# AI Tool Use and Adoption in Software Development by Individuals and
Organizations: A Grounded Theory Study

Ze Shi Li<EMAIL_ADDRESS>University of VictoriaVictoria, Canada ,  Nowshin
Nawar Arony<EMAIL_ADDRESS>University of VictoriaVictoria, Canada ,
Ahmed Musa Awon<EMAIL_ADDRESS>University of VictoriaVictoria, Canada ,
Daniela Damian<EMAIL_ADDRESS>University of VictoriaVictoria, Canada and
Bowen Xu<EMAIL_ADDRESS>North Carolina State UniversityUnited States

(2024; 20 February 2007; 12 March 2009; 5 June 2009)

###### Abstract.

AI assistance tools such as ChatGPT, Copilot, and Gemini have dramatically
impacted the nature of software development in recent years. Numerous studies
have studied the positive benefits that practitioners have achieved from using
these tools in their work. While there is a growing body of knowledge
regarding the usability aspects of leveraging AI tools, we still lack concrete
details on the issues that organizations and practitioners need to consider
should they want to explore increasing adoption or use of AI tools. In this
study, we conducted a mixed methods study involving interviews with 26
industry practitioners and 395 survey respondents. We found that there are
several motives and challenges that impact _individuals_ and _organizations_
and developed a theory of AI Tool Adoption. For example, we found creating a
culture of sharing of AI best practices and tips as a key motive for
practitioners' adopting and using AI tools. In total, we identified 2
individual motives, 4 individual challenges, 3 organizational motives, and 3
organizational challenges, and 3 interleaved relationships. The 3 interleaved
relationships act in a push-pull manner where motives pull practitioners to
increase the use of AI tools and challenges push practitioners away from using
AI tools.

artificial intelligence, machine learning, software engineering, AI practice,
software practitioners, adoption, challenges, interviews, survey

††copyright: acmlicensed††journalyear: 2024††doi: XXXXXXX.XXXXXXX††ccs: Do Not
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##  1\. Introduction

Large-language models (LLMs), generative artificial intelligence (GAI), and
other artificial intelligence (AI) products are quickly transforming the
software engineering landscape. AI assistance tool(s) such as Copilot (GitHub,
2023), ChatGPT (OpenAI, 2022), and Gemini (gem, 2024) offer the promise of AI
powered assistance to help software development teams. They offer enticing
marketing terms such as, ``The AI coding assistant elevating developer
workflows." (GitHub, 2023) Based on early adoption numbers (Porter, 2023; mic,
[n. d.]) and early empirical studies about these tool(s) (Liang et al., 2024;
Peng et al., 2023; Kalliamvakou, 2022), these tool(s) are creating substantial
value for software practitioners, and their employment organizations who
benefit from more happier and productive employees (Kalliamvakou, 2022).

Early empirical studies have often studied the usability aspects of AI
assistance tool(s) (Vaithilingam et al., 2022; Liang et al., 2024). Their
primary emphasis is on how developers perceive and use AI programming
assistants (Vaithilingam et al., 2022), as well as usability challenges that
developers experience (Liang et al., 2024). Industry surveys also tried to
capture the nature of software developer use, focusing on the specific tasks
that developers most commonly rely on AI assistance (Sta, 2023; GitLab, 2023).
Another recent study attempted to understand the complexity of generative AI
(GAI) adoption in software engineering (Russo, 2023) through survey
participants. In an exploration through surveys, the author looked to
understand adoption of generative AI in software engineering through
individual, technological, and social levels based on other theoretical
models. The author developed a preliminary model of adoption in software
engineering, however, 3/7 of the hypotheses in the model was not supported
upon further analysis (Russo, 2023) including impacts from social factors, and
personal and environmental factors. Moreover, the model proposed factors that
may influence the adoption but lacked details on how these factors played a
role in the adoption. Details on the different factors is essential for
organizations to understand how they can use the factors to increase adoption
of AI tools. Despite these earlier works, we still lack clarity on what is
impacting the adoption and usage of AI tools from practitioners and their
organizations, and how can practitioners improve usage?

To fill this research gap, we conducted a study following socio-technical
grounded theory (STGT) (Hoda, 2021), which is an iterative research method
that helped us generate our novel theory. Our research was guided by an
initial goal of gathering more understanding regarding: _What impacts AI
adoption and use in SE?_ In particular, we conducted 26 interviews with
diverse software practitioners from a variety of development roles (e.g.,
software engineers, developers, DevOps, AI engineers, tech leads, etc.),
organizational sizes, and geographic locations. Additionally, after our basic
and advanced stage of theory development, we also conducted a round of
theoretical category and relationship validation through a survey with 395
respondents. Our work improves on Russo's preliminary work by surveying a
significantly higher number of participants and developing our theory from
semi-structured interviews, offering deeper insights and how organizations can
support usage and adoption. We also developed a theory of motives and
challenges for adopting and using AI tool(s) from both an individual and
organizational level. For example, practitioners are limited in their work by
a lack of training provided by their organization as well as a lack of usage
guidelines, which can create uncertainty surrounding privacy risk. In
contrast, we found organizations who provided AI tool training, pulling
practitioners to increase usage of AI tools. We identified 3 push-pull
relationships between the motives and challenges that could negatively impact
the use and adoption of AI tool(s) if organizations do not adequately handle
the challenges.

The contributions of our study are as follows:

  * •

Identification and Analysis of Motivation: Our study identified and analyzed 2
individual and 3 organizational motive factors that increase practitioner use
and adoption of AI tool(s) in software development.

  * •

Identification and Analysis of Challenges: Our study identified and analyzed 4
individual and 3 organizational challenge factors that limit practitioner use
and adoption of AI tool(s) in software development.

  * •

Identification and Analysis of Relationships and Recommendations to
Practitioners: Our study identified and analyzed 9/12 factors that together
form 3 different push-pull relationships between motives and challenges. We
discuss these relationships in detail and provide recommendations for
practitioners, to assist them in navigating the challenges that limit use and
adoption of AI tool(s).

##  2\. Related Work

The recent surge of AI tool(s) such as ChatGPT (OpenAI, 2022), GitHub Copilot
(GitHub, 2023), Llama (Meta, 2023), Gemini (gem, 2024), Claude (Anthropic,
2023), and many more has transformed the software development industry,
introducing new capabilities and challenges and sparked discussions about
their usage. This section reviews existing research on AI tool(s) usage and
its broader implications on software development practices.

As one of the first AI tool(s) used in the software development industry,
GitHub Copilot has been the subject of several research studies. In the work
by Vaithilingam et al. (Vaithilingam et al., 2022), the authors studied 24
participants to understand how programmers use and perceive Github Copilot.
The authors found that while programmers appreciated Copilot for providing a
starting point and reducing the need to search online, the tool's generated
code was often difficult to debug, understand, and modify. These limitations
hindered their task solving effectiveness. Saki Imai (Imai, 2022) experimented
with 21 participants and found that while GitHub Copilot boosts productivity,
it compromises code quality. Yetistiren et al. (Yetistiren et al., 2022)
assess the quality of generated code provided by GitHub in terms of validity,
correctness, and efficiency. They found that Copilot successfully generated
valid code 91.5% of the time. However, in terms of correctness, only 28.7% of
code was fully correct, 51.2% was partially correct, and 20.1% was incorrect.
The study concluded that GitHub Copilot is promising but it requires further
evaluation. Likewise, Nguyen and Nadi (Nguyen and Nadi, 2022) and Dakhel et
al. (Dakhel et al., 2023) evaluated the code generated by Copilot and found
that the code quality varies depending on the programming language.
Furthermore, Barke et al. (Barke et al., 2023) observed 20 participants using
GitHub Copilot and developed a grounded theory that describes two modes in
which users interact with the tool: acceleration and exploration. In
acceleration mode, programmers use Copilot to quickly achieve known goals
without disrupting their workflow. In exploration mode, programmers use
Copilot as a starting points, involving slower, more deliberate interactions
with extensive validation. Based on their theory, the authors provide design
recommendations for future programming assistants. These studies indicate that
while AI tool(s) like GitHub Copilot is promising, there are still challenges
of code quality and errors which engineers need to validate before using.

Jaber et al. (Beganovic et al., 2023) conducted a systematic literature review
of 12 papers on the application of ChatGPT in software development. The study
indicates that ChatGPT has potentials in various domains of software
development including automated program repair and bug fixing, programming
numerical algorithms, software engineering decision-making and more. However,
the authors indicated concerns about over reliance, reinforcement of
misconceptions or biases, and ethical implications of AI use in education and
other sectors. Stemming from ethical concerns of AI tool(s), Pant et al. (Pant
et al., 2024a) deployed a survey to understand AI practitioners’ awareness of
AI ethics and their challenges in incorporating ethics. The study found that
most AI practitioners have a good grasp of AI ethics due to their workplace's
rules and policies on privacy and security. Although formal education and
training provide some help, practitioners face challenges related to
technological problems and human factors when trying to build ethical AI
systems. Pant et al. (Pant et al., 2024b) further studied the AI/ML fairness
and developed a framework outlining the relationship between AI practitioners'
understanding of fair AI/ML and their development challenges, the consequences
of creating unfair AI/ML, and the strategies used to ensure fairness. Another
area related to AI tool(s) is the trust. Johnson et al. (Johnson et al., 2023)
investigated engineers' trust in software tool(s) by interviewing 18 engineers
from within and outside Microsoft. The study resulted in the PICSE framework,
designed to analyze and enhance trust among engineers in their software
tool(s). These studies show the potential benefits and challenges of tool(s)
like ChatGPT and the current understanding among practitioners.

Prior works have further studied how practitioners leverage AI tool(s) in
software development and how these tool(s) impact developer productivity.
Ziegler et al. (Ziegler et al., 2022) conducted a case study on the impact of
GitHub Copilot on developers' perception of productivity by examining how
frequently suggestions were accepted by users. They identified that
developers' perceptions of productivity was mainly influenced by how often
they accepted Copilot's suggestions. Ross et al. (Ross et al., 2023) developed
a Programmer's Assistant and tested with 42 participants if conversational
agents based on large language models can help with software development.
Their study revealed that these agents effectively assist software engineers
by generating relevant code and engaging in useful, extended conversations
that improve productivity. In a study by Liang et al. (Liang et al., 2024),
the researchers conducted a survey with 410 developers to explore how they use
AI programming assistants and the usability challenges they encounter. The
results highlight that developers primarily use these tool(s) to reduce
keystrokes, speed up task completion, and assist with syntax recall. However,
they do not use AI tool(s) for brainstorming solutions. The study further
unveiled that the AI tool(s) fail to meet specific functional or non-
functional requirements and often produced undesired output.

Other prior works on generative models for programming found that users using
natural language for generative models still struggled with challenges because
they need to learn the model's ``syntax" (Jiang et al., 2022). A user study
where developers tasked to complete a variety of Python programming tasks were
not found to achieve statistically significant gains in measurable outcomes
using the assistance plugin, in categories such as completion time and
correctness (Xu et al., 2022).

The studies conducted so far have focused on various aspects of these tool(s)
such as code generation quality of these tool(s) (Yetistiren et al., 2022;
Imai, 2022), impact on developers productivity (Ziegler et al., 2022), trust
(Johnson et al., 2023), ethics (Akbar et al., 2023; Pant et al., 2024a), and
usability (Vaithilingam et al., 2022; Liang et al., 2024). More recently, a
study by Daniel Russo (Russo, 2023) investigated the adoption of generative AI
tool(s) in software development employing surveys based on frameworks like
Technology Acceptance Model (TAM), the Diffusion of Innovation Theory (DOI),
and the Social Cognitive Theory (SCT). The researcher developed an initial
model of generative AI tool adoption which included 7 hypothesis. However,
upon further analysis 3 were not supported. He concluded that compatibility
with existing workflows is the primary driver of AI tool adoption.

Despite previous efforts, there is a lack of understanding of the role that
practitioners and their organizations play in adopting and using AI tool(s).
More importantly, given the existing literature that provides evidence about
the advantages of leveraging AI tool(s) for software development, for
practitioners and organizations to fully reap the benefit of AI tool(s), it is
important to understand what should organizations do to increase the use and
adoption of AI tool(s). Thus, our research aims to fill this gap and
contribute to improving the adoption and increased use of AI tool(s) in
software development.

Figure 1. The Methodology of Our Study

##  3\. Methodology

Socio-Technical Grounded Theory (STGT) is ideal for studies like ours that
involve data collected through interviews, theory development, practice,
industry-relevant topics, and the human and social aspects of software
engineering (Hoda, 2021). Since its inception, STGT has been a commonly used
research method for software engineering research due to its suitability for
technology intensive domains (Madampe et al., 2023a, b; Gunatilake et al.,
2024; Graetsch et al., 2023; Gama and Lacerda, 2023; Hidellaarachchi et al.,
2023). Moreover, STGT is best suited for those studies that aim to develop
deep understandings through qualitative data (Madampe et al., 2023a, b;
Hidellaarachchi et al., 2023).

STGT is aptly suited for this research topic, since we aim to study the broad
emerging area of AI tool usage and adoption by practitioners and reduce
challenges in leveraging AI tools in their organizations. Additionally, the
research context of our work closely aligns with the four pillars of the STGT
research framework (Hoda, 2021) as discussed below.

  * •

Socio-Technical Phenomenon: Our study explores the use and adoption of AI
tools for practitioners in software organizations. This is a socio-technical
phenomenon because usage and adoption of new AI tools may be greatly impacted
by multiple factors, such as interactions with tools, technical systems, other
team members, organizational structures and processes.

  * •

Socio-Technical Data, Tools, and Techniques: Our research team collected
interview data via Zoom and Microsoft Teams, and used tools such as
Otter.AI111https://otter.ai/ to automatically transcribe audio recordings, and
Google Sheets to assist with coding and analyzing interviews.

  * •

Socio-Technical Domain: Because of the nature of the software engineering
field, there are various socio-technical actors in our study, including but
not limited to software developers, AI engineers, software engineers, DevOps
engineers, technical leads, and developers. Software engineering teams in
organizations and AI assistance tools for software development are socio-
technical domains.

  * •

Socio-Technical Researcher: The knowledge and skills of the research team
encompass a wide breadth of domain and research knowledge and experience. Each
interview was conducted by experienced practitioners and/or researchers.

Figure 1 illustrates the methodology of our study. STGT has two variants
according to the purpose, e.g., STGT for data analysis (Madampe et al., 2023a,
b; Gunatilake et al., 2024; Hidellaarachchi et al., 2023) and full STGT study
resulting in theory (Graetsch et al., 2023). The difference between the two is
that a full STGT study results in _``novel, useful, parsimonious, and
modifiable, theories grounded in evidence''_ , whereas STGT for data analysis
applies basic data analysis techniques with other research methods (e.g., case
study) and produces preliminary categories and propositions (Hoda, 2021). We
adhered to the full STGT method prescribed in the STGT guidelines (Hoda, 2021)
which include two stages _basic data collection and analysis_ and _advanced
stage of theory development._ Our application of the full STGT method also
involved conducting a questionnaire survey, in a validation stage, to further
validate our theoretical categories and relationships (Hoda, 2021). We
conducted 26 interviews in 3 rounds. Adhering to the STGT (Hoda, 2021)
process, the basic data collection involved interviews in two rounds: (1)
round 1 with P1 to P14, (2) round 2 with P15 to P22. The subsequent advanced
stage of theory development involved targeted interviews with 4 participants.

###  3.1. STGT: Basic Stage of Data Collection and Analysis

The basic stage starts with a lightweight review of the existing literature,
known as a lean literature review in STGT (Hoda, 2021). We began with
reviewing the existing research on the use of AI tool(s) in SE which we report
in the section Introduction (Section 1) and Related Works (Section 2).

We chose semi-structured interviews as our primary data collection approach.
The first 14 interviews revealed new insights related to several aspects such
as company culture, data privacy, and company guidelines surrounding AI
tool(s) usage. In the subsequent 8 interviews, we narrowed our questions to
these aspects of AI tool use and adoption. Upon analyzing the data from the
second round, the categories and relationships became more prominent,
highlighting motives and challenges influencing the AI tool use and adoption.
Then we embarked to the next stage, i.e., the Advanced stage.

####  3.1.1. Interview Design

Our initial literature review helped to partially design the interview
questions. In the literature review, we found survey studies related to the
usability of the AI tools and industry oriented surveys. For example, the
study by Liang et al., (Liang et al., 2022) surveyed developers on topics such
as _why and how often AI tool(s) are used, strategies used to these tools work
better, and why developers give up on using the AI-generated code._ We found
another study conducted by Stack Overflow (Sta, 2023), where they surveyed
practitioners about their perceptions of AI tool(s) and how these tools may or
may not impact their workflows. The study revealed that practitioners use AI
tool(s) throughout the various phases of Software Development Life Cycle
(SDLC) for tasks like coding, debugging, documentation, learning about
codebase, code commit and review, testing, as well as deployment and
monitoring. Thus, we developed our semi-structured interview questions by
building on top of these studies and following the general interview guide
(Patton, 2002), as it is widely used by researchers (Alahyari et al., 2017;
Alami et al., 2022; Itkonen et al., 2012; Raghavan R et al., 2020).

We prepared 40 broad base questions including questions such as _1) How do you
decide which AI tools to use? 2) How does using AI tools impact work
assignments? 3) How do you apply these tools to your workflow?_ We asked _how
AI tools impact work assignments_ , because previous literature indicated that
developers _saved time_ from using AI tools. Hence, we were interested to see
if these time improvements resulted in a difference in how practitioners
assigned tasks and divided work.

Benefiting from our careful interview design, we were able to find new
insights about potential influences on AI tool use and adoption, such as
culture of sharing insights with peers. For example, from P7's interview we
found that they share prompting techniques in their company Slack channel,
_``We use a company Slack channel, so we have several channels, sometimes we
just share, how do you prompt like best way to prompt some research papers?
Like React prompting.'' (P7)_ We also found discussions related to data
privacy, peer judgment, lack of guidelines, and more. These topics emerged as
motives and challenges that influenced the AI tool use and adoption in the
industry. The semi-structured nature of our interviews enabled us to unveil
these findings, as it allowed us to ask probing questions and delve deeper
into the emerging topics. Furthermore, STGT methodology emphasizes iterative
and incremental research, so we were able to adjust our interview questions in
subsequent interviews and ask questions that are typically not supported in
survey-based approach. Hence, we updated our questions to the emerged topics,
and added 15 new questions. The new set included questions like _``Is there a
culture of sharing practices or strategies for using AI tools within your
company?''_ , as we found many participants highlighting that talking to peers
often taught new prompting methods and tips to try on the AI tools. A subset
of the new questions also included, _1) Does your company provide any
guidelines and restrictions on using AI tools? 2) Ever since the new AI tools
have come out, how has the company culture changed?_ The full set of interview
questions are included in our replication
package.222https://zenodo.org/records/12165737

####  3.1.2. Sampling and Recruitment:

We began participant recruitment by using convenience sampling (Baltes and
Ralph, 2022) and invited industry practitioners from our personal contacts to
participate in the semi-structured interviews. The initial selection criteria
included any software practitioner who uses AI tool(s) for tasks related to
software development. We expanded our reach through recommendations from
previous interviewees. To mitigate bias, we aimed to interview practitioners
from diverse company sizes, years of experience, and countries to help us
understand the overall worldwide usage and adoption of the AI tool(s). In
total, we interviewed 26 participants from Asia, Europe, North America,
Oceania, and South America, whose experience in the software industry varied
from 1 to 15 years. Their companies also vary in size with small, medium,
large, and extra-large-sized. Table 1 shows the demographic information of our
participants.

Figure 2. Example Coding of Raw Quotes

####  3.1.3. Collection and Analysis Procedure:

In the first round of interviews, we interviewed 14 participants (P1 - P14).
Each interview was conducted via Zoom and lasted approximately 45-60 minutes.
At the beginning of each interview, we informed the participants that their
interview would be recorded for transcription purposes and would remain
confidential. To analyze the transcripts three authors used open coding to
inductively assign codes (Strauss and Corbin, 1990). As new codes emerged we
employed a constant comparison method and regularly met to discuss and compare
the derived codes. We further prepared memos for each interview to reflect on
the key points of the interview and draw connections between the codes. The
codes from the 14 interviews pointed us toward new topics, e.g., discussion
with peers, data privacy, peer judgment, lack of guidelines, and more. Hence,
we updated our questions accordingly using these insights and narrowed our
focus towards the aspects that influence AI tool use and adoption.

At this stage, we applied theoretical sampling which is the process of
narrowing the focus of the data collection to the main findings that emerged
from the analysis process. The process of theoretical sampling also involves
updating the interview questions based on the emerging concepts, categories,
and relationships, rather than keeping them broad as previous interviews
(Hoda, 2021). Thus, we conduct round 2 of the interviews which involved 8
participants (P15 - P22) using the updated questions. We continued open coding
and memoing the interviews. From the 22 interview transcripts, we first
generated 48 codes. Then, we categorized the 48 codes into 12 concepts. We
refer to these 12 concepts as motives and challenges _factors_ that influenced
the practitioners' AI tool use and adoption. For example, _discussion with
peers_ motivated a practitioner to use AI tool(s) during their work. Whereas,
a _lack of guidelines_ in the company made it challenging for them to use AI
tools, as they were unsure about how much information they could put into the
tool. We show an example of applying STGT analysis from raw quote to category
in Figure 2.

During the last 3 interviews of round 2, no new insights emerged, and we found
detailed and prominent concepts. Thus, we reached the end of the Basic Stage.
However, by the end of the Basic Stage of Data Collection and Analysis, we
still lacked a full theory. Therefore, in the next stage, we proceeded to the
Advanced Stage of Theory Development with the ``Emergent Mode'' (Hoda, 2021).
The STGT guidelines (Hoda, 2021) defines the Emergent Mode as,

> ``Enabling the emergence of theory through the iterative targeted data
> collection and analysis step which ends with theoretical saturation and
> results in a mature theory.''

Table 1. Interviewee Demographics

P# |  Role |  Exp |  Size |  Continent
---|---|---|---|---
P1 |  DevOps |  2 |  M |  North America
P2 |  Senior Dev. |  6 |  L |  Asia
P3 |  Developer |  3 |  S |  Asia
P4 |  Senior Dev. |  6 |  M |  Asia
P5 |  Senior Fullstack |  8 |  M |  North America
P6 |  DevOps |  3 |  M |  North America
P7 |  Developer |  3 |  L |  Europe
P8 |  Sw. Engineer |  4 |  L |  North America
P9 |  Sw. Engineer |  2 |  L |  North America
P10 |  Sw. Enginerr |  5 |  S |  Oceania
P11 |  Applied Scientist |  7 |  XL |  North America
P12 |  CTO |  5 |  S |  North America
P13 |  AI Engineer |  3 |  XL |  Asia
P14 |  Tech Lead |  15 |  XL |  Asia
P15 |  Tech Lead |  7 |  XL |  Asia
P16 |  AI Engineer |  7 |  L |  Asia
P17 |  AI Engineer |  10 |  XL |  Asia
P18 |  Tech Lead |  10 |  XL |  Asia
P19 |  Sw. Architect |  5 |  L |  Europe
P20 |  Sw. Engineer |  4 |  XL |  North America
P21 |  Sw. Engineer |  5 |  M |  Europe
P22 |  Sw. Engineer |  3 |  L |  North America
P23 |  Sw. Engineer |  3 |  L |  North America
P24 |  Sw. Engineer |  6 |  M |  South America
P25 |  Sw. Engineer |  5 |  L |  South America
P26 |  AI Engineer |  2 |  M |  North America

  * •

Organizational size: S (fewer than 50 employees), M (between 50 and 249
employees), L (between 250 and 4999 employees), and XL (5000 employees and
more).

Table 2. Number of survey participants who use each tool. Note: We only showed tool(s) that are used by more than 3 participants Tool | Num. Participants (%)
---|---
GitHub Copilot | 256 (64.8%)
ChatGPT | 245 (62.0%)
ChatGPT (Paid) | 185 (46.8%)
OpenAI APIs | 137 (34.7%)
Gemini | 124 (31.4%)
Claude | 91 (23.0%)
Bing Chat | 85 (21.5%)
Perplexitiy | 75 (19.0%)
Llama 2 | 58 (14.7%)
Google Bard | 57 (14.4%)
Tabnine | 49 (12.4%)
Codeium | 41 (10.4%)
Poe | 27 (6.8%)
Amazon CodeWhisperer | 25 (6.3%)
Mistral | 6 (1.5%)
Phind | 4 (1.0%)

###  3.2. STGT: Advanced Stage of Theory Development

We conducted two steps in this advanced stage: 1) _targeted data collection
and analysis_ , and 2) _theoretical structuring_. Targeted data collection and
analysis involves first targeting data sources (i.e., interviewees) to help
strengthen our concepts. The analysis involves targeted coding and constant
comparison, which refers to coding the most important concepts from the Basic
Stage.

####  3.2.1. Targeted Data Collection and Analysis:

To implement targeted data collection, we conducted a 3rd round of interview
with 4 participants (P23 - P26). In round 3 of the interviews, we focused on
strengthening the identified motives and challenges influencing AI tool use
and adoption. We interviewed 4 practitioners, 3 software engineers and 1 AI
engineer. These interviews lasted 30-40 minutes, slightly shorter than the
previous round, as the questions at this stage were more targeted and focused.

####  3.2.2. Theoretical Structuring:

The last 4 interviews strengthened the 12 concepts, indicating that we reached
theoretical saturation (Hoda, 2021). To structure the concepts, we decided to
leverage visualization techniques using diagramming software. From the
visualization, we found that the 12 concepts generally fall into four
categories: individual motives, individual challenges, organizational motives,
and organizational challenges. We further found a push-pull relationship among
the concepts and categories which we denote using double sided arrows. We
present the concepts, categories, and the push-pull relationship in Figure 4.
The 4 motives and challenges and relationships together influence the use and
adoption of AI tool(s) which we present as our ``A Theory of AI Tool Use and
Adoption for Software Development''. In section 4, we provide an in-depth
explanation of the theory with example quotes from our interviewees.

Figure 3. Number of AI tools used by each survey participant

###  3.3. Validation of Theoretical Categories and Relationships

As an additional step, we deployed a survey to validate the categories and
relationships developed through the STGT process.

####  3.3.1. Survey Design:

The survey began with a description of the purpose of our data collection and
a set of questions on demographics such as years of experience and number of
employees in respondent organization. To minimize participant fatigue, we
designed a 10 minute survey with few open-answer questions and made all of
them optional. For example, _in your opinion, what would improve your
organization's adoption of AI tool(s)?_ For the rest of the questions
regarding the factors impacting AI tool use and adoption, we refined the
questions so that respondents could easily answer yes or no or not applicable
whether a factor impacted their use and adoption. For example, _``After I used
AI tool(s), my use of tool(s) such as Google and Stack Overflow to help debug
or learn has decreased''_. Furthermore, we asked questions about the
relationships between different categories pulling and pushing AI tool use and
adoption. We asked survey questions about every motive and challenge and
piloted the survey with 41 participants (i.e., 27 from LinkedIn, 4 from X, 10
from Facebook). These 41 pilot participants were recruited from open calls to
participate from the author(s) social media posts on the three platforms. It
was from the piloting that we found that the survey could be further
optimized, therefore, we switched the answers for factors for adoption into
_yes_ or no or not applicable as opposed to the standard 5-level Likert scale
which we kept for the questions about relationships between different factors
for adoption. The results from the pilot surveys were not included in this
study.

####  3.3.2. Sampling Strategy:

To recruit potentially suitable participants, we focused our attention towards
GitHub as it provides a platform that allows to identify software
professionals who may be using AI assistance tool(s) based on their interests.
We adopted an approach similar to previous studies that used GitHub to recruit
survey participants (Liang et al., 2024, 2022; Huang et al., 2021). Similar to
Liang et al. (Liang et al., 2024), we identified developers interested in AI
assistant programming tool(s), which include Copilot.Vim (git, 2023e), ChatGPT
(git, 2023d), BetterChatGPT (git, 2023c), TabNine (git, 2023g), awesome-
chatgpt-prompts (git, 2023a), and _awesome-ChatGPT-repositories(git, 2023b)_.
Using GitHub's API (git, 2023f), we collected users who starred any of the
aforementioned repositories. Since it was possible for a user to star multiple
repositories, we eliminated duplicate users and also removed users who did not
disclose a publicly available email. Upon filtering we were left with over 20K
GitHub users with their respective emails. In line with previous survey
invitations (Liang et al., 2024), we randomly selected 14,525 users and sent
them the survey with a recruitment message. As part of our recruitment, we
also offered participants (i.e. both pilot and actual survey) to enter in a
draw to win one of four $40 gift cards. If a participant wanted to opt in for
the draw they could fill in their email. For the full list of survey
questions, we provide it in our replication
package.333https://zenodo.org/records/12165737

####  3.3.3. Survey Analysis:

The total number of respondents was 395, resulting in a response rate of 2.7%
(395/14525). For close-ended questions, we counted the number of responses for
each question. In contrast, for open-ended questions, two co-authors conducted
open coding (Saldaña, 2021), memoing, and constant comparison. The number of
responses for each question differed as participants were allowed to answer
``not applicable" or leave the survey at any time. Our response rate is in
line with other similar surveys (Liang et al., 2022, 2024). We note that our
response rate was initially around 3.2% for the first 301 responses (9,406).
However, the response rate dropped for the final batch (1.8%, 94/5,119)
because instead of sending emails directly from our own emails, we had to rely
on SurveyMonkey's built-in email service, which resulted in many unopened
emails, potentially going straight to recipients' junk inbox. Our respondents
are from 58 different countries and from the continents of Africa (10 people),
Asia (159 people), Europe (101 people), Oceania (10 people), South America (25
people), North America (87 people), and 3 undeclared. In addition, our
participants exhibit a diverse professional software-related experience with
$<$ 1 year of experience (30 people), 1 $-$ 3 years of experience (78 people),
3-5 years (79 people), 5-10 years (113 people), 10+ years of experience (95
people). Finally, our participants are also working in varied sizes of
organizations, $<50$ people organizations (203 people), $51-249$ people
organizations (65 people), $250-4999$ people organizations (65 people),
$>5000$ people organizations (62 people). This breakdown of the employer sizes
fits the economic reality. For countries in the OECD, small and medium
enterprises represent 99% of all firms (Matt and Rauch, 2020). In Table 2, we
provide the AI assistance tool(s) that are used by our survey participants. As
shown in the table, there are numerous tool(s) that software professionals
use, but the most prolific ones in our study are Copilot, ChatGPT (Paid)
ChatGPT (free), OpenAI APIs, Gemini, and Claude. There are some less popular
tool(s) that were mentioned only once or twice in total across the 301
participants, so we exclude them in the table.

Figure 4. A Theory of AI Tool Use and Adoption in Software Engineering

We use double sided arrows to make connections between concepts to show there
is a relationship between the two or more concepts.

##  4\. A Theory of AI Tool Use and Adoption for Software Development

This section details our theory (Figure 4) on the individual and
organizational motives and challenges that impact the use and adoption AI
tool(s). The theory encompasses the motives (i.e., pull) and challenges (i.e.,
push). In Table 3 and Table 4, we also detail the number of survey respondents
who experienced and/or agree with each factor. For Tables 3 and 4, we also
provide finer grained details for example _IM1 Learning and debugging_ contain
additional details: IM1.1 learning about new code, programming languages, or
concepts easier, IM1.2 debugging errors or problems easier, IM1.3 searching
new code, programming languages or concepts easier.

Table 3. Survey responses for motives that increase AI tool use and adoption ID |  Motives | #Votes (Ratios)
---|---|---
Individual Motives
IM1 |  Learning and debugging |
IM1.1 |  I realized AI tool(s) make learning about new code, programming languages, or concepts easier, my AI tool(s) usage has increased | 265/290 (91.4%)
IM1.2 |  I realized AI tool(s) make debugging my errors or problems easier | 242/291 (83.2%)
IM1.3 |  I realized AI tool(s) make searching about new code, programming languages, or concepts easier | 239/290 (82.4%)
IM1.4 |  I realized AI tool(s) reduce my use of tool(s) such as Google and Stack Overflow to help debug or learn | 200/295 (67.8%)
IM2 |  Discussion with peers |
IM2.1 |  We started having regular discussions about our use of AI tool(s) (e.g., best practices for prompting in AI tool(s)) within the team and/or the company | 155/247 (62.8%)
IM2.2 |  The employee wants to improve their AI usage skill by sharing | 187/224 (83.5%)
Organizational Motivates
OM1 |  Creating culture of sharing |
OM1.1 |  As company created a space for employees to openly discuss about the use of AI tool(s) (e.g., events, slack channels) | 128/189 (67.8%)
OM1.2 |  The employee's team has a strong culture of sharing insights | 188/223 (84.3%)
OM1.3 |  The employee's company has a strong culture of sharing insights | 151/213 (70.9%)
OM2 |  Providing and promoting the tool |
OM2.1 |  As company paid for the use of AI tool (e.g., provided subscription) | 124/191 (64.9%)
OM2.2 |  Because company provides AI tool plugin that is powered by external APIs (e.g., GPT4) | 100/166 (60.2%)
OM2.3 |  Company started notifying us on the availability of AI tool(s) (e.g., announced in a meeting) | 106/177 (59.9%)
OM2.4 |  Company started motivating us on the use of AI tool(s) (i.e., sending us email reminders) | 93/168 (55.4%)
OM2.5 |  Company mandated us to update our manager about our use of AI tool(s) | 61/139 (43.9%)
OM3 |  Providing guidance and training |
OM3.1 |  Once company started providing training on prompting AI tool(s) | 79/123 (64.2%)
OM3.2 |  After company provided a policy with rules and restrictions of AI tool(s) usage | 82/145 (56.6%)
OM3.3 |  Company provides an integration/tool that helps sanitize my prompt to protect company specific data | 74/122 (60.7%)
Table 4. Survey responses for challenges that limited AI tool use and adoption ID |  Challenges | #Votes (Ratios)
---|---|---
Individual Challenges
IC1 |  Fear of decreased skills |
IC1.1 |  A fear of a decreased coding ability | 53/74 (71.6%)
IC1.2 |  A fear of losing learning ability | 39/74 (52.7%)
IC1.3 |  A fear of over relying on AI tool(s) | 56/75 (74.7%)
IC2 |  Limited AI Capabilities for Software Development |
IC2.1 |  Tool does not have operation environment information (e.g., versioning, hardware) | 90/141 (63.8%)
IC2.2 |  Tool does not have access to codebase and is unable to generate the correct results | 125/163 (76.7%)
IC2.3 |  AI tool often refuses to provide a response for my question (e.g., "As a language model, I cannot answer your question") | 84/155 (53.8%)
IC3 |  Lack of prompting skill |
IC3.1 |  Due to AI tool(s) as it needs a lot of experimentation before getting a desired result | 146/264 (55.3%)
IC3.2 |  Tool getting me into an infinite loop of prompting and diverging from the actual task | 42/71 (59.2%)
IC4 |  Potential judgment |
IC4.1 |  Due to potential judgement from peers | 45/218 (20.6%)
Organizational Challenges
OC1 |  Lack of culture of sharing |
OC1.1 |  Everyone does different work, do not have similar project context | 24/31 (77.4%)
OC1.2 |  There is no culture of sharing within my team and/or company | 20/29 (68.9%)
OC1.3 |  Fear of potential judgment from others when sharing | 16/30 (53.3%)
OC2 |  Company does not cover the cost of the tool |
OC2.1 |  Due the high prices and you have to pay for it | 120/201 (59.7%)
OC2.2 |  Number of API calls allowed in a time interval (e.g., 40 prompts/4 hours on ChatGPT) | 73/142 (51.4%)
OC3 |  Lack of company guideline |
OC3.1 |  Due to company not providing any usage guideline | 68/201 (33.8%)
OC3.2 |  Due to company not providing training | 49/197 (24.9%)
OC3.3 |  Due to concerns about accidentally leaking company data | 93/201 (46.2%)

###  4.1. Individual Motives

####  4.1.1. IM1: Learning and Debugging:

AI tools improving practitioners' ability to learn and debug was widely
reported by our interviewees and surveyees. The ostensible reason for AI tool
use was that practitioners benefit largely from the _time_ saved when learning
about new code, debugging errors, or searching about concepts. Prior to AI
tools, when writing new code, practitioners relied on a combination of
personal knowledge, Stack Overflow, documentation, and Googling to reach their
conclusion (Wu et al., 2019). However, our participants reported that AI tools
such as Copilot and ChatGPT lowered their cost of _time_ to generate code,
particularly boilerplate or repetitive code (Liang et al., 2024). A previous
survey study also briefly highlighted that practitioners use AI tools for
learning, searching and debugging (Russo, 2023). However, the study failed to
discuss _how the use_ of AI tools impact these tasks. In contrast, our study
detailed that there are noticeable differences between time saved from using
AI tools between different roles, and that leveraging AI tools for various
tasks led to declined use of traditional software searching and debugging
tools such as Stack Overflow and Google.

Through using AI tools, almost all of our interviewees reported perceived
_time_ savings in their work between 5% to 50%. Admittedly, since virtually
none of interviewees adopted a systematic metric to measure their work
allocation and productivity, we relied on their self reported time savings. To
our surprise, software developers reported low time saving with a range
between 5%-25%. In contrast, people in more AI or research roles such as _P11,
P17, P26_ reported saving upwards of 50% in their code related work. Our
initial assumption was that since there were a plethora of training data on
backend and frontend code that software engineer's work would be significantly
simplified, but due to restrictions such as privacy, which we cover later, the
time savings is less than we assumed. On the other hand, AI engineers reported
to us that their coding work involved a fair amount of structured code
regarding models that they are building towards so much of that can be
generated with the help of AI tools.

Specifically, 13 interviewees _(P1, P2, P3, P4, P5, P10, P12, P15, P16, P20,
P21, P22, P24)_ admitted to using AI tool(s) to help learn a new or existing
piece of code. 265/290 (91.4%) of survey respondents agreed that their use of
AI tools increased after determining that AI tool(s) make learning about new
code and programming concepts easier. P12 recollects how they dealt with an
assortment of unfamiliar technologies such as dealing with cloud
infrastructure while wanting to release a prototype as soon as possible.
_``What's the first step to creating this service? I don't know how, so I ask
ChatGPT, and it tells me I could use FastAPI. ``But how do I get it online?"
Then I asked, ``how to do that", it says to use EC2, so I just hammered away
at it, right? And then I got it up and running. '' (P12)_ Before the
introduction of AI tools, P12 had to expend considerable resources reading and
studying manuals regarding these technologies.

Additionally, our interviews highlighted the advantages from searching with AI
about unfamiliar problems. Prior to AI tools, a developer would probably ask
Stack Overflow or search Google for a few possible solutions (Wu et al., 2019;
Ponzanelli et al., 2014; Yang et al., 2016), but _identifying the most
relevant search terms_ may be difficult. _``I asked ChatGPT and it gives me
some possible ways. Then I just pick those suggestions and do some searching
on Google and then read some blogs. '' (P2)_ To this point, 239/290 (81.4$)
survey respondents agreed that AI tool(s) make searching about new code and
programming languages easier, this is in line with 2023 Stack Overflow survey
(Sta, 2023).

Similarly, relying on AI tools to debug code errors was widely reported by our
interviewees _(P1, P3, P5, P6, P9, P11-P15, P17, P20, P21, P23, P25, P26)_ and
surveyees (242/291 (83.2%)). Before the use of AI tools, practitioners Googled
stack trace or other messages to determine potential causes for issues (Li et
al., 2022; Xia et al., 2017; Sadowski et al., 2015). With the ability of AI
tools that allowed uploading screenshots, interviewees explained that sifting
through Stack Overflow pages is rather unnecessary. _``The advantage is that I
don't have to spend time sifting through Stack Overflow to see which post
actually resembles my problem. Often on Stack Overflow, even though many
problems seem similar, they are not exactly the same.'' (P19)_

The reduction in effort has led to our interviewees and surveyees (200/295
(67.8%)) to describe that their reliance on help from Stack Overflow and
Google for debugging has decreased. _`` Our internal engineers now, whether
they are working on algorithms or on research and development, use [AI tools]
to debug, and they no longer need to go to Google anymore using GPT4 for
debugging.'' (P17)_.

####  4.1.2. IM2: Discussion with peers:

Practitioners in our study described that a key motive for increasing use of
AI tools is taking on a personal initiative in sharing experiences and
successes with AI tools with others in their organization. 16 interviewees
mentioned having experienced discussions with peers about using AI tool(s) and
155/247 (62.8%) of surveyees agreed that their AI tool usage increased after
having regular discussions about their use of tool(s) within the team and/or
the organization.

One reason of discussion with others in the team and company was beneficial is
that despite the seemingly high popularity of AI tools, as reported by our
participants there are still many colleagues who are not yet aware nor tried
using them. As described by Geoffrey Moore (Moore and McKenna, 1999), early
innovators and early adopters of emerging technologies and products represent
only a segment of the overall population. Hence, our participants recounted
examples of colleagues who emerged as strong proponents of AI tools as a
result of discussions with colleagues. _``We were coordinating with a third-
party vendor to adjust some logs and monitoring. A teammate from our group
could not figure it out even after a day. So I told him to try ChatGPT and it
helped him write the function. He shared about [this incident] in our big
group chat, saying, ``Look at this, it's amazing, I was able to write such a
function."'' (P15)_. The colleague was aware of ChatGPT, but never actually
attempted it for their work and now they share AI tool's usefulness to other
colleagues. It exemplifies the organic spread of AI tool use and adoption
driven by positive user experience and peer recommendations.

Another reason that discussion is beneficial is that it facilitated a debate
of ideas and insights, and practitioners could improve their own skills from
this discussion. 187/224 (83.5%) survey participants reported that their AI
usage increased from sharing best practices and improving their own usage
skills. These discussion sessions allowed practitioners to _learn_ about how
others in the organization were using AI tools and tips to achieve the best
prompts. _``We've done a few presentations ourselves everybody has tried and
we wanted to present what we have tried doing with AI … I think everybody's
really curious. They want to try different things.'' (P23)_

Our interviewees also told us about how tips are shared and others within the
team benefit from the collective knowledge base. _``We talk about it during
team meetings to figure out what has or has not been working for us. We do
talk about what kind of tool(s) we are using, what kind of processes need to
be we need to be using to help us do our work better. '' (P9)_ These meetings
also facilitate discussions about tips such as _``ideas about the best way to
prompt ChatGPT to get the kind of information you want.'' (P20)_

From our survey responses, we also found a difference between discussing and
sharing with peers. Respondents from Asia had the highest agreement rate for
both IM2.1 and IM2.2 at 90.6% (77/85) and 78% (78/100). In contrast, other
continents with large respondent populations like North America and Europe had
agreements of IM2.1 82.1% (46/56), IM2.2 54.5% (30/55) and IM2.1 78.2%
(43/55), IM2.2 47.5% (29/61). One probable reason for this phenomenon could be
the greater emphasis on collectivist benefits found in many Asian cultures
(Michailova and Hutchings, 2006). The opposite is often observed in western
cultures where individualism is given higher priority (Michailova and
Hutchings, 2006). The sentiment of everybody needing to become acquainted with
these tools is shared by P16, _``It's best to start using and learning or
mastering this tool as soon as possible because I believe that in the future,
it will become as essential as tools like Excel are today, something that
everybody values and everybody needs to use'' (P16)_.

###  4.2. Individual Challenges

####  4.2.1. IC1: Fear of decreased skills:

A variety of fears related to the use of AI tool(s) were articulated by
participants, including 9 interviewees _(P1, P2, P4, P11, P12, P14, P17, P19,
and P20)_. Although AI tools assist in boosting learning and debugging, which
lead to perceived speed ups in time, participants also reported fears that
hinder the use and adoption of AI tools. 53/74 (71.6%) and 39/74 (52.7%) of
survey respondents agreed that their usage of AI tool(s) is challenged by
their fears for a decreased coding ability and losing learning ability.

Despite the ability for AI tools to generate working code, P2 and P1 stresses
the importance of understanding the generated code _``If you're copying code
from the ChatGPT, then you have to understand and you have to think about it.
We are using ChatGPT or other tool(s) for increasing our productivity, but not
damaging our thinking, or our creativity.'' (P2)_ _`` I don't really like the
idea of going to chatgpt, copying a bunch of code that somebody else
implemented in a repository. Even even with stuff prompts that I am entering,
I try to think twice.'' (P1)_

Although AI tools offer access to rapid generation of potential solutions to
various software development challenges, our interviewees described that over-
reliance could lead to a knowledge gap, affecting the developers' ability to
grow and adapt. Over-reliance may hinder junior developers skills, whereas
prior to AI tools, juniors could grow by learning and understanding the
codebase. _``If they are juniors, if they can explore the codebase, they will
learn more. … there will be some knowledge gap about codebase for a developer
who did not use ChatGPT and a developer who, who is using ChatGPT from the
beginning of his software engineering career.'' (P4)_ This worry was exhibited
in how surveyees responded to our questions regarding whether they have a fear
of decreased skills IC1.1-IC1.4. For surveyees that had over 10+ years of
professional experience, that agreed with IC1.1) 88.9% (8/9), IC1.2) 88.9%
(8/9), IC1.3) 87.5% (7/8), and IC1.4) 100% (6/6). Contrastingly, respondents
with $<$ 1 year of professional experience agreed with IC1.1) 60.0% (6/10),
IC1.2) 40% (4/10), IC1.3) 60% (6/10), and IC1.4) 44.4% (4/9).

On a personal level our participants described the risk of decreased
creativity and critical thinking as a result of over-reliance. _`` When we use
ChatGPT people don't want to think for themselves anymore at all… Because over
time, if you're not engaging your mind, you're engaging yourself in thinking
about things, you become less creative, and you become more dependable on
these tool(s).'' (P20)_

####  4.2.2. IC2: Limited AI Capabilities for Software Development:

One of the major challenges of AI tools that impedes use and adoption is their
limited capabilities in specific instances, as mentioned by our interviewees.
Nearly all participants aspired for more functionalities, but 14 of them _(P1,
P5, P9-P12, P14, P17, P20-P22, P23, P25, P26)_ explicitly desired additional
improvements such as access to the operational environment and codebase.

Our interviewees highlighted that the lack of awareness of the operational
environment, including specific software versions, hardware configurations, or
the nuances of the programming languages in use often becomes a bottleneck.
This leads to AI generated responses that may be incompatible with the
developer's actual working environment. P11 explains this issue, _``Current
code can only understand the code, but it cannot comprehend the underlying
running environment. I think this is the biggest problem. If it doesn't
understand the running environment, it can only see the most surface-level
issues. But if it can see what the environment is like, I think it could go to
another level.'' (P11)_ Prior to AI tools, developers would have to dig
through Google or Stack Overflow and hope to find an answer with a compatible
environment. It is easier now for AI to generate a ``correct" sounding answer,
but without knowing the operational environment, developers run into the same
problem of hoping to find the right solution for their given circumstance. As
P1 described, [AI response] is just sort of like a quality distillation of
averages of what other people have already agreed upon in some sort of a
public context. 90/141 (63.8%) of surveyees also felt that tools not having
operation environment information was a challenge that reduced their AI use.

Our participants also described that tool(s) most times do not have access to
a user's codebase. 93/109 (85.3%) survey participants agreed that their usage
of AI tool(s) was limited by tooling not having access to the codebase and
inability to generate the correct results. This limitation hampers the ability
to generate context-aware solutions or debug effectively. Without direct
access to the codebase, these tool(s) often resort to generic responses that
may not be useful. _``It doesn't have the amount domain knowledge that my team
member would have. [It] just has general knowledge that the internet has.''
(P9)_ The limitation also translates into debugging due to a lack of context.
_``It's just really unreliable, you usually have to give it the full context.
It is a lot of context you [have to] give it or it just doesn't know what to
do.'' (P8)_ To workaround this limitation requires detailing the codebase and
context, but this may also lead to concerns about privacy. As we will describe
in Subsection 4.4.3, data privacy could have significant revenue and legal
ramifications.

The impact of limited AI capabilities is even more noticeable for software
organizations working in less common domains or programming languages. _``As
you get more and more into specific problems in your domain, it's less
helpful.'' (P23)_ _``We use a test framework called [removed], and it's a bit
older, and very unintuitive and unfriendly… But [AI tools] hasn't really been
that successful. I wonder if it's because [removed] is not that popular and it
didn't really train on that data a lot.'' (P5)_ For our interviewees who use
less popular programming languages, they all indicated that their desired
improvement is adding language support for their domain.

Our interviewees also expressed the need to be considerate of using AI tools
for writing reports or communicating with clients and other stakeholders.
Tools like ChatGPT are known for its ability to write speeches (Herbold et
al., 2023), articles (Wu et al., 2023), and even research papers (Lingard,
2023), but some interviewees expressed dissatisfaction regarding the
superfluous language and unnatural sounding tone from AI tools. _``I don't
like its writing style. It's a bit too verbose, and it is very much not my
style. '' (P5)_ _``The boss can tell at a glance that most of it is written by
GPT… He doesn't encourage it because he thinks you are genuinely slacking off
and you entrusted everything to GPT without full consideration.'' (P17)_

####  4.2.3. IC3: Lack of prompting skill:

Another notable challenge identified by 9 participants _(P1, P3, P5, P6,
P14-P16, P19, P21)_ revolves around the difficulty in crafting the right
prompts. The process of finding the right prompt often involves trial and
error which can be time consuming. 146/264 (55.3%) survey participants agreed
that their usage of AI tools is challenged due to AI tool(s) needing a lot of
experimentation before getting a desired result.

As P21 described in their case, _`` I've still been struggling with what kind
of prompts to use, how to do it more efficiently… I feel like for different
tasks, it's better to do different prompts.'' (P21)_ Or as P15 describes, they
have a lack of familiarity and understanding of using the AI tool(s). _`` I
feel that it is still a matter of familiarity or insufficient understanding,
so many more effective ways of using and functions have not yet been
discovered.'' (P15)_

We found a noticeable difference in survey respondents who have years of
professional experience and more junior practitioners regarding the pain of
experimenting with a lot prompts. Survey respondents who have more than 10+
years of experience reported this challenge in 45.2% (28/62) cases, whereas
87.5% (21/24) of respondents with less than 1 year of experience reported this
challenge. The effectiveness of a prompt can depend on the user's experience
and familiarity with the domain. _`` When you have worked for a certain number
of years and are in a certain field, I think you have that knowledge on what
to write what the prompt should be. If you have been working for a time, then
you know what to write and probably what to search. If you know what to search
you can figure out roughly the proper prompt. You can give the AI to give you
the best result.'' (P3)_ These results indicated that senior practitioners are
more proficient in crafting the correct prompt and asking the relevant
questions to reach their desired solution. Senior practitioners being more
proficient is to be expected, however, this difference may be more pronounced
in the age of AI since the barrier to finding _solutions_ is lowered. Previous
literature showed that senior engineers often spend time helping junior
engineers with inquiries (Meyer et al., 2019). _``The pressure that entry-
level programmers may face in the future, which will likely increase
employment difficulties, especially for those at the low to middle end of the
spectrum. However, high-end programmers should not be affected.'' (P17)_ One
strategy to lower this concern is prioritizing the need for training on
prompting. _``Most engineering peers, including myself, feel that we don't
clearly understand the underlying workings of the large models. How to use
them more efficiently is actually a very important point.'' (P15)_ We discuss
this in more details in Subsection 4.3.3.

In subsection 4.2.1, we described the fear that AI tools work _too well_ and
developers become dependent. However, in instances where AI tools fails to
work as expected, 42/71 (59.2%) of surveyees indicated that they face
limitations from going into a loop of prompting, leading away from their
actual tasks. As noted by P3 and P23, _``Cases where I found maybe using the
tool, leads to someone being stuck in a loop.'' (P3)_ _``Because sometimes you
can get stuck in ``this prompt isn't working", or you're in the loop, trying
to get some answers out.'' (P23)_ When this occurs, practitioners are
_``pushed backed to using Stack Overflow and Google to get help for
debugging.'' (P14)_ Despite the limited capabilities at times, the overall
cost to test AI responses is cheap. _``If I ask it a question, you tell me
something that's not very accurate, then I immediately test it and find
there's a bug, then I throw it back to the tool, and I say, 'Look, this is a
bug.' It says 'sorry,' and then it helps me to fix it.'' (P12)_

####  4.2.4. IC4: Potential judgment:

We found potential judgment for using AI tool as a concern among some of our
participants _(P4, P5, P14, P22, P24, P25)_. 45/218 (20.6%) of survey
participants agreed that their adoption of AI tool(s) may be limited due to
potential judgment from peers. This ratio is not as high as some of the other
factors, however, 1/5 of survey respondents feeling a potential judgment from
fellow colleagues is not an insignificant number as software engineering is a
collaborative activity that requires effective teamwork in communication,
coordination, and collaboration (Barkhi et al., 2006).

Our interviewees and surveyees indicated that a reliance on AI could be
interpreted as a lack of skill, leading to a reluctance to discuss AI tool use
openly, even when it might enhance productivity or creativity. The existence
of _imposter syndrome_ (i.e., ``person’s experience of feeling like an
intellectual phony despite having outstanding academic and professional
credentials" (Albusays et al., 2021)) has been often experienced by women
(Clance and Imes, 1978) and other minorities (Albusays et al., 2021), but can
also impact junior developers and other practitioners with less industry
experience. _`` I almost don't want my coworkers to know. And not wanting my
coworkers to know I use it for weird reasons. Like it makes me less of a
programmer or something. '' (P5)_ A fear of judgment exists in junior
developers who may worry about being judged by their senior colleagues for not
being proficient enough to code without AI assistance. There may be a
perception that using AI indicates incompetence or laziness. Power dynamics
plays a negative role in hierarchical environments, where there can be a power
imbalance between junior and senior developers. _``I guess [me and my
teammates], we are not seniors yet. Mostly. Just some juniors and me who's
kind of intermediate. So we, with us, we don't have that [negative]
perception. But with other teams and seniors, they have this kind of negative
perception.'' (P25)_

The negative connotations lead to interviewees reporting that are unwilling to
share or admit to using AI tool(s) as they may feel embarrassed. _``It seems
like it could really be possible [people are embarrassed to tell others they
use AI tool(s)], which is why I've observed the proportion is so low… Then
there might be a feeling of embarrassment'' (P14)_ P14 who is a technical lead
mentioned, _``I boldly guess that there must be more people [in my team] who
are probably using it secretly.'' (P14)_

We observed this difference in sentiment in the results of our survey as well.
When we asked surveyees about using AI tools and the feeling of judgment from
others, 12% (6/50) and 13% (7/53) of practitioners who have 10+ and 5-10 years
of experience respectively agreed. On the other end of the spectrum, 42%
(8/19) and 19% (9/47) of practitioners with less than 1 year and 1-3 years of
experience agreed. In particular, those with less than 1 year of professional
experience felt a much greater potential for scrutiny that the other groups.
This may have a been a result of cultural norms within certain organizations
that prioritize traditional coding methods over AI tools.

###  4.3. Organizational Motives

####  4.3.1. OM1: Creating culture of sharing:

From our interviews and surveys, we found that core to organizational motives
is fostering a culture of sharing of experiences and practices for using AI
tool(s). Participants _(P6, P7, P9, P10, P15, P18-P21, P23, P26)_ described
how their companies provided open spaces such as Slack channels to exchange
knowledge about AI tool use. 128/189 (67.8%) of survey participants agreed
that their organization created a space for employees to openly discuss the
use of AI tool(s). For example, _``we have a Teams channel and we have
something called Tech Tips, where people just either talk about tool(s) or how
to use the tool(s) and how to use better prompts.'' (P21)_

While organizational communication channels facilitate the quick sharing of
tips, our participants mentioned that a more centralized storage of best
practices and knowledge would be more ideal. _``It would be nice if there's
like a more centralized repository where people can just share all their tips
there.'' (P7)_ Prior to the use of AI tools, software organizations would
often rely on project management and documentation tools such as Confluence
and Jira to store important information (Froeschl et al., 2020). In the age of
AI, tips and tricks for using AI tools represent a new potential boost in
productivity that could dramatically improve organization efficiency (e.g.,
P15 colleague's one day workload solved within minutes). Hence, as P7 alluded,
a single, accessible, and searchable knowledge base would provide potential
benefits to streamline the process of learning and leveraging AI tool.

In contrast to organizations where individuals worry about a negative
perception for discussing the use of AI tool(s), practitioners described an
environment where sharing about AI tool(s) is met with enthusiasm rather than
embarrassment. _``Actually, nobody is feeling embarrassed or anything, it's
more like everyone is finding this thing quite magical and wondering if it can
play a significant role.'' (P15)_ The narrative is less fearing judgment over
using AI tools and more feeling excitement about their potential. Our findings
indicated that when companies facilitate a culture of sharing, it supports the
dissemination of knowledge to use AI tools. _``The only risk you're getting
when you're telling someone that you're using AI tool(s), is that he's going
to request you to show a demo because they also want to use it.'' (P26)_ The
only perceived _risk_ is the positive pressure of demonstrating tool
capabilities to interested colleagues.

In our survey responses, geographic region again seemed to play a role in
establishing a culture of sharing. For the question about whether
organizations that create open spaces for employees to discuss about AI tools
increased the use of AI tools, 80% (60/75) participants from Asia agreed,
which is significantly higher than the other continents of Europe (57.8%,
26/45), North America (61.9%, 13/21), South America (54.5%, 6/11). Similarly,
regarding whether participants were part of an organization that has strong
culture of sharing insights, 79.5% (62/78) participants from Asia agreed,
again higher than Europe (73.6%, 39/53), North America (66.0%, 35/53), and
South America (68.8%, 11/16). These findings indicated that culture seems to
influence whether organizations and team members help facilitate an
environment that is conducive for practitioners to share insights (Michailova
and Hutchings, 2006). Like IM2, collectivist cultures appeared more willing to
create spaces for the flow of insights.

####  4.3.2. OM2: Providing and promoting the tool:

In addition to creating a culture a sharing, we found 11 participants _(P5,
P7, P8, P9, P11, P15, P17, P18, P19, P20, P25)_ revealing that their companies
either paid for or showed willingness to cover the cost of subscriptions.

Our interviewees explained that the decision to invest in paid AI tools is
often driven by the potential for growth and the relatively minor financial
burden of these expenses. P10 highlights that to many companies, the
investment is not much. _``what is the subscription fee for GPT for two
months, something like 20 USD? From the company's perspective, it's nothing.''
(P10)_. P10 further adds that their company usually pays for tool if the
employee shows that it is essential to learn and grow. _``When I can convince
them that [AI tool] is necessary, and it will help us grow a little bit
further, the company will definitely will pay for it'' (P10)_ Interviewees
whose organizations paid for their subscription argued that over the course of
a month (i.e., 30 days), if developers could save at least a few hours of
work, then the cost benefit of paying for a subscription would be positive.

From our survey respondents we found that extra-large organizations were
slightly most likely to pay for the use of tools 69.7% (23/33). This
intuitively makes sense as larger organizations would most likely have more
resources at their disposal to cover the cost. We also found that the
difference between the size of organizations was small in comparison with
medium (63.3%, 19/30) and large organizations (65.8%, 25/38). As one survey
participant mentioned, organizational adoption can be improved when managers
know what can they achieve easier now with lower cost, like if their adoption
can increase the overall performance of company without increasing the number
of staff. Our interviewees described that when their organization provides the
tools, employees are empowered to use these tools as needed. _``Yeah,
[company] pays also for the API usage and all of that. So not only copilot,
but if you want to build like something internal, or just use the API for
anything, they pay for it. … and I know I can use it when I want to.'' (P7)_

In addition, organizations can enable employees to tailor models and versions
for specific use cases and needs. There is also the ability to build add-ons
and extensions from the tools. Prior to AI tools, this would be like an
organization providing a custom Stack Overflow to its employees (sta, [n.
d.]). While previously difficult to achieve, this is now possible if
organizations acquire subscriptions and API for its employees. _``You can
choose including several other models that you can pick from. We get either
1,000 or 500 instances of free conversation.'' (P15)_ Interviewees also
mentioned that such enterprise versions of AI tools helps mitigate risks
associated with data breaches and ensure a unified approach to tool usage.
_``In the company, there are ready-made [tool(s)], and because you need to
make extensive adjustments to your own account due to budget considerations,
you will definitely use the company's unified planning.'' (P18)_

In addition to just providing AI tools, six interviewees _(P2, P10, P15, P17,
P20, P25)_ described how their organizations formally and informally promoted
different tools. Due to constant notifications, it is possible for
practitioners to miss new tools introduced by their organization. _``We are
bombarded with a lot of information every day, some people might turn off
certain notifications. They might only find out about [new tool] after they
noticed a colleague using something, and then they became aware of it.''
(P17)_ Since the Covid pandemic, many teams have been working in remote and/or
hybrid settings, emphasizing the higher importance for coordination and
communication (de Souza Santos and Ralph, 2022). For organizations, the
primary worry of not keeping up with industry practices is whether their
developers are able to maintain a competitive edge. _``Our company encourages
us to use those AI tool(s), because it heavily boosted our productivity. If we
can't be familiar with those AI tool(s) and prompt engineering, then it can be
difficult for us in future to cope with the other developers in the world. ''
(P2)_

Our interviewees discussed the role played by senior management who
incorporated AI tools as part of the organization's broader strategic plan.
_``The senior management really prioritize this matter. They want
comprehensive intelligence, things like AI-driven initiatives. These are
strategic directives coming from the top echelon.'' (P15)_ Similar to creating
a culture of sharing, we find differences in promoting the tools depending on
a participant's geographic location. We found participants from Asia most
often agreeing that their organization notified and motivated employees on
using AI tools. For OM2.3, 68.9% (51/74) participants from Asia agreed,
whereas 51% (24/47) of participants from Europe and 55.3% (21/38) from North
America agreed. Similarly, 62% (44/71) participants from Asia, 45.2% (19/42)
from Europe and 55% (18/33) from North America agreed with OM2.4.

####  4.3.3. OM3: Providing guidance and training:

Apart from sharing experience and providing and promoting AI tools, our
participants also emphasized the importance of organizations providing
guidance and training. Of our interviewees, 16 _(P2-P9, P13, P15, P17-P20,
P23, P25)_ described the existence of some form of guidance related to using
AI tools in their organizations. Specifically, guidance refers to
organizational policies and guidelines that govern what and how practitioners
can use AI tools in their work.

Prior to the use of AI tools, organizations typically allocated some code of
conduct for employees which included internal rules and policies. For example,
an employee is forbidden to access customer data without authorization and
forbidden to disclose company content without permission. This meant that an
employee would not be able to create a set of company source code and upload
it to GitHub or some other publicly facing file repository (Mergel, 2015).
Providing guidelines allows organizations to manage the concerns regarding
responsible use of AI tool, especially pertaining to when using AI tools is
appropriate and what tasks are not allowed. Adding guidelines allows
organizations to prescribe structure and expectations on handling sensitive
data, which helps to prevent the leaking of company code and customer data.
_``In our company, it's not ok to release the code. There are also some very
sensitive data involved, such as daily active user data those involve
sensitive company information, which is also not permissible.'' (P17)_

Our interviews _(P15, P19)_ indicated one approach to provide clear guidelines
was dedicating resources to training practitioners. Training covered best
practices and techniques for prompting. In P15's organization, senior
management was heavily invested in pushing the entire company towards AI so
training became mandatory. _``AI is essentially a strategy of the company, and
requires everyone to get on board and take exams, earn credits, and then
teaches you how to use these tool(s). Otherwise, it will be reported to the
supervisor, and there will be some kind of reward and punishment measures in
place.'' (P15)_ The organization not only provided training, but also required
employees to demonstrate their knowledge through testing. Among the company's
courses include discussions about _``certain sensitive topics that you might
not be allowed to inquire [tool(s)] about.'' (P15)_ AI tools are rather new
and still an emerging research area especially in areas such as prompt
engineering. A number of active research is still exploring how to more
effectively work with large language models, code generation (Chen et al.,
2021; Weyssow et al., 2023; Du et al., 2024), and designing prompts (Reynolds
and McDonell, 2021; Brade et al., 2023; Liu and Chilton, 2022). Unlike
traditional software development tools such as Jira or GitHub or Visual Studio
Code, which have remained stable over the years, AI tools are constantly
undergoing change. P15 organization's training demonstrates the desire some
organizations have to keep pace with the AI tool transformation.

Our survey respondents agreed that organizations providing training on
prompting supports increased use and adoption of AI tools (79/123, 64.2%). In
particular, surveyees from Asia (77.4%, 41/53) strongly agreed that training
supports increased use. Contrastingly, participants from Europe (53.8%, 14/26)
and North America (58.6%, 17/29) had much lower agreement. These numbers seem
to align with earlier research on technology adoption that shows people from
Asia were less resistant to trying out new technologies (Seong et al., 2024;
Asia, 2024).

In addition to providing guidelines and training, one direct strategy to
tackle privacy exposure risk mentioned by interviewees is to develop a company
portal that sanitizes all prompts. A company created portal allows employees
to deal with customer data with an AI tool and lower the potential risk of
data disclosure to a third party provider. _``You can also choose to use the
internal resources of your company when you're dealing with customer data. You
still have to use the interface of the company.'' (P13)_ Developing a company
native portal is even more paramount for industries are under more security
and privacy scrutiny. For example, financial firms have to adhere to
regulations such as PCI (Williams and Adamson, 2022). _``Chatgpt, we use it as
a policy that chatgpt is only allowed to use with level one data. So we have
some kind of confidentiality level.'' (P26)_ To this end we found our
interviewees acknowledging and making progress in sanitizing the prompts they
send to external AI tools.

3 interviewees _(P13, P15, P17)_ indicated that their companies have
implemented structured mechanisms (i.e., sanitization) to ensure the safe and
responsible use of AI tool(s) by their employees. For instance, P15
highlighted _``When I visit ChatGPT, I definitely have to go through the
company's portal, and then on that end, there will be some verification and
filtering happening. … there may be a safety net where the company could help
take care of it.'' (P15)_ Similarly, for P13, their sanitization tool first
filters out sensitive information and can directly reject requests that may
compromise safety in the input. There is a specific team in charge of the
prompt filtration service and they use a combination of rule based and model
based approach across multiple layers to remove compromising content. These
measures prevent the sharing of confidential data, such as database passwords
or proprietary code, while allowing for the use of AI for non-sensitive tasks,
such as discussing code hierarchy or non-confidential snippets. From our
surveyees, 74/122 (60.7%) of participants agreed that a tool to sanitize
prompts helps protect company data. Like providing training, surveyees who
most strongly agreed about company providing tool to help sanitize prompt were
based in Asia (71.4%, 40/56), versus Europe (46.4%, 13/28) and North America
(54.2%, 13/24). Through providing sanitizations mechanisms and techniques,
employees are encouraged to engage with AI tools while complying with security
and confidentiality standards.

###  4.4. Organizational Challenges

####  4.4.1. OC1: Lack of culture of sharing:

While some organizations established a culture of sharing AI practices, our
interviewees _(P5, P8, P11, P13, P14, P22, P24, P25)_ also described how their
organizations did not have a culture of sharing and collaboration. A lack of
sharing is characterized by limited communication and interaction among
colleagues about AI tool use and best practices. Our interviewees highlighted
that their discussion about AI tools was limited to individual use rather than
summarizing lessons that could be disseminated company wide. Emerging in the
post-pandemic work environment, previous literature showed that organizations
working in remote and hybrid work environments, which are increasingly common,
has to watch for coordination, collaboration, and communication challenges (de
Souza Santos and Ralph, 2022). Coordinating and communicating about software
development can be challenging, without even considering ``what are the tools
that my colleagues are using?" We found a frequent narrative was that
practitioners are either unaware of their colleagues' use of tools or
perceived the use of these tools as irrelevant to their field of work. In
particular 77.4% (24/31) of survey participants agreed that everyone does
different work and they do not share a similar project context as the reason
for not communicating about AI best practices. 68.9% (20/29) of survey
participants believed that there is no culture of sharing with their team or
organization. For example, _``I feel that at least in our company, this kind
of atmosphere hasn't come about, or it's how we view ourselves in this
context.'' (P11)_

We found participants who have access to group messaging, but there is little
engagement from teams that result in productive discussions about the tools.
Referring to their company group chat, _``I don't think so. No [sharing], not
in my team.'' (P8)_ This results in small, unorganized discussion of tips and
best practices rather than meaningful discourse that may result in a
coordinated organizational wide strategy. P25 exemplifies this problem about
how a team does not know what tool(s) are being used in nearby teams. _``I
think the other two teams don't even know about it because the communication
it's not really good.'' (P25)_

Another limiting factor is that participants may be dissuaded when they
perceive an organization is against sharing of experiences. _``Absolutely not.
There's no culture there. I think like, I know that some people on my team…
they don't know that it's out there… There's no culture of sharing or anything
like that.'' (P5)_ _``No, there are very few [sharing activities]. It might be
that the whole company hasn't really promoted or initiated such activities.
Someone at my level, a middle-tier leader, will relatively follow the
company's policies in doing things, making plans.'' (P14)_ In P14's case, even
though they are a technical lead with dozens of employees working under their
supervision, they still need to follow the company's lead. Therefore, if an
organization demonstrates limited interest in promoting a culture of knowledge
sharing, the effect seems to cascade down.

####  4.4.2. OC2: Company does not cover the cost of the tool:

A significant concern that emerged from our interviewees and surveyees was the
cost of the premium version of the AI tool(s). 8 interviewees (P5, P6, P11,
P12, P13, P14, P22, P26) indicated that they bore the expense of these tool(s)
out of their own pockets to have access to better features. 120/201 (59.7%)
survey participants reported that their AI usage is challenged due to the high
prices associated with subscriptions and paying for it. In particular, we
found a discrepancy between geographic location regarding the payment of tools
perceived as a problem. For survey participants based in Asia, 76% (57/75)
agreed that their usage of AI tools is limited by the high cost of AI tools
and paying. There was a significant gap with the participants from Europe
(52.9%, 27/51) and North America (37.8%, 17/45). For instance, P16's
organization is based in Asia and has a large number of employees, but the
organization was unable to cover the cost of the tool due to payment. _``The
company is relatively poor. We don't have this paid version!'' (P16)_ Many of
the largest AI tool providers are based in North America so it seems
reasonable for organizations based there to establish partnerships with these
providers (Harvard Business Review, 2023).

Employees may purchase a subscription as a trial run and try it out for a
period of time, but the high cost of subscriptions can lead people to just
revert back to a standard free version. _``During the initial stage of trying
it out, I did purchase the pro version. But, the company didn't buy this kind
of license. This is a reflection of the company's real situation.'' (P14)_ Not
having access to the full paid version could have negative repercussions on
the benefits a practitioners could realize given that paid tools usually
provide bigger prompt limits, more recent knowledge base, and ability to more
frequently query (Diaz, [n. d.]). The paid version often offers better
features which can help accelerate the development work.

Related to the paid versus free versions, is the number of API calls granted
per unit of time. As of this time of writing, a plus version of ChatGPT allows
up to forty prompts per three hours. A developer can quickly reach the upper
bound if they are conducting a lot of experimentation. 73/142 (51.4%) of
survey respondents reported that the number of API calls allowed in a time
interval is limiting their usage of AI tools. In contrast, an organization
such as P15's employer grants developers up to 500 or 1000 prompts per day. We
found among the survey respondents that those participants who had less than 1
year experience more commonly reported the API problem with 75% (12/16) versus
52.5% (21/40) for participants with 5-10 years experience and 43.3% (13/30)
for participants with 10+ years experience. One cause for this discrepancy
could be that juniors have less experience with software development so they
may require more trial and error leading to higher API use. Seniors, in
contrast, have a wealth of experience and require less trial and error
prompting so they are more likely to reach the solution using less prompts.

####  4.4.3. OC3: Lack of company guideline:

In addition to some organizations not covering the cost of AI tools, we also
found 9 interviewees _(P10, P11, P12, P14, P16, P21, P22, P24, P26)_ who
indicated that there is a lack of formal training and guidelines in their
company, on the effective use of AI tools. Prior to AI tools, organizations
implemented restrictions preventing unauthorized sharing of sensitive
information on platforms like GitHub, but no such guidelines have been
prescribed to address the emerging challenges of AI tools. _``In the early
days it was mentioned that it was strictly forbidden to post anything on
[Github]. And it should be in the handbook. The company have not made any
specific and clear policies targeting AI tool(s).'' (P14)_ These interviewees
complained that their organizations adopted a laissez-faire attitude towards
governing the use of AI tools.

Sometimes not publishing any guidelines is a purposeful decision, because a
company may be unsure about how these tools handle data. _``We have a monthly
meeting where me and my colleagues gave a presentation about ChatGPT how to
use it in the work. However, there is not an concrete example, this is what
you're able to do. I think they one side is that they don't know yet.'' (P21)_
P21 further adds, _``We had a training about security, about what to do and
how to not get your information leaked, the basic things. But for AI, it's
still not that explicit.'' (P21)_ One positive and one negative consequence is
caused by this laissez-faire attitude. The positive is that employees may
choose to use AI tools how they see fit, the negative is that employees are
operating in a grey area of company policy. 36.2% (38/105) of participants in
small organizations and 40.5% (15/37) of participants in medium organizations
agreed that their use of AI tools was limited due to company not providing any
usage guidelines, in contrast to 29.6% (8/27) and 21.9% (7/32) of participants
from large and extra-large organizations. Larger organizations tend to have
more structure in place compared to smaller organizations who have flatter
organizational structure (Cummings and Berger, 1976). For example, during the
push for European organizations to comply with the General Data Protection
Regulation (gdp, 2019), one of the most stringent privacy regulations in the
world, smaller and medium organizations were found to be struggling to comply
more than their larger counterparts (Sirur et al., 2018). For guidelines
surrounding AI tools, smaller organizations again may not have the necessary
resources to form dedicated teams to analyze data collection and processing
conduct from AI tool companies.

Aside from the lack of guidelines, we found 20 participants _(P5-P14, P16-P18,
P20-P26)_ indicating that there was a lack of formal training from their
company on the effective use of AI tools. Participant P25 noted that AI
tool(s) were introduced to their company's Slack with no training or guidance.
_``It was dropped just like that.'' (P25)_ Employees in the company were left
to independently navigate and explore the tool's functionalities without any
organized support from the company. Our interviewees' perception was that
training could enhance developers' proficiency in crafting prompts and
leveraging AI tool(s)' capabilities. _``if there's a training, which is able
to efficiently help you like software developers, specifically, and how to
prompt, I think it will be very helpful.'' (P21)_ 49/197 (24.9%) survey
participants felt that their use of AI tools was challenged from a lack of
training provided by their company. This ratio is not considerably high, but
we note that most AI assistance tool(s) for software are relatively easy to
get a conversation started (Borsci et al., 2022; Jo, 2023). Training to use
the basic functionality of these tools is most likely unnecessary. However, as
described in Subsection 4.3.3, training can help employees become more
proficient in prompting. Like Subsection 4.3.3, we found noticeable
differences in survey demographics. Survey participants from Asia (32.9%,
22/67) agreed more often than those from North America (17.3%, 9/52) and
Europe (13%, 7/54) that company not providing training limited use of AI
tools.

11 interviewees _(P1, P4, P10, P13, P14, P17, P20-P23, P26)_ voiced concern
regarding the exposure of company data privacy as a challenge that limits
their adoption of AI tool(s). In recent years, a growing push from legislators
around the world was enacting stringent privacy regulations in an effort to
more effectively protect user privacy. Among the complex to navigate
regulations include the GDPR (gdp, 2019), California Consumer Privacy Act
(ccp, [n. d.]), Personal Data Protection Act (pdp, [n. d.]), Personal
Information Protection Law (pip, [n. d.]), and Consumer Privacy Protection Act
(cpp, [n. d.]). Famous cases of privacy infringements resulting in loss of
user data also costed company reputation and financial penalties (CSO Online,
2023). It was reasonable for interviewees to express worry about the potential
privacy consequences that may arise from the use of AI tools. There is also
some concern that policy makers are slow to react to technology changes.
_``For those who are against such technology, for lawmakers, it’s actually
because we often know that legislation usually lags behind. '' (P14)_

Previous literature has covered concerns regarding the integration of AI tools
like Copilot (Jaworski and Piotrkowski, 2023; Zhang et al., 2023) and the
security worries from their use. We found one primary concern from
participants is that AI tools are recording user prompts and then using the
prompt contents as further training data. _``Data security is really
concerning part for us, and that's why we can't depend on ChatGPT and we can't
use in our IDE. What will be the use cases of the consumed data and whats the
guarantee they will not leak it somehow and sell it to somewhere. Are they
consuming my credentials? Are they consuming my personal data? '' (P4)_
Moreover, many users of these AI tools rarely understand terms of service and
privacy policies even when they are available. This is a notorious problem
among research regarding how to ensure end users understand privacy risks
(Tang et al., 2021).

We found in our survey responses that there was a noticeable difference in
attitude between participants from larger organizations in comparison to those
from smaller organization. 68.4% (26/38) and 59.4% (19/32) from extra-large
and large organizations agreed that their use of AI was limited by privacy
versus 35.9% (37/103) and 39.4% (11/28) from smaller and medium organizations.
The deep-rooted concern with privacy is largely attributed to customer data.
Consequently, participants expressed hesitance to use AI tool(s) for tasks
that might involve any customer confidential data. _``Data from conversations.
We will try not to use real ones. For example, we also have e-commerce
platforms. The data from dialogues between customer service and users, we will
not use those. Especially when it involves buyers' addresses, phone numbers,
and such.'' (P17)_

Given the significant fines that have been handed out to companies for
violation of privacy regulations, (CSO Online, 2023) it seems reasonable for
employees from large organizations to be extra considerate about user data.
The most significant fines against organizations have largely all against
large organizations with significant financial resources. Based on the GDPR
fines, smaller organizations have for the most part avoided excessive
scrutiny. Practitioners in smaller organizations may not have organizational
guidelines governing AI tool terms of use but are less worried about privacy
because they may not survive. _``If I am a large corporate, I wouldn't want
user data to leak. For a small company, surviving until the day we receive a
legal notice is one thing, but if you're a large company and let's say you
have a major user data breach, that's a huge problem.'' (P26)_ Ultimately,
privacy regulations that are passed by jurisdictions have strong implications
for how much companies fear data breaches from using AI tools. Similar to the
Cambridge Analytica case (The New York Times, 2018), one privacy breach can
have irreparable damage to reputation and cause negative financial
consequences.

Table 5. Relationships between factors from interviewees and survey voting results (from 5-step Likert scale) ID |  Relationships |  Survey Prompt |  Top-3 #Votes (Ratios)
---|---|---|---
IM2 & OM1 & IC4 & OC1 |  Culture of sharing AI knowledge vs. Negative judgment and lack of support |  If organization builds a culture of sharing AI practices would it increase your use of AI tool(s)? |  173/223 (77.6%)
If organization builds a culture of sharing AI practices would it reduce the potential for negative judgment by others? |  158/216 (73.1%)
OM2 & OC2 |  Providing and promoting vs. Not paying the cost |  If organization starts paying for employee use of AI tool(s) would it increase employee's use of AI tool(s) |  193/226 (85.4%)
OM3 & IC3 & OC3 |  Providing guidance and training vs. Lack of prompting skills and usage guidelines |  If organization implements a policy on the rules, restrictions, and accepted terms for using AI tool(s) would it increase your use of AI tool(s)? |  131/205 (63.9%)
If organization provides guidance and training for AI tool(s) would it increase your use of AI tool(s)? |  168/231 (72.7%)
|  |  If organization provides tool(s) and techniques for prompt sanitization would it reduce your concern about data privacy leaks? |  176/227 (77.5%)

##  5\. Push-Pull Relationships between the Motives and Challenges

This section describes relationships that we observed between some of the
motives and challenges that have significant impact on the use and adoption of
AI tools. These relationships act in a push-pull manner, whereby motives and
challenges are interconnected with each other. Motives _pull_ practitioners
and organizations to increase use and adoption of AI tools, whereas challenges
act in the opposite way and _push_ practitioners and organizations away from
adopting and using AI tools. For example, _providing and promoting the tool_
motivates the use and adoption of AI tools, but in contrast, _company does not
cover the cost of the tool_ inhibits use and adoption. We derived these
relationships because they help indicate the factors that software
organizations can consider to improve their use of AI tools.

###  5.1. Culture of sharing AI knowledge vs. Negative judgment and lack of
support:

One of the most important relationships identified in our research was
establishing a culture of AI experience and knowledge sharing helps increase
AI tool use and adoption, but a lack of support and potential judgments impede
adoption and use. This relationship intersects all four categories identified
in our theory from individual motive, and organizational motive, to individual
challenge, and organizational challenge.

From an individual perspective, we found that participants often had a natural
inclination to share their experiences and best practices from their use of AI
tools. The willingness to share knowledge not only fosters a collaborative
environment but also informs colleagues who may not be aware of the
productivity boost. _``People give ideas about the best way to prompt ChatGPT.
I think that was usually somebody will share cheat sheets. This was something
that really helped them like over time. And people kind of like, keyed into
that and started to use that.'' (P20)_ This type of individual initiative was
also on display from practitioners who actively encouraged others in their
organization to start using AI tools. _``[Employees] encourage other people to
use it. But it doesn't come from an organizational level where it doesn't come
from the top down to say we should be using these to make us more efficient.''
(P9)_

However, this individual motive is limited by a counter factor, primarily
fuelled by a fear of judgment from other team members. This fear stems from a
perception that reliance on AI tools may be seen as a lack of capability or
laziness. _``Because you're relying on it so much that you're not using your
brain, which is that's a problem.'' (P6)_ The criticism is typically directed
towards junior developers who leverage AI tool(s) to help them learn, search,
and debug code. _``I've seen that passing judgment. Some of them is still
passing judgments when using the ChatGPT.'' (P4)_

In comparison, 73.1% (158/216) of survey participants selected the top 3
levels in a 5 level Likert scale indicating that if an organization builds a
culture of sharing AI practices, it would reduce the potential for negative
judgment from using AI tools. Some of our participant organizations
facilitated a culture of sharing, where team members were encouraged to
discuss their experiences and share tips. This sharing of knowledge is often
conducted through communication channels such as Microsoft Teams or Slack,
which act as avenues for knowledge transfer, as described by P7 and P21 in
Section 4.3.1. 78.3% (173/221) of survey respondents agreed that an
organization that builds a culture of sharing AI practices would increase
their use of AI tools. Meanwhile, the chief reasoning for a lack of sharing
was that employees conducted different work and organizations did not create
an atmosphere conducive for sharing with other team members. _``It's because
perhaps everyone has different habits, and so each individual faces different
problems, including even the fact that everyone is responsible for different
modules.'' (P17)_ Similarly, for P22, colleagues have few discussions about AI
tool use. _``None of the people like chatting.'' (P22)_ In such environments,
conversations about best practices, tips, and experiences are limited, and
there are less opportunities for disseminating AI tool related knowledge
within the organization.

###  5.2. Providing and promoting vs. Not paying the cost:

Another relationship identified in our work was providing and promoting AI
tools within an organization helps increase AI tool use and adoption, but
adoption and use is impeded when organizations do not pay for the cost of AI
tools We found participants agreeing that organizations covered the cost of AI
tools was a motivating factor for employees to use them. When companies do not
cover the cost, fewer people use the tools because the cost becomes a hurdle.
Depending on geographic location and available resources, the cost may be
significant for a practitioner, especially as the number of AI tools increases
every time a major AI company releases a new model or tool. 193/226 (85.4%) of
our survey respondents indicated that employee use of AI tools would increase
if an organization starts paying for them.

One trial and error approach seemed to work for some participants. An
organization may not initially pay for a tool, if an employee can show that
the tool is necessary for their job and boosts productivity, the company might
then agree to cover the cost. _``If I can prove that without chatGPT or
without the paid version, I cannot work or it will help a lot for me, as well
as the company to work along the way. If I'm using the paid version of
chatGPT, the company will definitely pay for it.'' (P10)_

###  5.3. Providing guidance and training vs. Lack of prompting skills and
usage guidelines:

The final use and adoption relationship we found is providing guidance and
training for using AI tools help increase AI tool use and adoption, but
adoption and use is impeded when individuals lack prompting skills and
organizations do not provide AI tool usage guidelines. As described earlier in
OM2 in Section 4.3.3, when organizations invest in training for developers
about AI tool(s) and prescribe clear guidelines, it clarifies the limits of
what employees can and cannot do. This can help reduce uncertainty where a
developer may not know if their actions are legally permitted by their
organization. P21 provides an example of how he believes training would help.
_``I think if there is training, which is able to efficiently help you like
software developers specifically, and how to prompt, I think it will be very
helpful.'' (P21)_

AI is a rapidly changing field with a plethora of active research. Sometimes
within the span of weeks, major updates are made to popular AI tools or a
completely new AI model is released altogether. Consequently, it can be
difficult for practitioners to keep up with the pace of AI change if they are
not actively paying attention to all the changes (Yahoo Finance, 2024). _``The
majority of my fellow colleagues, including myself, might not know in what
situations AI tool(s) can play a certain role, and also, with the current
large models at the front line, their speed of upgrading and iteration is very
fast…'' (P15)_ Should an organization provide prompt engineering and other
training, it would help guide practitioners to understand how to best use the
available tools and which software development task work best for each tool or
model. 72.7% (168/231) of survey participants agreed that if an organization
provides guidance and training for AI tool(s) it would increase their use of
AI tool(s).

In contrast, our work found challenges pertaining to when organizations failed
to provide clear guidelines on the use of AI tool(s), as discussed in detail
in Subsection 4.4.3. When there is a lack of clear guidelines, many
practitioners suffered from a fear stemming from an undesire to inadvertently
violate company policies, which could lead to negative consequences for both
the individual and organization. _``I think we should be more careful about
compliance this and things like that, I think sometimes in software dev, you
kind of forget, because you're more lenient on looking for things online.''
(P23)_ Correspondingly, 63.9% (131/205) of survey respondents agreed that
their use and adoption of AI tools would increase if an organization
implements a policy on the rules, restrictions, and accepted terms for using
AI tools.

Additionally, we found that when organizations implement measures to sanitize
inputs and outputs of AI tool(s) described in section 4.3.3, developers feel
more comfortable. These sanitization processes are particularly important for
practitioners to be assured that sensitive information, such as API keys, user
data, or proprietary code, is not inadvertently captured and misused by these
tool(s). _``We also maintain partnerships with organizations like OpenAI…
Could involve filtering out safety or safety issue-related inputs and
outputs.'' (P13)_ The absence of such measures leaves developers wary about
using AI tool(s) with sensitive or confidential code. 176/227 (77.5%) of
survey participants indicated that if an organization provides tool(s) and
techniques for prompt sanitization it would reduce their concern about data
privacy leaks.

The absence of such measures leaves developers wary about using AI tool(s)
with sensitive or confidential code. _``I never use names or brands,
especially in terms of my company, I never use any of the company names. I
usually obscure even references to environment variables. It's very important
that there's no reference to any real person.'' (P22)_ This fear often leads
them to alter code or create a hypothetical situation in their prompts to
avoid the risk of exposing sensitive data. _``I'll always have a sanity check
is this. Will this be okay to put in ChatGPT? I don't just copy-paste and put
it in there. I sometimes do like pseudocode.'' (P21)_ Therefore, our
participants suggest organizations implement a combination of clear policies
governing tool use and providing means of filtering sensitive data directly
inside prompts. Otherwise, the risk of privacy exposures can be costly (DPM,
2021; gdp, 2023, [n. d.], 2024).

##  6\. Discussion

Previous research studying AI tool(s) in software development has often
focused on the tool(s) themselves and the usability of those tool(s) for
software tasks (Liang et al., 2024; Sta, 2023; Peng et al., 2023; GitLab,
2023). These prior works found that developers reportedly gain immense boosts
in productivity when they leveraged AI assistance to help with development
(Peng et al., 2023; Liang et al., 2024; GitLab, 2023). As a result, AI tool(s)
such as ChatGPT experienced monumental user growth, where in less than 1 year,
it successfully attracted 100 million weekly users (Porter, 2023).

One previous study attempted to provide more contextualization regarding the
adoption of generative AI (Russo, 2023). Russo developed a preliminary theory
for the adoption of generative AI, however, 3/7 of his hypotheses were
rejected upon analysis (Russo, 2023). In particular, he suggested that
organizations play a limited role in impacting developer adoption of AI tools,
that more future empirical work is needed to investigate this fast-changing
landscape. Therefore, we still lacked clarity on the impact of organizations
and individuals gravitating toward or away from adopting these tools.

In our work, we identified challenges and motives that push and pull
practitioners into using AI tool(s). Unlike individual motives, which may have
a visible impact, our study also uncovered the importance that organizational
culture serves in the adoption and increased use of tool(s). Our findings on
the impact of organizations contradict with previous literature (Russo, 2023),
however, we surmise that this may be a result of our emphasis in our study
about organizational role in the use and adoption of AI tool(s). Moreover, it
is possible that aspects such as guidelines and data privacy are becoming more
important due to ongoing lawsuits and regulatory concerns.

In our study, we aimed to fulfill this gap through a Socio-Technical Grounded
Theory approach, which included 26 interviews with practitioners and a survey
with 395 respondents regarding their use and adoption of AI tool(s) for
software development. Our findings presented a list of 12 factors including 7
challenges that influence the use and adoption of AI tool(s). We also
presented 3 relationships between factors, which detailed how increasing
motives for use and adoption of AI tool(s) may help reduce its corresponding
challenges.

In the following subsections, we discuss how to increase the use and adoption
of AI tools in organizations and the importance of privacy concerns and ethics
amidst the use and adoption of AI tools. Finally, we propose directions for
future research.

###  6.1. Increasing adoption of AI tool(s) in organizations

The primary finding in our study is what are the factors that are motivating
and challenging the adoption and usage of AI tool(s) for software development.
Previous works have shown that developers who use tool(s) like Copilot and
ChatGPT should reap significant productivity improvements and become more
effective in their work (Kalliamvakou, 2022). While there is debate on exact
extent of improvement, the current literature and confirmed in our study
supports the notion that AI tool(s) boost practitioner productivity.

However, despite these benefits, our study unveiled that not all companies or
developers are embracing AI tool(s) to their fullest potential. Several
limiting factors were identified, affecting both the extent of adoption and
usage. At an individual level, knowledge gaps and uncertainty about the
optimal use of AI tool(s) can hinder their adoption. We found that AI tool
adoption in organizations is not trivial as simply creating a ChatGPT account
and using it to generate software code.

Previous works have stated productivity boosts (Peng et al., 2023; Liang et
al., 2024), materializing from auto completion, finding code solutions and
edge cases are motivations for developer use of AI tool(s). Whereas
motivations for not using AI tool(s) include lack of useful or relevant
output, particularly involving functional and non-functional requirements
(Liang et al., 2024).

One organizational challenge that has profound impact on software
professionals is simply organizations not covering the cost of AI tool(s) such
as ChatGPT or others. Although the cost of $20 USD may seem trivial to
resource rich organizations, numerous participants in our study indicated that
their organizations are not covering this cost. For participants who are in
less resource rich situations, the price of ``Plus" subscriptions may be cost
prohibitive. As one surveyee explained with respect to what practices their
organization should follow to improve the adoption, _``Offer cost reductions
for AI tool(s) and even assist employees in covering the expenses."_

Even for organizations who provide AI tool(s) directly to users the choice of
subscription could also have negative ramifications. For example, P25's
organization provides a company subscription to ChatGPT integrated into Slack,
but it is the lower 3.5 version. Consequently, P25's motivation to use the AI
tool was dampened due to poor generated results. _``But it just that version,
it just repeats things that I asked and answers me back with the same words,
like scrambled, I didn't like it. It didn't get to the point for the
documentation for me. It's just babble."_

From an organizational perspective, the culture surrounding knowledge sharing
plays a pivotal role. In environments where sharing and collaboration are
encouraged, knowledge of AI tool(s) and their potential benefits disseminates
more rapidly, fostering a more widespread and effective adoption. When
employees share insights with others in the company or team, it can
significantly improve the productivity of colleagues because some employees
are not aware of the best practices. In P16's organization, colleagues share
insights about how to use tool(s) to increase productivity. As an example, one
colleague demonstrated how they were able to use AI tool(s) to develop a
useful web scraper in 15 minutes, the previous human manual effort would have
been a whole day.

In contrast, practitioners should be cautious of potential judgment and lack
of culture of sharing manifesting in their organization as they may limit the
gains an organization could realize. As one surveyee respondent added,
_``Driving AI adoption from the top down is the only way to get people 'out of
the shadows'. And the only way to do that is for leaders to spend the time and
curiosity to understand limitations and opportunities from AI, and encourage
that sharing in the org."_ Organizations should avoid creating environments
where employees are negatively judged and rated depending on their usage of
AI. One survey participant explains that the reason they do not share their
experience of using AI tool(s) with other colleagues is a result of, _``Just
they might judge my performance if I say I do so_." Our findings emphasize the
importance of addressing both individual and organizational challenges to
maximize the benefits of AI tool(s) in software development. Software
professionals can use our theory to identify areas to watch out and practices
to adapt to increase motives for high use of AI tool(s).

###  6.2. Privacy concerns and ethics

Previous literature focusing on the ethics (Pant et al., 2024a) of AI
practitioners have shown that practitioners are aware of the risks of privacy.
In fact, _``Privacy protection and security"_ was the concept that majority of
AI practitioners in the study were aware about (Pant et al., 2024a). A
separate multiple case study on ethical concerns from implementing big data
and AI found that privacy was the only ethical principle that was prevalent in
all the case studies (Ryan et al., 2021). Despite the recognition of need for
ethical considerations by those who _build_ AI products, our findings indicate
that organizations and practitioners are nonetheless still significantly
concerned about privacy.

A large number of our respondents emphasized that they are limited in their
adoption and subsequent use of various AI tool(s) because they are
uncomfortable sharing company data in the form of code or documentation, as
well as feeding customer data directly to a tool. As one survey respondent
explained, _``Uncertain data privacy both internally and externally with AI
providers/tool(s)"_. A previous survey on usability of AI programming
assistants found that 54% of participants gave up on using AI assistants
because code generation tool(s) did not meet functional or non-functional
requirements, which includes security and other requirements (Liang et al.,
2024). However, our findings specifically highlight the anxiety surrounding
privacy.

Not only are respondents concerned about prompting itself, but also worried
about the unethical sourcing of data. As one surveyee described, _``Major
privacy problems. Also data sourcing, most data collected by AI has been done
unethically without consent."_ Similarly, P8 also pointed out this
uncertainty, _``So just always want to make sure that whatever you whatever I
input doesn't contain any kind anything that is tied to the company… It's an
internal policy. Because I think that data is also feedback to like train the
model to be more efficient."_

As another survey respondent elaborated, more to this problem about data
collection issue is that _``There are few LLM-based tool(s) which have clear
privacy manifests and have strict rules to a data collection, that is the
biggest show-stopper. There is little to no knowledge on how models were
trained as well, which leads to a question on how legal is it to use output of
such LL models."_ From the perspective of software professionals and their
organizations, it would help dissuade their concerns if AI tool developers
began clearly outlining what they are collecting and how the security will be
handled. _``They should outline clearly what they are consuming and how
they're consuming and what will be the security of this the consume data.''
(P4)_

However, the privacy discussion is ongoing, where the development of stringent
regulations is being discussed in various jurisdictions around the world (noa,
2024; Warren, 2024; Yang, 2024) amidst calls for more regulation (ope, 2023;
Díaz-Rodríguez et al., 2023; Hacker et al., 2023; Finocchiaro, 2023; Pascual,
2023). Advocates call for auditing and regulation of AI (Díaz-Rodríguez et
al., 2023; ope, 2023), with some industry giants even calling for an immediate
pause to AI experiments (ope, 2023). In contrary, there are also some debate
about balance to strike between constraints that may stifle innovation. _``If
you impose constraints too early and too strictly, you end up limiting what it
could achieve in the future, where it could go. We can see that LLMs have
already made some sort of breakthrough developments in certain areas. [With
stringent regulation] they might find themselves in an awkward competitive
spot, and may not be willing to invest in researching and studying this
technology.'' (P14)_

However, our findings still provide some suggestions that software
organizations and practitioners may consider to assist their adoption and
mitigate some concerns. Software organizations may alleviate some uncertainty
internal to organizations by establishing clear guidelines governing the use
of AI tool(s). These guidelines may include aspects such as names of the
tool(s) permitted by the organization. Moreover, the types of prompts and the
types of data permitted as input should also help alleviate practitioner
confusion.

Software organizations may also opt to develop or externally acquire tool(s)
that plugin into the AI tool(s) that support sanitizing or anonymizing
practitioner prompts so that confidential or sensitive information is filtered
before sending. For example P15's organization established a sanitization tool
that filters a prompt of sensitive content before sending it off to a third
party tool. This sanitization helps abstract software professionals from
checking themselves if their prompt satisfies organizational privacy
requirements.

###  6.3. Directions for Future Research

  * •

Prompt sanitization and privacy safeguards: Our research highlights the
importance that many practitioners place on effectively protecting company and
user privacy. Privacy and security of prompts when using AI tool(s) is hugely
critical for more widespread adoption and use of these tool(s). Further
research could investigate leveraging security and privacy mechanisms to
protect confidential data as built-in plugins when using AI tool(s).

  * •

Organizational aspects: We also found that organization play a significant
role influencing the use and adoption of AI tool(s). Whether it is the payment
for AI tool subscriptions or facilitating a culture of AI practice sharing,
organizational culture impacts the decisions that practitioners make about AI
tool(s). More empirical research into organizational aspects such as culture
and size are warranted to better understand how these factors prevail and
their specific impacts on practitioners. More importantly, more research into
this area could develop strategies into mitigating the challenges with
adoption and usage that we identified in this work.

  * •

Education: The participants of our study expressed how training and
understanding prompting is crucial to achieving the most benefits out of the
tool(s). While some organizations provide varying levels of guidance and basic
training for producing more effective prompts, more research is needed to
create more structured training. Future research could study the areas and
content that should be part of manuals and training material to help
practitioners become effective users of AI tool(s).

###  6.4. Threats to Validity

Despite our best efforts in mitigating threats to our research, there are
still some limitations. We use the total reliability framework from Roller
(Roller and Lavrakas, 2015) and the aspects of credibility, analyzability,
transparency, and usefulness to assess the threats to validity.

_Credibility_ refers to the accuracy and truthfulness of the data. It is about
ensuring that our research accurately represent the data collected and the
perspectives of our study participants. For our research, our selection of
interview participants is limited by our convenience sampling, which means
reaching out to practitioners from our personal contacts. However, we
mitigated this threat by ensuring that each participant matches our selection
criteria, which is aimed towards answering our research goal. We ensured that
each participant was a practitioner who uses AI tool(s) in their software
development work. In addition, we relied on a mixed methods approach where we
validated the findings from our interviews with a survey towards software
professionals.

_Analyzability_ refers to the ability to draw meaningful conclusions from the
data. For our study, we relied on the transcription tool Otter.ai (ott, 2023)
to help transcribe each interview. We also relied on SurveyMonkey to conduct
our questionnaire survey (sur, 2023). Three co-authors followed the steps of
STGT to collect and analyze the data, including conducting open coding,
memoing and constant comparisons until reaching saturation at which point the
theory was developed.

_Transparency_ refers to the the clear and completeness of documenting of the
research process. For our study, we provided thick descriptions of our entire
research methodology. We also provided examples and used quotes wherever
possible. Our provided as much details as possible to show the relationships
between the themes in the findings. In addition, we release a replication
package containing the interview questions, code book, and survey questions.
Unfortunately, we are bound by research ethics obtained from our institution
and unable to release raw transcripts nor answers from interview and survey
participants.

_Usefulness_ refers to the utility of our research and whether the findings
have value to the relevant audience. AI assistance tool(s) for software is
currently undergoing a rapid pace of change, which is challenging for our
research. However, many of our findings transcends cultural aspects that are
not easily _``solved"_ by an update in an AI tool, but otherwise require
further study to mitigate any of the challenges limiting adoption of AI
tool(s). This is why we conducted both interviews with diverse software
professionals and also conducted a validation round of questionnaire surveys
with over 395 professionals from around the world. Although we do not assume
that every software organization will encounter the same challenges and
motives identified in our research, we expect professionals and organizations
that share characteristics as those in our study to experience some similar
benefits and limitations.

##  7\. Conclusion

Practitioners and organizations are increasingly relying on AI assistance
tool(s) to help with software development, recognizing the importance these
tool(s) play in the present and future. We followed the steps of Socio-
Technical Grounded Theory to guide our research through 26 interviews with
industrial practitioners and theory validation with 395 survey respondents.
Our findings show that practitioners are motivated to adopt and use AI tool(s)
based on 2 individual motives and 3 organizational motives, but their use is
also challenged by 4 individual and 3 organization factors. 9 of these
challenges and motives are linked across 3 push-pull relationships. Given the
benefits that practitioners may acquire from using AI tool(s) in software
development tasks, we highlight the challenges which may limit increased
adoption and use. We also emphasize the importance for organizations to create
guidelines to help clarify potential privacy concerns and ethics from
practitioners.

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