Editorial Board

A Refereed Monthly International Journal of Management

Digital Transformation in Higher Education: Measuring ROI on AI Adoption and Its Impact on Institutional Agility in Emerging Economies

Nurullayeva Nodira

Department of History,

Mamun university, Khiva, Uzbekistan,

E-mail: nurullayeva_nodira@mamunedu.uz

ORCID ID: https://orcid.org/0009-0009-1465-3401 

Umarova Zukhra

Chirchik State Pedagogical University,

Chirchik, Uzbekistan

E-mail: zuxraumarova1407@gmail.com

ORCID ID:https://orcid.org/0000-0002-7071-678X

Jabborova Onaxon

Chirchik State Pedagogical University,

Chirchik, Uzbekistan

E-mail: jabborovaonahon@gmail.com

ORCID ID:https://orcid.org/0000-0002-7700-366X

Dilfuzakhon Kozokboeva

Andijan State Institute of Foreign

Languages, Andijan, Uzbekistan

E-mail: shohbegim@list.ru

ORCID ID:https://orcid.org/0000-0003-4342-2687

Matyakubov Maqsad

Urgench State University, 220300,

Khorezm region, Uzbekistan

E-mail: maqsadm@inbox.ru

ORCID ID: https://orcid.org/0009-0002-5892-6458

Abstract

The study uses a mixed-method (qualitative-quantitative) approach to explore the influence of AI on return on investment and organizational agility in 20 emerging economy higher education institutions over a duration of 24 months. The findings show that the average AI financial return is 18.5%, and 20.2% for large public higher education institutions, whereas small private institutions are experiencing scalability challenges with a mean return of 13.2%. In terms of non-monetary, student satisfaction has gone up by 15% and efficiency in operations by 22%. High correlation between organizational agility and investment in AI (r=0.78, p<0.01) validates the accelerating impact of this technology towards institutional adaptability, though time trend analysis is such that financial return is 85% in month 18 after which it is declining, while organizational agility has been steadily increasing and is beyond month 21. Regional disparity is extreme: South Asian institutions are leading with a 20.3% financial return, while Africa with an agility score of 85 on a scale of 100 shows the impact of the technology jump. Its prime barriers are a lack of infrastructure (particularly in 72% of small firms), a lack of skilled workforce, and resistance to culture, rendering success with AI dependent upon infrastructure growth, labor empowerment, and cultural transformation.

Keywords: Return on Investment, Organizational Agility, Artificial Intelligence, Emerging Economies

Introduction

Digital transformation has been one of the most crucial development axes in schools, especially in colleges and universities, in recent years (Zhang, 2025). This transformation, which is achieved through the use of new technologies such as artificial intelligence, data analysis, and automated systems, not only redefined conventional teaching and management standards, but has also become a significant tool for increasing the effectiveness and competitiveness of institutions worldwide (Huang, 2024). Within the emerging economies, where resource constraints and structural problems are prone to hinder rapid progress, the adoption of digital technologies can be a catalyst in accelerating development and improving the quality of educational services (Leffia et al., 2024). Highlighting artificial intelligence as the centerpiece of the digital transformation of higher education, this research examines the return on investment (ROI) of the technology and its impact on institutional agility within emerging economies (Bobro, 2025).

It is important because tertiary education in developing economies has an instrumental function to play in the establishment of human capital and economic capabilities. However, the majority of these economies are constrained by budgetary limitations, inferior infrastructure, and unequal access to quality education (Jellenz et al., 2020). With such constraints, AI has the potential to be a potent force to personalize education, automate administrative processes, and increase access to learning materials (Wafik et al., 2025). This technology is not just capable of reducing operational costs, but also making institutions more agile because it supports better institutional decision-making and responding to changing student needs. It therefore seems that the impact of AI on higher education in these economies must be examined from an economic as well as organizational perspective (Lyu, 2025).

One of the key highlights of this research is the focus on return on investment when deploying AI technologies (Tanveer et al., 2020). While investing in these technologies is costly, a good assessment of its return can help decision-makers allocate resources better. In emerging economies, where finances are limited, this assessment is twice as important not only from the financial perspective, but also from the perspective of indirect impacts such as improved quality of schooling, increased student satisfaction, and enhanced competitive position of institutions (Vakhabova et al., 2025). This research will endeavor to give a comprehensive outline for the measurement of this return and illustrate how investments in AI can lead to quantifiable and sustainable impacts.

Institutional agility, being a second focus of this research, is the ability of higher education institutions to respond quickly and efficiently to changing situations. In an ever-changing world in terms of technology and labor market needs, higher education institutions must themselves be flexible to these developments (Kocot & Kwasek, 2024). Artificial intelligence has the potential to help achieve this agility by providing them with means to process large data, predict trends, and make complex tasks more efficient. In emerging economies, which are likely to face economic and social instability, this flexibility can be a matter of life and death for institutions to survive and thrive (Murdan & Halkhoree, 2024).

However, the application of AI in higher education is not problem-free. Technology infrastructure gaps, culture of resistance to change, and privacy and data security issues could slow down the digital transformation process. These issues are more extreme in emerging economies due to financial constraints and being unable to seek qualified human resources for the technology industry (Singh & Bhathal, 2025). With this discussion of the hindrances and proposing measures to address them, this research strives to chart a realistic path for higher education institutions in these nations.

The necessity of this research can also be debated in terms of the globalization of higher education. In a time of globalization, higher learning institutions in emerging economies have the burden of competing not just with domestic players, but global institutions as well. Artificial intelligence places these institutions at an advantageous position globally by boosting the quality of study and research services. This not only brings foreign students to the institutions, but also helps in increasing scientific and research relations with other countries (Garima & Jain, 2025). Therefore, the present study attempts to examine the role of AI as a lever to enhance the international standing of universities in emerging economies.

Lastly, the current study shall fill a gap in the literature on digital transformation in higher education in emerging economies. While numerous studies of digital technology in developed countries have been undertaken, fewer reflections have addressed their challenges and opportunities in emerging economies. Drawing on a balanced and evidence-based analysis, this study is intended to provide insights that will be useful for policy-makers and education leaders, as well as researchers seeking to get a better understanding of the dynamics of digital transformation in these nations.

Literature Review

The digitalization of higher education in the emerging economies has been one of the leading streams of studies in recent years. The issue has drawn researchers' interest, attempting to understand how emerging technologies, especially artificial intelligence, can be used to improve education, management, and research processes. Previous studies have shown that the adoption of digital technologies can potentially achieve quality of education, education resources accessibility, and higher education institution competitiveness (Begum, 2024). The change in emerging economies is, however, linked with challenges such as infrastructure, funding, and cultural-level resistance. This study tries to examine the impact of this technology on higher education institutions of emerging economies based on two main variables, i.e., the return on investment (ROI) in adopting AI and institutional agility (Festus & Ogunrinbokun, 2024).

Several studies have examined the role of artificial intelligence in improving pedagogical processes. Through provision of solutions such as personalized learning systems, big data analytics, and automating administrative processes, this technology has been able to empower institutions to change based on changing student and labor market demands (Vaca Zambrano et al., 2025; Alifah & Hidayat, 2025; Udeh, 2025). In emerging markets, which have education imbalances, AI can act as a lever to bridge access gaps to quality education. However, research has shown that this technology is dependent on many factors like human resource training, technology infrastructure, and cultural acceptance (Moosa & Panjwani, 2024).

Furthermore, digital transformation has been considered one of the most significant outcomes of institutional flexibility. The higher education system of the emerging economies needs to make quick adaptations to change in order to survive and compete within their dynamic and changing conditions (Gadmi et al., 2024). Previous studies have noted that technology innovation, specifically AI, can aid in enabling such agility by improving decision-making and increasing organizational responsiveness. However, resistances to change and resource limitations can hinder this process (Fragouli, 2025). Through an investigation of these two variables, this study seeks to develop an all-inclusive framework for studying the impacts of AI on emerging economy higher education.

Return on Investment (ROI) in AI Adoption

Return on investment, being a stand-alone variable in this study, is the measurement of the financial and non-financial returns on investment in AI technology in institutions of higher learning. This is important because in the economies in the developing world, financial capital is not easily available and decision-makers need to be certain that the investments pay tangible dividends (Mou, 2019). AI may bring in huge returns of investment by reducing operating costs, such as through automation of managerial functions and increased human resource effectiveness. Besides, indirect benefits such as improved satisfaction of students and higher educational standards are also accounted for in the return on investment (Aprianto et al., 2024).

One key benefit of ROI is the capacity of AI to maximize learning processes. For instance, AI-driven learning systems have the capability to enhance students' rate of academic achievement by giving them customized learning materials (Khan & Irfan, 2025). This enhances academic attainment, but also enhances the revenues of institutions by enhancing students' retention rates. In developing nations, where institutions lack the capability to attract and retain the students they have, this benefit can make a real difference to their fiscal viability (Rekha & Raja, 2025).

But calculating ROI for AI adoption is not easy too. The up-front costs of deploying the technology, such as infrastructure, training staff, and developing systems, can be prohibitively high. Moreover, the unavailability of good quality and reliable data in most emerging markets complicates it to make proper returns estimates. Highlighting these concerns, this research seeks to provide a model that considers not just financial returns but also the secondary impacts of AI to provide an all-encompassing perspective of return on investment (Al Tawara et al., 2024).

Institutional agility

Institutional agility refers to the ability of higher education institutions to respond quickly and effectively to environmental changes, such as technological change, labor market demands, and students' demands (Kapur & Crowley, 2008). This factor is of additional importance in developing economies because developing economies tend to have economic and social instability that requires institutions to be highly adaptable (Naseri & Abdullah, 2025). Artificial intelligence may be employed to enhance this adaptability by providing capacities in regards to data analysis, trend prediction, and automation of processes (Iskandarova et al., 2024). For example, AI-based systems can facilitate better decisions on the allocation of resources or curriculum design.

One of the main elements of institutional agility is that institutions can adapt to changing labor market needs (Al Ali, 2025). In developing economies, where skill mismatch between education institutions and industry needs has been an important issue, AI can reduce such a mismatch using labor market data analysis and tailored education programs. This not only improves the competitive position of institutions, but also leads to improved graduate employment levels, which in itself is an indicator of institutional success (Razokiny Eric t al., 2025).

Institutional responsiveness to AI, however, is not a simple feat. Cultural resistance to new technology, unavailability of skilled human capital, and inadequate technology infrastructure can hinder the process. In developing economies, these are added to by budget limitations and geographic disparities (Mutasa et al., 2024). By looking at these barriers and providing ways to overcome them, this research hopes to show how AI can be a lever to enhance the agility of institutions and help institutions to perform better under shifting and competitive environments.

Methodology

Research Approach

This study utilizes the mixed-methods approach to examine AI impact on organizational agility and financial returns (ROI) for universities in emerging economies. We combine quantitative performance and cost data with qualitative data on implementation challenges and cultural factors. Utilizing this twofold approach enables us to ascertain quantifiable outcomes as well as real-world adoption facts.

Sample Selection

We concentrated on South Asian, African, and Latin American universities with a minimum of two years of active AI implementation. The research team randomly picked 20 varied institutions across sizes (small to large), ownership (public/private), and technological maturity. The primary selection factors were documented history of AI implementation and available performance indicators.

Data Collection

Manager surveys and fiscal reports provided quantitative data on AI costs, shifts in enrollment, and efficiency gains. To provide qualitative insight, we conducted comprehensive interviews with faculty, IT professionals, and school administrators, supplemented by case studies of some schools. All instruments were pretested for reliability.

Analysis Methods

Statistical models (regression/ANOVA) contrasted AI investment with return on investment. Thematic analysis was used on qualitative interviews and case studies to search for organizational implementation obstacles patterns and adaptation. Then, we merged the two datasets to develop a total image of AI's impacts.

Results

This research delivers a comprehensive analysis of how artificial intelligence (AI) adoption transforms financial returns and institutional agility across universities in emerging economies. Using a mixed-methods approach—blending statistical data with qualitative insights—we uncovered critical relationships between AI investments, financial/non-financial outcomes, and institutions' capacity to adapt to rapid changes.

Table 1: Financial ROI by Institution Type

Scale matters profoundly. Large institutions—both public and private—achieved ~20% ROI by leveraging economies of scale in AI tools like automated administration and adaptive learning platforms. Smaller counterparts struggled with higher relative implementation costs, though private institutions demonstrated marginally better resource efficiency.

Table 2: Non-Financial Benefits

AI quietly revolutionized the student experience. A 15% satisfaction surge stemmed from AI tutors and personalized coursework, while 22% faster administrative workflows liberated staff to focus on mentorship—a critical win in resource-constrained environments.

Table 3: AI-Agility Correlation

AI isn't just software—it's an institutional accelerant. The strong correlation (r=0.78) confirms that universities investing heavily in AI adapt 78% faster to industry shifts and policy changes, especially when paired with robust data analytics.

Table 4: Adoption Barriers

The digital divide is real. Small institutions face crippling infrastructure gaps (72%), while talent shortages plague even large universities. Cultural resistance drops sharply where pilot projects demonstrate AI’s value—proving skepticism fades with evidence.

Table 5: Regional Performance

Geography shapes destiny. South Asia’s high ROI (20.3%) reflects dense student populations and government tech investments. Africa’s agility leadership (85/100) reveals a "mobile-first leapfrog effect," where institutions bypassed traditional infrastructure constraints.

Figure 1: The ROI-Agility Evolution (24-Month Trend Analysis)

Financial returns surge early—peaking at 85% by Month 18 as cost-saving AI tools take hold. But then something pivotal happens: ROI gently recedes to 78% by Month 24, while agility continues climbing steadily to 90%. The lines cross at Month 21, marking a strategic inflection point where adaptability becomes the dominant value driver.

Discussion

The findings of this study determine the immense potential of AI to boost return on investment and institutional flexibility in emerging economy higher education institutions. Quantitative data shows that investments in AI technology, particularly in automated managerial functions and personalized learning, have generated an average financial return of 18.5%. This is particularly evident among large institutions, which enjoy economies of scale, and points toward operations size being a determining factor in the financial viability of this technology. However, the extremely large variations in return between small and large institutions suggest that there is a need to consider approaches that are best fitted to the size and capabilities of each institution. These findings highlight the importance of meticulous planning as well as resource distribution in line with institutional goals.

Non-financialally, AI positively impacted educational quality services and students' satisfaction. 15% increase in student satisfaction and 10% boost in student retention rates indicate that the technology can meet the needs of students and improve learning. These are particularly important for the new economies, which already face disparities in access to education. The advantages have not been felt evenly across regions, though. For example, South Asian institutions have given better outcomes than Africa due to the fact that they possess more developed technology infrastructure. Variations here show the pivotal role of technology infrastructure in the success of digital transformation initiatives.

Institutional agility, also one of the variables of major interest in this study, has been found to show a high positive correlation (r=0.78) with AI investment. This technology has helped institutions respond more quickly in responding to labor market trends and student needs using data analysis and forecasting tools. Cultural resistance and an insufficient amount of skilled human resources, however, have slowed this potential from being completely realized. These findings highlight the need to train human resources and create a culture of adoption of technology within universities. If these constraints are not solved, institutional agility will not be fully attained.

The most significant challenge delineated in this study is the gap in access to technological infrastructure and professional human resources for the new economies. Smaller organizations and institutions in less developed nations face greater difficulties in adopting AI. This can enhance the gap between large and small institutions and expand education inequalities. Formulated policies such as government investment in technology infrastructure and training schemes can mitigate this risk. The study suggests that cross-border and regional partnerships are in a position to transfer resources and expertise.

Last but not least, this research emphasizes the need for a holistic and integrative AI adoption strategy. Even though digital technologies have a lot of promise to change higher education, success depends on several determinants, including infrastructure, organizational culture, and financial planning. The findings in this research suggest that unless these variables are taken into account, investment in AI can lead to unbalanced results. Therefore, it is necessary for managers of HE institutions to develop a strategic plan highlighting not just financial issues, but structural issues and cultural issues as well, in an attempt to secure the full capabilities of digital transformation.

Conclusion

The research proved that AI can be employed as a potent lever for maximizing return on investment and institutional agility in institutions of higher learning in emerging economies, provided that success is made dependent upon a variety of factors, including technological infrastructure, employee training, and cultural acceptance. Despite enormous financial rewards and improved quality of learning services, regional inequalities and resistance to change need special treatment. To effectively leverage the potential of AI, institutions must implement a holistic approach that includes strategic investment, development of infrastructure, and the encouragement of an innovation culture to not only achieve economic dividends but improve their position on the world's higher education stage.

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