Generative AI technology has taken the spotlight in consumer usage; however, its adoption in businesses and organizations has been notably lower, despite its potential to boost business operations by driving productivity, improving decision-making, and providing a competitive edge.
RESEARCH GOALS
Understand the fundamental reasons for the slower adoption of generative AI in businesses and organizations.
Develop a set of concrete design recommendations for the UX team to help increase responsible generative AI integration into company teams and workflows.
PROTO-PERSONA DEVELOPMENT
Based on our research goals, I developed three proto-personas to identify the user base for future Salesforce generative AI products and experiences. We used these personas to help define target criteria to find representative participants for our primary research.
SECONDARY RESEARCH
I began by reviewing existing research on generative AI adoption in business vs. consumer applications to understand the gaps in current knowledge, identify tenets of generative AI adoption (i.e. what factors influence whether a GenAI product is adopted or not), and help inform our survey and interview questions.
SURVEYS
I designed and conducted persona-specific surveys to gather quantitative data from a larger number of respondents in a relatively short amount of time. The goal of the surveys was to learn about users' experiences, concerns, and needs surrounding generative AI usage and adoption in their organization.
We recruited via LinkedIn, Slack, Discord, and in person at the UC Berkeley Haas School of Business and School of Information.
We received 58 responses: 23 individual contributors (ICs), 22 decision-makers, and 13 marketers.
Sample survey questions:
For ICs: How valuable have GenAI technologies been for achieving the following? Please rank in the order of importance (1 = most important): time savings, increased creativity or inspiration, improved quality of output, learning/skill development
For decision-makers: Please rate the significance of each of the following concerns to adopting GenAI technologies in your organization (1 = not a concern, 5 = major concern): cost, complexity, data privacy and security, ethical considerations, reliability/accuracy, job displacement, other: ___.
INTERVIEWS
I wrote and conducted 10 semi-structured, 30-minute user interviews to develop a more comprehensive, in-depth understanding of the goals, pain points, motivations, and behaviors of each user group regarding generative AI usage and adoption within their organizations.
Out of the 10 interviewees, there were 8 ICs (3 from the UX industry, 1 from ed tech, and 4 graduate students), 1 decision-maker from the ed tech industry, and 1 marketer from the aviation industry.
I wrote and conducted 10 semi-structured, 30-minute user interviews to develop a more comprehensive, in-depth understanding of the goals, pain points, motivations, and behaviors of each user group regarding generative AI usage and adoption within their organizations.
Out of the 10 interviewees, there were 8 ICs (3 from the UX industry, 1 from ed tech, and 4 graduate students), 1 decision-maker from the ed tech industry, and 1 marketer from the aviation industry.
COMPETITIVE ANALYSIS
I conducted a competitive analysis of 9 GenAI products commonly used for businesses and consumers across 8 dimensions: cost, learning curve, accuracy, data security, type of input, type of output, customization ability, and user interface. This allowed us to identify common weaknesses across products that may hinder business adoption rates and draw insights from their strengths.
We chose these products because they covered the highest variety of use cases – including using GenAI for content creation, for image generation, as a chatbot, for coding, etc.
Ethnographic Research: Observe how generative AI tools fit into everyday work practices, how they alter workflow dynamics, and their impact on organizational culture.
Usability Testing: Because we didn’t have an existing product to work off of, there was no usability testing to be done, but once a first prototype is developed, it will be essential to conduct usability testing.
Longitudinal Studies: Observe the long-term effects of generative AI integration on organizational performance; this is especially important because of the rapidly-evolving nature of generative AI.
Good UX researchers must be adaptable, resourceful, and persistent. My biggest challenge during this project was participant recruitment, as our personas were quite niche and budget constraints meant that we could not offer incentives to participants. Instead of viewing these limitations as setbacks, I treated them as an opportunity to be more creative and intentional in my approach.
I led our team in exploring alternative recruitment strategies, identifying suitable proxy users, and leveraging existing networks and communities in more strategic ways. By continually refining our approach and adjusting our criteria, we were able to build a participant pool that provided meaningful insights.
These constraints pushed me to become a more flexible and inventive researcher, and the data we gathered played a critical role in shaping our final design recommendations.
SECONDARY RESEARCH
I began by reviewing existing research on generative AI adoption in business vs. consumer applications to understand the gaps in current knowledge, identify tenets of generative AI adoption (i.e. what factors influence whether a GenAI product is adopted or not), and help inform our survey and interview questions.
SURVEYS
I designed and conducted persona-specific surveys to gather quantitative data from a larger number of respondents in a relatively short amount of time. The goal of the surveys was to learn about users' experiences, concerns, and needs surrounding generative AI usage and adoption in their organization.
We recruited via LinkedIn, Slack, Discord, and in person at the UC Berkeley Haas School of Business and School of Information.
We received 58 responses: 23 individual contributors (ICs), 22 decision-makers, and 13 marketers.
Sample survey questions:
For ICs: How valuable have GenAI technologies been for achieving the following? Please rank in the order of importance (1 = most important): time savings, increased creativity or inspiration, improved quality of output, learning/skill development
For decision-makers: Please rate the significance of each of the following concerns to adopting GenAI technologies in your organization (1 = not a concern, 5 = major concern): cost, complexity, data privacy and security, ethical considerations, reliability/accuracy, job displacement, other: ___.
INTERVIEWS
I wrote and conducted 10 semi-structured, 30-minute user interviews to develop a more comprehensive, in-depth understanding of the goals, pain points, motivations, and behaviors of each user group regarding generative AI usage and adoption within their organizations.
Out of the 10 interviewees, there were 8 ICs (3 from the UX industry, 1 from ed tech, and 4 graduate students), 1 decision-maker from the ed tech industry, and 1 marketer from the aviation industry.
COMPETITIVE ANALYSIS
I conducted a competitive analysis of 9 GenAI products commonly used for businesses and consumers across 8 dimensions: cost, learning curve, accuracy, data security, type of input, type of output, customization ability, and user interface. This allowed us to identify common weaknesses across products that may hinder business adoption rates and draw insights from their strengths.
We chose these products because they covered the highest variety of use cases – including using GenAI for content creation, for image generation, as a chatbot, for coding, etc.
Ethnographic Research: Observe how generative AI tools fit into everyday work practices, how they alter workflow dynamics, and their impact on organizational culture.
Usability Testing: Because we didn’t have an existing product to work off of, there was no usability testing to be done, but once a first prototype is developed, it will be essential to conduct usability testing.
Longitudinal Studies: Observe the long-term effects of generative AI integration on organizational performance; this is especially important because of the rapidly-evolving nature of generative AI.
Good UX researchers must be adaptable, resourceful, and persistent. My biggest challenge during this project was participant recruitment, as our personas were quite niche and budget constraints meant that we could not offer incentives to participants. Instead of viewing these limitations as setbacks, I treated them as an opportunity to be more creative and intentional in my approach.
I led our team in exploring alternative recruitment strategies, identifying suitable proxy users, and leveraging existing networks and communities in more strategic ways. By continually refining our approach and adjusting our criteria, we were able to build a participant pool that provided meaningful insights.
These constraints pushed me to become a more flexible and inventive researcher, and the data we gathered played a critical role in shaping our final design recommendations.
