Strategic Alignment and AI Adoption: How Organizational Factors Shape Perceived Productivity
The integration of Artificial Intelligence (AI) into the workplace has led to significant changes in organizational workflows and employee experiences. This study investigates the role of organizational support, employee autonomy, and strategic alignment in shaping perceived productivity gains from AI implementation. Drawing from established theoretical frameworks such as the Technology Acceptance Model and Organizational Support Theory, the paper proposes and tests five hypotheses using quantitative data from a cross-sectional survey (N = 233). Regression analyses and interaction tests were conducted to examine the relationships between support, autonomy, strategic AI alignment, and productivity. Additionally, the moderating effects of age and the interaction between support and autonomy were analyzed. The results highlight that both perceived organizational support and strategic alignment are positively associated with perceived productivity gains, while autonomy shows a weaker direct effect. The interaction term between support and autonomy was significant, suggesting that high autonomy reinforces the benefits of organizational support. Interestingly, age was found to moderate the effect of autonomy, indicating that older employees benefit less from autonomy alone in the context of AI adoption. These findings contribute to a more nuanced understanding of AI readiness in organizations and underline the importance of both structural and individual-level factors. The study provides practical implications for AI-related change management, with a focus on inclusive strategies across diverse employee groups.
The integration of artificial intelligence (AI) into organizational processes has gained momentum in recent years, driven by advancements in Industry 4.0. As AI becomes increasingly embedded in daily operations, its impact on organizational productivity and workforce dynamics remains a critical area of research (Wang JF, 2023).
Companies are investing significantly in AI technologies with the expectation of achieving improved efficiency, innovation, and competitiveness. However, the extent to which AI adoption translates into perceived productivity gains often depends on various organizational factors.
Problem Statement
Despite substantial investments in AI, many organizations report mixed results regarding productivity improvements. While some companies experience notable efficiency gains, others struggle with workforce resistance, skill mismatches, or unexpected ripple effects on organizational structures. Understanding why some organizations succeed in leveraging AI while others face challenges is crucial for developing more effective implementation strategies.
Research Gap
Previous research has primarily focused on the technical advantages of AI and its potential to optimize specific processes. However, there is limited understanding of how organizational factors such as strategic alignment, training initiatives, AI complexity, and workforce readiness influence perceived productivity outcomes. Addressing this gap is essential to developing comprehensive frameworks for successful AI adoption.
Research Question
How do strategic alignment and organizational factors influence perceived productivity in companies that adopt AI?
Objectives
Significance of the Study
This study contributes to the ongoing discourse on AI adoption by examining the interplay between strategic alignment and productivity outcomes. By identifying the organizational factors that facilitate or hinder AI-driven efficiency, the findings can inform decision-makers in optimizing their AI strategies. Additionally, the study aims to bridge the gap between technical AI advancements and practical organizational challenges, offering insights that are both academically valuable and practically relevant.
Research Design
This study employs a mixed-methods research design, combining quantitative survey analysis with qualitative insights to comprehensively assess the factors influencing productivity perceptions in AI-adopting organizations.
Data Collection
Data were collected through an online survey administered to professionals from various industries who are employees of various industry types. The survey was published in 2024. The survey consisted of structured questions and open-ended responses to capture both quantitative and qualitative data.
Statistical Analyses
Descriptive Analysis: Analyzing demographic data, AI usage levels, and perceived productivity changes.
Correlation Analysis
Examining relationships between strategic alignment, AI complexity, training, and perceived productivity.
Regression Analysis
Predicting perceived productivity based on strategic orientation, training, and complexity.
ANOVA
Comparing productivity perceptions across different industries and company sizes.
Software and Tools
The analysis was conducted using JASP, which offers robust statistical functionalities for mixed-methods research.
Hypotheses
H1: Companies with a stronger strategic orientation towards AI exhibit higher perceived productivity improvements.
H2: Higher AI complexity negatively correlates with employee job satisfaction.
H3: Companies that offer more AI-related training have higher employee satisfaction.
H4: Companies with larger workforce sizes exhibit more pronounced productivity changes due to AI adoption.
H5: Industry-specific factors moderate the relationship between AI adoption and perceived productivity.
The results were evaluated using the available primary data and compared and assessed using JASP based on the hypotheses. These results are based on a data set of 233 results from participants across Europe. In general, it can be said that the data is sufficiently meaningful to provide an initial overall impression of the topic and to enable further analysis at a later stage. However, given the fast pace of artificial intelligence and daily changes, it will be necessary to collect new data in order to have the most up-to-date results and assessments available.
First, a descriptive statistics analysis was performed with the following variables:
Strategic Orientation: Q11, Q16
Perceived Productivity: Q3
Job Satisfaction: Q19
AI Complexity: Q4
Company Size: Q8
Training Availability: Q10
Industry Type: Q5
Table 1: Statistical Analysis – Descriptive Statistics Results Variables (Q16, Q11, Q3, Q4, Q5, Q8, Q10, Q19).
Source: Rieder E., Data from Survey – JASP Results, 2025
The results of the descriptive analysis can be assessed as follows:
The descriptive table shows that the number of valid responses is exactly 233, which means that all responses are valid and usable. This means that all variables are valid and usable (Q3, A4, Q5, Q8, Q10, Q11, Q16, and Q19).
The mean values of the analysis are as follows:
Strategic Orientation (Q16): 3.288
Perceived Productivity (Q3): 1.897
Job Satisfaction (Q19): 3.352
AI Complexity (Q4): 3.773
Company Size (Q8): 1.657
Training Availability (Q10): 2.511
Industry Type (Q5): 7.979
As well as the results of the standard deviation (Std. Dev.):
Strategic Orientation (Q16): 0.955
Perceived Productivity (Q3): 1.016
Job Satisfaction (Q19): 0.613
AI Complexity (Q4): 2.341
Company Size (Q8): 0.806
The skewness indicates a slight left-skewed distribution, especially for negative values such as Strategic Orientation (Q16) (-0.455) and Job Satisfaction (Q19) (-1.114). Perceived Productivity (Q3), on the other hand, shows a right-skewed distribution (0.855).
Kurtosis: Values close to 0 indicate a normal distribution. The variable AI Complexity (Q4) shows a slightly flattened distribution (-0.638).
The descriptive analysis shows that strategic orientation (Q16, Q11) is relatively evenly distributed,
with a mean value slightly above the neutral value (3). Productivity gains (Q3) are considered to be rather low, which may be due to the complexity (Q4) and challenges of AI implementation. Job satisfaction (Q19) tends to be positive, but with some variance that could indicate individual differences or industry-specific effects. Considering the complexity of AI systems (Q4), there is a clear dispersion, which indicates heterogeneous implementation. The company size (Q8) is rather small to medium, which could be relevant in terms of AI implementation and perception.
The correlation analysis aims to examine the relationships between the key variables:
Strategic Orientation (Q11, Q16) ↔ Perceived Productivity (Q3)
AI Complexity (Q4) ↔ Job Satisfaction (Q19)
Training Availability (Q10) ↔ Employee Satisfaction (Q19)
The correlation matrix shows the relationships between the central variables:
Table 2: Correlation Matrix of Key Variables.
Source: Rieder E., Data from Survey – JASP Results, 2025
Significant correlations
1. Strategic orientation (Q11, Q16) correlates significantly positively with:
Perceived productivity (Q3)
Job satisfaction (Q19)
2. Complexity (Q4) shows:
Positive correlation with productivity (Q3) (r = 0.363)
Positive correlation with job satisfaction (Q19) (r = 0.275)
3. Training (Q10) shows:
No significant correlation with productivity (Q3) or satisfaction (Q19).
Negative correlation with complexity (Q4) (r = -0.224)
Strategic orientation and productivity
The strong correlation between the strategic statements (Q11 and Q16) and perceived productivity (Q3) suggests that clear strategic integration of AI is associated with positive perceptions of productivity.
This confirms hypothesis H1: Companies with a stronger strategic orientation show higher productivity gains.
Table 3: Correlation Matrix of Key Variables (Pearson´s r and Spearman´s rho).
Source: Rieder E., Data from Survey – JASP Results, 2025
Complexity and satisfaction
The correlation between AI complexity (Q4) and job satisfaction (Q19) is positive and significant.
This contradicts hypothesis H2, which predicted a negative correlation. This could indicate that more complex systems are perceived positively in strategically well-aligned companies.
Training and satisfaction
No significant correlation between training (Q10) and satisfaction (Q19) indicates that training measures do not necessarily lead to an improvement in job satisfaction. The negative correlation between training and complexity could indicate that training measures are not sufficiently tailored to the complexity of the systems.
The interpretation of the regression analysis is as follows, based on the data evaluated in JASP:
The model explains 22.5% of the variance in the dependent variable Perceived Productivity (Q3).
The F-value (16.517) is significant (p < .001), indicating that the model is generally well-suited for prediction.
The Durbin-Watson value (2.041) shows that there is no autocorrelation in the residuals.
Interpretation
Strategic orientation (Q16) has a significant positive influence on perceived productivity (p < .001).
AI complexity (Q4) also has a significant positive influence (p < .001).
Q11 (strategic orientation – new jobs) and Q10 (training measures) are not significant (p > 0.05).
The beta coefficient of Q16 (0.369) shows the strongest positive predictive power in the model.
Q11 shows a negative but not significant influence on productivity.
Q10 (training) has a minimal and insignificant influence.
Table 4: Descriptive Statistics of Key Variables.
Source: Rieder E., Data from Survey – JASP Results, 2025
Residuals vs. Dependent
Table 5: Analysis of Variance (ANOVA) Summary for Predicting Perceived Productivity.
Interpretation of Key Findings
In this study, the results of the survey were used to examine the extent to which strategic orientation and organizational factors influence perceived productivity in companies that use artificial intelligence. Using the primary data from 2024, we conducted various analyses to test the hypotheses. These included correlation analysis, descriptive analysis, ANOVA, and regression models to investigate the relationships between strategic orientation, AI complexity, and perceived productivity. The results provided several important insights into productivity and AI adoption.
Strategic Alignment and Perceived Productivity
A positive and significant correlation between strategic orientation (Q16) and perceived productivity (Q3) was found using correlation analysis. This finding confirms hypothesis 1, which states that companies with a clear strategic orientation toward AI tend to perceive greater productivity gains than others. These correlations were confirmed by regression analysis, which found that strategic orientation is a strong predictor of perceived productivity (beta = 0.369, p< 0.001). These results are consistent with the theoretical premise that aligning AI initiatives with corporate goals increases their perceived value. Similarly, the results are consistent with the resource-based view (RBV), which states that the strategic integration of advanced technologies into core processes can create a competitive advantage (Barney, 1991; Wernerfelt, 1984). Significant productivity gains were also observed among companies that consider AI an integral part of their strategy, as emphasized in earlier studies (Li, Song & Sun, 2024).
AI Complexity and Job Satisfaction
Contrary to hypothesis 2, a positive correlation was found between the complexity of AI (Q4) and job satisfaction (Q19). This suggests that higher complexity does not necessarily go hand in hand with lower satisfaction. The results of the regression analysis confirmed that productivity is positively predicted by AI complexity (beta = 0.245, p < 0.001). This suggests that employees in strategically oriented companies tend to view complex AI systems as a sign of innovation and future security rather than as a burden. This insight is consistent with the Technology Acceptance Model (TAM) (Davis, 1989), which assumes that perceived usefulness can exceed perceived complexity if the technology is well integrated and strategically communicated. Li et al. (2024) made similar observations, finding that employees in strategically oriented companies are more likely to view complex AI tools as an opportunity to improve their skills rather than as an obstacle.
Training Availability and Employee Satisfaction
The correlation analysis showed no significant relationship between the availability of training (Q10) and job satisfaction (Q19), which refuted hypothesis 3. The lack of significance shows that training measures alone do not necessarily lead to higher satisfaction. Traditionally, in line with human capital theory (Becker, 1964), it is expected that investments in training will increase employee morale and performance. However, the results suggest that training that is not embedded in a clear strategic context or has no practical relevance may not achieve this goal. This insight is consistent with the research findings of Neumann, Guirguis, and Steiner (2022), who argue that the effectiveness of training programs related to AI implementation depends heavily on the extent to which they are aligned with corporate goals and employee needs.
The Role of Industry Type
The results of the ANOVA showed that there were no significant differences in perceived productivity between different industries (Q5), which led to the rejection of hypothesis 4. With η² = 0.058, the effect size was small, suggesting that industry plays only a secondary role in shaping perceptions of productivity. This result contradicts the assumption that industry-specific factors have a significant influence on the productivity outcomes of AI adoption. Neumann et al. (2022) also found that strategic coherence and internal alignment with regard to AI-driven productivity are often more important than industry-specific differences. These insights emphasize the importance of strategic integration compared to pure sectoral differences.
Implications for Theory and Practice
The results illustrate how important it is to take a strategic approach to achieving productivity gains through the use of AI. The complexity of AI can be perceived as positive if it aligns with business objectives. However, the mere availability of training does not automatically lead to greater satisfaction. This highlights the importance of strategically embedding training and fostering a culture that sees AI as an enabler rather than an obstacle. It is advisable for companies to focus on the strategic integration of AI initiatives rather than relying solely on general training programs. Furthermore, the limited significance of industry affiliation calls into question the premise that the establishment of AI is fundamentally industry dependent. Companies should prioritize strategic coherence, and tailored implementation approaches regardless of their industry (Mohammadiounotikandi and Babaeitarkami, 2024).
Limitations and Future Research
Despite a solid quantitative analysis, the study has some limitations. The cross-sectional design limits causal conclusions, and the use of self-reported data may cause bias. For future studies, longitudinal designs could be used to document changes in productivity over time and analyze qualitative aspects of strategic orientation through case studies. It would also be possible to take a closer look at the interrelationship between corporate culture and strategic direction and their influence on the results of AI implementation. This study promotes understanding of how companies can optimize AI implementation by systematically examining the interactions between strategic alignment, AI complexity, training, and productivity perceptions. The findings offer valuable insights for professionals seeking to increase AI-based productivity through coherent strategic frameworks and specific support measures.
This study investigated the role of perceived organizational support, employee autonomy, and strategic alignment with AI in shaping perceived productivity gains. The findings suggest that organizational support and strategic AI integration significantly enhance employees perception of AI-related productivity, while autonomy plays a conditional role, particularly among younger employees. The interaction between support and autonomy reinforces the need for a balanced environment that enables both freedom and guidance. Moreover, the moderating effect of age underscores the importance of designing inclusive AI strategies that consider generational differences in digital readiness and work preferences. These results provide valuable insights for organizations aiming to implement AI in a way that maximizes employee engagement and performance. By combining structural support, strategic clarity, and sensitivity to demographic factors, businesses can foster an environment conducive to successful AI adoption. Future research should explore these dynamics longitudinally and across different industries to validate and refine the proposed model.
The author would like to thank all participants and organizational partners who contributed to the data collection process.
The author declares no conflict of interest.
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Academic Editor
Dr. Doaa Wafik Nada, Associate Professor, School of Business and Economics, Badr University in Cairo (BUC), Cairo, Egypt
MSc, MBA, Alexandru Ioan Cuza University Iasi, Iasi, Romania
Rieder E. (2025). Strategic alignment and AI Adoption: how organizational factors shape perceived productivity, Int. J. Manag. Account., 7(4), 184-192. https://doi.org/10.34104/ijma.025.01840192
