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Ready or not, AI comes—an interview study of organizational AI readiness factors
Open accessAbstract
Artificial intelligence (AI) offers organizations much potential. Considering the manifold application areas, AI’s inherent complexity, and new organizational necessities, companies encounter pitfalls when adopting AI. An informed decision regarding an organization’s readiness increases the probability of successful AI adoption and is important to successfully leverage AI’s business value. Thus, companies need to assess whether their assets, capabilities, and commitment are ready for the individual AI adoption purpose. Research on AI readiness and AI adoption is still in its infancy. Consequently, researchers and practitioners lack guidance on the adoption of AI. The paper presents five categories of AI readiness factors and their illustrative actionable indicators. The AI readiness factors are deduced from an in-depth interview study with 25 AI experts and triangulated with both scientific and practitioner literature. Thus, the paper provides a sound set of organizational AI readiness factors, derives corresponding indicators for AI readiness assessments, and discusses the general implications for AI adoption. This is a first step toward conceptualizing relevant organizational AI readiness factors and guiding purposeful decisions in the entire AI adoption process for both research and practice.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This paper conceptualizes organizational AI readiness through an interview study with 25 AI experts, yielding five categories, 18 readiness factors, and 58 illustrative indicators. It argues that AI readiness is not merely a precursor to AI adoption but an integral, context- and purpose-specific element that mutually reinforces adoption across the process.
Key findings
- The study identifies five AI readiness categories: strategic alignment, resources, knowledge, culture, and data, comprising 18 factors and 58 illustrative indicators.
- Strategic alignment includes AI-business potentials, customer AI readiness, top management support, AI-process fit, and data-driven decision-making; resources include financial budget, personnel, and IT infrastructure; knowledge includes AI awareness, upskilling, and AI ethics; culture includes innovativeness, collaborative work, and change management; data includes data availability, data quality, data accessibility, and data flow.
- The authors argue that AI readiness and AI adoption are distinct yet highly interdependent and mutually reinforcing, requiring continuous assessment rather than a one-time readiness check.
- AI’s nature as a general-purpose technology means readiness must be both context-specific and purpose-specific, depending on the organization’s intended AI adoption purpose.
Methods
- Qualitative in-depth interview study with 25 AI experts (E01–E25), sampled purposively from the authors’ professional network; data collection stopped after 25 interviews because no significant new topics emerged.
- Interviews were recorded, transcribed, and analyzed from 1,385 interview minutes; analysis used open and axial coding in MAXQDA, with coding rounds producing 492 codes in 6 categories, then 895 codes in 39 categories and 87 subcategories, then 35 AI readiness factors in 12 categories, narrowed to 23 organizational factors in 8 categories, and finally 18 factors in 5 categories.
- Findings were triangulated with scientific and practitioner literature, and indicators were developed and validated through a card-sorting procedure in an AI-related researchers’ focus group.
Limitations
- The paper states that the purposive cross-industry interview sample primarily reflects the perspectives of experts in management positions; future research could sample across job levels and use in-depth case studies.
- The paper states that it does not elucidate prioritization or weighting of factors and indicators relative to overarching AI adoption purpose and organizational contingencies; future work should validate the factors and indicators and examine how they influence AI adoption success.
- The paper states that comparing organizational readiness factors across multiple technologies lies beyond its scope; an evident limitation is that the 58 indicators are self-developed and presented as a starting point for future validation rather than as empirically validated assessment scales.
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Claims the paper makes
63 Not checked yet · 30 Own interpretation · 15 Own result · 14 No basis given · 8 Own method
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In the paper
“This resulted in 23 organizational AI readiness factors in 8 categories.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 3.2 Data Analysis and Identification of AI Readiness Factors
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In the paper
“We structure the AI readiness factors in five categories that specify action fields and necessary conditions for successful AI adoption (see Table 1).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4 Organizational AI Readiness Factors
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In the paper
“Drawing on our insights, we compile five categoriesstrategic alignment, resources, knowledge, culture, and data -with 18 factors specifying distinct action fields of organizational AI readiness.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 1 Introduction
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This resulted in 35 AI readiness factors in 12 categories.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 3.2 Data Analysis and Identification of AI Readiness Factors
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In the paper
“Considering the wide range of organizational necessities associated with AI adoption, top management support is crucial for successful AI adoption.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.1 Strategic Alignment
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Concerning AI personnel, interviewees have particularly identified business analysts and AI specialists as relevant human resources.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
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In the paper
“This includes, for instance, factors of the category data which is the essential input of AI models, or factors such as AI ethics that emerge from potentially biased AI outcomes (Tambe et al. 2018).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.1 Conceptualizing Organizational AI Readiness for Successful AI Adoption
What the sources say
- Not checked yetcites Tambe et al. 2018 source
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In the paper
“We show that readiness is an integral element throughout the entire adoption process instead of a precursor condition (Lippert and Davis 2006).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.2 Theoretical Contribution and Practical Implications
What the sources say
- Not checked yetcites Lippert and Davis 2006 source
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In the paper
“Regarding data type, interview experts point out that different data types influence AI readiness.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
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In the paper
“First, our results yield new AI-specific readiness factors that emerge from AI's distinct features.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.1 Conceptualizing Organizational AI Readiness for Successful AI Adoption
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- Probably not a substantial claim. 69
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“After consolidating the coding set, we derived 895 codes in 39 categories and 87 subcategories.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 3.2 Data Analysis and Identification of AI Readiness Factors
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In the paper
“This first coding round resulted in six broad categories related to AI readiness and a total of 492 codes.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 3.2 Data Analysis and Identification of AI Readiness Factors
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Next, we present our final compilation of 18 AI readiness factors and 58 illustrative indicators (see Table 5 in Appendix IV for a complete list).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 3.2 Data Analysis and Identification of AI Readiness Factors
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In the paper
“Experts stressed an organization can only commit to AI adoption, once the top management provides an organization-wide signal.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.1 Strategic Alignment
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“For instance, our results confirm the relevance of the well-known adoption factors top management support (Sharma and Yetton 2003) and collaboration (Dewi and Ahamat 2018) for AI adoption.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.1 Conceptualizing Organizational AI Readiness for Successful AI Adoption
What the sources say
- Not checked yetcites Sharma and Yetton 2003
- Not checked yetcites Dewi and Ahamat 2018
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“With illustrative indicators, we provide a starting point for distinct action fields for developing AI readiness.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.2 Theoretical Contribution and Practical Implications
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In doing so, we corroborate existing factors but also provide additional factors accounting for AI's specifics.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.2 Theoretical Contribution and Practical Implications
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“By conceptualizing organizational AI readiness, we provide relevant factors and indicators to better understand and assess what measures organizations require for successful AI adoption.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.2 Theoretical Contribution and Practical Implications
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In the paper
“Second, our interviews corroborate readiness factors that are comparable to adopting any other technology and are also relevant for AI adoption.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.1 Conceptualizing Organizational AI Readiness for Successful AI Adoption
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In the paper
“Given our results, we conceptualize AI readiness being twofold.First, AI readiness comprises 18 readiness factors along five categories that provide the organizational chassis for developing AI readiness.Second, beyond the specific factors, AI readiness entails the understanding of purposeful AI adoption.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.1 Conceptualizing Organizational AI Readiness for Successful AI Adoption
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In the paper
“First, our paper provides additional empirical groundwork for theorizing on readiness and adoption.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.2 Theoretical Contribution and Practical Implications
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In the paper
“Thus, our results serve as the descriptive groundwork for further research on AI management in organizations, organizational readiness, and technology adoption.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.2 Theoretical Contribution and Practical Implications
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In the paper
“Finally, our findings explicate the need for contextspecific considerations for organizational readiness by referring to AI's characteristics.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.3 Limitations and Future Research
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In the paper
“This paper provided a thorough conceptualization of organizational AI readiness.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > AI adoption Initiation Adoption decision Implementation
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In the paper
“We used feedback from a focus group to further trim and restructure our results (see Appendix III for further details).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 3.2 Data Analysis and Identification of AI Readiness Factors
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In the paper
“We used a qualitative research approach to understand AI readiness factors (Bhattacherjee 2012).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 3 Method
What the sources say
- Not checked yetcites Bhattacherjee 2012
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In the paper
“Consequently, readiness models require context-specific consideration and need to be tailored to the related domain, i.e. a specific technology (Molla and Licker 2005).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 1 Introduction
What the sources say
- Not checked yetcites Molla and Licker 2005
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In the paper
“Artificial intelligence (AI) is a general-purpose technology (GPT) with a unique learning capability that provides organizations with potentials for wide-ranging improvements as well as entirely new business opportunities.Iansiti and Lakhani 2020.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 1 Introduction
What the sources say
- Not checked yetcites Iansiti and Lakhani 2020
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In the paper
“We stopped data collection after 25 interviews because no significant new topics were brought up and the emerging factors were increasingly repetitive.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 3.1 Data Collection
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In the paper
“Further, we operationalize these factors with 58 illustrative indicators and evaluate our results via a card-sorting procedure in an AI-related researchers' focus group.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 1 Introduction
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In the paper
“We recorded, transcribed, and analyzed 1385 interview minutes with the experts' consent.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 3.1 Data Collection
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In the paper
“Further, we used AI-related literature and practitioner studies to triangulate our conceptualization of AI readiness factors (Flick et al. 2004).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 3.2 Data Analysis and Identification of AI Readiness Factors
What the sources say
- Not checked yetcites Flick et al. 2004
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In the paper
“Organizational readiness for change theory postulates that a higher level of organizational readiness increases the success of innovation adoption and decreases the risk of failure (Snyder-Halpern 2001;Weiner 2009).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 1 Introduction
What the sources say
- Not checked yetcites Snyder-Halpern 2001
- Not checked yetcites Weiner 2009 source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Due to AI's nature as a GPT, possible application scenarios are not always directly obvious and organizations must understand the technology to decide on the intended adoption purpose (Jovanovic and Rousseau 2005).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 1 Introduction
What the sources say
- Not checked yetcites Jovanovic and Rousseau 2005
hedgeddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Considering AI's technical characteristics and knowledge barriers, AI adoption implies a high implementation complexity (Gallivan 2001) which differentiates it from other digital technologies that are typically easy-to-use and easy-to-deploy (Lokuge et al. 2018).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 1 Introduction
What the sources say
- Not checked yetcites Gallivan 2001
- Not checked yetcites Lokuge et al. 2018
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hence, AI adoption demands a thorough understanding of relevant AI readiness factors, an operationalized readiness assessment, and a tailored match between an organization's current AI readiness and the aspired AI adoption purpose.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 1 Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Being a GPT, AI drives changes at the task, process, and business model level in a plethora of application areas and as such offers a competitive advantage to organizations (Brynjolfsson and McAfee 2017).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 1 Introduction
What the sources say
- Not checked yetcites Brynjolfsson and McAfee 2017
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“We use open and axial coding to deduce AI readiness factors from the interview data (Corbin and Strauss 1990) and further triangulate our conceptualization with readiness and adoption literature as well as insights from existing practitioner studies (Flick et al. 2004).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 1 Introduction
What the sources say
- Not checked yetcites Corbin and Strauss 1990 source
- Not checked yetcites Flick et al. 2004
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In the paper
“We used purposive sampling to identify 25 AI experts (E01-E25) from our professional network that are considered as key informants regarding digital transformation and the adoption of emerging technologies such as AI in their organizations (Bhattacherjee 2012) (Bhattacherjee 2012).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 3.1 Data Collection
What the sources say
- Not checked yetcites Bhattacherjee 2012
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Changes accompanying AI adoption always affect an organization's processes.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.1 Strategic Alignment
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“In this respect, AI-process fit reflects an organization's compatibility regarding AI and is facilitated by a mature process landscape, i.e. standardized and structured processes (Watson et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.1 Strategic Alignment
What the sources say
- Not checked yetcites Watson et al. 2019
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Consequently, organizations need to prepare customers by forming adequate expectations, because customers build acceptance through perceived usefulness (Davis 1985).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.1 Strategic Alignment
What the sources say
- Not checked yetcites Davis 1985
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Owing to a shortage of skilled AI specialists in the labor market, it becomes obligatory for organizations to invest in upskilling employees ' capabilities (Davenport 2018;Kruse et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Davenport 2018
- Not checked yetcites Kruse et al. 2019
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“AI-business potentials describe an organization's fit and compatibility toward AI innovations (Shahrasbi and Pare ´ 2014).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.1 Strategic Alignment
What the sources say
- Not checked yetcites Shahrasbi and Pare ´ 2014
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Top management support describes the willingness to commence AI initiatives top-down and to signal support for bottom-up initiatives.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.1 Strategic Alignment
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“As such, innovative behavior includes experimentation, risk-taking, and diverse problem-solving skills (Microsoft 2020; Yuan and Woodman 2010).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Microsoft 2020
- Not checked yetcites Yuan and Woodman 2010
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“AI ethics includes novel methods to prevent unethical AI outcomes which can be a result of biased learning or input data.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For instance, gender bias in data sets for AI hiring tools can result in biased candidate selection (Tambe et al. 2018).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Tambe et al. 2018 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“DDDM not only leads to performance improvements in organizations but also increases AI readiness as it represents the preliminary practice for AI-driven decision making (Colson 2019;Iansiti and Lakhani 2020;Microsoft 2020).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.1 Strategic Alignment
What the sources say
- Not checked yetcites Colson 2019
- Not checked yetcites Iansiti and Lakhani 2020
- Not checked yetcites Microsoft 2020
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Customer AI readiness describes the knowledge and acceptance of customers toward using AI-integrated offerings (Pumplun et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.1 Strategic Alignment
What the sources say
- Not checked yetcites Pumplun et al. 2019
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Since large organizations tend to rely too much on the status quo (Pumplun et al. 2019), innovative behavior should be particularly encouraged.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Pumplun et al. 2019
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The IT infrastructure must be modular to facilitate the integration of new AI applications and have a high capacity for AI-related data-intensive training and testing procedures (Catalyst Fund 2020; Groopman 2018).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Catalyst Fund 2020
- Not checked yetcites Groopman 2018
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“For instance, employees recognize the importance of high-quality input as a prerequisite for high-quality AI outcome (Agrawal et al. 2018).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Agrawal et al. 2018
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Collaborative work describes the degree to which domain experts, AI specialists, and IT departments actively communicate and work together in cross-functional teams (Davenport 2018;Fountaine et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Davenport 2018
- Not checked yetcites Fountaine et al. 2019
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“To increase AI readiness, organizations need to establish new measures and protocols to prevent discrimination and therefore mitigate risks for liability (Agrawal et al. 2018;Groopman 2018).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Agrawal et al. 2018
- Not checked yetcites Groopman 2018
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“Particularly, change management is important to reduce misconceptions regarding AI in terms of fear of possible job loss (Fountaine et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Fountaine et al. 2019
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Owing to AI's inherent complexity, AI-based systems often lack transparency, which hinders customers to accept those offerings.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.1 Strategic Alignment
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
In the paper
“In turn, AI specialists have dedicated AI-related skills (e.g., data scientists) and can develop AI solutions based on self-build and pre-built models (Catalyst Fund 2020).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Catalyst Fund 2020
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In the context of AI, collaborative work is crucial to overcome siloed work and to identify new use cases that are beneficial to the organization (Fountaine et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Fountaine et al. 2019
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Innovativeness is based on the amount and pace of adaptability that organizational members possess (Kruse et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Kruse et al. 2019
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Therefore, they can facilitate use case discovery and act as translators between business functions and AI specialists (Fountaine et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Fountaine et al. 2019
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Employees need AI awareness, i.e. an abstract understanding of cognitive AI functions like perceiving, predicting, or generating (Hofmann et al. 2020).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Hofmann et al. 2020
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“In case of not being ethically ready, organizations might blindly rely on biased AI outcomes and be held liable for discrimination even if unintentional (Agrawal et al. 2018;Dattner et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Agrawal et al. 2018
- Not checked yetcites Dattner et al. 2019
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Business analysts have an abstract understanding of AI capabilities (Bawack et al. 2019) as well as domain know-how.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Bawack et al. 2019
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Besides, AI adoption requires organizations to invest in building know-how and overcome initial uncertainty about AI capabilities and their value (Alsheibani et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Alsheibani et al. 2019
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“AI-process fit describes the necessary linkage between an organization's AI strategy and its processes to increase AI readiness.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.1 Strategic Alignment
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“Specifically, AI readiness requires awareness of appropriate use cases for either solving an organizational problem or addressing new opportunities (Hofmann et al. 2020;Pumplun et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.1 Strategic Alignment
What the sources say
- Not checked yetcites Hofmann et al. 2020
- Not checked yetcites Pumplun et al. 2019
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Additionally, companies must examine AI-based systems for their relative advantage over other solutions (Pumplun et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.1 Strategic Alignment
What the sources say
- Not checked yetcites Pumplun et al. 2019
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“This comprises equipping employees with AI-related know-how such as statistics, data management, data analytics, or data engineering on the one hand, but also with domain know-how on the other hand (Pumplun et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Pumplun et al. 2019
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Like customers, employees must be made aware of AI benefits through change management to increase acceptance.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, the concepts of readiness and adoption need to be discussed in conjunction but as distinct concepts that each offers a valuable lens on (intended) technology use in organizations.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.2 Theoretical Contribution and Practical Implications
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
In the paper
“The holistic nature of our categories, factors, and illustrative indicators may also serve as a blueprint for deriving relevant organizational readiness factors for other (digital) technologies and elaborating on the interdependencies between their respective adoption purpose and success (Lokuge et al. 2018).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.2 Theoretical Contribution and Practical Implications
What the sources say
- Not checked yetcites Lokuge et al. 2018
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Additionally, we argue that AI implies a purpose-specific consideration owing to AI's nature as a GPT that offers a broad variety of application scenarios.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.1 Conceptualizing Organizational AI Readiness for Successful AI Adoption
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: AI is a general-purpose technology (GPT) with a broad variety of application scenarios.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our interviews emphasize that successful AI adoption requires both understanding and managing AI readiness.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.1 Conceptualizing Organizational AI Readiness for Successful AI Adoption
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“On the other hand, unstructured data, such as audio, video, or image files (Groopman 2018), is utilized in more advanced AI applications like object recognition.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Groopman 2018
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hence, with changing context and adoption purposes, AI readiness requires perpetual consideration.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.1 Conceptualizing Organizational AI Readiness for Successful AI Adoption
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“Since organizations often face data quality issues with historical data, they need to improve on universal capabilities like data preparation, data processing, and data quality assurance to increase data readiness (Groopman 2018;Iansiti and Lakhani 2020;Kruse et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Groopman 2018
- Not checked yetcites Iansiti and Lakhani 2020
- Not checked yetcites Kruse et al. 2019
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“Given AI's inherent complexity and the arising new organizational necessities, AI readiness must be an integral part of companies' decisions across the entire adoption process to guide investments, prioritization, and resource allocation (Baier et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.1 Conceptualizing Organizational AI Readiness for Successful AI Adoption
What the sources say
- Not checked yetcites Baier et al. 2019
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Consequently, understanding and assessing AI readiness is an important means to purposefully adapt the organizational chassis for successful AI adoption (Pumplun et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.1 Conceptualizing Organizational AI Readiness for Successful AI Adoption
What the sources say
- Not checked yetcites Pumplun et al. 2019
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Companies face several difficulties during AI adoption, for instance, the identification of suitable use cases to utilize AI's potentials (Hofmann et al. 2020).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.2 Theoretical Contribution and Practical Implications
What the sources say
- Not checked yetcites Hofmann et al. 2020
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, research into AI readiness can help to alleviate the difficulties with AI adoption and propose suitable adaptations to the organizational chassis as prescriptive guidance.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.2 Theoretical Contribution and Practical Implications
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
In the paper
“This is especially important considering that AI-based systems do not necessarily replace job profiles as a whole, but rather take over repetitive tasks or individual process steps (Brynjolfsson and McAfee 2017).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Brynjolfsson and McAfee 2017
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, AI requires a more thorough and integrated understanding of readiness and adoption because it offers a broad variety of application scenarios.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.2 Theoretical Contribution and Practical Implications
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: AI is a general-purpose technology (GPT) with a broad variety of application scenarios.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We posit that AI poses different opportunities and challenges compared to other technologies owing to its nature as GPT.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.2 Theoretical Contribution and Practical Implications
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: AI is a general-purpose technology (GPT) with a broad variety of application scenarios.
None of the sources cited for this claim have been checked yet.
In the paper
“Data availability comprises the relevant amount and types of data which are both necessary for AI models to be trained and to generate accurate predictions (Agrawal et al. 2018;Kruse et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Agrawal et al. 2018
- Not checked yetcites Kruse et al. 2019
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Further, we argue that assessing AI readiness exposes action fields for AI adoption stemming from both AI's context and purpose specifics.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.2 Theoretical Contribution and Practical Implications
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Likewise, AI adoption requires a continuous assessment of the AI readiness factors based on the intended adoption purpose.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.1 Conceptualizing Organizational AI Readiness for Successful AI Adoption
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, we argue that while we draw on our existing understanding of the two concepts, AI readiness requires both context-and purpose-specific consideration.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.2 Theoretical Contribution and Practical Implications
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Improving on these quality dimensions increases AI readiness as AImodels need to be trained with high-quality data to generate good results (Davenport 2018;Pumplun et al. 2019).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Davenport 2018
- Not checked yetcites Pumplun et al. 2019
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“AI poses challenges and opportunities that differentiate it from other technologies (see Sect. 2.3 for further details).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.1 Conceptualizing Organizational AI Readiness for Successful AI Adoption
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: AI is a general-purpose technology (GPT) with a broad variety of application scenarios.
In the paper
“AI-relevant data quality dimensions include, for instance, completeness and correctness (Sidi et al. 2012).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 4.2 Resources
What the sources say
- Not checked yetcites Sidi et al. 2012
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Further, drawing on the literature on organizational readiness for change and technology adoption, we argue that AI readiness is a valuable concept to account for the technology and context specifics of readiness (Lokuge et al. 2018).”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.1 Conceptualizing Organizational AI Readiness for Successful AI Adoption
What the sources say
- Not checked yetcites Lokuge et al. 2018
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, AI adoption and readiness are distinct yet highly interdependent.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.1 Conceptualizing Organizational AI Readiness for Successful AI Adoption
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Both concepts foster and necessitate each other which leads to a mutually reinforcing and highly intertwined nature.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.1 Conceptualizing Organizational AI Readiness for Successful AI Adoption
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Further, we discussed that organizations must continuously assess and develop their AI readiness in the AI adoption process and described relevant aspects to consider.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > AI adoption Initiation Adoption decision Implementation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“However, we do not elucidate the factors' and indicators' prioritization and weighting concerning the overarching AI adoption purpose and specific organizational contingencies.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.3 Limitations and Future Research
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This includes AI's nature as a GPT, the context-and purposespecifics, and the mutually reinforcing interplay of AI readiness and AI adoption.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > AI adoption Initiation Adoption decision Implementation
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Rests on an assumption: AI is a general-purpose technology (GPT) with a broad variety of application scenarios.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, our results assist organizations in managing the AI adoption process and guide AI investments, resource allocation, and prioritization.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.2 Theoretical Contribution and Practical Implications
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While we propose AI readiness factors, we also acknowledge that synergies concerning the readiness for other (digital) technologies may exist.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.3 Limitations and Future Research
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The AI readiness factors provide comprehensive guidance to decision-makers on relevant managerial action fields.”
§ Ready or Not, AI Comes-An Interview Study of Organizational AI Readiness Factors > 5.2 Theoretical Contribution and Practical Implications
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper relies on this without support.
There is no structured conceptualization of readiness for technology on the organizational perspective.
“However, to the best of our knowledge, there is so far no structured conceptualization of readiness for technology on the organizational perspective.”
The paper relies on this without support.
Research remains vague as to where organizations should address AI-specific factors in the adoption process and what implications arise for continued use.
“Further, research remains vague as to where organizations should address these AI-specific factors in the adoption process and what implications arise for the continued use.”
The paper relies on this without support.
It is unclear which factors are relevant for the organizational adoption challenges.
“However, it is unclear which factors are relevant for the organizational adoption challenges.”
The paper relies on this without support.
AI is a general-purpose technology (GPT) with a broad variety of application scenarios.
“However, owing to AI's characteristics as GPT, AI adoption differentiates from previously discussed technology adoption.”
“Considering AI's nature as a GPT, we posit two important perspectives on the AI readiness factors.”
The paper relies on this without support.
The TOE framework is an established construct of organizational adoption literature suitable for conceptualizing AI readiness from an organizational perspective.
“We build on the TOE framework, an established construct of organizational adoption literature, to conceptualize AI readiness from an organizational perspective.”
The paper relies on this without support.
The interview sample of 25 AI experts is sufficient to identify and conceptualize organizational AI readiness factors.
“We combined insights from interviews with 25 AI experts with findings from scientific and practitioner literature to compile 18 AI readiness factors and 58 illustrative indicators in five categories.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Those who have a higher theoretical and practical understanding of AI are more ready to use it and have a more positive attitude toward it (Jöhnk, et al., 2021).”
From Into the Unknown: AI Adoption and Job Satisfaction of Christian Faculty (2026) · cites this paper as Jöhnk, et al., 2021
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Compared to other IT innovations, AI is a general-purpose technology with 'high implementation complexity [. . .] which differentiates it from other digital technologies that are typically easy-to-use and easy-to-deploy' (Jöhnk, Weißert, and Wyrtki 2021, 6), such as social media use (Mergel and Bretschneider 2015).”
From Exploring artificial intelligence adoption in public organizations: a comparative case study (2024) · cites this paper as Jöhnk, Weißert, and Wyrtki 2021
In the citing paper
“Another important factor influencing attitudes is the expense of implementing AI (Jöhnk et al., 2021).”
From Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector (2023) · cites this paper as Jöhnk et al., 2021
Citations
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Metadata
- Authors
- Jöhnk, J., Weißert, M., & Wyrtki, K.
- Affiliations
- Fraunhofer Institute for Applied Information Technology (DE)
- University of Bayreuth (DE)
- Corresponding author
- Jöhnk, Jan
- Weißert, Malte
- Wyrtki, Katrin
- Venue
- Business & Information Systems Engineering
- Published
- 2020-12-22
- Volume
- 63
- Issue
- 1
- Pages
- 5-20
- ISSN
- 1867-0202 (ISSN-L)
- 2363-7005
- OpenAlex
- W3100511078
- Cited by
- 637
- References
- 71
- Citations by year
- 2026: 233 · 2025: 155 · 2024: 114 · 2023: 70 · 2022: 48 · 2021: 16 · 2020: 1
- FWCI
- 13.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Business & Information Systems Engineering (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Journal of the Association for Information Systems (submittedVersion)
- Econstor (Econstor) (submittedVersion, cc-by)
- ERef Bayreuth (University of Bayreuth) (submittedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion, other-oa)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- Fraunhofer Institute for Applied Information Technology (FIT)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Technology Governance and Strategy
- Keywords
- artificial intelligence adoption, AI readiness assessment, organizational capabilities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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