AI Capabilities Scale

1. Abstract

The AI Capabilities Scale (AICAP) is an organizational psychometric and diagnostic instrument conceptualized and validated by Abou-Foul, Ruiz-Alba, and López-Tenorio (2023). Developed to address the expanding operationalization of artificial intelligence within contemporary enterprises, the AICAP scale extends beyond narrow technical definitions of computational machinery to capture organizational capabilities across business model innovation, information management, and applied computer science. The scale operationalizes AI capability as a multidimensional, higher-order construct composed of four distinct operational dimensions: AI Customer Value Proposition, AI Key Processes Optimization, AI Key Resources Optimization, and AI Societal Good.

The scale construction followed the rigorous C-OAR-SE (Construct definition, Object classification, Attribute classification, Rater identification, Scale formation, and Enumeration) framework developed by Rossiter (2002). The final instrument consists of 17 items assessed via a standardized 5-point Likert scale ranging from 1 (“Strongly disagree”) to 5 (“Strongly agree”). The empirical validation was conducted on a multinational sample of senior organizational respondents, technical leaders, and operational directors from manufacturing enterprises located across the United States and the European Union navigating the strategic transition toward digital servitization and automated ecosystem integration.

Psychometric evaluation demonstrated robust structural validity, internal consistency, and convergent and discriminant properties. Exploratory Factor Analysis (EFA) utilizing principal axis factoring and Promax oblique rotation corroborated the four-factor structural dimensionality, accounting for substantial common variance without problematic cross-loadings. Confirmatory Factor Analysis (CFA) confirmed satisfactory goodness-of-fit indices across subscales following the elimination of items exhibiting high modification indices. Reliability analyses established high internal consistency across all sub-dimensions, with Cronbach’s alpha coefficients consistently exceeding the classical psychometric threshold of α > 0.70. Convergent validity was substantiated through Average Variance Extracted (AVE) values exceeding the 0.50 benchmark, while discriminant validity was confirmed via inter-construct correlation analyses remaining well below the 0.85 threshold. The AICAP scale represents a standardized tool for empirical researchers, organizational psychologists, and enterprise leaders examining technological capability deployment, dynamic capabilities, and sustainable technological transformation.

2. Keywords

AI Capabilities Scale, Artificial Intelligence, Business Model Innovation, Servitization, Dynamic Capabilities, Machine Learning, Predictive Maintenance, Organizational Psychology, Psychometrics, Process Optimization

3. Authors

The AI Capabilities Scale was developed and published by an international team of researchers specializing in strategic operations, service management, digital transformation, and industrial marketing:

• Mohamad Abou-Foul, Ph.D. — Al-Azhar University, Jamal Abdl Naser St, Gaza, Palestinian Territory. Email: [email protected]. Focus areas: Dynamic capabilities, industrial servitization, enterprise artificial intelligence implementation, and quantitative operational modeling.
• Jose L. Ruiz-Alba, Ph.D. — School of Organizations, Economy and Society, Westminster Business School, University of Westminster, London, United Kingdom. Email: [email protected]. Focus areas: Servitization, customer relationship management, sales management, organizational learning, and capability development.
• Pablo J. López-Tenorio, Ph.D. — Facultad de Ciencias Sociales Aplicadas y de la Comunicación, UNIE Universidad, Madrid, Spain. ORCID: 0000-0003-4601-0733. Focus areas: Strategic consumer psychology, technological adoption, business transformation metrics, and structural equation modeling.

4. Purpose

The foundational purpose of the AI Capabilities Scale (AICAP) is to address a critical theoretical and empirical gap in the measurement of organizational behavior, strategic information systems, and industrial management. Prior to the development of the AICAP scale, investigations of artificial intelligence in organizational contexts frequently relied either on blunt binary proxies (e.g., presence or absence of algorithmic systems) or overly technical metrics (e.g., algorithmic latency, parameter volume, computational infrastructure investments) that failed to capture how organizations mobilize, integrate, and transform digital assets into organizational competence. The AICAP instrument measures artificial intelligence capability not as isolated software artifacts, but as an integrated, organizational dynamic capability that bridges business model innovation, information management, customer value generation, operational orchestration, and social responsibility.

From an applied perspective, the AICAP scale fulfills critical diagnostic and analytical roles. In organizational research, it serves as a standardized, reliable measurement model capable of evaluating how technological capabilities interact with dynamic routines, absorptive capacity, organizational agility, and market performance. It allows empirical investigators to model complex structural relationships, such as how distinct dimensions of AI capability enable industrial manufacturing firms to transition from traditional product-dominant business models to digital servitization models, including predictive maintenance agreements, outcome-based contracts, and advanced operational optimization services.

For executive leaders, organizational psychologists, and enterprise transformation consultants, the scale provides a granular benchmarking instrument. Traditional internal audits often overestimate algorithmic readiness while underestimating downstream integration and ethical alignment. By disaggregating AI capability into four functional domains, the AICAP scale enables organizations to identify structural asymmetries in technological deployment. For instance, an enterprise may exhibit sophisticated internal predictive maintenance protocols within manufacturing processes while lagging in customer-facing value proposition personalization or societal decarbonization initiatives. By standardizing these operational dimensions, the AICAP provides a reliable baseline for strategic resource allocation, workforce skill augmentation, change management interventions, and socio-technical system alignment.

5. Psychological Construct

The psychological and organizational construct operationalized by the AICAP scale is Artificial Intelligence Capability, defined as an organization’s latent, multidimensional capacity to orchestrate, embed, and mobilize advanced machine learning, algorithmic intelligence, data architectures, and predictive analytics across operational routines, strategic client interactions, asset allocations, and societal impacts. Rather than treating AI capability as an exogenous technological resource, the construct is conceptualized as an endogenous socio-technical capability embedded in human expertise, organizational structures, and algorithmic workflows. In alignment with established psychometric standards, the AICAP models this construct across four distinct, correlated subscales:

1. AI Customer Value Proposition

This subscale captures an enterprise’s capacity to deploy artificial intelligence to fundamentally transform customer engagement, value co-creation, and external client satisfaction. It evaluates how machine intelligence extracts actionable insights from post-deployment behavioral and telemetry streams to personalize customer interactions, tailor operational outcomes, and ensure long-term client success. Key manifestations include the utilization of machine learning models to dynamically calculate warranty terms and pricing structures, the implementation of automated quoting engines, and the ingestion of embedded Industrial Internet of Things (IIoT) sensor data to execute continuous predictive maintenance, minimizing client downtime and moving commercial relationships toward performance-guaranteed agreements.

2. AI Key Processes Optimization

The operational process dimension measures the deployment of automated computational intelligence to elevate, streamline, and reconfigure core internal business processes. This subscale reflects how organizations integrate cognitive computing, conversational agents, machine vision, and big data systems across the organizational workflow. Manifestations of this construct include the strategic deployment of advanced data science for granular demand forecasting and supply stocking, the acquisition of targeted data pipelines to fulfill order commitments reliably, the deployment of intelligent conversational agents (chatbots) within next-generation Customer Relationship Management (CRM) architectures, edge analytics for industrial yield optimization, and data mining platforms designed to refine the product innovation lifecycle and structural Bill of Materials (BOM) management.

3. AI Key Resources Optimization

This subscale evaluates an organization’s capability to mobilize machine intelligence for internal and external resource orchestration, factor cost minimization, and infrastructural asset protection. Rooted in the dynamic configuration of organizational inputs, this dimension assesses the systematic use of centralized analytical data warehouses to monitor and coordinate complex multi-tier supply networks, algorithmic workforce management to balance labor allocation and talent utilization, algorithmic procurement tools to pinpoint lowest-cost and highest-reliability vendors, and continuous machine learning monitoring to reinforce cybersecurity architectures, detect network anomalies, and safeguard operational data integrity.

4. AI Societal Good

The fourth dimension captures an organization’s commitment to deploying computational intelligence in service of broad social, environmental, and human well-being objectives. This construct moves beyond immediate financial profitability to assess responsible and sustainable AI adoption. It measures the organizational capacity to deploy algorithmic systems to protect frontline employee health and safety (e.g., computer vision hazard detection, algorithmic physical safety assistants), the deployment of advanced optimization techniques such as deep reinforcement learning to drive down energy consumption, operational carbon footprints, material waste, and environmental inequities, and the provision of data-driven, customer-facing green solutions targeted at global decarbonization challenges.

6. Theoretical Framework

The AICAP scale is grounded in two primary theoretical foundations of strategic management and organizational psychology: the Dynamic Capabilities Perspective (Teece, Pisano, & Shuen, 1997; Teece, 2007) and the Resource-Based View (RBV) of the firm (Barney, 1991). These foundational frameworks are combined with psychometric scale development principles codified in the C-OAR-SE methodology (Rossiter, 2002).

Under the classical Resource-Based View, physical computing hardware, proprietary datasets, and off-the-shelf software algorithms represent static resources. While valuable, these assets are often mobile, imitable, and readily acquired across open factor markets. Consequently, technological hardware alone cannot deliver sustained competitive advantage. The Dynamic Capabilities framework extends this perspective by positing that sustained advantage arises from an organization’s repeatable routines to sense, seize, and transform enterprise architectures in response to volatile environmental shifts. Within the AICAP model, artificial intelligence is conceptualized as an overarching dynamic capability: algorithmic assets must be bound to human cognitive routines, cross-functional communications, and organizational mechanisms to generate transformative business models.

The operational framework explicitly models how artificial intelligence capabilities catalyze industrial servitization—the strategic transition from selling discrete, physical manufactured goods to delivering integrated product-service systems, ongoing advisory maintenance, and outcome-based guarantees. Building on Cohen and Levinthal’s (1990) theory of Absorptive Capacity, the authors posit that an organization’s ability to recognize external value, assimilate modern algorithmic techniques, and apply them commercially moderates the relationship between latent AI capabilities and servitization outcomes. Dynamic adaptation cannot occur in a vacuum; it requires cognitive readiness, technological receptivity, and systematic learning routines.

Methodologically, the construction of the AICAP scale adhered strictly to Rossiter’s (2002) C-OAR-SE framework:

• Construct Definition: Conceptualization of organizational AI capability across functional business domains, separating it from general IT infrastructure or consumer software acceptance.
• Object Classification: Identification of the focal object as the enterprise or strategic business unit operating within competitive, data-intensive markets.
• Attribute Classification: Formulation of the multidimensional attributes representing value propositions, operational workflows, resource allocations, and societal impacts.
• Rater Identification: Designation of expert respondents (C-level executives, operations managers, data science directors) capable of evaluating enterprise-wide functional realities rather than idiosyncratic personal software attitudes.
• Scale Formation: Engineering items with balanced semantic structures that measure concrete operational capabilities rather than abstract managerial intentions.
• Enumeration: Derivation of valid scoring rules and index summations supported by psychometric factor modeling.

7. Validity

The validation of the AICAP scale followed rigorous empirical testing designed to substantiate its structural, convergent, and discriminant validity across complex organizational environments. The psychometric evaluation utilized empirical survey data gathered from managerial respondents across manufacturing firms in the United States and the European Union, testing the theoretical measurement model against established thresholds.

Convergent Validity

Convergent validity was evaluated by assessing factor loadings and the Average Variance Extracted (AVE) for each of the four hypothesized latent dimensions, adhering to the criteria established by Bagozzi and Yi (1988). All retained 17 items exhibited standardized factor loadings well above the conventional 0.60 threshold for newly developed psychometric scales. Furthermore, the AVE values for all latent constructs consistently exceeded the 0.50 cutoff benchmark, confirming that the latent factors explain more than half of the variance observed across their respective indicator sets rather than residual measurement error.

Discriminant Validity

To establish that the four sub-dimensions of the AICAP scale capture distinct operational capabilities rather than redundant conceptual space, discriminant validity was tested following criteria proposed by Henseler, Ringle, and Sarstedt (2015). The empirical correlation matrices between latent dimensions were examined. All average bivariate correlations between subscales were found to be well below the conservative 0.85 threshold. In addition, the square root of the AVE for each latent variable exceeded its highest correlation with any other construct, fulfilling the classical Fornell-Larcker criterion and establishing clear psychometric divergence among the four subscales.

Construct and Predictive Validity

Construct and nomological validity were established by embedding the AICAP measure within a structural equation model evaluating the transition of traditional manufacturers toward advanced service-oriented business models (servitization). As hypothesized by Abou-Foul et al. (2023), AI capabilities exhibited a statistically significant, positive predictive path toward industrial servitization capabilities. Furthermore, this predictive relationship was significantly moderated by firm absorptive capacity, demonstrating nomological validity in enterprise research contexts.

8. Reliability

The reliability of the AICAP scale was evaluated through comprehensive assessments of internal consistency across all latent dimensions. Because the scale was operationalized as a multidimensional measure with reflective sub-constructs, reliability metrics were calculated independently for each subscale to prevent inflated composite figures.

Across the four empirical dimensions, internal consistency was evaluated using Cronbach’s alpha (α) and Composite Reliability (CR) coefficients:

• AI Customer Value Proposition: Demonstrated high internal consistency, exhibiting a Cronbach’s alpha well above the classical threshold (α > 0.70) and a CR exceeding 0.80, confirming strong item covariance and minimal error variance.
• AI Key Processes Optimization: Produced Cronbach’s alpha and composite reliability values well above acceptable psychometric standards (α > 0.80, CR > 0.85), indicating that its 6 operational items consistently capture internal workflow optimization routines.
• AI Key Resources Optimization: Maintained high internal consistency with alpha coefficients exceeding the 0.70 benchmark and high inter-item correlation, demonstrating reliable capture of factor input orchestration.
• AI Societal Good: Established strong internal consistency (α > 0.75), indicating cohesive measurement of sustainability and workplace safety routines.

No subscale exhibited Cronbach’s alpha values below the standard 0.70 benchmark defined by Nunnally (1978). Item-total correlation analyses confirmed that the removal of any individual item would not yield a substantial increase in overall subscale alpha values, corroborating the stability of the final 17-item instrument.

9. Factor Analysis

The structural dimensionality of the AICAP scale was established through a two-stage factor-analytic workflow combining Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

Exploratory Factor Analysis (EFA)

To examine the latent structure of the preliminary item pool, an EFA was conducted using principal axis factoring paired with an oblique Promax rotation, allowing for theoretical correlation among underlying capability dimensions. Eigenvalues greater than 1.0 were retained under the Kaiser criterion. The scree plot and factor extraction confirmed a clear four-factor solution aligning with the theoretical conceptualization. With the exception of three preliminary items, all items loaded cleanly onto their intended factors with factor loadings exceeding the 0.60 threshold recommended by Kline (2014) for newly developed scales. No cross-loadings exceeded 0.30, demonstrating structural clarity across dimensions.

Confirmatory Factor Analysis (CFA)

To evaluate the structural integrity of the derived model, CFA was conducted using maximum likelihood estimation. During initial single-factor and baseline testing, three items were found to be psychometrically suboptimal due to elevated modification indices, indicating localized residual covariance (Bagozzi & Yi, 1988). These three items were removed to ensure structural independence and parsimony.

The respecified measurement model comprised the final 17 items across the four latent dimensions. Goodness-of-fit indices demonstrated acceptable fit to the empirical data:

• Chi-Square to Degrees of Freedom Ratio (χ²/df): Well within the recommended range of < 3.0, reflecting adequate structural parsimony.
• Comparative Fit Index (CFI): Exceeded the 0.90 threshold, indicating adequate comparative fit against the independence baseline.
• Tucker-Lewis Index (TLI): Exceeded the 0.90 acceptable fit criterion.
• Root Mean Square Error of Approximation (RMSEA): Exhibited values below 0.08, confirming an acceptable degree of approximation error.
• Standardized Root Mean Square Residual (SRMR): Maintained values below 0.08, confirming minimal standardized residual discrepancies.

The CFA results established the 17-item, four-factor model as an empirically sound and theoretically coherent measurement tool.

10. Instrument / Measurement Tool

• Test Type: Original psychometric scale / Organizational diagnostic survey instrument.
• Measurement Format: Standardized self-report / Key-informant perceptual rating scale.
• Number of Items: 17 items distributed across 4 subscales:
  • AI Customer Value Proposition: 4 items
  • AI Key Processes Optimization: 6 items
  • AI Key Resources Optimization: 4 items
  • AI Societal Good: 3 items
• Response Scale: All 17 items of the AICAP scale are measured using a 5-point Likert scale. The response options are:
  • 1 = Strongly disagree
  • 2 = Disagree
  • 3 = Neither agree nor disagree
  • 4 = Agree
  • 5 = Strongly agree
• Target Population: Organizations, manufacturing firms, enterprise digital transformation units, and enterprise leaders (aged 18 and older).
• Eligible Informants: C-suite executives, Chief Technology Officers (CTOs), Operations Directors, IT and Data Science Managers, and Business Model Strategists.
• Scoring Protocol: Dimensional scores are calculated by averaging the response scores within each respective subscale (ranging from 1.0 to 5.0). Alternatively, an overall composite AI Capabilities index can be calculated through a global summation or second-order structural equation model. Higher scores indicate greater organizational AI capability maturity.

11. Permissions & Fee and Test Year

The AI Capabilities Scale (AICAP) was developed and published in 2023 by Mohamad Abou-Foul, Jose L. Ruiz-Alba, and Pablo J. López-Tenorio. The scale was formally introduced in the Journal of Business Research (Volume 157, Article 113609).

Permissions & Usage Rights: The scale is available for academic, educational, and empirical research purposes without royalty fees. Academic researchers and educators are permitted to utilize the instrument in non-commercial empirical investigations, doctoral dissertations, and university curricula, provided that appropriate scholarly attribution is given to the original authors (Abou-Foul et al., 2023). Commercial applications, proprietary organizational audit tools, and consulting diagnostic frameworks may require formal licensing agreements and institutional permission from the copyright holders and the publishing entity.

12. References

• Abou-Foul, M., Ruiz-Alba, J. L., & López-Tenorio, P. J. (2023). The impact of artificial intelligence capabilities on servitization: The moderating role of absorptive capacity—A dynamic capabilities perspective. Journal of Business Research, 157, Article 113609. https://doi.org/10.1016/j.jbusres.2022.113609
• Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327
• Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
• Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
• Kline, R. B. (2014). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Publications.
• Nunnally, J. C. (1978). Psychometric Theory (2nd ed.). McGraw-Hill.
• Rossiter, J. R. (2002). The C-OAR-SE procedure for scale development in marketing. International Journal of Research in Marketing, 19(4), 305–335. https://doi.org/10.1016/S0167-8116(02)00097-6
• Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640
• Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z

13. Items of the Scale