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Cited by 772Artificial Intelligence ApplicationsImpact of AI and Big Data on Business and SocietyInformation Technology Governance and Strategy

Ready or not, AI comes—an interview study of organizational AI readiness factors

Open access
Jöhnk, Jan, Weißert, Malte, Wyrtki, Katrin·2021·Business & Information Systems Engineering
DOI 10.1007/s12599-020-00676-7
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Abstract

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.

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.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1007/s12599-020-00676-7

Cites

70 of 70 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 82 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Narrowing the focus on organizational factors in the TOE framework resulted in 23 organizational AI readiness factors in 8 categories.

    “This resulted in 23 organizational AI readiness factors in 8 categories.”
    Quote in the paperSubstantial claim
  • Findingcitation

    AI readiness factors are structured in five categories that specify action fields and necessary conditions for successful AI adoption.

    “We structure the AI readiness factors in five categories that specify action fields and necessary conditions for successful AI adoption (see Table 1).”
    Quote in the paperSubstantial claim
  • Findingown data

    The study compiled five categories-strategic alignment, resources, knowledge, culture, and data-with 18 factors specifying distinct action fields of organizational AI readiness.

    “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.”
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  • Findingown data

    Axial coding resulted in 35 AI readiness factors in 12 categories.

    “This resulted in 35 AI readiness factors in 12 categories.”
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  • Findingreasoning

    Considering the wide range of organizational necessities associated with AI adoption, top management support is crucial for successful AI adoption.

    “Considering the wide range of organizational necessities associated with AI adoption, top management support is crucial for successful AI adoption.”
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    Findingown data

    After consolidating the coding set, the study derived 895 codes in 39 categories and 87 subcategories.

    “After consolidating the coding set, we derived 895 codes in 39 categories and 87 subcategories.”
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    Findingown data

    The first coding round resulted in six broad categories related to AI readiness and a total of 492 codes.

    “This first coding round resulted in six broad categories related to AI readiness and a total of 492 codes.”
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    Findingown data

    The final compilation is 18 AI readiness factors and 58 illustrative indicators.

    “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).”
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    Findingnone given

    An organization can only commit to AI adoption once the top management provides an organization-wide signal.

    “Experts stressed an organization can only commit to AI adoption, once the top management provides an organization-wide signal.”
    Quote in the paperSubstantial claim

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    Findingown data

    The results confirm the relevance of top management support and collaboration for AI adoption.

    “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.”
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    Findingreasoning

    With illustrative indicators, the paper provides a starting point for distinct action fields for developing AI readiness.

    “With illustrative indicators, we provide a starting point for distinct action fields for developing AI readiness.”
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    Findingreasoning

    The paper corroborates existing factors but also provides additional factors accounting for AI's specifics.

    “In doing so, we corroborate existing factors but also provide additional factors accounting for AI's specifics.”
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    Findingreasoning

    By conceptualizing organizational AI readiness, the paper provides relevant factors and indicators to better understand and assess what measures organizations require for successful AI adoption.

    “By conceptualizing organizational AI readiness, we provide relevant factors and indicators to better understand and assess what measures organizations require for successful AI adoption.”
    Quote in the paperSubstantial claim

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    Findingown data

    The interviews corroborate readiness factors that are comparable to adopting any other technology and are also relevant for AI adoption.

    “Second, our interviews corroborate readiness factors that are comparable to adopting any other technology and are also relevant for AI adoption.”
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    Findingown data

    Given the results, AI readiness is conceptualized as twofold: 18 readiness factors along five categories, and the understanding of purposeful AI adoption.

    “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.”
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    Findingown data

    The paper provides additional empirical groundwork for theorizing on readiness and adoption.

    “First, our paper provides additional empirical groundwork for theorizing on readiness and adoption.”
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    Findingreasoning

    The paper's results serve as the descriptive groundwork for further research on AI management in organizations, organizational readiness, and technology adoption.

    “Thus, our results serve as the descriptive groundwork for further research on AI management in organizations, organizational readiness, and technology adoption.”
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    Findingnone given

    The findings explicate the need for context-specific considerations for organizational readiness by referring to AI's characteristics.

    “Finally, our findings explicate the need for contextspecific considerations for organizational readiness by referring to AI's characteristics.”
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    Findingown data

    The paper provided a thorough conceptualization of organizational AI readiness.

    “This paper provided a thorough conceptualization of organizational AI readiness.”
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    Study design and conductown data

    The study used feedback from a focus group to further trim and restructure the results.

    “We used feedback from a focus group to further trim and restructure our results (see Appendix III for further details).”
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    Study design and conductcitation

    The study used a qualitative research approach to understand AI readiness factors.

    “We used a qualitative research approach to understand AI readiness factors (Bhattacherjee 2012).”
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    Premisecitation

    Readiness models require context-specific consideration and need to be tailored to the related domain, i.e. a specific technology.

    “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).”
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    Premisecitation

    AI is a general-purpose technology with a unique learning capability that provides organizations with potentials for wide-ranging improvements and new business opportunities.

    “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.”
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    Study design and conductown data

    Data collection stopped after 25 interviews because no significant new topics were brought up and the emerging factors were increasingly repetitive.

    “We stopped data collection after 25 interviews because no significant new topics were brought up and the emerging factors were increasingly repetitive.”
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    Study design and conductown data

    The study operationalized these factors with 58 illustrative indicators and evaluated results via a card-sorting procedure in an AI-related researchers' focus group.

    “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.”
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    Study design and conductown data

    The study recorded, transcribed, and analyzed 1385 interview minutes with the experts' consent.

    “We recorded, transcribed, and analyzed 1385 interview minutes with the experts' consent.”
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    Study design and conductcitation

    The study used AI-related literature and practitioner studies to triangulate the conceptualization of AI readiness factors.

    “Further, we used AI-related literature and practitioner studies to triangulate our conceptualization of AI readiness factors (Flick et al. 2004).”
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    Premisecitation

    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.

    “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).”
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    Premisecitation

    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 intended adoption purpose.

    “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).”
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    Premisecitation

    AI adoption implies a high implementation complexity which differentiates it from other digital technologies that are typically easy-to-use and easy-to-deploy.

    “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).”
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    Interpretationreasoning

    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.

    “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.”
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    Premisecitation

    Being a GPT, AI drives changes at task, process, and business model level in many application areas and offers competitive advantage to organizations.

    “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).”
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    Study design and conductcitation

    The study used open and axial coding to deduce AI readiness factors from interview data and triangulated the conceptualization with readiness and adoption literature and practitioner studies.

    “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).”
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    Study design and conductcitation

    The study used purposive sampling to identify 25 AI experts from the professional network considered key informants regarding digital transformation and adoption of emerging technologies such as AI in their organizations.

    “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).”
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    Premisenone given

    Changes accompanying AI adoption always affect an organization's processes.

    “Changes accompanying AI adoption always affect an organization's processes.”
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    Premisecitation

    AI-process fit reflects an organization's compatibility regarding AI and is facilitated by a mature process landscape, i.e. standardized and structured processes.

    “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).”
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    Interpretationcitation

    Organizations need to prepare customers by forming adequate expectations, because customers build acceptance through perceived usefulness.

    “Consequently, organizations need to prepare customers by forming adequate expectations, because customers build acceptance through perceived usefulness (Davis 1985).”
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    Premisecitation

    Owing to a shortage of skilled AI specialists in the labor market, it becomes obligatory for organizations to invest in upskilling employees' capabilities.

    “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).”
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    Premisecitation

    AI-business potentials describe an organization's fit and compatibility toward AI innovations.

    “AI-business potentials describe an organization's fit and compatibility toward AI innovations (Shahrasbi and Pare ´ 2014).”
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    Premisenone given

    Top management support describes the willingness to commence AI initiatives top-down and to signal support for bottom-up initiatives.

    “Top management support describes the willingness to commence AI initiatives top-down and to signal support for bottom-up initiatives.”
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    Premisecitation

    Innovative behavior includes experimentation, risk-taking, and diverse problem-solving skills.

    “As such, innovative behavior includes experimentation, risk-taking, and diverse problem-solving skills (Microsoft 2020; Yuan and Woodman 2010).”
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    Premisenone given

    AI ethics includes novel methods to prevent unethical AI outcomes which can be a result of biased learning or input data.

    “AI ethics includes novel methods to prevent unethical AI outcomes which can be a result of biased learning or input data.”
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    Premisecitation

    Gender bias in data sets for AI hiring tools can result in biased candidate selection.

    “For instance, gender bias in data sets for AI hiring tools can result in biased candidate selection (Tambe et al. 2018).”
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    Premisecitation

    Data-driven decision-making not only leads to performance improvements in organizations but also increases AI readiness as it represents the preliminary practice for AI-driven decision making.

    “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).”
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    Premisecitation

    Customer AI readiness describes the knowledge and acceptance of customers toward using AI-integrated offerings.

    “Customer AI readiness describes the knowledge and acceptance of customers toward using AI-integrated offerings (Pumplun et al. 2019).”
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    Since large organizations tend to rely too much on the status quo, innovative behavior should be particularly encouraged.

    “Since large organizations tend to rely too much on the status quo (Pumplun et al. 2019), innovative behavior should be particularly encouraged.”
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    Premisecitation

    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.

    “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).”
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    Premisecitation

    Employees recognize the importance of high-quality input as a prerequisite for high-quality AI outcome.

    “For instance, employees recognize the importance of high-quality input as a prerequisite for high-quality AI outcome (Agrawal et al. 2018).”
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    Premisecitation

    Collaborative work describes the degree to which domain experts, AI specialists, and IT departments actively communicate and work together in cross-functional teams.

    “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).”
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    Interpretationcitation

    To increase AI readiness, organizations need to establish new measures and protocols to prevent discrimination and mitigate risks for liability.

    “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).”
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    Premisecitation

    Change management is important to reduce misconceptions regarding AI in terms of fear of possible job loss.

    “Particularly, change management is important to reduce misconceptions regarding AI in terms of fear of possible job loss (Fountaine et al. 2019).”
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    Interpretationnone given

    Owing to AI's inherent complexity, AI-based systems often lack transparency, which hinders customers to accept those offerings.

    “Owing to AI's inherent complexity, AI-based systems often lack transparency, which hinders customers to accept those offerings.”
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    Premisecitation

    AI specialists have dedicated AI-related skills (e.g., data scientists) and can develop AI solutions based on self-build and pre-built models.

    “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).”
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    Premisecitation

    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.

    “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).”
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    Premisecitation

    Innovativeness is based on the amount and pace of adaptability that organizational members possess.

    “Innovativeness is based on the amount and pace of adaptability that organizational members possess (Kruse et al. 2019).”
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    Premisecitation

    Business analysts can facilitate use case discovery and act as translators between business functions and AI specialists.

    “Therefore, they can facilitate use case discovery and act as translators between business functions and AI specialists (Fountaine et al. 2019).”
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    Premisecitation

    Employees need AI awareness, i.e. an abstract understanding of cognitive AI functions like perceiving, predicting, or generating.

    “Employees need AI awareness, i.e. an abstract understanding of cognitive AI functions like perceiving, predicting, or generating (Hofmann et al. 2020).”
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    Premisecitation

    In case of not being ethically ready, organizations might blindly rely on biased AI outcomes and be held liable for discrimination even if unintentional.

    “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).”
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    Business analysts have an abstract understanding of AI capabilities as well as domain know-how.

    “Business analysts have an abstract understanding of AI capabilities (Bawack et al. 2019) as well as domain know-how.”
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    Premisecitation

    AI adoption requires organizations to invest in building know-how and overcome initial uncertainty about AI capabilities and their value.

    “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).”
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    Premisereasoning

    AI-process fit describes the necessary linkage between an organization's AI strategy and its processes to increase AI readiness.

    “AI-process fit describes the necessary linkage between an organization's AI strategy and its processes to increase AI readiness.”
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    Interpretationcitation

    AI readiness requires awareness of appropriate use cases for either solving an organizational problem or addressing new opportunities.

    “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).”
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    Interpretationcitation

    Companies must examine AI-based systems for their relative advantage over other solutions.

    “Additionally, companies must examine AI-based systems for their relative advantage over other solutions (Pumplun et al. 2019).”
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    Premisecitation

    Upskilling comprises equipping employees with AI-related know-how such as statistics, data management, data analytics, or data engineering, and domain know-how.

    “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).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Pumplun et al. 2019

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning

    Like customers, employees must be made aware of AI benefits through change management to increase acceptance.

    “Like customers, employees must be made aware of AI benefits through change management to increase acceptance.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationreasoning

    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.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationcitation

    The holistic nature of the paper's categories, factors, and illustrative indicators may 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.

    “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).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Lokuge et al. 2018

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    AI implies a purpose-specific consideration owing to AI's nature as a GPT that offers a broad variety of application scenarios.

    “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.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: AI is a general-purpose technology (GPT) with a broad variety of application scenarios.

  • May combine two claims.

    Interpretationown data

    Successful AI adoption requires both understanding and managing AI readiness.

    “Our interviews emphasize that successful AI adoption requires both understanding and managing AI readiness.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Unstructured data, such as audio, video, or image files, is utilized in more advanced AI applications like object recognition.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Groopman 2018

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    With changing context and adoption purposes, AI readiness requires perpetual consideration.

    “Hence, with changing context and adoption purposes, AI readiness requires perpetual consideration.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Premisecitation

    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.

    “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).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites Groopman 2018

      Not checked in the free run.

    • cites Iansiti and Lakhani 2020

      Not checked in the free run.

    • cites Kruse et al. 2019

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationcitation

    AI readiness must be an integral part of companies' decisions across the entire adoption process to guide investments, prioritization, and resource allocation.

    “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).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Baier et al. 2019

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationcitation

    Understanding and assessing AI readiness is an important means to purposefully adapt the organizational chassis for successful AI adoption.

    “Consequently, understanding and assessing AI readiness is an important means to purposefully adapt the organizational chassis for successful AI adoption (Pumplun et al. 2019).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Pumplun et al. 2019

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Companies face several difficulties during AI adoption, for instance, the identification of suitable use cases to utilize AI's potentials.

    “Companies face several difficulties during AI adoption, for instance, the identification of suitable use cases to utilize AI's potentials (Hofmann et al. 2020).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Hofmann et al. 2020

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationreasoning

    Research into AI readiness can help to alleviate the difficulties with AI adoption and propose suitable adaptations to the organizational chassis as prescriptive guidance.

    “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.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    AI-based systems do not necessarily replace job profiles as a whole, but rather take over repetitive tasks or individual process steps.

    “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).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Brynjolfsson and McAfee 2017

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    AI requires a more thorough and integrated understanding of readiness and adoption because it offers a broad variety of application scenarios.

    “Thus, AI requires a more thorough and integrated understanding of readiness and adoption because it offers a broad variety of application scenarios.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: AI is a general-purpose technology (GPT) with a broad variety of application scenarios.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    AI poses different opportunities and challenges compared to other technologies owing to its nature as GPT.

    “We posit that AI poses different opportunities and challenges compared to other technologies owing to its nature as GPT.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: AI is a general-purpose technology (GPT) with a broad variety of application scenarios.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    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.

    “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).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Agrawal et al. 2018

      Not checked in the free run.

    • cites Kruse et al. 2019

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Assessing AI readiness exposes action fields for AI adoption stemming from both AI's context and purpose specifics.

    “Further, we argue that assessing AI readiness exposes action fields for AI adoption stemming from both AI's context and purpose specifics.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    AI adoption requires a continuous assessment of the AI readiness factors based on the intended adoption purpose.

    “Likewise, AI adoption requires a continuous assessment of the AI readiness factors based on the intended adoption purpose.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    AI readiness requires both context- and purpose-specific consideration.

    “Thus, we argue that while we draw on our existing understanding of the two concepts, AI readiness requires both context-and purpose-specific consideration.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Improving on data quality dimensions increases AI readiness as AI models need to be trained with high-quality data to generate good results.

    “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).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Davenport 2018

      Not checked in the free run.

    • cites Pumplun et al. 2019

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    AI poses challenges and opportunities that differentiate it from other technologies.

    “AI poses challenges and opportunities that differentiate it from other technologies (see Sect. 2.3 for further details).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: AI is a general-purpose technology (GPT) with a broad variety of application scenarios.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    AI-relevant data quality dimensions include completeness and correctness.

    “AI-relevant data quality dimensions include, for instance, completeness and correctness (Sidi et al. 2012).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Sidi et al. 2012

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationcitation

    AI readiness is a valuable concept to account for the technology and context specifics of readiness.

    “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).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Lokuge et al. 2018

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    AI adoption and readiness are distinct yet highly interdependent.

    “First, AI adoption and readiness are distinct yet highly interdependent.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationnone given

    Both AI readiness and AI adoption foster and necessitate each other, leading to a mutually reinforcing and highly intertwined nature.

    “Both concepts foster and necessitate each other which leads to a mutually reinforcing and highly intertwined nature.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Organizations must continuously assess and develop their AI readiness in the AI adoption process.

    “Further, we discussed that organizations must continuously assess and develop their AI readiness in the AI adoption process and described relevant aspects to consider.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The paper does not elucidate the factors' and indicators' prioritization and weighting concerning the overarching AI adoption purpose and specific organizational contingencies.

    “However, we do not elucidate the factors' and indicators' prioritization and weighting concerning the overarching AI adoption purpose and specific organizational contingencies.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationreasoning

    Relevant aspects to consider include AI's nature as a GPT, the context- and purpose-specifics, and the mutually reinforcing interplay of AI readiness and AI adoption.

    “This includes AI's nature as a GPT, the context-and purposespecifics, and the mutually reinforcing interplay of AI readiness and AI adoption.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: AI is a general-purpose technology (GPT) with a broad variety of application scenarios.

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationreasoning

    The paper's results assist organizations in managing the AI adoption process and guide AI investments, resource allocation, and prioritization.

    “Thus, our results assist organizations in managing the AI adoption process and guide AI investments, resource allocation, and prioritization.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    The paper proposes AI readiness factors and acknowledges that synergies concerning the readiness for other digital technologies may exist.

    “While we propose AI readiness factors, we also acknowledge that synergies concerning the readiness for other (digital) technologies may exist.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    The AI readiness factors provide comprehensive guidance to decision-makers on relevant managerial action fields.

    “The AI readiness factors provide comprehensive guidance to decision-makers on relevant managerial action fields.”
    Quote in the paperNot a substantial claim
90 of 90

  • 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.”
6 of 6

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?

  • 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.

    “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

    The Knowledge discussion (section 4.3; page unavailable in our copy) says AI awareness helps employees understand AI’s applications and form adequate expectations. The change-management discussion says explaining AI’s benefits can increase employee acceptance. But the paper addresses organizational readiness; it does not establish that people with greater theoretical and practical understanding are individually more ready to use AI or have more positive attitudes than others. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • 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.

    “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

  • The expense of implementing AI is an important factor influencing attitudes.

    “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

Cites

Flagged first, then by citations

1 of the 71 references couldn’t be matched to a known paper.

Research works printed but not linked: 4

  1. Brynjolfsson E, McAfee A (2017) The business of artificial intelligence: What it can – and cannot – do for your organization. Harvard business review. https://starlab-alliance.com/wp-content/uploads/2017/09/The-Business-of-Artificial-Intelligence.pdf. Accessed 12 Mar 2020
  2. Fishbein M, Ajzen I (1975) Belief, attitude, intention and behavior: an introduction to theory and research. Addison-Wesley series in social psychology. Addison-Wesley, Reading, Boston
  3. Fountaine T, McCarthy B, Saleh T (2019) Building the AI-powered organization. Harv Bus Rev 97:62–73
  4. Pumplun L, Tauchert C, and Heidt M (2019) A new organizational chassis for artificial intelligence – exploring organizational readiness factors. In: ECIS 2019 Proceedings, Stockholm-Uppsala

Cited by

Sorted by citations

Related

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
ORCID
Venue
Business & Information Systems Engineering
Publisher
Springer Science and Business Media LLC
Published
2020-12-22
Volume
63
Issue
1
Pages
5-20
ISSN
  • 1867-0202 (ISSN-L)
  • 2363-7005
DOI
10.1007/s12599-020-00676-7
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
Licence
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.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.

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.

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