# Deconstructing the Veneer of Simplicity: Co-Designing Introductory
Generative AI Workshops with Local Entrepreneurs

Yasmine Kotturi<EMAIL_ADDRESS>Carnegie Mellon University5000 Forbes
AvePittsburghPennsylvaniaUSA15213 ,  Angel Anderson<EMAIL_ADDRESS>Community ForgeWilkinsburgPennsylvaniaUSA ,  Glenn Ford<EMAIL_ADDRESS>Community ForgeWilkinsburgPennsylvaniaUSA ,  Michael Skirpan
<EMAIL_ADDRESS>Carnegie Mellon
UniversityPittsburghPennsylvaniaUSA and  Jeffrey P. Bigham<EMAIL_ADDRESS>Carnegie Mellon UniversityPittsburghPennsylvaniaUSA

(2024)

###### Abstract.

Generative AI platforms and features are permeating many aspects of work.
Entrepreneurs from lean economies in particular are well positioned to
outsource tasks to generative AI given limited resources. In this paper, we
work to address a growing disparity in use of these technologies by building
on a four-year partnership with a local entrepreneurial hub dedicated to
equity in tech and entrepreneurship. Together, we co-designed an interactive
workshops series aimed to onboard local entrepreneurs to generative AI
platforms. Alongside four community-driven and iterative workshops with
entrepreneurs across five months, we conducted interviews with 15 local
entrepreneurs and community providers. We detail the importance of communal
and supportive exposure to generative AI tools for local entrepreneurs,
scaffolding actionable use (and supporting non-use), demystifying generative
AI technologies by emphasizing entrepreneurial power, while simultaneously
deconstructing the veneer of simplicity to address the many operational skills
needed for successful application.

generative artificial intelligence, entrepreneurship, community-based research

††ccs: Human-centered computing Empirical studies in HCI††ccs: Human-centered
computing Empirical studies in HCI††journalyear: 2024††copyright:
rightsretained††conference: Proceedings of the CHI Conference on Human Factors
in Computing Systems; May 11–16, 2024; Honolulu, HI, USA††booktitle:
Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI
’24), May 11–16, 2024, Honolulu, HI, USA††doi: 10.1145/3613904.3642191††isbn:
979-8-4007-0330-0/24/05

##  1\. Introduction

Figure 1. We designed an introductory generative AI workshop series with
entrepreneurs and tech providers which centered communal experience,
supportive exposure, tangible and actionable exercises, and long-term
technical support for maintenance and repair. Image created with Midjourney
(Midjourney, 2023).

This image, generated with Midjourney, includes a conference table with about
20 entrepreneurs and providers working on laptops and with each other in a
room at a conference table. Entrepreneurs and providers appear to be from
various age groups, and race and gender identities. This image is in the style
of a watercolor painting.

The implications of pre-trained, generative AI models–deemed a “general
purpose technology” (Eloundou et al., 2023)–for the workforce are vast
(Agrawal et al., 2019). Local entrepreneurs, who primarily target their local
economy and operate at a small scale (Hui et al., 2020), are uniquely
positioned to derive the benefits of these tools by outsourcing tasks,
increasing efficiency, and cutting costs (Short and Short, 2023). For example,
consider an entrepreneur with an event planning business and who posts
frequently on social media platforms about upcoming occasions, services
offered, and prior events; by integrating ChatGPT, Canva text-to-image, and
cross-platform management tools, she can quickly generate content and automate
publishing workflows, saving her hours worth of work each week (with Canva,
2023a). Beyond efficiency gains, equipping local entrepreneurs with generative
AI technologies presents a rare opportunity to provide entrepreneurs with
support essential to overcome the inertia required to maintain a new venture
alone (Mollick and Euchner, 2023). Whether it’s brainstorming with ChatGPT to
generate new product ideas or marketing plans, local entrepreneurs can
leverage generative AI technologies to overcome creative blocks and catalyze
the everyday momentum required for success (Mirowski et al., 2023; Swanson et
al., 2021).

Yet, despite user-friendly packaging and seemingly simple chat-based interface
designs, generative AI technologies like ChatGPT (Bang et al., 2023) and
DALL-E (Ramesh et al., 2022) are primarily used by those who have backgrounds
in technology or who are college educated (Center, 2023). Thus, generative AI
risks further widening the digital divide (BBC, 2023), as entrepreneurs who
have access to formal and informal education, devices, and technical capital
are more quickly learning how to harness this nascent and powerful technology
for their benefit (Mollick and Euchner, 2023; Bell and Bell, 2023). This paper
explores how to address this growing disparity by engaging local entrepreneurs
in a community setting where disparities are heightened given uneven access to
technological resources and a history of resource deprivation stemming from
post-industrial blight and systemic racial inequalities.

Previous work in human-computer interaction (HCI) which focuses on local
entrepreneurs from lean economies—economies where citizens exude resilience
and resourcefulness to overcome minimal resources (Dillahunt et al.,
2018)—highlights the critical role of building small, local networks among
entrepreneurs when onboarding digital technologies for their business
(Dillahunt et al., 2018; Hui et al., 2018; Avle et al., 2019; Raval and Pal,
2019; Pei and Crooks, 2020; Hui et al., 2020; Ogbonnaya-Ogburu et al., 2019;
Kotturi et al., 2022). These in-person support structures enable entrepreneurs
to more effectively vet relationships, reputations, and technological guidance
for trustworthiness (Hui et al., 2018; Dillahunt et al., 2018). Trust is
particularly important for entrepreneurs from low-income communities as
entrepreneurship is often pursued out of economic necessity (Hui et al.,
2018). For instance, small, vetted groups of local entrepreneurs and experts
can alleviate the risks and burdens of maintaining technology, and help to
foster effective technology use tailored to a specific business domain (Hui et
al., 2020). In particular, such groups excel when they are informal, non-
hierarchical, and showcase all members’ expertise (Dillahunt et al., 2022). In
addition to small groups, one-on-one technology support between entrepreneurs
and technology experts can provide needed flexibility to tailor technical
advice to the unique backgrounds and domains of local entrepreneurs (Kotturi
et al., 2022).

While prior work details the role of local networks to support maintenance of
existing technologies, it is unclear how social support should be structured
to onboard local entrepreneurs to generative AI technologies. In this setting,
social support models must respond to fast-paced development and
unpredictability (Bommasani et al., 2021), astounding accuracy alongside
hallucinations (Jiang et al., 2020), and ill-defined notions of AI literacy
(Ng et al., 2021), especially in the context of local entrepreneurship.
Therefore, to inform configurations of social support in this context, we
followed a community-driven protocol (Harrington et al., 2019) building on a
four-year partnership with a local entrepreneurial hub dedicated to racial
equity in technology and entrepreneurship. Together, we co-designed an
interactive workshop series across five months to onboard entrepreneurs to
generative AI technologies, where workshops were embedded in a monthly
entrepreneur mixer (Lu et al., 2023a) and addressed specific community needs
for business support. To support our iterative design process, we conducted 15
semi-structured interviews with local entrepreneurs and community providers
who participated in a workshop(s) and, in doing so, sought to answer the
following research questions: RQ1 What is the role of social support when
onboarding local entrepreneurs to pre-trained generative AI platforms and
features? Because of entrepreneurs’ diverse backgrounds and business domains,
as well as their various technological goals and aversions, we asked: RQ2 What
are community-driven outcomes of an interactive workshop series intended to
onboard local entrepreneurs to generative AI technologies? By co-designing one
configuration of social support which centered community-driven outcomes and
values, we then asked: RQ3 How do local entrepreneurs use (and prefer not to
use) generative AI technologies for their business? What concerns do local
entrepreneurs have when adopting generative AI in their business?

Three core findings emerged from our analysis. First, we found that centering
communal experience was critical when onboarding entrepreneurs to generative
AI technologies in order to demystify these technologies and mitigate techno-
anxieties these tools can elicit when participants’ livelihoods are implicated
in use. Second, while generative AI technologies are often presented with a
veneer of simplicity, we detail a laundry list of operational skills beyond
prompt engineering that are required for successful use (e.g., browser
literacy, successful password management, knowledge of cloud and local
storage, keyboard shortcuts). Through centering within-community expertise, we
further detail the steps before and after use of generative AI technologies
(i.e., “pre-” and “post-processing” of inputs to, and outputs of, generative
AI technologies) in order for technologies to provide actual and sustained
value for local entrepreneurs. Third, after introduction, we detail how
entrepreneurs used, and preferred not to use, generative AI technologies,
which technologies they used, as well as initial concerns entrepreneurs had
when using generative AI technologies for their business such as bias and
intellectual property infringement.

Taken together, this paper makes the following three contributions. First,
building on models low-tech social support (Hui et al., 2020; Dillahunt et
al., 2022), we present empirical findings for an interactive workshop
structure tailored for generative AI technologies that emphasized communal
exposure and actionable opportunity for use (and non-use (Baumer et al.,
2014)). In particular, we present an early look at the various operational
skills required for AI literacy in the context of local entrepreneurship, and
how interactive workshops can support these literacies. Second, we contribute
empirical findings of local entrepreneurs’ use of generative AI technologies,
as well as their concerns for use as it relates to their business. Third, we
contribute details of an approach to designing community-driven AI workshops
(Cooper et al., 2022) that prioritize long-term commitment (e.g., a four year
and ongoing tech support program (Kotturi et al., 2022)), community-driven
goals (e.g., workshop series embedded in ongoing community initiatives), and
community-centered value generation (e.g., workshop series primarily aimed to
support entrepreneurs and improve services within the community center).

##  2\. Related Work

Three bodies of scholarship motivate our work: (1) entrepreneurialism in the
digital age, and the importance of social support when onboarding local
entrepreneurs to digital technologies, (2) generative artificial intelligence,
and recent HCI research on how people use generative AI technologies (e.g.,
large language models), and (3) community-driven research methodologies in
computing research.

###  2.1. Entrepreneurialism in the Digital Age

Entrepreneurship in the 21st century takes various shapes from tech
entrepreneurship (Bailetti, 2012) to side-hustles managed entirely on mobile
devices (Pew Research, 2019). Digital platforms and tools enable a wide range
of entrepreneurial pursuits which are less dependent on brick and mortar
storefronts and local material sourcing and manufacturing (Agrawal et al.,
2013). Yet, while entrepreneurialism in the digital age is touted to be a
democratizing force (Von Hippel, 2006), scholars increasingly critique the
burden placed on entrepreneurs to keep pace with technological innovation
(Irani, 2019). In particular, pressures of digitization disproportionately
affect entrepreneurs in resource-constrained communities or “lean economies”
due to lack of access to technology resources, education, and capital (Hui et
al., 2018). These effects are further compounded based on race and class due
long-standing systemic and institutionalized racism and classism in the U.S.
(Benjamin, 2023). To better understand these inequities, prior work has
detailed the types of technology challenges faced by local entrepreneurs in
lean economies (Dillahunt et al., 2018; Hui et al., 2018; Raval and Pal, 2019;
Pei and Crooks, 2020; Hui et al., 2020; Ogbonnaya-Ogburu et al., 2019; Avle et
al., 2019)—entrepreneurs who primarily engage their local economy to overcome
a lack of job opportunity and upward mobility (Hui et al., 2018, 2020)—who are
often driven by economic necessity rather than choice (Hui et al., 2018); such
challenges include a lack of trust in technology platforms due to tech-based
erasure and harm (Dillahunt et al., 2018), unsupported technical skill
acquisition alongside constant need for “upskilling” (Kotturi et al., 2022),
unreliable devices and maintenance difficulties (Hui et al., 2020), and more.

####  2.1.1. The Importance of Low-Tech Social Support Among Local
Entrepreneurs

To overcome barriers to economic mobility, HCI scholars have detailed the
importance of low-tech social support among local entrepreneurs. For instance,
Dillahunt et al. presented a model of social support for individuals
experiencing financial hardships called “the Village”—a community-based
mentorship model which centered non-hierarchical relationships in non-
institutional settings (Dillahunt et al., 2022). In doing so, they
differentiate a village model of mentorship from predominant mentorship models
that exist primarily in the workplace and educational settings, and that
assume expert-novice relationships (such as in the case of legitimate
peripheral participation (Lave and Wenger, 1991)). In the context of poverty-
stricken adults in the United States, the authors found that in-person
interactions and trust building were required to facilitate economic mobility,
and that technological mediation of relationships may prohibit such relational
foundations (Dillahunt et al., 2022). In the context of local
entrepreneurship, entrepreneurs in resource-constrained communities similarly
preferred to become digitally engaged by leveraging social networks of peers
and experts (Hui et al., 2018, 2020). Hui et al. articulated one model of
social support called “Community Collectives” which involved small informal
and formal groups of like-minded individuals who joined together in a
collective pursuit to become entrepreneurs, specifically local tour guides
(Hui et al., 2020). In their analysis, they found that digital platforms
assumed access to basic resources, and that low-tech social supports were
critical (e.g., resource-connecting organizations, regular in-person meetings,
and paper planning tools).

While the “Community Collectives” model focused on entrepre-neurs with a
shared business domain (i.e. local tourism), “Tech Help Desk” provided a
strategic and relational model of technical support for entrepreneurs from
diverse backgrounds, business domains, and tech preferences through one-to-
one, in-person, and long-term technical support (Kotturi et al., 2022).
Technical support staff worked collaboratively with entrepreneurs to solve the
“long tail of computing challenges”, or a large number of distinct challenges
that were surfaced and solved. In doing so, this work detailed the various
digital literacies entrepreneurs needed to become digitally engaged which
included both strategic skills—high-level goal setting—and operational
skills—low-level implementation (Van Deursen and Van Dijk, 2011; Ala-Mutka,
2011). This paper builds on this scholarship in two ways; first, by
investigating the role of social support when onboarding local entrepreneurs
to generative AI platforms and features. Second, this paper then investigates
which operational and strategic skills may be helpful for local entrepreneurs
in order to apply these technologies to their business pursuits.

###  2.2. Generative Artificial Intelligence

Generative artificial intelligence has introduced a paradigm shift in
computing, illustrated through the recent deployments and high adoption rates
of pre-trained models wrapped in user-friendly platforms such as Midjourney
(Midjourney, 2023) and ChatGPT (Bang et al., 2023). Large language models
(LLM) like GPT exhibit high task performance with minimal training (Bang et
al., 2023; Ramesh et al., 2021). Recent interface designs which support end-
user interactions with large language models, such as ChatGPT, rely on prompt-
based interaction techniques where a user providers a set of instructions
which can be written with natural language or code-like syntax (Liu et al.,
2023). In addition to text generation, text-to-image generation is yet another
paradigm shifting technology deployed for public consumption within the last
year, such as diffusion models (Ramesh et al., 2022). Platforms like
Midjourney and DALL-E 2 leverage such models to enable high-fidelity imagery
generation by end-users (Midjourney, 2023; Ramesh et al., 2022).

####  2.2.1. Prompt Engineering

A critical part of effective use of generative AI technologies is “prompt
engineering” or writing natural language instructions that models respond to
(Bach et al., 2022). Researchers have detailed prompting techniques such as
personas (i.e., a user provides a LLM with persona or role to play when
generating output), flipped interaction (i.e., a user requires a LLM to ask
questions rather than generate output), context manager (i.e., a user
specifies the context for a LLM’s output) (White et al., 2023), meta-prompting
(i.e., a user asks a LLM to create its own prompt (Reynolds and McDonell,
2021)), repetition in prompts (Ramesh et al., 2021), giving examples of
desired interaction (Brown et al., 2020; OpenAI, 2023b), adopting code-like
syntax and structure (Bach et al., 2022), and even adopting a well-known Q&A
structure from online forums called “ask me anything” (Arora et al., 2022).

####  2.2.2. HCI Applications of Generative AI

HCI researchers are rapidly charting the design space of generative artificial
intelligence (Morris et al., 2023), as well as developing applications of
generative AI. For instance, recent work designed novel interaction modalities
(Park et al., 2023, 2022) and prompt chaining (where the output of one prompt
automatically becomes the input in another prompt (Wu et al., 2022)), as well
as applying large language models to support idea generation in the context of
creative writing (Gero et al., 2022; Swanson et al., 2021) and script writing
(Mirowski et al., 2023). In addition, HCI researchers have studied effective
prompt engineering such as in the context of software engineering (Jiang et
al., 2021; Zamfirescu-Pereira et al., 2023). For instance, Zamfirescu-Pereira
et al. studied how non-AI experts “intuitively” approached chat-based LLMs and
constructed prompts for programming tasks (Zamfirescu-Pereira et al., 2023).
They found that end-users approached prompt designs opportunistically and were
overconfident, without a clear strategy nor assessment protocol. Therefore,
the authors called for further studies with users from more diverse
backgrounds, noting that even though their users were non-AI experts, they
were, in fact, graduate students or professionals in STEM-related fields. The
authors also suggested an area for future work: how can tools help set
expectations for end-users to make them more accurate? Such inquiries are
relevant to this paper, as existing reference guides—even those dedicated to
“beginners”—require a high level of technical knowledge to parse and make
actionable (OpenAI, 2023c, d).

While there is limited scholarly work specifically applying generative AI in
the context of entrepreneurship, there is a rapidly growing body of
tangentially-related scholarship which may be applicable for local
entrepreneurs, such as using generative AI to increase productivity (Noy and
Zhang, 2023) and improved creative outcomes with ideation support (Haase and
Hanel, 2023), such as personalized brand material (Wang et al., 2023b).
Alongside these empirical results, there are hundreds of accounts and videos
of self-proclaimed entrepreneurs and influencers providing advice online for
how to use AI for your business, often providing demos of ChatGPT (Shopify,
2023), DALL-E 2 (with Canva, 2023b), Canva’s Text-to-Image feature (with
Canva, 2023a), Vidyo.ai (Vidyoai, 2023). Yet, despite user-friendly packaging
and seemingly simple chat-based interface designs, generative AI tools like
ChatGPT and DALL-E are primarily used by those who have backgrounds in
technology or who are college educated (Center, 2023). Thus, there is a
further widening of the digital divide (BBC, 2023), as entrepreneurs who have
access to formal and informal education, devices, and technical capital are
rapidly learning how to fully harness this nascent and powerful technology for
their benefit (Mollick and Euchner, 2023; Bell and Bell, 2023). This paper
considers how to address this growing disparity by investigating the skills
that are presumed to be implicit knowledge among end-users. In other words,
this paper aims to detail the “long tail” of technical skills (Kotturi et al.,
2022) that are required to use generative AI technologies effectively but are
often overlooked.

####  2.2.3. Dangers, Ethics, and Responsibility

The opportunities presented by the emergence of generative AI technologies
come with many ethical concerns and potential downfalls as we careen into a
new paradigm of human-computer interaction, or “human-centered AI” (Li, 2018).
Even in the short period of time these technologies have been available,
manifest risks have emerged (Bommasani et al., 2021). Scholars have detailed
issues such as nonfactual responses and misinformation (Jiang et al., 2020),
toxicity (Gehman et al., 2020), ethical, legal and environmental concerns
(Bender et al., 2021), and more. Such issues stem in part from model training
techniques which often lack human oversight due to the large scale. For
example, since the corpus of text used to train LLMs is riddled with the same
biases found across the internet, stereotypes and prejudice sentiments are
inculcated into models that repeat and reinforce these harmful viewpoints
(Bender et al., 2021).

With many risks present and looming, questions may be posed on the
responsibility and ethics of propagating these technologies into new spaces,
especially when the digital and AI literacy in a space may be inadequate for
minimizing these risks. However, the reality of the digital divide is that we
diminish human autonomy and thus human flourishing when perpetuating an access
gap. Our stance is that we need to take responsibility and be honest about the
risks and limitations of technology when sharing with new people. We also
believe that we must take an approach to this work that is empowering for new
users, and focus on increasing the agency of people who are otherwise more
likely to miss out on technological advancements. In doing so, we situate our
work in the larger discourse of responsible AI (Lee et al., 2020),
specifically by considering how to introduce generative AI technologies into
new spaces in ways that foster critical conversations and community
empowerment to use (and not use) these novel technologies.

###  2.3. Community-Driven Research in Computing

When engaging underserved communities in the design of computing technologies,
standard user-centered methods often falter as they assume a positive
relationship between researchers and participants (Dillahunt et al., 2017),
encode infantilizing treatment of participants (Harrington et al., 2019), and
may perpetuate forms of institutional racism (Tran O’Leary et al., 2019;
Williams, 2019). Community-based research, where community members and
researchers work in tandem to conduct research and derive solutions, can
result in outcomes which center community ideas, assets, desires, and needs
(Wong-Villacres et al., 2022; Israel et al., 1998). Doing so successfully
requires an awareness of the power dynamics at play across stakeholders
(Sabiescu et al., 2014). This can also require reorienting the traditional HCI
paradigm, and providing support for non-experts to actively shape research
objectives (Winschiers-Theophilus and Bidwell, 2013). When community
stakeholders assume a more directive role in research processes, community-
driven collaboration can hearken community wisdom and showcase alternative
types of knowledge not traditionally surfaced in the design process (Cooper et
al., 2022; Le Dantec and Fox, 2015; Tandon et al., 2022). In particular, Lu et
al. detailed how community events can play a critical role in fostering
participatory action research with underserved communities (Lu et al., 2023a).
The authors leveraged community-driven events to spark conversation about
community surveillance in order to center lived experience (Lu et al., 2023c)
and bolster participatory noticing through photovoice (Lu et al., 2023b). By
designing for the context of community events, the authors prioritized meeting
community stakeholders where they are, both physically in local community
centers and on a topic which was pertinent to the community. Therefore, our
work draws on community-driven scholarship to consider the role of community
events when fostering ongoing conversations around generative AI technologies
and their implications in the context of entrepreneurship.

##  3\. Methods

###  3.1. Location and Site

We conducted our research within a coworking space and community hub for
entrepreneurs based in Wilkinsburg, PA called Community Forge.

Wilkinsburg, PA  Wilkinsburg is a borough of Allegheny County. The population
of Wilkinsburg is roughly 49.5% Black and 23% of people are living at or below
the poverty line (Census, 2020). Wilkinsburg immediately borders but is not
part of Pittsburgh, and it is one of the many unincorporated municipalities
that acutely struggles with resource deprivation and long-term disinvestment
(County, 2023). In 2021, the Pittsburgh Metropolitan Area was considered to be
one of the U.S.’s “Apartheid Cities” (of Pittsburgh, [n. d.]), as the
structures of power within the city continue to perpetuate systemic racial
inequality and injustice (Massey and Denton, 1993), magnified by the post-
industrial blight the region experiences.

Community Forge  Community Forge is a former elementary school repurposed into
a space that hosts mixed programming geared towards developing a more
equitable economy for Wilkinsburg and the Greater Pittsburgh Metropolitan
Area. Towards this goal, Community Forge provides financial resources, jobs,
job training, business development, youth empowerment programs (e.g., courses,
summer camps, hands-on-learning), and community outreach events (e.g., food
and supply giveaways, music and movie nights), and voting resources. Community
Forge’s business development resources include: coaching and professional
service referrals, technical assistance, networking opportunities, financial
support, and affordable office rentals (repurposed classrooms with coworking
and individual office space). Community Forge works with roughly 50 local
businesses each year through a variety of programs where 95% of the businesses
are Black-owned, approximately 90% of entrepreneurs do not have a college
degree, and 80% are first-time entrepreneurs. To spread information about
resources available within the space, Community Forge relies on word-of-mouth
and social media, as well as working with existing organizations in
Wilkinsburg and Pittsburgh which support entrepreneurs. Community Forge also
hosts monthly entrepreneur nights, where local entrepreneurs can network,
enjoy free food, share updates and hear any announcements with the space. To
support itself and provide various programming for the community, 65% of
Community Forge’s budget is earned revenue from a mixture of sources such as
building revenues (i.e., leases and rentals of space for coworking),
government contracts, school contracts, and other partnership contracts. The
remaining 35% of Community Forge’s budget is funding from philanthropic
foundations.

Business Service Center and Tech Help Desk  Community Forge’s resource center
for entrepreneurs, called the Business Service Center, provides a variety of
services at a subsidized rate including accounting, bookkeeping, and marketing
services. In addition, to provide technical support for entrepreneurs and
residents, the research team and Community Forge leadership collaboratively
designed Tech Help Desk, which has been running for four years and is free for
entrepreneurs to use (Kotturi et al., 2022). Tech Help Desk connects local
engineering Ph.D. students, trained in community-based methods, with
entrepreneurs to provide weekly technical support for a range of computing
issues such as website building and design, file organization and management,
cybersecurity monitoring, and more. To date, the service has provided
technical support to over 70 entrepreneurs addressing over 200 distinct
computing issues. Central to the success of Tech Help Desk is the emphasis on
relationship building and trust throughout long-term and reliable technical
support. As described in the following sections, these ongoing services
provided a steady foundation that the academic-community partnership relied on
for successful implementation of the workshop series.

###  3.2. Co-Designing Introductory Workshops to Generative AI for Local
Entrepreneurs

To facilitate the co-design process of the generative AI workshop series, the
academic team members and community stakeholders (i.e., staff and leadership
at Community Forge) met weekly April 2023-September 2023. Early meetings
outlined community goals, while later meetings served as a way to refine goals
and continue iteration of workshop design based on entrepreneurs’ and
providers’ feedback. At a high-level, Community Forge leadership wanted
workshops to be a multi-part series, where each workshop focused on a
separate, yet connected topic. Because of the prevalent need for business
marketing assistance for entrepreneurs participating in the Business Service
Center and Tech Help Desk, workshops focused on using generative AI for
marketing and branding (See Table 1).

####  3.2.1. Workshops Goals

Community Forge leadership and research-ers co-articulated three primary goals
of the workshop series: first, the workshop series needed meet entrepreneurs
where they are in terms of their level of comfort and trust with technology—or
lack thereof—and provide support beyond technological means (e.g., provide
food for workshops during dinner time). It was especially important to frame
engagement with technology as highly optional to support non-use for those who
were uninterested (Baumer et al., 2014), and facilitate other activities
alongside such as peer networking (See Table 1). Second, the workshop series
needed to be actionable and tangible: entrepreneurs needed to be able to do
hands-on work (rather than solely listening to lecture-style presentations),
and the work needed to be directly tailored to their business (as opposed to
generic assignments). Finally, the workshop series needed to be embedded in a
network of trust. In this way, the workshop series needed to be hosted at
Community Forge (rather than at Carnegie Mellon), and draw on existing
technical services already present within Community Forge which have built a
reputation for providing trustworthy technical support. By embedding the
workshop series in other programming within Community Forge, this also meant
the workshops better connected entrepreneurs to ongoing support for them to
access between and after workshops. Another way we prioritized trust building
was by supporting various levels of engagement in workshop and research
activities. In particular, as done in prior work with service and event-based
community engagement (Lu et al., 2023a; Kotturi et al., 2022), participation
in the study was optional, and attendees could opt-in to the study after
meeting with the research team, asking questions about the research process,
compensation, and so on.

The first two workshops served as a soft launch for the latter two workshops
and were more informal as a way to gauge entrepreneurs’ initial reactions,
interactions and preferences when it came to using generative AI for their
businesses. To facilitate effective iteration of workshop structure, the
Business Service Center conducted pre- and post-questionnaires with attendees.
Pre-questionnaires asked entrepreneurs to share their level of experience with
various generative AI tools. Post-questionnaires solicited feedback from
entrepreneurs as to what were the most and least valuable aspects of the
workshop, among other kinds of internal data collection used to improve the
Business Service Center and Tech Help Desk services in concert with the
generative AI workshops. In addition, providers were asked to complete a
feedback form after workshop completion to provide quick feedback on the
event. Part of evolution focused on fine-tuning the particulars of the
workshop format. For instance, initial workshops provided entrepreneurs with
prompt libraries, both paper and digital copies (Hui et al., 2020), as
exploring prompt libraries is often a recommended approach for initial use
(OpenAI, 2023d). But Community Forge leadership and community providers noted
how these libraries, while applicable and tangible, presumed several critical
steps of knowing how to situate prompts in the context of use, indicated by
entrepreneurs’ overwhelm when presented with prompt libraries. Therefore,
latter workshops shifted away from prompt libraries upon introduction, and
instead relied on community providers to facilitate co-articulation of prompts
with entrepreneurs upon discussion with entrepreneurs about their business and
tech goals.

Over the course of the workshop series, training materials were assembled and
distributed to providers as an overview of learning objectives (e.g., prompt
engineering techniques and examples such as personas, flipped interaction,
repetition (White et al., 2023)). Learning objectives were meant to provide
guidance when needed, but providers were encouraged to customize their
approach to working with each entrepreneur in order to meet entrepreneurs’
unique needs. Examples of learning objectives included breadth-oriented
learning objectives such as “Entrepreneurs are aware of at least three AI
tools or features they can use to create images for this business (e.g.,
Canva, Pixlr, DALL-E 2, Midjourney),” and depth-oriented objectives such as
“Entrepreneurs know how to write text-to-image prompts which create useful
images for their business branding.” All learning objectives were paired with
a measurable outcome. In the final workshop, there was a live demonstration by
a local entrepreneur who used generative AI for his apparel business;
specifically, he used AI image-generation (DALL-E 2) to create custom designs.

|  Workshop #1 |  Workshop #2 |  Workshop #3 |  Workshop #4
---|---|---|---|---
Workshop date and duration |  May 2023, 2 hours |  June 2023, 2 hours |  July 2023, 2 hours |  August 2023, 2 hours
Workshop theme |  Marketing (SEO) |  Marketing (Social Media) |  Marketing (Copy and Email) |  Marketing (Images and Branding)
Number of attendees |  9 Entrepreneurs & 4 Providers |  7 Entrepreneurs & 3 Providers |  10 Entrepreneurs & 4 Providers |  10 Entrepreneurs & 7 Providers
Generative AI Technologies covered |  ChatGPT, Bard |  ChatGPT, Bard |  ChatGPT, Bard |  DALL-E 2, Canva Text-to-Image App, Pixlr, Midjourney
Additional activities offered |  Board games, peer networking |  Record business pitch, peer networking |  Informal tours of Community Forge, peer networking |  In-house demonstration of DALL-E 2, peer networking
Table 1. Overview of the workshops series. Given the need for marketing
support among entrepreneurs at Community Forge, all workshops focused on
different aspects of small business marketing. However, providers were
encouraged to work on whichever tasks were most pertinent to the entrepreneurs
and the low provider-entrepreneur ratio supported this flexibility. The
additional activities offered were essential to support non-use, and food from
minority-owned restaurants was provided.

###  3.3. Workshop Series Overview

In total, we offered four workshops on the third Wednesday of May, June, July
and August of 2023 in the evening, 5:00-7:00 PM (See Table 1). All workshops
were embedded in Community Forge’s monthly “Entrepreneur Night” which had been
running since March 2022. Workshop attendance was typically 50% of event sign-
up rate; providers were recruited based on sign ups to achieve a 2:1 ratio or
lower between entrepreneurs and providers. Workshops included 30 minutes of
meet-and-greet time with other entrepreneurs and providers. Next,
entrepreneurs and providers gathered and everyone briefly introduced
themselves and their business, as well as responded to an “AI Icebreaker”
prompt such as: “share an emotion that arises when you think of using AI for
your business”. Then, entrepreneurs and providers formed small groups to begin
a one-hour interactive portion of the workshop, co-articulating and iterating
on prompts relevant to the workshop’s theme. The layout of this interactive
portion of the workshops evolved from having small groups of providers and
entrepreneurs distributed across a large room to sitting side-by-side at a
large conference table in order to better support sharing (See Figure 1).

Each workshop included devices which were ready for use: iPads and Dell
Laptops. We set up free accounts and created paid accounts when needed,
associated with Community Forge, and signed in to all relevant platforms on
each device. Entrepreneurs typically had paper and pen note-taking tools with
them, or they were provided with this if not. At the end of the workshop,
entrepreneurs were encouraged to share what they had created during the
workshop with the group. See supplemental materials for an event timeline
example. Workshops were not recorded and our analysis of the workshops was
based on the interview participants’ reflective experience after their
participation in a workshop(s), as described in the next section.

###  3.4. Interviews with Local Entrepreneurs and Community Providers

We conducted 15 interviews (ranging from 30 minutes to two hours long) with
seven local entrepreneurs and eight community providers who participated in
one or more of the workshops. We interviewed all but two community providers
who participated in the workshop series. We recruited entrepreneurs from the
workshops by announcing the opportunity to everyone during the workshops and
posting in the Community Forge entrepreneur Facebook group; we did observe
that entrepreneurs who responded to interview requests were typically those
who were highly engaged during the workshops (e.g., they attended multiple
workshops, asked the most questions during the workshop, or came to Tech Help
Desk between workshops). Participants were compensated $20/hr. Interviewing
both entrepreneurs and providers helped to gain a more well-rounded
understanding of the workshops. See supplemental materials for full interview
protocols.

####  3.4.1. Participants

On average, the seven entrepreneurs we interviewed participated in 1.4
workshops. These entrepreneurs had various product and service-based
companies, with little overlap in domains such as a podcast producer, gift
basket maker, event planner, clothing designer, candle maker, and more (See
Table 2). Participants’ demographics reflected the communities that Community
Forge aims to support: individuals with low to moderate income, who primarily
engage in entrepreneurship out of necessity or to overcome a lack of local job
opportunities. Participants’ age ranged from 22 of over 65. Community
providers included Community Forge staff from the Business Service Center,
Tech Help Desk, as well as the youth tech programs (See Table 2). Providers
had post-graduate degrees in education, business or computer science. Academic
volunteers had training in community-based research methods and had been
vetted by community stakeholders.

###  3.5. Data analysis

For semi-structured interviews, the research team conducted audio recording
and took detailed field notes. 13 out of 15 interviews were conducted remotely
via Zoom. All audio recordings were transcribed (with Zoom or Temi
transcription services). We followed participant quote editing conventions
consistent with applied social science research practices (Corden and
Sainsbury, 2006)—i.e., removed filler words and false starts, and re-
punctuated and used ellipses to indicate substantial omissions. The research
team analyzed these data through a process of open coding to identify initial
themes across the interviews (Charmaz and Belgrave, 2007). The first author
wrote analytic memos for each interview with an average word count of 810
words (Charmaz and Belgrave, 2007), which summarized the interviews along
three themes: feedback on workshop structure and experience, skills needed to
use generative AI for business application, and uses of AI (and concerns). All
memos were reviewed by our community partner, serving as a member check
(Charmaz and Belgrave, 2007).

###  3.6. Community-Driven Research Process

In community-collaborative approaches, it is critical that community partners
are involved in all stages of research processes (Cooper et al., 2022). We
centered community directives in the following ways: university and community
teams had a four-year working relationship established through running Tech
Help Desk. By having research team members physically at Community Forge every
week for four years, this created effective work relations and clear
understanding of intent. In addition, the IRB protocol was informed by the
four year relationship between the university team and Community Forge, where
the university-community team co-articulated the protocol’s structure as done
in prior work (Boser, 2006). In particular, participation in the study was not
required in order to access technical services, and data collection was
community-driven and connected to ongoing programming at Community Forge.
Community partners received ethics certifications, which was paid for by the
university team.  Lastly, the first two authors—each a representative from the
university team and community partner—worked together throughout each stage of
the collaboration to design workshops, interview protocol, conduct and analyze
interviews together, and engage in reflexive discussion.

###  3.7. Positionality Statement

We disclose the identities and positionality of the researchers and authors of
this paper, as a concern for reflexive design research practice (Liang et al.,
2021; Schlesinger et al., 2017). This research team comprised one white woman
(a U.S. immigrant from Canada); two white men from the rural Midwest of the
U.S. and an impoverished, post-industrial part of Eastern U.S.; one person who
identifies as a triple minority as a non-binary, queer person of color from
the Eastern U.S; one African-American man who is neurodivergent and is from a
low-income background. The research team comprises two researchers who are
upper management at the field site, two staff members at the field site, and
three researchers in a technical department at a private U.S. university. In
particular, we note how the three middle-aged, white researchers do not have
certain lived experiences that are relevant to this study such as the impact
of forms of violence due to racism, ageism, or xenophobia (especially in the
context of technology education). Given the predominantly white research team,
we took measures to mitigate power imbalances and to cultivate a more
equitable relationship between the research team and Community Forge members
(as well as within the research team). For instance, all members of the
research team committed to the Community Forge mission statement (exhibiting
such commitment through a four year working relationship through hosting Tech
Help Desk), prioritized generating immediate value for the community members
rather than optimizing the research agenda, maintained transparency with
research practices, deprioritized data collection, and routinely sought
feedback from Community Forge members and staff.

[Participant demographics]This table provides an overview of participants,
their job or business description or business type, duration of using AI/ML
technologies, as well as which tools they use and for which business purposes.

Table 2. Overview of participants, their job or business description
(community provider “P_”) or business type (local entrepreneurs “E_”),
duration of using AI/ML technologies, as well as which tools they use and for
which business purposes. Entrepreneurs highlighted in blue. Note that E3
provided the expert demonstration described in Sections 3.2.1 and 4.2.3.

ID |  Participant Type |  Job/Business Description |  Prior experience with AI |  Uses of Generative AI |  Main Tools Used
---|---|---|---|---|---
P1 |  Community Provider |  Educator, Community Forge leadership |  1 year |  Portraits, media, branding materials |  Wonder AI, ChatGPT
P2 |  Community Provider |  Educator, Community Forge staff |  3 months |  Flyers, media |  Wonder AI, PhotoRoom
E3 |  Local Entrepreneur* |  Clothing Brand, Community Forge fellow |  1 year |  Podcast editing and imagery for products |  DALL-E 2, ChatGPT, Nomad Sculpt
P4 |  Community Provider |  Maintenance Technician, Community Forge staff |  3 months |  Video and audio file editing |  Capcut, Vidyo.ai
E5 |  Local Entrepreneur |  Youth mentorship |  3 months |  Website copy, editing video/audio content, hashtags |  ChatGPT, Vidyo.ai
E6 |  Local Entrepreneur |  Luxury candles |  None |  Grant proposal, SEO, lead generation |  ChatGPT, DALL-E 2
P7 |  Community Provider |  Educator, Community Forge leadership |  ¿5 years |  Research, education |  ChatGPT
E8 |  Local Entrepreneur |  Notary and podcast producer |  None |  Podcast scripts, logos, flyers |  ChatGPT, DALL-E 2
E9 |  Local Entrepreneur |  Gift basket and party planning |  None |  Website copy, flyers |  Wix Copy Generator, Canva Text-to-Image, Copy.ai
E10 |  Local Entrepreneur |  Clothing brand |  None |  N/A |  N/A
P11 |  Community Provider |  Educator, Community Forge leadership |  ¿5 years |  Media |  ChatGPT, Midjourney
E12 |  Local Entrepreneur |  Marketing |  None |  Website copy, captions, hashtags |  ChatGPT
P13 |  Community Provider |  Community Coordinator, Community Forge staff |  3 months |  Copy, short-form video content |  Capcut, Copy.ai, Speech-to-text
P14 |  Community Provider |  Tech support volunteer |  1 year |  Idea and outline generation |  ChatGPT
P15 |  Community Provider |  Entrepreneurial support, Community Forge staff |  6 months |  Flyer, copy writing, research |  ChatGPT

##  4\. Findings

Three core findings emerged from our analysis. First, while generative AI
technologies are presented with a veneer of user-friendly simplicity, we found
the entrepreneurs needed to navigate a large set of operational and strategic
skills, as well as engage in “pre-” and “post-processing” to derive actual and
sustained value from these technologies. Second, we found that centering
communal experience (and supporting non-use) was critical when onboarding
entrepreneurs to generative AI technologies in order to overcome overwhelm and
techno-anxieties these tools elicit. Third, after introduction, we found how
local entrepreneurs used generative AI technologies, which technologies they
used, as well as initial concerns entrepreneurs had when using generative AI
technologies for their business.

###  4.1. Taking Stock of Skills Needed for Successful Application of
Generative AI

Throughout the co-design process, workshops, and interviews, providers and
entrepreneurs repeatedly pointed to a tension: the way the tools had been
marketed and the rhetoric surrounding these tools as simple-to-use magic
conveyed an unrealistic expectation that the tools would provide immediate
value to users out-of-the-box. For instance, after participating in a
workshop, E10 reflected on what he observed to be an oversimplification of
generative AI online: “People on the internet kind of simplify AI [saying] if
I use AI it will decrease project time lickity split”. As P7 shared, this
oversimplification was connected to technologists’ assumptions that software
tools are ready for use immediately: “We do too much of, ‘Hey here’s the
software, start using it.’ ” P7 went on to describe, as a leader at Community
Forge and a tech educator at a local university, how he frequently witnesses a
pattern of inaccurate expectations technologists have for end-users, as they
assume their technology is designed well enough for easy use by all. While
this issue was more generalizable than newly developed AI technologies,
participants noted the heightened “smoke and mirrors” for how generative AI
was portrayed and marketed, like P4 who shared: “a lot of these tools are
still in beta…there is a lot of smoke and mirrors.” Taken together,
participants expressed how the workshops simultaneously needed to make
generative AI technologies more approachable by showing entrepreneurs that
“you can do this real quick on your phone” (P2), while also clearly conveying
the amount of work required for actual utility.  P4 went on to say that, in
order to make generative AI tools actually useful for entrepreneurs, “there is
so much wraparound support that is needed.”  This “wraparound support” is what
we detail in the next section.

####  4.1.1. Detailing the “Pre-” and “Post-Processing” Required

Part of this “wraparound support” included being upfront with entrepre-neurs
about the necessary steps required so that these technologies may provide
business utility, as P4, an audio engineer and Community Forge staff shared:
“If you’re using artificial intelligence for your accounting, you can’t just
throw a bunch of receipts in [ChatGPT]. You gotta organize [the receipts] in
certain ways. It’s the same thing with audio and video. It’s the same thing
with everything.”  This “same thing” P4 highlighted was the need for what he
later referred to as “pre- and post- processing” or the steps that a user must
take to organize and clean any inputted data to generative AI tools (pre-
processing), as well as the steps required to take any generated outputs and
turn them into useful artifacts (post-processing). In the context of audio and
video, P4 detailed how he worked with an entrepreneur to use Vidyo.ai (a
generative AI platform for video editing and content creation), which required
file type conversations; he listed five different file conversions required to
use just one of the entrepreneur’s videos recorded from an iPhone.

Similarly, E3, who provided the demonstration for how he used generative AI to
create custom designs for his clothing line, shared that he hoped
entrepreneurs would remember the importance of “getting photos out of DALL-E
and into Photoshop, Illustrator or Procreate” in order to edit the images for
them become usable. E3 shared that “nothing is ever picture perfect off the
Internet,” and he noted that the images generated were helpful for a
brainstorming step. However, in order to use the generated images for his
business, E3 detailed the variety of tools needed to edit the images and
change file types before they could be sent to his clothing supplier (e.g. he
used Photoshop to edit and overlay images onto apparel mockups). Entrepreneurs
in the audience during E3’s demonstration took note of these steps. For
instance, one entrepreneur shared how this perspective of generative AI
technologies was both inspiring and sobering, specifically noting E3’s time
investment: “just seeing how [E3] was able to create his own clothing line.
And how much he had to [do], basically going through a ton of different
images…who knows how long it took him?”  Taken together, P1, P2, P4, P7, and
E3 all highlighted the importance of the workshops to make salient the full
work that was required to use these tools effectively; only then would
entrepreneurs be able to cut through the often disillusioned marketing and
rhetoric surrounding generative AI and decide for themselves if these tools
would work for them and their business.

####  4.1.2. The Steps which Preempt Prompt Libraries

Of course, when it came to introducing generative AI technologies to local
entrepre-neurs, reviewing prompt engineering techniques was central. As
described in Section 3.3, we leveraged state-of-the-art prompting techniques
such as personas (White et al., 2023), and populated example prompts related
to each workshop theme. However, we found that providing prompt libraries,
both paper and digital copies, was typically ineffective during introductory
workshops. As P15 shared, “we were skipping steps with having the prompts on
paper, prompt [engineering] in itself is its own workshop. It’s its own tool
that people have to learn how to use. What I found, especially working with
[E9], it was easier to architect a prompt as I was explaining to her what a
prompt is.” Here, P15 described their interaction with E9, whom they were
partnered with during a workshop. Together, they co-articulated the prompt as
they discussed the nature of a prompt. P15 went on to explain how “without
[entrepreneurs] having some sort of frame of reference in general…how can you
be a prompt architect if you don’t know the nature of AI? How do you tell it
to give you a 4k image without people inside if you don’t know what a prompt
is?” Similarly, E9, E12, P14, and P15 discussed how it was critical for
entrepreneurs and providers to engage in back and forth discussion about
entrepreneurs’ businesses, business goals, as well as their technology goals,
and then write initial prompts together to “see tangible outcomes for what AI
can do for them, so that they know what it is.”  As described in the next
section, breaking down prompt engineering into the distinct operational skills
required was essential to ensure workshops provided adequate support, as well
as supporting the other skills beyond prompt engineering needed for successful
use.

####  4.1.3. A Laundry List of Operational Skills Needed to Use Generative AI
Technologies

P1 dissected the operational skills required for prompt engineering: using
language in a dialogue box, entering a search query (or prompt), and then
knowing how to iteratively refine a search query (or prompt) to get better
results. However, beyond prompt engineering, P1, P2, E3, P4, P7, and P11
compiled a list of foundation, digital skills entrepreneurs should acquire for
successful use of generative AI technologies: basic browser literacy such as
opening a new tab and switching between tabs and windows (such as when moving
between tools for pre- and post-processing); understanding file types and file
conversions in order to transition files between generating, editing and
publishing software; understanding files systems both locally and cloud-based
to save generated results; understanding storage management to edit the output
or share with providers, employees, customers, mentors, or peers; word
processing system skills in order to customize generated text; graphic design
knowledge to take generated images and “put it into Canva” or other visual
editing software to edit photos effectively (i.e., online aesthetics and
design guidelines); password management skills to sign in and out of various
tools; typing skills and keyboard shortcuts to quickly enter and edit prompts,
as well as copy and paste generated media.

Taken together, this list included seven operational skills that entrepreneurs
needed in order to effectively use generative AI tools for their business.
Beyond these operational skills, P1, P2, P4, P7, P11, and P13 expressed the
importance of entrepreneurs’ sense of self-efficacy when using generative AI.
For instance, when discussing the workshop format, P1 shared, “Start with
self-efficacy. ‘The tool is not above you. It’s not smarter than you. You have
the intelligence inherent within you. You have the wherewithal to leverage
these tools just as much as anyone else.”’ Here, P1 spoke to the importance of
emphasizing entrepreneurs’ self-efficacy as he described how workshops can
directly mitigate any doubts entrepreneurs may have when it comes to their
abilities to use a novel technology. Similarly to P1, P13 further emphasized
the importance of self-efficacy messaging throughout the workshop series as he
shared:“That’s all [entrepreneurs] need, their brain…Everyone has the capacity
within them to learn this skill.” By taking into account self-efficacy and
often overlooked skills, the workshop series made strides towards achieving
community-driven objectives, as described in the next section.

####  4.1.4. The Larger Goal: Supporting Use, and Non-Use

Providers (P1, P2, P4, P7, P13, P14, P15) emphasized that workshops needed to
support a range of levels of use of generative AI technologies: supporting
entrepreneurs who wanted to dive in and incorporate generative AI throughout
their business processes, as well as supporting entrepreneurs who were
interested to try out generative AI technologies, but ultimately may decide
not to use them. For instance, P13 emphasized how it was important that the
workshops were “not putting [generative AI] in the participants’ face[s]”, and
instead workshops provided various other activities and peer networking
opportunities (See Table 1). Instead, P15 viewed the ideal outcome of the
workshops to be “a frame of reference,” for entrepreneurs to have a sense as
to what generative AI technologies are, and what these technologies can do for
their business. Similarly, P1, P2, P4, and E10 emphasized that exposure to the
technology was more important than being concerned with immediate proficiency,
as a matter of entrepreneurial agency: “If you know it exists, [then] you can
decide how to use it.” P4 continued on to share that workshops needed to “show
[entrepreneurs] what the AI is capable of [and] not capable of, show all of
the pre- and post- processing. Don’t blow smoke. It might make [entrepreneurs’
tasks] better, faster, [or] it might take longer.”  As P4 described, a
sobering perspective of generative AI technologies was essential in order to
align workshops with community-driven goals to support both use and non-use.
In either case, as described in the next section, centering entrepreneurs’
shared experiences and commonalities such as geographic proximity or
affiliation with Community Forge was an important way to structure
introductory workshops.

###  4.2. The Importance of Supportive Exposure as a Communal Experience

Community providers and local entrepreneurs frequently discussed the
importance of communal and supportive exposure to generative AI technologies
in the context of entrepreneurship (P1, E3, E5, E6, P7, E8, E9, P13, P14,
P15). Since entrepreneurs were from the surrounding community, and the
workshops took place in a shared community space, this provided commonality
for entrepreneurs to build a shared experience. P1 argued this was essential
to successfully introduce entrepreneurs to generative AI: “all the people
attending are members in the local society, the local economy, all of them are
entrepreneurs…They also have familiarity with Community Forge. [The workshops]
are building commonality right there.”  As P1 described, building commonality
through a shared community space was important for both during and after the
workshops; in several cases, after connecting during workshops, entrepreneurs
continued to meet at Community Forge or elsewhere locally to provide ongoing
support or even collaborate.

####  4.2.1. Overcoming Overwhelm, Together

Centering communal experience during workshops (through a shared space, round
robin introductions, ice breakers, peer networking, small group work sessions,
and so on) was important for several reasons. First, communal exposure helped
to provide a safe space to navigate a range of emotional responses
participants experienced towards generative AI technologies. Entrepreneurs
expressed anxiety about being left behind by not understanding or using the
technology (E3, E5, E6, E8, E9), sharing “whether I like it or not, technology
is the future. You have to be equipped or you are going to be left behind”
(E5), and “jump on the train before [you’re] left behind”(E8). Entrepreneurs
also expressed being intimidated and fearful of generative AI technologies.
For instance, E6 reflected on the first time she opened ChatGPT, and, upon
seeing the text response, immediately closed the window and shut her laptop:
“someone told me about ChatGPT a while ago…When [I opened] it up, and then I
put something in there, and it just spit out all this information. I was
overwhelmed. I was like, ‘Nope, it’s not for me. Log off.”’  Similarly to E6,
before the workshop, E9 shared that her impression of generative AI
technologies was primarily fear-based: “It was total fear…the fear of the
unknown kept me from jumping in sooner.”

As with E6 and E9, E8 shared how the first workshop he participated in was
primarily memorable for the experience of seeing others in a similar
situation; this helped him to overcome a sense of overwhelm he felt towards
generative AI technologies: “I won’t lie, the first session was a little
overwhelming. But it was just nice being in the space where everybody was all
coming from the same place, just trying to learn and get an understanding of
what we have in front of us.” In this way, the first workshop for E8 was
impactful because he witnessed that he was not alone by observing others’
trepidation and excitement within a shared space. He went on to share: “The
second session was more impactful because I had time to absorb what
[generative AI] can do, things of that nature.” As E8 described, after the
first workshop where he established a sense of community, he was better able
to digest information in the following workshops. E8 shared how, one of the
critical aspects of both workshops he attended was how the workshops provided
a “safe space”: “It was a safe space that everyone is…unfamiliar with, and
they just wanna get familiar [with AI tools]. While it is overwhelming…it
doesn’t linger ‘cause then you can look around the room and see people just
like you in the same space…there’s no stupid questions.”  Here, E8’s
reflection makes salient a few aspects of the workshops which contributed to a
sense of safety. For instance, E8 felt comfortable to ask questions freely and
without judgement from peers or providers. In addition, he witnessed other
local entrepreneurs’ reactions and questions as they digested information
about the technologies for the first time, too. Taken together, E6, E8, and
E9’s reflections highlight the importance of convening in a community space in
order to overcome the overwhelm associated with generative AI technologies,
together.

####  4.2.2. Building Long-Term Community and Technical Capital

In addition to overcoming overwhelm, communal experience helped to catalyze
technical capital building and continued support among entrepreneurs and
Community Forge. This was especially important when onboarding entrepreneurs
who were less likely to be embedded in a network of “techies”, as P7
described: “if you’re not a techie, I think it really helps that you have a
community to explore this craft and tool with…especially if you’re in a
community that’s generally not using [AI] tools.” Here, P7 pointed to the
importance of being embedded in a technical network, where members can easily
share and discuss new software releases, hacks, problems, and more, providing
both critical information and encouragement. One way that the workshop series
supported technical capital and long-term support was by connecting the
workshop structure to other ongoing technical services at Community Forge. For
instance, workshop attendees were encouraged to partake in the weekly (free)
technical office hours, called Tech Help Desk, hosted at Community Forge for
continued support between and after workshops; several entrepreneurs from the
workshops (including E5, E6, and E9) attended multiple offerings for continued
support. P13 reflected on the role of this continued support: “Tech Help Desk
is a piece of [the workshops] that is super important. It allows entrepreneurs
a way to come back and stay engaged [and is] really impactful in terms of
meeting people where they are.” With this continued engagement, P14 observed
that by the fourth workshop, “Most of the people involved were not strangers
to [each other]…it’s important to do something like this in a community that
they feel safe with.”  For P1, building technical capital through a communal
onboarding experience played a role in claiming power, especially among
entrepreneurs who may have been systemically disempowered and under-supported
to use novel technologies: “Where do [entrepreneurs] go to get instruction on
how to claim power? We have to go to our community, and strategize within our
community…you have to be around people who have skill and strategy of how to
be effective with the tool.” Importantly, P1 emphasized the role of within-
community expertise to embolden entrepreneurs who historically have been
disempowered by novel technologies. One way the workshops built on this point
and showcased “skill and strategy from within the community”, was through a
live demonstration from a local entrepreneur who was also a Community Forge
member, as detailed in the next section.

####  4.2.3. Showcasing In-House Expertise

As described in Section 3.2.1, the fourth workshop featured a local
entrepreneur’s (E3) live demonstration of how he used generative AI for his
business. Almost all participants who attended this workshop commented on its
impact, especially because E3 was a member of Community Forge. For instance,
P7 commented: “the presentation by [E3] was really powerful…for people to see
a young African-American dude who [is] around Community Forge as an
[entrepreneur], here’s how quickly he made a product that he’s selling using
an AI tool…I even audibly heard people being like, ‘whoa.”’  Here, P7, who was
in the audience during E3’s demonstration, commented on the auditory reactions
audience members had while witnessing the live demonstration of E3 using
ChatGPT to generate complementary color schemes via HEX codes, DALL-E 2 to
create images of the Pittsburgh skyline, and then Procreate and Photoshop to
edit images and overlay creations onto clothing mockups. E9, who had a custom
gift basket and event planning business, shared how E3’s demo inspired her and
helped her better understand what was possible with tools like DALL-E 2 and
ChatGPT: “To be able to hear how that young man used AI to create the shorts,
it had my mind spinning. Now that I know you can do that, I want to be able to
make my own ribbon, a custom banner.”  Taken together, showcasing in-house
expertise presented an opportunity to further bolster technical capital
building and create an empowered sense of community which was critical to
overcome overwhelm associated with generative AI technologies. From this
standpoint, entrepreneurs could then more confidently decide how to use (or
not use) these technologies for their business, as detailed in the next
section.

###  4.3. Entrepreneurs’ Initial Uses (and Non-Use) of Generative AI, and
Concerns

Participants shared their initial uses of generative AI technologies to make
logos and flyers, write grant applications, optimize search engine results,
and more (See Table 2). In addition, entrepreneurs shared how their
preferences developed with respect to how they did not want to use generative
AI for their business, such as in the case of handling material which was
sensitive (e.g., concerns relating to sentimentality, intellectual property,
inauthenticity, and biases).

####  4.3.1. How Entrepreneurs Used Generative AI for Business

Entre-
preneurs used ChatGPT in a variety of ways to boost their business’ marketing
capabilities upon taking a workshop. E5 referred to ChatGPT as a “24-hour
secretary”, and he described asking ChatGPT to become a content creator for
his social media accounts. E5 estimated that he used ChatGPT almost daily, and
was still experimenting and getting comfortable formulating persona-based
prompts such as, “You are a fortune 500 CEO…” “you are an owner of a mom and
pop store…”  E8 used Canva Text-to-Image and DALL-E 2 to make a logo for his
podcast, which used his initials: “I don’t consider myself the greatest
creator. I wouldn’t say I’m very imaginative or anything like that. So that’s
where the software helped me…I just put in [my initials]…and I just wanted to
see what different variations it would create for me. And it did!”  During a
workshop, E12 used DALL-E 2 to create a flyer for an upcoming business event.
Specifically, she made an image of people drinking wine on a roof during
sunset, where she started with a basic rooftop and then included “tech” and
“future” in the prompt to make the resulting image more futuristic. During a
workshop, P2 showed an entrepreneur with a hair braiding business how to
generate images of African-American women with different kinds of hairstyles,
and then how to add her logo to the images, all on her mobile phone with
Wonder.ai and Photoroom. Between workshops, E6 attended multiple offerings of
Tech Help Desk. After working on a social media marketing plan during a
workshop, she sought continued guidance on writing an application for a local
small business grant using ChatGPT. When asked what her vision was for how to
incorporate AI in her business, she noted that in the immediate future, she
wanted to use ChatGPT to improve her website’s SEO and lead generation (e.g.
through blog writing), and create designs for flyers. E9, another consistent
attendee of Tech Help Desk and the workshops, used the Wix.com AI-generated
product descriptions to zhuzh her existing product descriptions within the
website editor interface: “I used it to reword my descriptions…For gift basket
descriptions, if you want to reach a broader audience, your wording needs to
cover a wide variety of people.”  For E9, Wix’s generative AI feature helped
her to add more detail that she had not considered such as other types of
customers who may be interested in a gift basket.

####  4.3.2. Reasons for Non-Use: Participants’ Concerns for Using Generative
AI for Business

Entrepreneurs expressed several concerns when it came to using generative AI
for their business, and sometimes these concerns led them to opt not to use
these technologies. Detailing such concerns is critical, in order to be able
to develop a curriculum which can formally and proactively address concerns by
equipping entrepreneurs with relevant skills to navigate these concerns, and
support critical non-use. One common concern entrepreneurs expressed was
whether the use of generative AI for content creation would make it harder to
convey their business’ unique brands (E3, E5, E6, E9, E12). For instance, E6
emphasized that she wanted to “make sure that I’m authentic.” To achieve this,
she actively edited the synthetic text to customize it and to make sure it was
accurate, and also used a specific persona in ChatGPT: “I put the role in the
text, but I’m very specific. I go back and forth about how authentic it is…Let
me go back and rewrite this, personalize it, look [it] up to make sure these
numbers are accurate.” E9 echoed similar authenticity concerns to E6, however,
her concerns amounted to her decision to not use tools like ChatGPT to
communicate with her customer base: “I never want people to read my own stuff
and say, ‘oh wow, she doesn’t even write her own stuff.’ ” For E9, her
underlying concern was that she would lose her unique communication style with
her audience and ultimately damage her reputation. Taken together, E6 and E9’s
shared concerns reflected two issues with generated outputs. First—as an issue
of the quality of generated outputs—E6 needed to repeatedly revise her prompts
such that the generated outputs were to her standards and this took
considerable effort. Second, even with revised prompts, E9 expressed a
fundamental concern that her audience may perceive text as synthetic and judge
accordingly, and decided to remove this risk entirely by not using the
technology. E12 shared an internal dilemma as well, as she reflected on when
it was and was not “appropriate” to use generative AI, which was partly
informed by whether she thought the artifacts needed to be created by her, or
not. In particular, she focused on whether AI-generated content could be
construed as false advertising, and if so, she wanted to avoid use.

Entrepreneurs and providers also expressed anticipated concerns for long-term
use such as becoming overly dependent on generative AI (P1, E3, E9, E10, P11,
E12). For instance, E10 shared that, over the long term, he was “afraid that
people might see AI less of a tool and more of a co-dependent.”  If co-
dependence prevailed, P1 noted that long-term use may contribute to feelings
of imposter syndrome, where entrepreneurs may attribute any business success
to generative artificial intelligence over their own, triggering thoughts such
as: “Did I cheat? Do I have something to hide?” E10, one of the participants
in our study who reported he was less likely to use generative AI for his
business shared: “I do not want to become brain dead in the future. You still
have to know the general basis of your audience.”  P1 and P11 detailed how
this technology was similar to other technologies like auto-fill and
predictive typing, where now, users “don’t need to know how to type…This is an
example of tool that came to help us, but in its use we have lost the ability
to [spell].” To address this concern, P11, leadership at Community Forge who
developed tech curriculum for youth, emphasized the importance of maintaining
balance between tool use and skill development: “We try to be balanced. We
recognize there is an opportunity [and] teach the complementary skills that AI
is taking away from [users].” In doing so, P11 went on to say that use of
generative AI needs to be reframed from “outsourcing” to “collaboration.”

In addition to perceived inauthenticity, accusations of false advertising, and
overreliance, participants detailed additional concerns which motivated non-
use focused on ethical and legal dilemmas such as issues of ownership and
intellectual property (E3), transparency of use (P1), racial and gender bias
(P1, P2, P14, P15), and tech and data extraction (P1, P4, P14, P15). For
instance, E3, who demonstrated how he used DALL-E 2 and ChatGPT to create
imagery for his clothing line, considered the role of ownership: “I believe
OpenAI has it so that you actually own the images that you generate with your
prompts, so legally they’re your property now. So I feel like in that area
[it] is fine.” Based on OpenAI’s current policy, users do own the images they
create, regardless if you used free credits or paid (OpenAI, 2023a). However,
during the process of writing this paper, a federal court ruling prohibited
copyright of AI generated art (K and Gates, 2023), which speaks to the rapid
evolution of policy regulating this novel technology (Jiang et al., 2023), and
the need to keep entrepreneurs’ up-to-date on legal considerations of use.
Even with this in mind, E3 had additional benchmarks for ethical use: “I feel
like as long as I’m not taking the image, putting it on like a T-shirt, and
then screen printing that T-shirt, doing the bare minimum [and] gaining profit
from it, then I feel like we should be okay since nobody was hurt in the
process.”  He continued on to state how this benchmark was based on a version
of the “golden rule”: he did not want someone else to query: “create art in
the style of [E3]” and then be able to sell or own the generated images, so he
applied the same principle to himself. For E5, who was authoring a book based
on his experiences while incarcerated, he contemplated using ChatGPT to help
with copy editing and proofreading, but decided against it because the topic
was too personal. It was unclear to him how his experiences and emotions would
be used or extracted by the technology.

In addition to legal concerns, P14 reflected on how these generative AI
technologies perpetuate systemic biases based on the biases embedded in
training data. In particular, she reflected on one instance when an
entrepreneur in the workshop uploaded a photo of their late relative to
inspire a logo design. This reference image was a portrait of a Black woman
who had short black curly hair, yet the resulting images generated by
Midjourney included a woman with light, straight hair pulled back into a loose
ponytail. While the entrepreneur and provider were able to work together to
create a prompt which did not white-wash the reference image, this experience
highlighted critical issues with these tools. P14 continued: “I am very
conflicted around these tools, how they collect data, privacy concerns, how
big tech is so extractive. But that all goes out the window, if you want
community input, they need to use these tools…gatekeeping information is
actually really detrimental.”  P13, who worked with the same entrepreneur
after the workshop on her business’s logo, shared that the entrepreneur
decided to work with a graphic designer instead due to the sentimental nature
of the photo and the need for a careful human touch. Taken together,
entrepreneurs and providers shared several concerns for using generative AI
for their business. And, as illustrated in the above examples, sometimes these
concerns amounted to entrepreneurs deciding to not use these technologies at
all.

##  5\. Limitations

There were several limitations of this study. First, while recruitment
targeted all workshop attendees, we observed that the entrepreneurs who opted
to participate in interviews tended to be more engaged in the workshops; this
biased data collection towards positive experiences. We attempted to offset
this bias in two ways. First, we solicited critical feedback from community
providers with whom we had long-standing rapport. Second, we detailed many
examples where entrepreneurs who, overall, were open to using generative AI
for their business, yet decided certain use cases were not appropriate.
Relatedly, a second challenge was that not all entrepreneurs wanted to engage
with generative AI technologies. While we frame this lack of use as less of a
limitation, and instead an important consideration (Baumer et al., 2014), we
understand that this reduced the quantity of usage data collected. As a
result, our study presented limited data on the quality of generated outputs.
Future work can analyze entrepreneurs’ assessments of quality of generated
outputs, accounting for novelty effects by analyzing use over time. Finally,
this study was exploratory in nature, and we did not formally assess knowledge
transfer. Instead, we provide initial insights that could be developed in
future work to assess the efficacy of a more formalized curriculum for
generative AI in the context of entrepreneurship.

##  6\. Discussion

This work detailed the importance of social support when onboarding local
entrepreneurs to generative AI. Through a close partnership with Community
Forge, we co-designed an interactive workshop series focused on meeting
entrepreneurs where they are, provided actionable and tangible outcomes, and
embedded technical support in a network of vetted and trusted relationships.
Our approach enabled us to gain knowledge about (RQ1) the importance of
centering communal experience when introducing generative AI to support local
entrepreneurs’ due to a range of comfort levels, (RQ2) the importance of
cutting through hyped rhetoric to provide entrepreneurs with a practical
understanding of the work (and time and skills) required to derive value when
applying generative AI technologies for business, and (RQ3) an early look at
how local entrepreneurs use—and prefer not to use—generative AI for their
business.

In answering these research questions, this paper makes three core
contributions. First, by building on models of low-tech social support
(Dillahunt et al., 2018; Hui et al., 2018; Avle et al., 2019; Raval and Pal,
2019; Pei and Crooks, 2020; Hui et al., 2020; Ogbonnaya-Ogburu et al., 2019;
Kotturi et al., 2022), we presented empirical findings for an interactive
workshop series tailored for generative AI technologies. With an eye towards
future work, in this section we consider other forms of low-tech social
support—beyond small, local networks—which may uniquely respond to the need to
keep pace with rapid technological advancements and policy changes related to
generative AI technologies. Second, we contribute empirical findings of local
entrepreneurs’ use of generative AI technologies, as well as their concerns as
usage relates to their business and motivations for non-use. In this
discussion section, we further consider how future work can create more
formalized AI literacy curricula tailored for local entrepreneurship as well
as more formally support concerns and instances of non-use. Finally, we
contribute the details of an approach to designing community-driven workshops
(Cooper et al., 2022) such as building on a four year and ongoing tech support
program, embedding the workshop series in ongoing community initiatives, and
ensuring value generation for the community center by emphasizing community-
driven data collection and goals. We argue that our approach was essential to
conducting this study given the techno-anxieties—nervousness and apprehension
(Marcoulides, 1989)—entrepreneurs shared. Taken together, in this discussion
section, we discuss these themes and contributions to formulate considerations
for future work, interweaving feedback from entrepreneurs and providers on
suggestions to improve the workshop series.

###  6.1. Deconstructing the Veneer of Simplicity

Community providers and entrepreneurs in our study surfaced a tension between
the way pre-trained generative AI was marketed to entrepreneurs by platforms
and self-proclaimed experts alike, versus the actual time, skills, and even
additional tools needed to move beyond superficial interaction towards
valuable use. We consider this tension alongside current disparities of use
where generative AI technologies like ChatGPT and DALL-E 2 are primarily used
by those who have backgrounds in technology or are college educated (Center,
2023). In particular, this tension merits inspection of the kinds of digital
skills required to leverage such innovative technologies.

Digital literacy involves various types of skills such as strategic
skills—high-level, goal setting—and operational skills—low-level
implementation (Van Deursen and Van Dijk, 2011; Ala-Mutka, 2011). Given rapid
technological advancement and recent end-user access, digital literacy in the
context of generative AI has not yet been clearly defined, inside nor outside
of a classroom context (Ng et al., 2021). For instance, some say that the
operational and strategic skills involved with using generative AI
technologies effectively are similar to those required for effective online
search (Metzler et al., 2021), involving periods of exploration and
exploitation as in an information retrieval task (Pirolli and Card, 1999).
However, other researchers argue that this analogy is flawed given the
differences between information retrieval and information generation. For
instance, Shah and Bender investigated how chat-based interfaces limit a
user’s ability to engage in typical sensemaking which is afforded in online
search (e.g., comparing across other links, identifying patterns to detect
misinformation) (Shah and Bender, 2022). They argued that because generative
models often lack references, predominant interface design detracts users’
abilities to verify information generated. To start to articulate general
framework for AI literacy, Ng et al. conducted a literature review of AI
literacy in education to create a framework highlighting four levels of
competency: “know and understand AI, use and apply AI, evaluate and create AI,
ethical implications of AI” (Ng et al., 2021). In this framework, it is
assumed that these are levels which must be acquired linearly for competency.
However, their work focuses on a pedagogical context and is, therefore, only
partially applicable in the context of entrepreneurship in lean economies. For
instance, in our study, entrepreneurs wanted to “know and understand AI” in
the context of entrepreneurship (not necessarily focusing on the details of
how diffusion models work), and shortly after this level, entrepreneurs
considered the fourth “level”: “ethical implications of AI”.

When considering a framework for AI literacy in the context of local
entrepreneurship, this study provided a preliminary and exploratory
opportunity to surface the skills needed which “democratizing” promises of
tech usually overlook (Hui et al., 2020; Von Hippel, 2006). In doing so, we
found that there were many more skills than simply prompt engineering involved
(and during initial use, prompt libraries were less helpful). The additional
strategic and operational skills entrepreneurs needed to have to use these
tools—on top of access to Internet-enabled devices—included: browser literacy,
understanding file types and file conversions, storage management literacy,
word processing skills, and more. The workshop series explored one way to
support these skills through a communal experience (e.g. providing devices,
pre-logging into platforms, co-articulating prompts). Here, we are not
suggesting a finalized curriculum, but instead are sharing insights that could
be used to support more formalized curriculum development focused on equity in
future work. In this way, future work can develop a more formalized approach
to supporting AI literacy in the context of local entrepreneurship. For
instance, building on the importance of meeting entrepreneurs where they are
(Gautam et al., 2020), researchers can create AI literacy modules embedded in
digital and physical community spaces such as community centers’ websites and
in-person workshops which target specific literacies required for successful
use of AI technologies.

In addition to providing an initial look at the skills needed to effectively
use AI tools, it was also critical that the workshops supported self-efficacy
of entrepreneurs and demystified generative AI technologies through collective
inquiry. After entrepreneurs’ established a “frame of reference”, only then
could discussions of larger implications of using generative AI for business
occur. We related this premise to the “Consentful Tech Project” (Project,
2023), which focuses on tech use which is freely given, reversible, informed,
enthusiastic and specific (FRIES), building on the feminist idea of
enthusiastic sexual consent (Strengers et al., 2021). In our study, many of
the providers were privy to the many ethical implications of generative AI and
discussed the importance of first having entrepreneurs, who were less familiar
with generative AI technologies, to be introduced within a communal space.
Having a clear understanding of the issues as a form of consent, we reflect on
the concerns entrepreneurs had for using generative AI technologies soon after
introduction, as detailed in follow-up interviews. For instance, entrepreneurs
expressed concern for becoming overly reliant and dependent, damaging their
reputation and becoming disconnected from their audience, being subject to
intellectual property infringement, as well as concerns due the systemic bias
embedded in generative AI technologies. While there have been studies which
find end-users are overly reliant on these models (Wang et al., 2023a), we
found that entrepreneurs in our study were actively, and intuitively,
skeptical of these technologies even if they were unsure of how to change
their use to address their concerns. Future work can investigate how to
support entrepreneurs’ critical use of generative AI technologies. In
particular, future work could explore how to more readily illuminate
entrepreneurs’ reasons for non-use or limited use as a way to highlight key
concerns, and provide strategies for entrepreneurs facing similar dilemmas.
For instance, building on HCI scholarship which focuses on digital
storytelling (Halperin et al., 2023), researchers could collaboratively create
a repository of anonymous stories of how entrepreneurs choose to not use
generative AI technologies for their business, as well as the reasoning behind
this decision. Such a repository could provide nuanced and granular
documentation of the everyday tensions entrepreneurs navigate with generative
AI technologies as a form of distributed and scalable mentorship (Hui et al.,
2019).

###  6.2. Extending the Role Social Support When Onboarding Local
Entrepreneurs to Generative AI

Both entrepreneurs and community providers reflected on the opportunity for
generative AI to level the playing field in entrepreneurship, but many were
simultaneously skeptical. Such skepticism was well-founded and reflected the
often superficial and unwarranted promises technologists make of more
democratic futures with each technological advancement (Hui et al., 2020). As
our providers and entrepreneurs described, actual equitable tool use required
wraparound support, such as continued engagement with Tech Help Desk, low
provider-entrepreneur ratios, and building a sense of community through shared
experience. Here, we first revisit Hui et al. ’s recent model of low-tech
social support for maintaining digital engagement for entrepreneurs (Hui et
al., 2020), and consider how our findings build on this model. For instance,
similarly to Hui et al. ’s model, the workshops benefited from small scale (to
encourage trust building), resource connecting organizations (such as with
Business Service Center and Tech Help Desk), paper planning tools (which
entrepreneurs were provided with if they did not bring them), regular in-
person meetings (both the workshops and Tech Help Desk), and validation and
practice.

However, our findings also provided an initial understanding of the unique
aspects of wraparound support that were critical when onboarding entrepreneurs
to generative AI technologies in particular, such as parsing instances when
generative AI may be more or less appropriate to use given fast-paced
developments and rapidly evolving policy (K and Gates, 2023). In these cases,
we consider how small, local networks may find it challenging to keep pace
with these rapid advancements (as did our university-community team), and
therefore we suggest that future work explore community-driven technology
interventions to pool unfolding information (such as a policy changes) and
extract relevant implications to be shared with local, small business owners
about these advancements. To incorporate community perspectives, future work
could leverage Erete’s framework for designing community-driven technological
interventions (Erete, 2014), and apply intersectional analyses of power in
design (Erete et al., 2023). In particular, such scholarship provides a road
map for designing technology which acknowledges the structural oppression and
institutionalized racism embedded in HCI and computing more generally.

##  7\. Conclusion

This paper investigated how to onboard local entrepreneurs to generative AI
technologies through a community-driven protocol which built on a four-year
relationship between university and community team members. In doing so, we
highlighted the importance of centering communal experience when introducing
generative AI to support local entrepreneurs’ range of comfort levels and
situate technologies in shared experience. In addition, we detailed the
importance of cutting through hyped rhetoric to provide entrepreneurs with a
practical understanding of the actual work, time and skills that were required
for successful application. Taken together, this paper provided an early look
at how entrepreneurs used generative AI, and how they preferred to not use
these technologies for their business. And while we are not the first
researchers in HCI to describe how technology is embedded in and intertwined
within entrepreneurial practices, this age of rapid apparent technological
change may cause some to question whether this time is different. Our answer
is a resounding “no”, however, the details and particulars are different in
important and impactful ways, as this research begins to unpack.

###### Acknowledgements.

This work was funded in part by the National Science Foundation (1928474). We
thank the entrepreneurs and providers who were involved in the workshop
series, and all of the Community Forge staff and leadership for their support,
especially TJ Johnson, Amil Cook, Patrick Cooper, Arturo Lozano, Hamza Perez,
Bethany Hallam, and Nora Reed.

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