, anthropomorphization intersects with the mere tool view, recognizing that interacting with AI is not the same as interacting with a person, as the following participant illustrates: It’s kind of a weird social kind of interaction, isn’t it? Like ‘cause, (...) if you were doing this with a person and they made mistakes you probably wouldn’t be that annoyed about it, but you’ve got higher expectations for a computer. They shouldn’t make mistakes. (Frank, Europe, semi-senior). Since generative AI can make mistakes, managing expectations starts with understanding its capabilities and limits. Researchers, however, find watsonx.ai lacks transparency, highlighting the need for explainable AI to verify data and analysis processes. Trust remains a key factor in the adoption of generative AI. Regarding AI’s roles, some researchers see it as an assistant handling administrative and tedious tasks; others view it as a “junior” researcher still learning. A new concept called “Agentic AI” has also emerged. AI agents, unlike assistants, can perform tasks autonomously without human intervention, which opens up immense opportunities for automation and raises many doubts concerning human autonomy. As this participant points out, the speed with which innovations are introduced risks leaving users behind in adapting to new ways of interacting with technology: “And we’re trying to introduce that into the industry now in the market and yeah, it’s just a crazy amount of speed. And the technology is moving faster than people can adapt to the concepts and ideas” (Jake, US/CA, semi-senior). Finally, when considering the future of their work, all the UX Researchers interviewed agreed that generative AI cannot replace them, as replicating their contributions would require interpretive skills and deep contextual understanding. One participant reached this conclusion after experimenting with watsonx.ai: It’s descriptive, it’s not analytical. (...) and so that’s good to know that it’s going to pick up on the words, you know, the denotation, not the connotation as much. And that’s where really the trick is what you need it for versus what can a human only do because we know deeper context and you know the subtleties between the lines... (Iris, Europe, semi-senior). 4.3.5 Transformations in job satisfaction and motivation Most researchers are motivated to use generative AI to improve efficiency, while some remain skeptical due to limited knowledge or concerns about data quality, biases, and algorithm flaws. The second round of interviews shows that their motivation increases as UXRs gain positive experiences with the technology. However, no changes in job satisfaction were observed. 4.4 Opportunities and expectations of working with generative AI All UX Researchers envision multiple applications of generative AI for their work (see Table 3 ). As to watsonx.ai, they expect the tool to improve in the future, as they estimate that at the time of the interviews, it is still early to see results, as shown in the quote: “I think we are still a little early in the curve to get the benefits out of it.” (Aisha, India, junior). Table 3 “What UXRs want to use gen AI for? Expectations”. Own elaboration. Gen AI use expectations round 1 Gen AI use expectations round 2 Extract the main themes from interviews Idem Generate images, illustrations, data visualization Idem Generate videos from text to foster engagement in presentations Idem Generate the presentation outline, story, or narrative structure. Generate variations of presentations based on target audience Formatting insights the IBM way Translating from one language to another Do secondary research, have the knowledge base of all IBM products incorporated. Detect which candidates used AI in their screener answers Summarizing meetings, interviews Correct and clean up transcripts with errors Manage and update documentation Solutions accessibility check It is interesting to note that current uses of generative AI also emerge when discussing expectations. For instance, generative AI’s ability to “extract main themes from interviews” highlights that researchers have yet to experiment with these capabilities and achieve the desired results. 5. Discussion and conclusions This study was conducted at an embryonic moment when companies began developing and applying generative AI. It examined the role of IBM Software UX Researchers, their experiences working with generative AI, and how these experiences transform how they approach their current and future work. Amidst the complex context facing the UX Research organization, there is a mainly exploratory and occasional use of IBM’s watsonx.ai tool available to UX Researchers. Researchers see AI as an opportunity to make their work more efficient, although they maintain a nuanced view of it; they recognize its possibilities but also have concerns about its limitations, such as the lack of transparency in its operations and the accuracy or veracity of its results. That means that the level of trust in AI is low. The study concludes that incorporating generative AI tools is transforming the work of UX Researchers. While generative AI is often associated with promises of increased productivity and efficiency, researchers’ experiences show that, in many cases, it currently requires more time and effort to achieve satisfactory results. Nevertheless, with continued practice and refinement of their methods, researchers are expected to eventually consolidate their workflows and realize the anticipated gains in productivity and efficiency. Beyond the productivity challenges tied to the tool’s capabilities, the main barrier to adopting AI is overcoming the learning curve. Exploring and testing different uses is not a quick process; it demands time, interest, and dedication from UX Researchers to make experimentation iterative and effective. Achieving this level of engagement requires researchers to feel comfortable with the technology, highlighting the need to understand the factors influencing their current attitudes. In addition to requiring determination, researchers have concerns about how technically skilled they must be to use AI effectively. For now, deciding whether to invest time in generative AI remains within the researchers’ autonomy. Still, in a rapidly evolving tech industry, this could soon shift toward stronger encouragement from the company. Other barriers to using AI involve trust and concern about the quality and accuracy of the results. UX Researchers mention that AI can make mistakes and that it is always important to verify results and “Do not blindly trust what it says” (Christian, Europe, junior). There are concerns about AI reinforcing existing biases in the data and questions about interpreting generative AI processes when the algorithms lack transparency. Regarding how generative AI could transform their role and impact their work, UX Researchers are skeptical about the ability to replace humans in the research process and believe that generative AI should be used as an auxiliary tool, either as an accelerator, starting point, inspector, or guide. UX Researchers view generative AI as limited compared to human expertise but are open to delegating it administrative tasks, like participant recruitment. While it may not entirely meet their expectations today, most UX Researchers have a positive vision for the future and envision countless use cases where AI could help them do their jobs better. When considering opportunities for gen AI use, challenges in communication and stakeholder relations, generating insights, structuring research results, and creating visual support materials serve as areas where generative AI could contribute. A key challenge in the evolving relationship between people and AI is preserving the human aspect in the digital era. As technology advances, the question becomes how to stay involved and relevant. In this context, UX Researchers have a unique perspective since their role is to understand people. As Neha (US/CA, senior) points out: “I enjoy listening to what their comments are and then making sure that I understand what they’re saying, I think that means a lot to them too.” Maintaining and nurturing human relationships is one aspect that can strengthen users’ trust in researchers and, ultimately, in IBM. 5.1 Recommendations 5.1.1 Training The interview results indicate that UX Researchers feel more confident using generative AI when provided with clear guidelines or frameworks for interaction. Therefore, it is crucial to continue developing skills such as designing effective prompts, understanding the technology’s limitations, and critically evaluating results. Additionally, training should align with IBM’s established protocols and ethical standards for generative AI use. The study also highlighted the need for advanced training targeted at those with a better grasp of AI, providing more profound knowledge and work in complex use cases. 5.1.2 Teamwork Encouraging collaboration among UX Researchers is key to sharing challenges and lessons learned with generative AI, boosting confidence in using the technology. Workshops, mentorships, and tutorials showcasing real examples can provide insight into other researchers’ strategies to achieve successful outcomes, creating a more effective AI work environment. Additionally, it is recommended to involve younger or junior UX Researchers, leveraging their enthusiasm and time, to develop use cases they can share with their peers. 5.1.3 Set objectives and metrics Researchers still lack clarity about the future of generative AI. IBM must establish a straightforward narrative on why AI is relevant to UX Researchers and set clear expectations about its role in augmenting, rather than replacing, their work. Emphasizing UXR’s invaluable role is essential. Additionally, the company must maintain active channels for UXRs to provide feedback on this issue. 5.1.4 Explore new areas for developing AI tools The teams working on gen AI tools should focus on the current pains of the UXR process to discover more opportunities where generative AI can contribute. These include managing communication engagement, influencing stakeholder decision-making, structuring results, recruiting participants, managing bias, and enabling and teaching stakeholders about research, among others, as they arise. 5.2 Future work Research on the future of work and generative Artificial Intelligence is in fertile ground. The following are some lines in which this fascinating and current topic can be further developed: Extend the research to quantitatively explore which tasks UXRs would like to delegate to AI and which ones would not, taking inspiration from Ethan Molick’s book “Co-Intelligence: Living and Working with AI.” The author invites us to classify tasks into three groups: those we want to perform independently, those we want AI support, and those we wish to delegate entirely to AI (Molick 2024 ). Knowing this can help to generate tools focused on tasks where aid is indeed required and not on tasks that are meaningful to researchers. Study the adoption of generative AI according to the gender and cultural context of UX Researchers. To delve into aspects of subjective job quality and well-being of UX Researchers as AI adoption progresses, to see how their job satisfaction, motivation, and work meanings are transformed in time. Declarations This work was supported by the Núcleo Milenio de Desigualdades y Oportunidades Digitales (NUDOS), a research center funded by the Agencia Nacional de Investigación y Desarrollo de Chile (ANID), the State agency that promotes the development of science, technology, and innovation in Chile. The author has no financial or proprietary interests in any material discussed in this article. Author Contribution The author is the only contributor, taking primary responsibility for the research described in the manuscript. Data Availability To preserve individuals’ privacy and IBM’s data-sharing policy, all data generated from this study is not openly available. References Acemoglu D & Restrepo P (2020) Robots and Jobs: Evidence from US Labor Markets. Journal of Political Economy, 128 (6): 2188–2244. https://doi.org/10.1086/705716 Barros A, Prasad A, & Śliwa M (2023) Generative artificial intelligence and academia: Implication for research, teaching and service. Management Learning, 54(5), 597-604. https://doi.org/10.1177/13505076231201445 Bellet C, De Neve J, & Ward G (2019) Does Employee Happiness have an Impact on Productivity? Saïd Business School WP 2019-13. http://dx.doi.org/10.2139/ssrn.3470734 Berg J & Gmyrek P (2023) Automation hits the knowledge worker: ChatGPT and the future of work. UN Multi-Stakeholder Forum on Science, Technology and Innovation for the SDGs (STI Forum) 2023. https://ssrn.com/abstract=4458221 Butts D (2024) AI is the talk of the town, but businesses are still not ready for it, survey shows. https://www.cnbc.com/2024/03/06/generative-ai-holds-massive-potential-but-businesses-arent-ready-yet.html Carbonero F, Ekkehard E, Weber E (2018) Robots Worldwide: The Impact of Automation on Employment and Trade. https://www.ilo.org/publications/robots-worldwide-impact-automation-employment-and-trade Coffey A & Atkinson P (2003) Making sense of qualitative data: complementary research strategies. 1st ed. (in Spanish) Editorial Universidad de Antioquía Creswell J & Creswell J.D (2018) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. SAGE Publications, Inc. Dell’Acqua F, McFowland III E, Mollick E, Lifshitz-Assaf H, Kellogg K, Rajendran S, Krayer L, Candelon F, & R. Lakhani K (2023) Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. Harvard Business School Working Paper, No. 24-013, September 2023. Equitable Growth (2022) The importance of understanding how job quality affects U.S. workers and the entire economy, and how to boost access to good jobs. Washington Center for Equitable Growth. https://equitablegrowth.org/the-importance-of-understanding-how-job-quality-affects-u-s-workers-and-the-entire-economy-and-how-to-boost-access-to-good-jobs/ Eurofound (2012) Trends in job quality in Europe. Publications Office of the European Union, Luxembourg. Eurofound (2019) Working conditions and workers’ health. Publications Office of the European Union, Luxembourg. Flick U (2007) Introducción a la investigación cualitativa, Ediciones Morata, Madrid, España Fornino M, & Manera A (2022) Automation and the future of work: Assessing the role of labor flexibility. Review of Economic Dynamics, Volume 45, 2022, Pages 282-321, ISSN 1094-2025, https://doi.org/10.1016/j.red.2021.07.002 Frank MR, Autor D, Bessen E, Brynjolfsson E, Cebrian M, Deming J, Feldman M, Groh M, Lobo J, Moro E, Wang D, Youn H & Rahwan I (2019) Toward understanding the impact of artificial intelligence on labor. Proc. Natl. Acad. Sci. U.S.A. 116 (14) 6531-6539. https://doi.org/10.1073/pnas.1900949116 Gmyrek P, Berg J, Bescond D (2023) Generative AI and Jobs: A global analysis of potential effects on job quantity and quality. ILO Working paper 96. (Geneva, ILO) https://www.ilo.org/global/publications/working-papers/WCMS_890761/lang--en/index.htm Goldstein J, Lobig B, Fillare C, Nowak C (2023) Augmented work for an automated, AI-driven world. Boost performance with human-machine partnerships. IBM Institute for Business Value. https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/augmented-workforce Gordon, C (2023) ChatGPT Is The Fastest Growing App In The History Of Web Applications. https://www.forbes.com/sites/cindygordon/2023/02/02/chatgpt-is-the-fastest-growing-ap-in-the-history-of-web-applications/?sh=1df542f2678c IBM AI Ethics Board (2024) Key Performance Indicators for Augmenting Human Intelligence with AI. https://www.ibm.com/downloads/documents/us-en/10a99803d6afd426 International Organization for Standardization (2019) ISO 9241-210:2019(en). Ergonomics of human-system interaction — Part 210: Human-centred design for interactive systems. 3.15 user experience. https://www.iso.org/obp/ui/en/#iso:std:iso:9241:-210:ed-2:v1:en Jarrahi MH, Newlands G, Lee MK, Wolf CT, Kinder E, & Sutherland W (2021) Algorithmic management in a work context. Big Data & Society, 8(2). https://doi.org/10.1177/20539517211020332 McKinsey Global Institute (2023) Generative AI and the future of work in America. https://www.mckinsey.com/mgi/our-research/generative-ai-and-the-future-of-work-in-america Molick E (2024) Co-Intelligence: Living and Working with AI. Portfolio/Penguin Nassaji H (2015) Qualitative and descriptive research: Data type versus data analysis. Language Teaching Research 2015, Vol. 19(2) 129–132. https://journals.sagepub.com/doi/pdf/10.1177/1362168815572747 Nielsen J (2023) AI: First New UI Paradigm in 60 Years. https://www.nngroup.com/articles/ai-paradigm/ Nurski L, & Hoffmann M (2022) The impact of artificial intelligence on the nature and quality of jobs. Bruegel Working Paper, No. 14/2022, Bruegel, Brussels. https://www.econstor.eu/handle/10419/270468 Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act) http://data.europa.eu/eli/reg/2024/1689/oj Sears J (2023) How artificial intelligence can augment a people-centered workforce. https://www.ey.com/en_gl/insights/workforce/how-artificial-intelligence-can-augment-a-people-centered-workforce Schreier M (2014) Qualitative Content Analysis. In: Flick U (ed) The SAGE Handbook of Qualitative Data Analysis, SAGE, pp 170-183 Stryker C & Scapicchio M (2024) What is generative AI? https://www.ibm.com/topics/generative-ai Van Zoonen W, Sivunen A & Treem J (2024) Algorithmic management of crowdworkers: Implications for workers’ identity, belonging, and meaningfulness of work. Computers in Human Behavior, Volume 152, 2024, 108089, ISSN 0747-5632. https://doi.org/10.1016/j.chb.2023.108089 Vasilachis de Gialdino I (Coordinator) (2006) Estrategias de investigación cualitativa. Editorial Gedisa, Barcelona, España Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6550600\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":451662313,\"identity\":\"75056176-fbd8-450f-b70a-8f1013e977ec\",\"order_by\":0,\"name\":\"Tamara Aillali Reyes Ponce de León\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYNADiQoGBjZ2IIMHiPlwKOJB4VmcAWphhgqzEaWlsg1IENJiz3468XNFDUM+v0Ty4Q83522T52NmYJN4u4NBHqctPLmbJc8cY7CcOSMtTXLmttuGbUAtknPPMBi24XRY7gbJBjYGA4MbOWbMkttuM4K0SPO2MSTgtIX/7eafDf8YDOxv5H/+/HfObXvCWiRyt0k2tgFtkchhkJBsuJ1IWMuNt9ssG/skDCTOPDOTkDh2O7mNmbHZcu4ZCZx+Ye/P3Xyz4ZuNAX978uMPEjW3bee3Nx+88XaHjTw/Di1QIMHAIJAA4zA2AJEEfg1gwH8AiQPUNQpGwSgYBaMABgD+6E6JHseamQAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Diego Portales University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Tamara\",\"middleName\":\"Aillali Reyes Ponce\",\"lastName\":\"de León\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-04-28 20:08:04\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6550600/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6550600/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":82152915,\"identity\":\"8c207acb-d529-4657-8692-01f1dc480d05\",\"added_by\":\"auto\",\"created_at\":\"2025-05-07 07:24:19\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":320041,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eIntegrative definition of job quality\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6550600/v1/3bd7775fafe1e3312d726978.jpg\"},{\"id\":83333300,\"identity\":\"4562dadc-ee5b-40f7-be7f-054bc0b614fe\",\"added_by\":\"auto\",\"created_at\":\"2025-05-23 08:31:43\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1238787,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6550600/v1/939a039f-c01e-4f35-9873-f2199f7fc066.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"The \\\"Augmented\\\" Researcher: UX Researchers' experiences with incorporating gen AI into their work\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eThe present study stems from the current context of the Fourth Industrial Revolution, driven by technological advances such as cloud computing, advanced analytics, machine learning, and Artificial Intelligence (AI). Within this landscape, IBM (International Business Machines) stands out as one of the most prominent U.S. multinational companies with over a century of history, pioneering transformative technological developments, including the introduction and advancement of AI. Operating in more than 170 countries and led by CEO Arvind Krishna since 2020, IBM is currently at the forefront of promoting generative AI both inside and for its clients.\\u003c/p\\u003e \\u003cp\\u003eAs companies around the globe seek to embrace this new trend and take advantage of its potential benefits, one of the significant questions that arise is how generative AI will transform the labor market and workers\\u0026rsquo; experiences. Research claims that this innovation will transform not only the landscape for \\u0026ldquo;manual\\u0026rdquo; workers (commonly affected by automation) but even more so for \\u0026ldquo;knowledge workers\\u0026rdquo; such as data scientists, software developers, and UX researchers, who have more education and more specialized skills. In this scenario, the availability of jobs is at stake, as is their quality.\\u003c/p\\u003e \\u003cp\\u003eIn this setting, introducing and promoting AI across IBM presents challenges and opportunities for its employees. This research focuses on UX Researchers at IBM Software, who play an essential role in the design and development cycle of IBM's digital products and services. This function faces a dual challenge with generative AI: They are responsible for researching its use in IBM\\u0026rsquo;s products that integrate the technology and learn to use it in their day-to-day work.\\u003c/p\\u003e \\u003cp\\u003eThrough a qualitative approach, this study explored what transformations generative AI produces in UX Researchers\\u0026rsquo; work, how these changes redefine their role, and what it means to be a researcher in the industry. Understanding the interaction between UX Researchers and generative AI is necessary because the company can incorporate their perspectives into designing and developing AI tools and recognize ways to collaborate effectively with them, yielding learnings that can be leveraged throughout the organization (Dell\\u0026rsquo;Acqua et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). In addition to broadening the understanding of how this technology impacts contemporary labor markets (Frank et al. \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), especially the technology industry.\\u003c/p\\u003e \\u003cp\\u003eThis document is structured as follows: Section 2 presents the background and context of the research. Section 3 discusses the methods used to achieve the research objectives. Section \\u003cspan refid=\\\"Sec12\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e describes the research\\u0026rsquo;s main findings, and section \\u003cspan refid=\\\"Sec22\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e concludes the study with the discussion and recommendations. The references are included in section 6.\\u003c/p\\u003e\"},{\"header\":\"2. Research background\",\"content\":\"\\u003cp\\u003eThis section reviews the study’s main background, which concerns technological advances in generative Artificial Intelligence and their impact on the labor market and job quality.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Artificial Intelligence\\u003c/h2\\u003e \\u003cp\\u003eThe European Union (EU) defines Artificial Intelligence in its “EU AI Act” as: “a machine-based system that is designed to operate with varying levels of autonomy and that (...) infers how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.” (Article 3(1) Regulation (EU) 2024/1689). Within this broader category, generative Artificial Intelligence (gen AI), is the one which “can create original content—such as text, images, video, audio or software code—in response to a user’s prompt or request.” (Stryker \\u0026amp; Scapicchio \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eGenerative AI is trending in scientific circles, industry, and the general public. One of the milestones that made this a reality was the launch of ChatGPT in 2022. This tool brought natural language generation capabilities to a personal-use application that can generate everything from a marketing plan to commenting on aviation history. ChatGPT is the fastest-growing application in history, reaching over 1\\u0026nbsp;million users in just 5 days (Gordon \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSome of the common benefits of generative AI include increasing productivity and creativity, speeding up decision-making, and contributing to hyper-personalization. However, it also poses risks, such as “hallucinations” or the creation of fictitious/false but plausible-sounding content, the possibility of transmitting and perpetuating biases present in the data used in training, and the lack of explainability of its results (Stryker \\u0026amp; Scapicchio \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Generative Artificial Intelligence and the future of work\\u003c/h2\\u003e \\u003cp\\u003eThere is extensive literature on how automation impacts job displacement and job creation (Berg \\u0026amp; Gmyrek \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Acemoglu \\u0026amp; Restrepo \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Carbonero et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Studies on the impact of automation on jobs have generally focused on examining the case of low-skilled, low to medium-wage workers whose tasks have a very high potential for automation - thus, their roles are more likely to be displaced by technology (Fornino \\u0026amp; Manera \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Acemoglu \\u0026amp; Restrepo \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Generative AI, on the other hand, introduces a less explored phenomenon: It is expected to have a more significant impact on roles that typically offer better working conditions and require higher levels of education, commonly referred to as “knowledge workers.”\\u003c/p\\u003e \\u003cp\\u003eDifferent reports (Gmyrek et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Berg \\u0026amp; Gmyrek \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Sears \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Goldstein et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) claim that rather than displacing knowledge workers by fully automating their tasks, AI will transform the way they work, modifying some tasks and creating new ones. “Augmentation” or “technological complementarity” refers to integrating technologies to enhance and complement human capabilities rather than replace them entirely. The promise of AI companies to workers and employers is clear: instead of doing routine and repetitive activities, “people can be left with more interesting work that requires creativity, problem-solving, and collaborating with others” (McKinsey Global Institute \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eDespite the AI hype, as of 2024, the employment impact of generative AI has been little researched, and its adoption by companies remains limited. On this last point, a study by MIT Technology Review and Telstra found that: \\\"while about 75% of respondents experimented with generative AI in 2023, only 9% said they had adopted the technology widely.” (Butts \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). This finding underscores the relevance of analyzing the perceptions of those who use this technology in different sectors of the economy to detect good practices and common challenges.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 Generative AI for UX Research\\u003c/h2\\u003e \\u003cp\\u003eOne of the many areas in the technology industry where the application of generative AI is being tested is User Experience (UX). According to ISO 9241 − 210:2019, UX refers to the perceptions, beliefs, emotions, and behaviors that result from using a product. This experience occurs during, before, and after use (International Organization for Standardization \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe UX Research (UXR) specialization has emerged in the intersection of Human-Computer Interaction (HCI) with social and market research to understand how people interpret and use a product, such as an application, website, or other interactive systems. UX Researchers (UXRs) conduct studies to reduce uncertainty in the product development lifecycle for design and development teams through usability testing, user interviews, and participant observation. Generative AI has caused great interest for UX Researchers, and its possible uses and implications are part of the AI debate. Some researchers anticipate that “we will be dealing with analysis that are a hybrid between human reflection and computer based suggestions” (Barros et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e p. 601).\\u003c/p\\u003e \\u003cp\\u003eIBM strongly believes in the potential of AI to find new ways to increase teams’ productivity. For this reason, several organizational initiatives aim to develop strategic skills in employees (IBM AI Ethics Board \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), such as using generative AI in daily work, mainly through the IBM watsonx™ platform. Within its AI application, watsonx.ai—which supports various LLMs for information analysis—is the “Prompt Lab”, a tool similar to ChatGPT that helps users create and refine prompts to interact with generative AI.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 Job quality\\u003c/h2\\u003e \\u003cp\\u003eThe ILO (International Labour Office) argues that the potential consequences of AI on the quality of work may be greater than the quantitative impacts, both in the creation of new roles and in “the potential effects on work intensity and autonomy when the technology is integrated into the workplace.” (Gmyrek et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e p.1). The latter is alarming due to negative consequences derived from \\u003cem\\u003ealgorithmic management\\u003c/em\\u003e, understood as the supervision of workers’ activities through algorithms (Van Zoonen et al. \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), where organizations increase power inequalities in the workspace through surveillance and control practices (Jarrahi et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eNurski and Hoffmann (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) define a good job quality as one that contributes to workers’ well-being: “subjectively, in terms of engagement, commitment and meaningfulness; and objectively, in terms of material welfare and physical and mental health.” (p.2). These components, “objective” and “subjective”, characterize job quality as a multidimensional concept (Eurofound \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eIn Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, the authors illustrate the model’s comprehensive view. Institutional elements or “antecedents” shape the “job dimensions”; which in turn impact the well-being of workers. Subjective components explore employees’ lived experiences, while objective ones refer to the work context that shapes those perceptions. This study excludes “physical working conditions,” as AI does not alter the physical work environment of UX Researchers, and “contractual conditions” since legal aspects fall outside the study’s scope.\\u003c/p\\u003e \\u003cp\\u003eDiscussing the quality of jobs is important because, firstly, it allows us to recognize that in addition to the wage level, “there are other employment attributes that shape workers’ experience, opportunities, and employment outcomes.” (Equitable Growth \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Secondly, job quality affects workers' well-being, general health, and satisfaction with their lives (Eurofound \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Thirdly, in the interest of organizations, more satisfied employees are more productive (Bellet 2023).\\u003c/p\\u003e \\u003cp\\u003eFor IBM, understanding the experiences of UX Researchers is valuable for integrating their insights early in designing AI solutions, helping to establish standards that enhance user experience. This study also sheds light on how workers perceive the digitization of employment, offering a fuller view of AI’s impact on knowledge work and contributing to a rapidly expanding field of research. On this basis, the present study has the following goals:\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5 General objective\\u003c/h2\\u003e \\u003cp\\u003eExplore how UX Researchers experience the transformations in their work with the incorporation of generative Artificial Intelligence tools.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.6 Specific objectives\\u003c/h2\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\u003col\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eTo identify what transformations UX Researchers have perceived in their tasks, work methodology, and work intensity by incorporating generative AI tools.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eTo understand the transformations that UX Researchers perceive in their skills, satisfaction, and motivation after incorporating generative AI tools.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eTo collect the expectations of UX Researchers regarding the use of generative AI tools in their work.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e \\u003cp\\u003e\\u003c/p\\u003e \\u003c/div\\u003e \"},{\"header\":\"3. Methods\",\"content\":\"\\u003cp\\u003eA qualitative approach explored how generative AI transforms UX Researchers’ experiences, practices, interactions, and relationships with technology. This strategy offered flexibility to study an emerging phenomenon and revealed unexpected themes through participant conversations (Vasilachis de Gialdino (coord.) 2006).\\u003c/p\\u003e\\u003ch2\\u003e3.1 Information production techniques\\u003c/h2\\u003e\\u003cp\\u003eGiven the rapidly evolving AI landscape, a phased research design was adopted to explore how UX Researchers experience the introduction of gen AI and what changes they perceive in their work practices over three months (August to October 2024).\\u003c/p\\u003e\\u003cp\\u003eSemi-structured in-depth interviews were conducted in two moments with the same people, one at the beginning and one at the end of the research, through the virtual conferences platform Microsoft Teams. A second observation period helped participants reflect on their incremental use of gen AI.\\u003c/p\\u003e\\u003cp\\u003eThe sample type was intentional and composed of 14 UX Researchers from the IBM Software organization (see Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eParticipants’ recruitment table, own elaboration.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003c/colgroup\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUX Research seniority\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWorld Region\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGender - Female\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eGender - Male\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eJunior (0–3 years)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUSA/Canada\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEurope\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIndia\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eSemi-senior (4–9 years)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUSA/Canada\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEurope\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIndia\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eSenior (10 + years)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUSA/Canada\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEurope\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIndia\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003ch2\\u003e3.2 Analysis techniques\\u003c/h2\\u003e\\u003cp\\u003eQualitative content analysis, which broadly consists of an inductive exploration of the data to discover recurring themes, patterns, or concepts and subsequently describe and interpret these categories (Nassaji \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e), was used to analyze the information produced from the interviews. It was an appropriate technique as it allowed for organizing and making sense of the data by identifying and interpreting recurrent and meaningful codes.\\u003c/p\\u003e\\u003cp\\u003e The analysis began checking the automatic interview transcripts from Teams, followed by an in-depth review using an initial coding framework based on the operationalization. This framework was tested, refined, and applied across the dataset to develop codes, categories, and subcategories (Schreier in Flick 2014). In interpreting the material, the goal was not to create a definitive portrait of the research subject but to acknowledge that “these analytical processes (...) can help to reveal different facets of the data” (Coffey \\u0026amp; Atkinson \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e p.18). Striking that balance was one of the key challenges of the analysis exercise.\\u003c/p\\u003e\\u003ch2\\u003e3.3 Positionality reflection\\u003c/h2\\u003e\\u003cp\\u003eIn the qualitative approach, the researchers’ subjectivity is an inherent part of the construction of knowledge, so it is essential to consider the researcher's position as an IBM employee. This setting presented challenges in balancing the roles of researcher and designer within her daily work.\\u003c/p\\u003e\\u003cp\\u003eResearch within a private company presents challenges when entering the field, since “Research is always an intervention in a social system” and “is a disturbing factor for the system, (...) to which it reacts defensively.” (Wolff 2004 in Flick \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e p. 71). Consequently, it was vital to achieve “rapport”, or a relationship of trust with the participants so that they could express themselves freely in a safe and respectful space.\\u003c/p\\u003e\\u003cp\\u003eThe study took place in the workplace with colleagues from different business units, requiring recognition of the power imbalances between the researcher and the participants (Creswell \\u0026amp; Creswell \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), especially since participants described work experiences that might include sensitive and personal issues. In addition, the interviews included diverse people from different world regions, so the interpretation had to consider cultural, ethnic, and gender aspects. The process began by acknowledging cultural differences, aiming to represent participants’ perspectives and mitigating personal bias.\\u003c/p\\u003e\"},{\"header\":\"4. Results\",\"content\":\"\\u003cp\\u003eThis section presents the study\\u0026rsquo;s findings, starting with a description of the UX Researchers. It then explores their experiences and expectations using watsonx.ai and highlights the changes in their work and barriers to gen AI\\u0026rsquo;s smooth adoption.\\u003c/p\\u003e \\u003cp\\u003eIBM Software UX Researchers work on different products that belong to IBM\\u0026rsquo;s offerings in Automation, Data and Artificial Intelligence, Cybersecurity, and Sustainability. As generative AI features are being integrated into various IBM products, UX Researchers play a dual role with this technology, as they are both researchers and users. The sample includes UX Researchers with an average of 5.2 years in the industry and 2.8 years at IBM, ranging from recent hires to employees with over 15 years at the company, spanning junior to senior roles.\\u003c/p\\u003e \\u003cp\\u003eThe first round of interviews included seven women and seven men; the second included seven women and six men from North America (the United States, Canada), India, and Europe (Germany, Ireland). They had multiple backgrounds, including Social Sciences, Computer and Information Sciences, Design, and Communications. Most (9) had postgraduate studies, either a Master\\u0026rsquo;s or PhD, with six beginning their academic careers before moving into the business world. The varied disciplines bring enriching perspectives to the research practice.\\u003c/p\\u003e \\u003cp\\u003eUX Researchers\\u0026rsquo; relationships can be separated into the UX Research and the Product areas. Their peers can be anywhere worldwide, meaning they must be interconnected and aligned in different latitudes. UXRs in Product work within \\u0026ldquo;product teams,\\u0026rdquo; collaborating mainly with Product Managers, UX Designers, and Developers. Strong relationships with these stakeholders are key to conducting timely, business-relevant research that supports the product roadmap.\\u003c/p\\u003e \\u003cp\\u003eUXRs are driven by curiosity and a passion for learning, discovery, and solving complex problems. Working at the intersection between human behavior and technology allows them to lead research on how emerging tools can benefit people. In that line, research is a mobilizer and agent of change: \\u0026ldquo;It\\u0026rsquo;s our job to find things that need to change and need to be improved\\u0026rdquo; (Robert, Europe, senior).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1 Previous gen AI training\\u003c/h2\\u003e \\u003cp\\u003eOnly four participants (two men and two women, all semi-senior and from Europe) had prior AI-related education before joining IBM. The others learned about AI upon joining, highlighting IBM\\u0026rsquo;s commitment to enabling its employees.\\u003c/p\\u003e \\u003cp\\u003eRegarding the \\u0026ldquo;AI for Product Teams: Generative AI Fundamentals\\u0026rdquo; training, which covers definitions, ethics, and prompt design, nine UXRs completed it; two did not, and three could not remember. The usefulness of this resource varied based on participants\\u0026rsquo; prior knowledge and experience with it. Newcomers shared an optimistic view, while those with more experience viewed it as less useful, which is understandable given its introductory nature.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2 Gen AI professional use\\u003c/h2\\u003e \\u003cp\\u003eParticipants were asked about using watsonx.ai\\u0026rsquo;s \\u0026ldquo;Prompt Lab\\u0026rdquo;; 11 out of 14 had used it at work. The three who had not\\u0026mdash;senior and semi-senior women from North America and Europe\\u0026mdash;cited either limited time at IBM or discomfort with the technology, which persisted in both rounds of interviews. These findings are insufficient to declare that there is a gender gap concerning generative AI use.\\u003c/p\\u003e \\u003cp\\u003eRegarding the participants\\u0026rsquo; expectations before using the platform, some held an almost \\u0026ldquo;magical\\u0026rdquo; view of what AI can achieve: \\u0026ldquo;I thought it would like read my mind and give me exactly what I want.\\u0026rdquo; (Neha, US/CA, senior). Their previous experiences with ChatGPT, a tool all participants use personally, might have shaped this feeling. Nevertheless, most participants were enthusiastic about experimenting with watsonx.ai and exploring its capabilities.\\u003c/p\\u003e \\u003cp\\u003eIn summary, four general uses of generative AI emerged from the interviews:\\u003c/p\\u003e \\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eAI as an accelerator\\u003c/b\\u003e: This is the most commonly mentioned use and refers to the expectation that AI will speed up the work process and make it more efficient. An example of this is to speed up the search for user quotes.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eAI as a starting point\\u003c/b\\u003e: It intends to overcome the blank page or start from scratch. For example, it facilitates writing interview guidelines by providing an initial structure.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eAI as inspector\\u003c/b\\u003e: This refers to using AI as another \\u0026ldquo;pair of eyes\\u0026rdquo; with a critical lens and an awareness of detail. Researchers already have advanced work and use AI to give them a different perspective.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eAI as a guide\\u003c/b\\u003e: In these cases, AI is used as a guide to request explanations, recommendations, or advice on how to proceed in specific situations.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe following table summarizes the specific uses mentioned in the interviews:\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003e\\u0026ldquo;What are UX Researchers using gen AI for?\\u0026rdquo;, own elaboration.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGen AI uses round 1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGen AI uses round 2\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDoing thematic analysis, generating categories, coding interviews, finding patterns in the data\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIdem\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGet participant\\u0026rsquo;s quotes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIdem\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eParaphrasing, evaluating writing, and grammar\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIdem\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCode generation (Python or R)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCreate research plan\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePerform sentiment analysis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCreate an interview protocol or guide\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSummarizing data, transcripts, meetings\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFormatting insights the IBM way\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChat with documents\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSimplify or explain technical concepts, acronyms, etc.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRanking insights\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eDuring the second round of interviews, the participants extended and diversified the uses declared in the first round, showing a proactive and creative attitude toward the technology.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3 Transformations in UX Researchers\\u0026rsquo; Work\\u003c/h2\\u003e \\u003cp\\u003eThe role of UX Researchers transforms as they incorporate generative AI tools. This leads to performing different steps or processes or considering aspects that were not relevant before. Below are five dimensions of job quality in which AI is transforming the work of UXRs. The first four involve \\u0026ldquo;objective\\u0026rdquo; job quality characteristics, and the last is \\u0026ldquo;subjective.\\u0026rdquo;\\u003c/p\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e4.3.1 Transformations in tasks, processes, and work intensity\\u003c/h2\\u003e \\u003cp\\u003eOne of the main questions when talking about processes and tasks is how AI affects the productivity and efficiency of UX Researchers. The first way in which generative AI transforms the work of UXRs is in terms of time. Different participants indicated that using generative AI took longer than it would have taken them to do a task themselves:\\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\u003cp\\u003e...it's probably more efficient for me to just do it manually, because I spent like at least an hour trying to put in different prompts to get it to do what I wanted it to and it just wasn't doing it\\u0026hellip; So that's a waste of time, right? (Neha, US/CA, senior).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eIn another similar case, a researcher sought to generate a research plan for which he detailed the context, objectives, and expected results and realized that the AI was merely repeating his \\u003cem\\u003einput\\u003c/em\\u003e in the \\u003cem\\u003eoutput\\u003c/em\\u003e: \\u0026ldquo;And so if I'm already coming up with that text, I might as well just put it into a Word document, and that's my research plan.\\u0026rdquo; (Serge, US/CA, junior).\\u003c/p\\u003e \\u003cp\\u003eBoth examples illustrate how AI is not always the most efficient or straightforward. It involves going back and forth, adjusting prompts, trying different models, and editing inputs so AI can better process them. Researchers face a trade-off between using gen AI or their current methods, which impacts their consideration of task time allocation, as illustrated by the following quote:\\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\u003cp\\u003e\\u0026hellip;my big question for myself is how much do I rely on this? How much time should I spend doing my traditional synthesis process separate from this? \\u0026hellip;should I use this as a shortcut and just go from here? Or should I actually start from the beginning again? (Peter, Europe, semi-senior).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe doubts regarding the concrete benefits of AI mean that not all participants are convinced to use generative AI for their work. In addition, the process of trial and error with prompts is something that not all researchers are willing to do, and for them, the value of using generative AI is questionable. From the experiences analyzed, the efficiency they aspire to is still out of reach. This fact undermines the promise of productivity that generative AI claims, putting researchers\\u0026rsquo; expectations in check. The following participant eloquently describes the researchers\\u0026rsquo; frustrations:\\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\u003cp\\u003eI think many people have kind of given up quite quickly when it's not really returning what they expected, and I think a lot of people kind of come into it thinking I can just dump stuff in here, ask it to do something and it'll do it for me, where it doesn't really work like that. There's a lot of backwards and forwards and kind of negotiating with the LLM\\u0026hellip; (Frank, Europe, semi-senior).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe next point where generative AI could contribute to UX Researchers\\u0026rsquo; performance is in the research quality. However, Peter (Europe, semi-senior) and Diya (India, junior) found the AI results unhelpful or unspecific, providing general summaries rather than relevant insights. The variability of results characteristic of generative AI becomes relevant, which makes the data-checking process practically mandatory.\\u003c/p\\u003e \\u003cp\\u003eThe last is a good example of how AI transforms or causes a recomposition of UXR tasks. We find that new tasks emerge and take center stage, such as preparing and cleaning data for the machine, running prompts, and reviewing AI outputs. As the next participant describes, uncertainty regarding the quality and thoroughness of the results, added to having to perform revision tasks manually, increases the intensity of the researchers\\u0026rsquo; work:\\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\u003cp\\u003eI think once we get a hang of it, it would actually help us in streamlining the process a lot because right now we are doing a lot of extra work and spending a lot of time in it, like going through the transcripts, discovering the findings, connecting the transcripts. (Aisha, India, junior).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eTo omit this new task risks delivering untrue findings, which could damage the researchers\\u0026rsquo; credibility. To avoid this, some even advise that they \\u0026ldquo;don't get it (AI) to do things you couldn't do by yourself.\\u0026rdquo; (Christian, Europe, junior).\\u003c/p\\u003e \\u003cp\\u003eThe third point related to the UXR work process leads to the question: \\u0026ldquo;Is researching a product with Generative AI features different from one without?\\u0026rdquo; For some, generative AI implies a significant change in UX Research. Why? Now, there is not only the experience with software or digital products but also the experience with the AI model embedded in them, which involves issues related to ethics, trust, transparency, output quality, data protection, and more. All these factors make the work of UX Researchers more complex.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e4.3.2 Transformations in skills and autonomy\\u003c/h2\\u003e \\u003cp\\u003eIncorporating generative AI into the UX Researchers\\u0026rsquo; work means acquiring new knowledge and skills. The most critical competency mentioned by all participants is \\u0026ldquo;Prompt Engineering\\u0026rdquo;, i.e., the art and science of creating \\u0026ldquo;prompts\\u0026rdquo; or effectively communicating to generative AI to obtain desired results. Understanding how AI models work and their limitations (for example, token limit) is also relevant. Another related skill is familiarity with programming. On this point, some researchers perceive that watsonx.ai and other platforms are created by and for people with technical backgrounds, which makes them difficult to approach:\\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\u003cp\\u003eI mean, it definitely showed me as like I came from a qualitative background (...) It's immediately obvious that this is made for people who know coding and do more quant because, like quant analysis, you know it's all about the pre structured setup... (Iris, Europe, semi-senior).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe topic of adapting to new technology is compelling. More than background or experience, the key differences lie in researchers\\u0026rsquo; openness toward AI and the training they receive. Proper training helps them understand how AI works, which leads to more realistic expectations about its capabilities.\\u003c/p\\u003e \\u003cp\\u003eThe last point on skills refers to the ethical dimension. Researchers say they should consider the contexts of use and the risks and harms that AI can cause and ask questions such as: What data does the model use, how is it trained, and what biases can it bring? They should also think critically and be wary of the information they receive from AI.\\u003c/p\\u003e \\u003cp\\u003eResearchers hold differing views on integrating generative AI with existing UXR tools. Some see value in incorporating it into their workflows, while others are concerned about potential disruptions and prefer to use it as a separate tool. In either case, it is essential to preserve researchers\\u0026rsquo; autonomy in deciding when and how to use generative AI and ensure they retain control over their results.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e4.3.3 Transformations in interpersonal relationships\\u003c/h2\\u003e \\u003cp\\u003eCollaboration between UX researchers, product teams, and other stakeholders is crucial to implementing research results, and support networks are essential to sharing challenges and knowledge. A couple of researchers recounted their participation in a collaborative activity organized by other UXRs to work with generative AI, which positively affected building confidence in using AI:\\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\u003cp\\u003e...But before the working group had their meeting, I really didn\\u0026rsquo;t feel comfortable doing this independently. I felt like I didn\\u0026rsquo;t have enough direction and it was gonna actually take way too much time to figure out how to use the AI by myself. So it did really help give me some confidence to get started. (Peter, Europe, semi-senior).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eCases like this one show that incorporating generative AI demands more interaction and collaboration between UX Researchers, where those with more experience can encourage their peers to participate and try AI for themselves.\\u003c/p\\u003e \\u003cp\\u003eWhile today the use of generative AI is optional, researchers believe that may change depending on business decisions, as the following quote indicates: \\u0026ldquo;I think as more and more managers start becoming more familiar with the tools then they probably (will increase) the encouragement to actually use them across their teams\\u0026rdquo;. (Frank, Europe, semi-senior). This case also shows that relationships with others can be a catalyst for exploring AI.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e4.3.4 Transformations in the relationship with generative AI\\u003c/h2\\u003e \\u003cp\\u003eGenerative AI implies a new paradigm of interaction (Nielsen \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) where the user\\u0026rsquo;s \\u0026ldquo;intent\\u0026rdquo; or instruction is the raw material for the technology to work. In navigating how researchers conceive of generative AI, anthropomorphization intersects with the mere tool view, recognizing that interacting with AI is not the same as interacting with a person, as the following participant illustrates:\\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\u003cp\\u003eIt\\u0026rsquo;s kind of a weird social kind of interaction, isn\\u0026rsquo;t it? Like \\u0026lsquo;cause, (...) if you were doing this with a person and they made mistakes you probably wouldn\\u0026rsquo;t be that annoyed about it, but you\\u0026rsquo;ve got higher expectations for a computer. They shouldn\\u0026rsquo;t make mistakes. (Frank, Europe, semi-senior).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eSince generative AI can make mistakes, managing expectations starts with understanding its capabilities and limits. Researchers, however, find watsonx.ai lacks transparency, highlighting the need for explainable AI to verify data and analysis processes. Trust remains a key factor in the adoption of generative AI.\\u003c/p\\u003e \\u003cp\\u003eRegarding AI\\u0026rsquo;s roles, some researchers see it as an assistant handling administrative and tedious tasks; others view it as a \\u0026ldquo;junior\\u0026rdquo; researcher still learning. A new concept called \\u0026ldquo;Agentic AI\\u0026rdquo; has also emerged. AI agents, unlike assistants, can perform tasks autonomously without human intervention, which opens up immense opportunities for automation and raises many doubts concerning human autonomy.\\u003c/p\\u003e \\u003cp\\u003eAs this participant points out, the speed with which innovations are introduced risks leaving users behind in adapting to new ways of interacting with technology: \\u0026ldquo;And we\\u0026rsquo;re trying to introduce that into the industry now in the market and yeah, it\\u0026rsquo;s just a crazy amount of speed. And the technology is moving faster than people can adapt to the concepts and ideas\\u0026rdquo; (Jake, US/CA, semi-senior).\\u003c/p\\u003e \\u003cp\\u003eFinally, when considering the future of their work, all the UX Researchers interviewed agreed that generative AI cannot replace them, as replicating their contributions would require interpretive skills and deep contextual understanding. One participant reached this conclusion after experimenting with watsonx.ai:\\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\u003cp\\u003eIt\\u0026rsquo;s descriptive, it\\u0026rsquo;s not analytical. (...) and so that\\u0026rsquo;s good to know that it\\u0026rsquo;s going to pick up on the words, you know, the denotation, not the connotation as much. And that\\u0026rsquo;s where really the trick is what you need it for versus what can a human only do because we know deeper context and you know the subtleties between the lines... (Iris, Europe, semi-senior).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e4.3.5 Transformations in job satisfaction and motivation\\u003c/h2\\u003e \\u003cp\\u003eMost researchers are motivated to use generative AI to improve efficiency, while some remain skeptical due to limited knowledge or concerns about data quality, biases, and algorithm flaws. The second round of interviews shows that their motivation increases as UXRs gain positive experiences with the technology. However, no changes in job satisfaction were observed.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.4 Opportunities and expectations of working with generative AI\\u003c/h2\\u003e \\u003cp\\u003eAll UX Researchers envision multiple applications of generative AI for their work (see Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). As to watsonx.ai, they expect the tool to improve in the future, as they estimate that at the time of the interviews, it is still early to see results, as shown in the quote: \\u0026ldquo;I think we are still a little early in the curve to get the benefits out of it.\\u0026rdquo; (Aisha, India, junior).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003e\\u0026ldquo;What UXRs want to use gen AI for? Expectations\\u0026rdquo;. Own elaboration.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGen AI use expectations round 1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGen AI use expectations round 2\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eExtract the main themes from interviews\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIdem\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGenerate images, illustrations, data visualization\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIdem\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGenerate videos from text to foster engagement in presentations\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIdem\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGenerate the presentation outline, story, or narrative structure.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGenerate variations of presentations based on target audience\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFormatting insights the IBM way\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTranslating from one language to another\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDo secondary research, have the knowledge base of all IBM products incorporated.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDetect which candidates used AI in their screener answers\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSummarizing meetings, interviews\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCorrect and clean up transcripts with errors\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eManage and update documentation\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSolutions accessibility check\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eIt is interesting to note that current uses of generative AI also emerge when discussing expectations. For instance, generative AI\\u0026rsquo;s ability to \\u0026ldquo;extract main themes from interviews\\u0026rdquo; highlights that researchers have yet to experiment with these capabilities and achieve the desired results.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Discussion and conclusions\",\"content\":\"\\u003cp\\u003eThis study was conducted at an embryonic moment when companies began developing and applying generative AI. It examined the role of IBM Software UX Researchers, their experiences working with generative AI, and how these experiences transform how they approach their current and future work.\\u003c/p\\u003e \\u003cp\\u003eAmidst the complex context facing the UX Research organization, there is a mainly exploratory and occasional use of IBM\\u0026rsquo;s watsonx.ai tool available to UX Researchers. Researchers see AI as an opportunity to make their work more efficient, although they maintain a nuanced view of it; they recognize its possibilities but also have concerns about its limitations, such as the lack of transparency in its operations and the accuracy or veracity of its results. That means that the level of trust in AI is low.\\u003c/p\\u003e \\u003cp\\u003eThe study concludes that incorporating generative AI tools is transforming the work of UX Researchers. While generative AI is often associated with promises of increased productivity and efficiency, researchers\\u0026rsquo; experiences show that, in many cases, it currently requires more time and effort to achieve satisfactory results. Nevertheless, with continued practice and refinement of their methods, researchers are expected to eventually consolidate their workflows and realize the anticipated gains in productivity and efficiency.\\u003c/p\\u003e \\u003cp\\u003eBeyond the productivity challenges tied to the tool\\u0026rsquo;s capabilities, the main barrier to adopting AI is overcoming the learning curve. Exploring and testing different uses is not a quick process; it demands time, interest, and dedication from UX Researchers to make experimentation iterative and effective. Achieving this level of engagement requires researchers to feel comfortable with the technology, highlighting the need to understand the factors influencing their current attitudes. In addition to requiring determination, researchers have concerns about how technically skilled they must be to use AI effectively. For now, deciding whether to invest time in generative AI remains within the researchers\\u0026rsquo; autonomy. Still, in a rapidly evolving tech industry, this could soon shift toward stronger encouragement from the company.\\u003c/p\\u003e \\u003cp\\u003eOther barriers to using AI involve trust and concern about the quality and accuracy of the results. UX Researchers mention that AI can make mistakes and that it is always important to verify results and \\u0026ldquo;Do not blindly trust what it says\\u0026rdquo; (Christian, Europe, junior). There are concerns about AI reinforcing existing biases in the data and questions about interpreting generative AI processes when the algorithms lack transparency.\\u003c/p\\u003e \\u003cp\\u003eRegarding how generative AI could transform their role and impact their work, UX Researchers are skeptical about the ability to replace humans in the research process and believe that generative AI should be used as an auxiliary tool, either as an accelerator, starting point, inspector, or guide. UX Researchers view generative AI as limited compared to human expertise but are open to delegating it administrative tasks, like participant recruitment.\\u003c/p\\u003e \\u003cp\\u003eWhile it may not entirely meet their expectations today, most UX Researchers have a positive vision for the future and envision countless use cases where AI could help them do their jobs better. When considering opportunities for gen AI use, challenges in communication and stakeholder relations, generating insights, structuring research results, and creating visual support materials serve as areas where generative AI could contribute.\\u003c/p\\u003e \\u003cp\\u003eA key challenge in the evolving relationship between people and AI is preserving the human aspect in the digital era. As technology advances, the question becomes how to stay involved and relevant. In this context, UX Researchers have a unique perspective since their role is to understand people. As Neha (US/CA, senior) points out: \\u0026ldquo;I enjoy listening to what their comments are and then making sure that I understand what they\\u0026rsquo;re saying, I think that means a lot to them too.\\u0026rdquo; Maintaining and nurturing human relationships is one aspect that can strengthen users\\u0026rsquo; trust in researchers and, ultimately, in IBM.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec23\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.1 Recommendations\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec24\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e5.1.1 Training\\u003c/h2\\u003e \\u003cp\\u003e The interview results indicate that UX Researchers feel more confident using generative AI when provided with clear guidelines or frameworks for interaction. Therefore, it is crucial to continue developing skills such as designing effective prompts, understanding the technology\\u0026rsquo;s limitations, and critically evaluating results. Additionally, training should align with IBM\\u0026rsquo;s established protocols and ethical standards for generative AI use. The study also highlighted the need for advanced training targeted at those with a better grasp of AI, providing more profound knowledge and work in complex use cases.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec25\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e5.1.2 Teamwork\\u003c/h2\\u003e \\u003cp\\u003eEncouraging collaboration among UX Researchers is key to sharing challenges and lessons learned with generative AI, boosting confidence in using the technology. Workshops, mentorships, and tutorials showcasing real examples can provide insight into other researchers\\u0026rsquo; strategies to achieve successful outcomes, creating a more effective AI work environment. Additionally, it is recommended to involve younger or junior UX Researchers, leveraging their enthusiasm and time, to develop use cases they can share with their peers.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec26\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e5.1.3 Set objectives and metrics\\u003c/h2\\u003e \\u003cp\\u003eResearchers still lack clarity about the future of generative AI. IBM must establish a straightforward narrative on why AI is relevant to UX Researchers and set clear expectations about its role in augmenting, rather than replacing, their work. Emphasizing UXR\\u0026rsquo;s invaluable role is essential. Additionally, the company must maintain active channels for UXRs to provide feedback on this issue.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec27\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e5.1.4 Explore new areas for developing AI tools\\u003c/h2\\u003e \\u003cp\\u003eThe teams working on gen AI tools should focus on the current pains of the UXR process to discover more opportunities where generative AI can contribute. These include managing communication engagement, influencing stakeholder decision-making, structuring results, recruiting participants, managing bias, and enabling and teaching stakeholders about research, among others, as they arise.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec28\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.2 Future work\\u003c/h2\\u003e \\u003cp\\u003eResearch on the future of work and generative Artificial Intelligence is in fertile ground. The following are some lines in which this fascinating and current topic can be further developed:\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003eExtend the research to quantitatively explore which tasks UXRs would like to delegate to AI and which ones would not, taking inspiration from Ethan Molick\\u0026rsquo;s book \\u0026ldquo;Co-Intelligence: Living and Working with AI.\\u0026rdquo; The author invites us to classify tasks into three groups: those we want to perform independently, those we want AI support, and those we wish to delegate entirely to AI (Molick \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Knowing this can help to generate tools focused on tasks where aid is indeed required and not on tasks that are meaningful to researchers.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eStudy the adoption of generative AI according to the gender and cultural context of UX Researchers.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eTo delve into aspects of subjective job quality and well-being of UX Researchers as AI adoption progresses, to see how their job satisfaction, motivation, and work meanings are transformed in time.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eThis work was supported by the N\\u0026uacute;cleo Milenio de Desigualdades y Oportunidades Digitales (NUDOS), a research center funded by the Agencia Nacional de Investigaci\\u0026oacute;n y Desarrollo de Chile (ANID), the State agency that promotes the development of science, technology, and innovation in Chile. The author has no financial or proprietary interests in any material discussed in this article.\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eThe author is the only contributor, taking primary responsibility for the research described in the manuscript.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eTo preserve individuals\\u0026rsquo; privacy and IBM\\u0026rsquo;s data-sharing policy, all data generated from this study is not openly available.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col class=\\\"decimal_type\\\"\\u003e\\n\\u003cli\\u003eAcemoglu D \\u0026amp; Restrepo P (2020) Robots and Jobs: Evidence from US Labor Markets. Journal of Political Economy, 128 (6): 2188\\u0026ndash;2244. https://doi.org/10.1086/705716\\u003c/li\\u003e\\n\\u003cli\\u003eBarros A, Prasad A, \\u0026amp; Śliwa M (2023) Generative artificial intelligence and academia: Implication for research, teaching and service. Management Learning, 54(5), 597-604. https://doi.org/10.1177/13505076231201445\\u003c/li\\u003e\\n\\u003cli\\u003eBellet C, De Neve J, \\u0026amp; Ward G (2019) Does Employee Happiness have an Impact on Productivity? Sa\\u0026iuml;d Business School WP 2019-13. http://dx.doi.org/10.2139/ssrn.3470734\\u003c/li\\u003e\\n\\u003cli\\u003eBerg J \\u0026amp; Gmyrek P (2023) Automation hits the knowledge worker: ChatGPT and the future of work. UN Multi-Stakeholder Forum on Science, Technology and Innovation for the SDGs (STI Forum) 2023. https://ssrn.com/abstract=4458221\\u003c/li\\u003e\\n\\u003cli\\u003eButts D (2024) AI is the talk of the town, but businesses are still not ready for it, survey shows. https://www.cnbc.com/2024/03/06/generative-ai-holds-massive-potential-but-businesses-arent-ready-yet.html\\u003c/li\\u003e\\n\\u003cli\\u003eCarbonero F, Ekkehard E, Weber E (2018) Robots Worldwide: The Impact of Automation on Employment and Trade. https://www.ilo.org/publications/robots-worldwide-impact-automation-employment-and-trade\\u003c/li\\u003e\\n\\u003cli\\u003eCoffey A \\u0026amp; Atkinson P (2003) Making sense of qualitative data: complementary research strategies. 1st ed. (in Spanish) Editorial Universidad de Antioqu\\u0026iacute;a\\u003c/li\\u003e\\n\\u003cli\\u003eCreswell J \\u0026amp; Creswell J.D (2018) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. SAGE Publications, Inc.\\u003c/li\\u003e\\n\\u003cli\\u003eDell\\u0026rsquo;Acqua F, McFowland III E, Mollick E, Lifshitz-Assaf H, Kellogg K, Rajendran S, Krayer L, Candelon F, \\u0026amp; R. Lakhani K (2023) Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. Harvard Business School Working Paper, No. 24-013, September 2023.\\u003c/li\\u003e\\n\\u003cli\\u003eEquitable Growth (2022) The importance of understanding how job quality affects U.S. workers and the entire economy, and how to boost access to good jobs. Washington Center for Equitable Growth. https://equitablegrowth.org/the-importance-of-understanding-how-job-quality-affects-u-s-workers-and-the-entire-economy-and-how-to-boost-access-to-good-jobs/\\u003c/li\\u003e\\n\\u003cli\\u003eEurofound (2012) Trends in job quality in Europe. Publications Office of the European Union, Luxembourg.\\u003c/li\\u003e\\n\\u003cli\\u003eEurofound (2019) Working conditions and workers\\u0026rsquo; health. Publications Office of the European Union, Luxembourg.\\u003c/li\\u003e\\n\\u003cli\\u003eFlick U (2007) Introducci\\u0026oacute;n a la investigaci\\u0026oacute;n cualitativa, Ediciones Morata, Madrid, Espa\\u0026ntilde;a\\u003c/li\\u003e\\n\\u003cli\\u003eFornino M, \\u0026amp; Manera A (2022) Automation and the future of work: Assessing the role of labor flexibility. Review of Economic Dynamics, Volume 45, 2022, Pages 282-321, ISSN 1094-2025, https://doi.org/10.1016/j.red.2021.07.002\\u003c/li\\u003e\\n\\u003cli\\u003eFrank MR, Autor D, Bessen E, Brynjolfsson E, Cebrian M, Deming J, Feldman M, Groh M, Lobo J, Moro E, Wang D, Youn H \\u0026amp; Rahwan I (2019) Toward understanding the impact of artificial intelligence on labor. Proc. Natl. Acad. Sci. U.S.A. 116 (14) 6531-6539. https://doi.org/10.1073/pnas.1900949116\\u003c/li\\u003e\\n\\u003cli\\u003eGmyrek P, Berg J, Bescond D (2023) Generative AI and Jobs: A global analysis of potential effects on job quantity and quality. ILO Working paper 96. (Geneva, ILO) https://www.ilo.org/global/publications/working-papers/WCMS_890761/lang--en/index.htm\\u003c/li\\u003e\\n\\u003cli\\u003eGoldstein J, Lobig B, Fillare C, Nowak C (2023) Augmented work for an automated, AI-driven world. Boost performance with human-machine partnerships. IBM Institute for Business Value. https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/augmented-workforce\\u003c/li\\u003e\\n\\u003cli\\u003eGordon, C (2023) ChatGPT Is The Fastest Growing App In The History Of Web Applications. https://www.forbes.com/sites/cindygordon/2023/02/02/chatgpt-is-the-fastest-growing-ap-in-the-history-of-web-applications/?sh=1df542f2678c\\u003c/li\\u003e\\n\\u003cli\\u003eIBM AI Ethics Board (2024) Key Performance Indicators for Augmenting Human Intelligence with AI. https://www.ibm.com/downloads/documents/us-en/10a99803d6afd426\\u003c/li\\u003e\\n\\u003cli\\u003eInternational Organization for Standardization (2019) ISO 9241-210:2019(en). Ergonomics of human-system interaction \\u0026mdash; Part 210: Human-centred design for interactive systems. 3.15 user experience. https://www.iso.org/obp/ui/en/#iso:std:iso:9241:-210:ed-2:v1:en\\u003c/li\\u003e\\n\\u003cli\\u003eJarrahi MH, Newlands G, Lee MK, Wolf CT, Kinder E, \\u0026amp; Sutherland W (2021) Algorithmic management in a work context. Big Data \\u0026amp; Society, 8(2). https://doi.org/10.1177/20539517211020332\\u003c/li\\u003e\\n\\u003cli\\u003eMcKinsey Global Institute (2023) Generative AI and the future of work in America. https://www.mckinsey.com/mgi/our-research/generative-ai-and-the-future-of-work-in-america\\u003c/li\\u003e\\n\\u003cli\\u003eMolick E (2024) Co-Intelligence: Living and Working with AI. Portfolio/Penguin\\u003c/li\\u003e\\n\\u003cli\\u003eNassaji H (2015) Qualitative and descriptive research: Data type versus data analysis. Language Teaching Research 2015, Vol. 19(2) 129\\u0026ndash;132. https://journals.sagepub.com/doi/pdf/10.1177/1362168815572747\\u003c/li\\u003e\\n\\u003cli\\u003eNielsen J (2023) AI: First New UI Paradigm in 60 Years. https://www.nngroup.com/articles/ai-paradigm/\\u003c/li\\u003e\\n\\u003cli\\u003eNurski L, \\u0026amp; Hoffmann M (2022) The impact of artificial intelligence on the nature and quality of jobs. Bruegel Working Paper, No. 14/2022, Bruegel, Brussels. https://www.econstor.eu/handle/10419/270468\\u003c/li\\u003e\\n\\u003cli\\u003eRegulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act) http://data.europa.eu/eli/reg/2024/1689/oj\\u003c/li\\u003e\\n\\u003cli\\u003eSears J (2023) How artificial intelligence can augment a people-centered workforce. https://www.ey.com/en_gl/insights/workforce/how-artificial-intelligence-can-augment-a-people-centered-workforce\\u003c/li\\u003e\\n\\u003cli\\u003eSchreier M (2014) Qualitative Content Analysis. In: Flick U (ed) The SAGE Handbook of Qualitative Data Analysis, SAGE, pp 170-183\\u003c/li\\u003e\\n\\u003cli\\u003eStryker C \\u0026amp; Scapicchio M (2024) What is generative AI? https://www.ibm.com/topics/generative-ai\\u003c/li\\u003e\\n\\u003cli\\u003eVan Zoonen W, Sivunen A \\u0026amp; Treem J (2024) Algorithmic management of crowdworkers: Implications for workers\\u0026rsquo; identity, belonging, and meaningfulness of work. Computers in Human Behavior, Volume 152, 2024, 108089, ISSN 0747-5632. https://doi.org/10.1016/j.chb.2023.108089\\u003c/li\\u003e\\n\\u003cli\\u003eVasilachis de Gialdino I (Coordinator) (2006) Estrategias de investigaci\\u0026oacute;n cualitativa. Editorial Gedisa, Barcelona, Espa\\u0026ntilde;a\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Generative Artificial Intelligence, Future of Work, UX Research, Human-AI collaboration, Augmentation\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6550600/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6550600/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis study aimed to explore the use of generative Artificial Intelligence (AI) by User Experience Researchers (UX Researchers) in the context of the technology company IBM. Using a qualitative approach, it involved two rounds of in-depth interviews with 14 participants from five countries within the IBM Software organization. The goal was to examine their practices, experiences, and challenges in adopting generative AI.\\u003c/p\\u003e \\u003cp\\u003eThe research delved into how generative AI transforms their work in at least five dimensions, including (1) tasks, processes, and work intensity, (2) skills and autonomy, (3) interpersonal relationships, (4) relationship with AI, and (5) job satisfaction and motivation. In terms of productivity (the organization\\u0026rsquo;s primary concern), generative AI has not met UX researchers\\u0026rsquo; expectations of efficiency and quality. Its adoption is heavily influenced by exaggerated expectations that do not fully reflect its capabilities.\\u003c/p\\u003e \\u003cp\\u003eIn addition, other challenges in its adoption arose, such as the lack of trust in generative AI and the quality of its results, and navigating the learning curve in the effective use of AI tools available within the enterprise. Regarding the potential threats of AI to employment, researchers view AI as an auxiliary tool rather than a replacement.\\u003c/p\\u003e \\u003cp\\u003eFinally, to enhance UX Research practices and achieve greater productivity and efficiency through generative AI, it is recommended to invest in training, foster collaborative work, establish clear goals, and explore new AI tools to address pain points such as participant and stakeholder management.\\u003c/p\\u003e\",\"manuscriptTitle\":\"The \\\"Augmented\\\" Researcher: UX Researchers' experiences with incorporating gen AI into their work\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-05-07 07:24:14\",\"doi\":\"10.21203/rs.3.rs-6550600/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"cd9f650d-38e2-4081-8abb-3a817417e2c0\",\"owner\":[],\"postedDate\":\"May 7th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-05-23T08:23:36+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-05-07 07:24:14\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6550600\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6550600\",\"identity\":\"rs-6550600\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}