Due to the accelerated progress in artificial intelligence (AI), the world has completely reorganized its business environment, altering the functions of business owners and administrators on an entirely new level. As the core of the fourth industrial revolution, AI has evolved from merely being an automated helper for data processing and automation to serving as a strategic partner in making decisions and innovative solutions (Chalmers et al., 2021; Chowdhury et al., 2024). Unlike the earlier technological transitions, AI's capacity to co-create value and transform organizational structures is the main focus of the current transition making it central to entrepreneurial and managerial practices (Ali 2023; Balaji 2025).

The digital era of entrepreneurship is closely linked with hi-tech technologies. Digital entrepreneurs use AI to create new business models, become more agile, and act to respond to external crises like the COVID-19 pandemic (Al-Omoush et al., 2020; Baig et al., 2022). Managers, parallelly, are responsible for shaping AI-driven transformations involving integrating strategic foresight and operational execution (Oppioli et al., 2023).

The role of AI is aiding international and cross-cultural contexts, where it is used to foster knowledge generation, cross-border collaboration, and fast-track internationalization strategies.15, 16, 17 Secinaro et al., 2023 C. Secinaro, R. Monlat, P. Pien, and C. Llorente. However, with all these developments, there still remains a gap in empirical research studies that can concomitantly consider the perception and integration of AI among entrepreneurs and managers in their respective roles. Much of the available literature is inclined to adopt either a leadership approach to entrepreneurship or a managerial approach, and does not consider the relative interaction of the two groups (Calandra et al., 2023; Battisti and Brem, 2021).

This research helps fill this gap by examining the role of AI adoption in decision-making effectiveness and entrepreneurial performance and disaggregates the experiences of entrepreneurs and managers. By utilizing a holistic approach to statistical methodology, the research not only validates the transformative role of AI, but redefines its role as a strategic partner in helping to shape professional identities for the digital future.

Artificial intelligence (AI), entrepreneurship, and management is one such area of research in the recent past. According to scholars, AI is not just a supplementary tool but a transformative factor that redefines organizational agility and entrepreneurial strategy (Chalmers et al., 2021; Chowdhury et al., 2024). In digital entrepreneurship, AI encourages innovation through data-driven decision-making, predictive analytics, and developing new business models (Ali, 2023; Baig et al., 2022). On the downside, managers are turning to AI more and more to simplify their decision-making processes and become more strategic (Oppioli et al., 2023).

The literature also highlights the idea that digital entrepreneurship does not qualify as the same as traditional entrepreneurship. As Antonizzi and Smuts (2020) emphasize, it is influenced by ongoing digital change, and Christine and Ileana (2022) remind that new technologies play a revolutionary role in redefining the nature of entrepreneurship. Similarly, Elia et al. (2020) have argued that digital ecosystems are conducive to creating collective intelligence in which entrepreneurs are able to co-create value for their stakeholders.

In the global context, AI has enabled quick globalization of business enterprises. Crespo et al. (2023) suggest that digitalization can be beneficial in early internationalization strategies, and Secinaro et al. (2025) indicate that AI can facilitate intercultural collaboration. At the same time, however, as Battisti and Brem (2021) note, technology-driven partnerships blur traditional boundaries between the public and private sectors.

Although there is an increasing interest, research tends to focus on entrepreneurship or management separately. This leaves a void in the understanding of the concurrent influence AI plays in shaping both roles and the need for comparative research to place AI as a strategic partner to entrepreneurs and managers alike.

Although the current literature has emphasized the importance of artificial intelligence in operations and predictive analytics, little has been done concerning AI as an entrepreneurial and management partner when it comes to developing entrepreneurship and management functions. Existing studies have tended to view entrepreneurs and managers as a homogenous group, missing the subtle distinctions between the ways entrepreneurs and managers perceive and embrace AI. Furthermore, empirical studies that examine overarching frameworks that intersect AI adoption, decision-making, and entrepreneurial outcomes are limited. This research attempts to fill these gaps by providing a comparative analysis and supporting structural relationships between these constructs.

Conceptual Framework

The research has been structured in such a way that makes the implementation of AI the pivotal variable of the effectiveness of decision-making and entrepreneurial performance. The deciosn-making is viewed as a dependent and mediating variable, with dual reference to the role of AI in improving both immediate managerial procedures and long-term business outcomes. The framework identifies the differences between entrepreneurs and managers and recognizes contextual differences in AI integration.

Figure 1.1: Conceptual Framework

Hypotheses

• H1: the use of AI positively influences the effectiveness of decision-making.
• H2: There is a positive impact of AI adoption on the results of an entrepreneurship.
• H3: The effectiveness of decision making mediates the relationship between AI adoption and entrepreneurial outcomes
• H4: Entrepreneurs and managers differ significantly in their attitude towards AI use.

Here we have implemented a quantitative research design to investigate the role of artificial intelligence (AI) as a strategic collaborator in transforming the roles played by entrepreneurs and managers in the 21st century. This research used a survey-based methodology because it enabled the researcher to obtain a large amount of data and test hypotheses objectively. The data were collected via online questionnaires, which were structured and sent through the professional markets, entrepreneurship forums, and managerial associations. We received about 420 responses, 392 of which were valid after excluding the incomplete submissions. The survey was the main data collection instrument, with industry reports on AI adoption trends as a supplementary data input. The statistical analysis of data was performed with the help of SPSS version 28 and AMOS version 26 that are accepted statistical and structural modeling packages.

In order to sample participants, purposive sampling was employed to both sample the entrepreneur and managers. The eligibility criterion was three years of professional experience and personal experience in decision-making processes where the respondents have used AI tools. By so doing, sample of individuals with relevant expertise was acquired rather than a general population. To develop the questionnaire tool, validated instruments of past research in the field of AI adoption, strategic management, and entrepreneurship were adapted. A Likert scale of 5 points (from strongly disagree to strongly agree) was used for all items to ensure that subtle perceptions of the strategic value of AI were included.

Then to establish that the instrument is of good quality, internal consistency was verified by use of Cronbachs alpha and Confirmatory Factor Analysis (CFA) was conducted in AMOS to establish the construct validity. These methods are selected because they are reliable and rigorous in studies of behavior. Descriptive statistics were first applied for summarizing the demographic characteristics such as age, gender, education, years of experience, and industry sector as well as the baseline patterns of AI adoption. The testing of this less complex relationship had to precede the more complex relationships.

The descriptive analysis showed that the sample of respondents mostly constituted of multicultural participants; there was an equal number of entrepreneurs and managers. In the Table 1 the demographic distribution of participants is depicted where 56 percent were male and 44 percent female with the majority being between the age range of 30-45 years. The representation of the industry was broad (technology (32%), manufacturing (28%), services (25%), and others (15%). Figure 1.2 also illustrates this population distribution wherein it is socialised in terms of age group, gender, and industry sectors.

Figure 1.2: Distribution of Respondents by Demographics

Table 1. Descriptive Statistics of Respondents

In order to determine the quality of measurement, reliability and validity tests were conducted. Table 2 gives a value of Cronbachs alpha of greater than the recommended value of 0.70 and this signifies internal consistency. The confirmatory factor analysis outcomes indicated the acceptable fit index (CFI = 0.93, RMSEA = 0.05), which indicates the evidence of the construct validity.

Table 2. Reliability and Validity Testing of Constructs

Further analysis was the relationships between variables. Table 3 summarizes outcomes of correlation and regression studies where it was discovered that the adoption of AI had a positive correlation with decision-making effectiveness (r = 0.61, p < 0.01) and entrepreneurial outcomes (r = 0.57, p < 0.01). The regression outcomes revealed that the use of AI was significantly predictive in the decision-making outcomes (p<0.001) (v = 0.48). The regression line on AI adoption versus the effectiveness of decision-making is the visual summary of these relations as presented in Figure 3.

Table 3. Correlation and Regression Analysis of AI Integration and Decision-Making

(p < 0.01)

The comparisons of groups showed a high degree of difference. The results of ANOVA and SEM will be provided in Table 4, which shows that the managers ranked higher on AI (M = 3.9) adoption as compared to entrepreneurs (M = 3.5), and the difference in the outcomes is statistically significant (F = 6.24, p < 0.01). The SEM analysis confirmed the hypothesized framework by demonstrating that the implementation of AI had both direct effects on the decision-making process and indirect effects on the outcome of entrepreneurship. Figure 4 also indicates the relationship between the relationships that were tested in the way of the structure.

Table 4. ANOVA and SEM Results: Comparing Entrepreneurs and Managers

(p < 0.01)

To complement these findings, Figure 2 shows relative degrees of AI use among entrepreneurs and managers where managers are progressively reporting a greater integration in different levels of decision-making. Altogether, the results indicate that the measures were quite reliable, that the positive associations between AI adoption and decision-making and entrepreneurial performance were meaningful, and that group findings were used to show that the managerial environment did affect the perceptions of AI.

The analysis itself began with an analysis of the participants. Table 1 and Figure 1 indicate that the sample was balanced between entrepreneurs and managers, but also representative in regard to gender, age and industry. Through such diversity, the results can be made more applicable and ensure that no single group or industry considers AI adoption only.

Reliability and validity tests were done to ascertain the quality of measurement. Table 2 indicated that, all the constructs had levels that were above the acceptable levels of Cronbach alpha and average extracted variance. These findings are indicative that the constructs applied in the present study were internally consistent as well as valid measures of the concepts of interest.

The relations between the adoption of AI, the decision-making process, and the results of entrepreneurial outcomes were analyzed as the core. According to Table 3, there was a high level of correlation between AI adoption and decision-making effectiveness and entrepreneur performance. The regression findings also showed that decision-making was greatly predicted by AI adoption with a standardized coefficient of 0.48. This visual tendency is supported in Figure 3, which demonstrates a positive regression line with a strong tendency, revealing the strong connection between the integration of AI and better managerial decision-making.

Important differences were identified on group-level comparisons. Table 4 shows that managers reported more mean scores in AI adoption than entrepreneurs and that this was statistically significant according to the results of the ANOVA. The same fact is reflected in Figure 2, where managers reported greater degrees of AI adoption in operational, decision-making and strategic areas. These observations indicate that there is a managerial environment in which a decision-making process is more structured to enable more AI integration.

Lastly, SEM was used to test the larger structural model. The adoption of AI positively affected the entrepreneurial outcomes both directly and indirectly as shown in Table 4 and Figure 4.

Particularly, the adoption of AI not only directly but also indirectly affected the effectiveness of decision making but also the performance of entrepreneurs through better decision-making. The indices of good fit to the model suggest that the data was lying within the conceptual framework because it supports the main idea of the study, which states that AI is a strategic partner in the formation of managerial and entrepreneurial functions.

The analysis was initiated by the description of the demographics of participants. Table 1 and Figure 1 reveal that there was a balanced mix of entrepreneurs and managers with representation of both genders, age groups and industries. Such diversity enhances the overall applicability of the results, as no one in a specific demographic or industry will be left with looking at the attitudes of AI adoption.

 The quality of measurement was ensured using the reliability and validity tests. Table 2 indicated that the values of all constructs were higher than the recommended values of Cronbach alpha and average variance extracted. Such results show that the constructs used in this study were both internally consistent and a valid measure of the concepts under study.

The correlation of AI adoption and decision-making and entrepreneurial outcomes was the focus of analysis. Table 3 has shown that the use of AI was significantly correlated with the effectiveness of decision making and performance of entrepreneurship. The regression results also showed that the use of AI had an important predictor of decision-making with a coefficient of 0.48. Figure 3, its turn, leads to the reinforcement of this tendency as it indicates the apparent positive line of regression, putting the heavy emphasis on the high correlation between AI integration and the increased decision-making by the managers.

 Group-level comparison gave significant differences. Table 4 revealed that the mean scores of managers in AI adoption were higher than those of the entrepreneurs and this was statistically significant based on the outcome of ANOVA. This can also be observed in Figure 2 where managers reported higher AI-adoption in operational, decision-making, and strategic fronts. These lessons show that the managerial environment is systematic and can be more integrated with AI because of involving systematic decision-making procedures.

Lastly, SEM was used to test the extended structural model. As Table 4 shows and Figure 4 depicts, there were both positive and negative indirect relationships between AI adoption and the entrepreneurial outcomes. In particular, the adoption of AI directly and indirectly affected the effectiveness of decision-making and positively impacted the performance of entrepreneurs as a result of better decision-making. The high model fit indices show that the conceptual framework received good support in the data which supported the main claim made by the study that AI is a strategic partner in the formulation of managerial and entrepreneurial functions.

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