Social entrepreneurship is not a new idea that can be described as a powerful instrument in assisting to address systemic inequities through a creative, mission-driven business model. Conventionally, social businesses ventures have always been humanistic in order to reduce poverty, enhance education and formulate and enforce inclusion. Nevertheless, the current high rate of the evolution of the Artificial Intelligence (AI) could be exploited as the opportunity to make such activity larger and more effective. The application of AI technologies, such as predictive analytics and machine learning, to natural language processing are becoming more and more a practical application to the operations of social enterprises, to maximize resource allocation, improved decision-making, and social interventions tailored to each individual.

The lack of data, lack of funds, and limited reach are some of the most endemic issues to social entrepreneurs in most developing and marginalized communities. The latter can be addressed with the help of AI which will allow implementing more accurate principles of determining community needs and providing social goods in a more effective way. In the given instance, the analysis conducted through AI will be capable of identifying the previously unnoticed social trends, and the organizations will be able to provide certain solutions to underserved populations. The automation can also streamline the administrational process in this respect also, and this will allow the social entrepreneur to allocate more work towards mission-driven innovation. This is where AI intersects with social entrepreneurship and this is a paradigm shift in responding to problems rather than being a data-driven social innovator. It also raises other ethical concerns, however, regarding equity, transparency, and digital inclusion.

With AI becoming an incorporated tool in the social sector, it is now critical to consider how it can be used in a responsible manner so that it can be utilized to bridge social differences instead of increasing the gaps between people. This study examines the reposition of social entrepreneurship as the AI-driven strategies reshape the role and influence of this phenomenon, providing the new avenues of inclusive, sustainable, and technology-based social change.

Background of the study

Over the last twenty years, social entrepreneurship has become a critical process of dealing with the long standing social, economic and environmental problems. In the past, social entrepreneurs have been depending on community-based strategies, invention, and mobilization of grassroots to develop inclusive and sustainable models of development. Nevertheless, the accelerated development of Artificial Intelligence (AI) has started to reconsider the ways in which such social missions can be developed, brought into practice, and expanded. AI, previously viewed as an instrument of the exclusive technology industry, is becoming more of an instrument of the social enterprise ecosystem- providing data insights, predictive modelling and automation to support the improved delivery of social impact.

The increasing social and economic inequalities in the world and particularly in areas of access to education, health, work, and financial inclusion have raised the alarm of the necessity of technology-based social innovation. According to the World Economic Forum (2023), using AI technologies opens the possibility of filling complicated gaps in the society by increasing efficiency, transparency, and access in social interventions. In this case, AI-based platforms can be used to recognise underserved groups and tailor support initiatives as well as optimize the utilisation of scarce resources. As a result, social entrepreneurship is no longer a human-centric system but is becoming tech-enhanced by integrating compassion with smart data systems.

New opportunities and challenges of AI and social entrepreneurship are violated. On the one hand, AI can be used to offer scalable solutions to the acute challenges in the world such as the alleviation of poverty, environmental sustainability, and access to healthcare. Artificial intelligence applications, such as chatbots, mental health counselling, machine learning, and farming forecasting, or algorithmic microfinance inclusion have demonstrated to achieve measurable social outcomes in social businesses. On the other hand, the challenge of automated bias, personal information confidentiality, and ethical regulations refers to the need to practice responsible and inclusive AI. This is what makes the field of study very crucial in the field of social entrepreneurship because AI has the enabler and disruptive functionality of being both a facilitator and a catalyser.

In addition, the concept of digital empathy is also accepted in terms of the need to make AI-based technological intervention more human. Social entrepreneurs are looking at how AI can do more than maximize efficiency, but also social connectedness and trust in disfavored groups of individuals. It is paradigm shift since technology is not a support tool any more but now it is a collaborator in the success of the social missions. The ability of social enterprises to reconcile innovation and social responsibility makes the ability of companies in various industries to close social divide more reliant on the AI-based model used.

Although there is increased interest, the scientific studies that investigate the synergistic connection between AI and social entrepreneurship are few especially in the developing world where technological literacy and infrastructure levels are not uniform. The necessity to learn more about the way AI-driven social enterprises can be made inclusive and thus, make sure that technological innovations do not support the given inequalities but, on the contrary, break them down is pressing. The current study, then, aims to discuss how Artificial Intelligence can be used as a radical change agent in the context of reducing social discrepancies, and reconstituting the norms and habits surrounding social entrepreneurship today.

Justification

Considering the social inequalities and a rapidly developing technology, the idea of integrating Artificial Intelligence (AI) as the social inclusion and empowerment tool has gained more and more significance. Social entrepreneurship, which started as a quest to find solutions to the issues of the community, is now experiencing a trace of change and transformation. The application of AI to social endeavours is a trend of multiplying impact and improving performance, as well as a systemic inefficiency in education, medical, access to finances, and social services. The abstract need to redefine social entrepreneurship in the digital age and the practical need to investigate AI and implement it to achieve fair development justify the research.

First of all, even though AI technologies have already achieved important milestones in the business sphere, the opportunities of social innovations have not been utilized yet. Even with limited resources and resources, social nonprofits can be able to reach wider audiences and increase their accuracy in addressing community needs with the aid of AI-based technologies, such as predictive analytics, natural language processors, and machine learning.

The exploration of this integration gives a critical framework to the way AI can reinforce social missions instead of substitute human empathy and discrimination which are at the core of social entrepreneurship.

Secondly, the research answers an increasing divide between technology capacity and social availability. Although the world has made strides in the digital transformation, the disadvantaged groups are not usually included in the benefits of the technology because of the factors of cost, literacy and infrastructure. This study aims to understand how to apply the idea of inclusive technology adoption to boost the social enterprises and minimize the disparity by analysing how AI could be strategically integrated into the enterprises to empower vulnerable populations.

Thirdly, the study is important as it is policy and economically relevant as it works to offer empirical and concept insights that can guide the strategies of development, particularly in the emerging economies. The use of the public-private partnership continues to grow among governments and development agencies to tackle social issues. The introduction of AI into socially focused businesses may introduce information-based decision-making systems, enhance the distribution of resources, and will be able to quantify social impact in a better way. Therefore, this study helps to contribute to the academic discussion and to the policy making process as AI will act as a mediator between technological advancement and societal benefit.

Further, the rationale behind this study is the need to fill research conducted gap. Although the literature on AI ethics and on social entrepreneurship alone has become quite numerous, there is hardly any literature discussing the overlap of the two as a synergistic model to ensure inclusive development. Through the idea of AI being a social equalizer once ethically applied and contextually, this paper will provide an innovative view of how the technology can be brought to the value of human beings.

Finally, the research study is morally and developmentally justifiable. The ongoing social challenges, including poverty, sex disparity, and education access, need comprehensive, quantifiable, and sustainable solutions. With AI being deployed, social entrepreneurs can be transformers of the systemic change, able to find looming factors, forecast social trends, and offer solutions to the needy communities individually. Such a paradigm shift places AI as not a technological advancement, but as a ethical tool of justice and inclusion.

Objectives of the Study 

1. To examine how Artificial Intelligence is performing its changing role in social entrepreneurship.
2. To explore how AI applications contribute to bridging social inequalities and promoting inclusive development.
3. To determine important opportunities and challenges related to the introduction of AI in social entrepreneurial activities.
4. To assess the impact of AI-enabled decision-making on the sustainability and scalability of social enterprises.
5. To suggest a theoretical framework of how AI can be used in social entrepreneurship to foster social inclusion and fair development.

1. Introduction: AI meets social entrepreneurship

The further development of Artificial Intelligence (AI) has brought about fresh opportunities and fatal threats to people who are interested in bringing social transformation. AI in social initiatives can make programs more powerful and expansive, and new threats are also introduced (Vinuesa et al., 2020). In the meantime, social entrepreneurship that can be regarded as a purposeful venture action that targets at potentialization of social value, in addition to sustainable form of organization, have famous approaches to convert innovations into community action (Mair and Marti, 2006; Dees, 1998). In order to investigate the morality and performance of implementing AI tools to the environment of social entrepreneurship, one will need to relate the study of AI applicability to the social good to the theory and practice of social ventures.

2. AI for social good: potentials and limits

The problematic assessment of the connection between AI and sustainable development provides an ambivalent image: AI may enable a positive shift in a wide spectrum of Sustainable Development Goal (SDG) outcomes (e.g., by improving health diagnostics, distributing resources more efficiently, and making more services accessible), but on the other hand, it can contribute to the growth of inequality and sustainability issues when it is not used appropriately (Vinuesa et al., 2020). This duality is reflected in the reviews of practitioners and industry: AI projects have the potential to drastically scale up and data-driven targeting of social interventions but frequently fail when the situation in social contexts, data poverty, or unintended bias is not properly considered (McKinsey, 2024; DataKind, 2020). In such a way, the literature emphasizes the potential of technical opportunities, as well as the socio-institutional obstacles that intermediate results.

3. Social entrepreneurship: theory, models, and impact pathways

The social entrepreneurship literature views social ventures as institutions which locate the opportunities to generate social value, marshal resources, and internalize new institutional practices (Mair and Marti, 2006). Dee and later reviews highlight hybrid organizational logics (a combination of social mission and earned revenue), the importance of context-sensitive innovation and scaling approaches that may go all the way to copying and enabling ecosystem change. Three such pathways of impacts have been identified within this body of work and include; (a) capability amplification (enhancing service delivery), (b) model innovation (development of new value propositions), and (c) ecosystem influence (setting of norms, policy, and markets) (Mair and Marti, 2006; Nicholls, 2006).

4. Where AI and social entrepreneurship intersect

Early applications to social entrepreneurs and practitioners have been reported in recent case reviews and practitioner reports targeting clients, detecting fraud in microfinance, predicting crop yields in smallholder farming, and automated counselling tools to access mental-health (WEF, 2024; SEWF/SEWForum case series). An example of how AI can reinforce the impact channels is the provision of more detailed diagnostics, real-time monitoring, and individual solution, which can also be used to make social ventures more efficient and quantify the results to funders (McKinsey, 2024; SEWF/SEWForum). The intersection, however, requires data availability, local collaboration, and human-focused design practices that ensure keeping the social mission first and foremost instead of prioritizing technological optimization (DataKind, 2020).

5. Ethical, governance, and inclusion concerns

One of the most prominent motifs in literature is governance: AI systems have the potential to recreate or increase social biases, undermine privacy, and create a lack of accountability in case of ethical design and oversight (Jobin, Ienca, and Vayena, 2019; Mittelstadt, 2016). Broad agreement on principles of transparency, fairness, non-maleficence, responsibility, and privacy is found in comparative reviews of AI ethics guidance, but divergent interpretations and poor implementation mechanisms are also observed (Jobin et al., 2019). In the case of social entrepreneurs, these ethical issues are stretched since the legitimacy of their missions lies in serving marginal populations; therefore, the mismanaged AI may destroy trust, ostracize the same beneficiaries the venture is targeting, and make the process less likely to survive.

6. Practical constraints: data, capacity, and scaling

Three common limitations have been cited in the use of AI by social ventures in the empirical reviews: (a) insufficient data and quality in underserved communities, (b) in-house technical capacity and resources to build and maintain AI solutions, and (c) the challenge of scale of context-sensitive interventions in diverse socio-political contexts (Vinuesa et al., 2020; DataKind, 2020). Researchers hold that lighter solutions to AI-technology, such as enabling lightweight AI usage alongside participatory data collection, collaborative relationships, and capacity building, have a higher likelihood of maintaining ethical and effective implementation than more substantial solutions to technological issues (Nasir, 2023; McKinsey, 2024).

7. Gaps in the literature and research agenda

Although the interest is on the increase, there remain a number of gaps in research. To start with, the longitudinal evidence on the results of AI-enabled social ventures is minimal, the majority of reports are descriptive or pilot analyses, which restricts the possibility of causal statements regarding the impact (Vinuesa et al., 2020). Second, comparative work on models of governance that balance high speed of technological innovation and accountability in mission-driven organizations is relatively minimized (Jobin et al., 2019). Third, intersectional analyses, the impact of AI adoption on gender, the caste/ethnic minorities, disability, and other dimensions of marginalization, are under-developed (Mittelstadt, 2016). The solution to such gaps requires mixed-method research that integrates randomized or quasi-experimental impact evaluation and participatory research and policy analysis.

Research Design:

This paper adopted a mixed-method research design, which incorporated both quantitative and qualitative research designs to identify how Artificial Intelligence (AI) is incorporated in social entrepreneurship to eliminate social and economic disparities. The study was to find out the magnitude, nature, and the effects of AI applications on women-led, youth-led, and community-based social enterprises in emerging and developed economies. The quantitative phase included a designed survey of 150 social enterprises that used AI-based tools to conduct their social inclusion, whereas a qualitative phase was based on semi-structured interviews with 20 social entrepreneurs and innovation managers. The mixed-method design also enabled the thorough triangulation of attaining quantitative data on the form of measurable trends, and qualitative information on the contextual meaning and motivation of adopting AI.

Data Collection Methods:

1. Quantitative Data

The quantitative data were gathered with the help of online questionnaire which was provided to registered social enterprises by the means of professional networks, nonprofit directories and social innovation hubs. The tool consisted of five questions on demographic information, adoption habits of AI, organizational performance, measures of social impact, and perceived barrier issues.

This questionnaire was that the respondents were asked to answer on a five-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree) to state their perception of AI in reference to social inclusion. The Cronbach alpha (0.87) was used to prove the internal consistency of the instrument and reliability.

2. Qualitative Data

The semi-structured interviews with selected respondents (referring to the various regions and sectors) were used to complement the survey results (education, healthcare, microfinance, and environment). Each interview took between 45 and 60 minutes and it was dedicated to:

• Cases of using AI in social impact strategies.
• Alterations to the beneficiary engagement and inclusiveness outcomes.
• One of them is ethical issues, including information privacy, bias, and access.

All interviews were tape-recorded (with consent) and transcribed verbatim and thematically analysed with NVivo software to uncover patterns and new themes.

Inclusion and Exclusion Criteria:

Inclusion Criteria

• Social enterprises operational for at least two years with documented community engagement activities.
• Organizations that have adopted or are piloting AI technologies (e.g., machine learning, chatbots, data analytics, predictive algorithms).
• Founders, managers, or innovation leads directly involved in AI-based program implementation.
• Participants aged above 21 years, willing to provide informed consent.

Exclusion Criteria

• Traditional businesses without a defined social mission or community development objective.
• Nonprofits or NGOs not utilizing AI or digital technologies in their operations.
• Enterprises that failed to complete the survey or provide verifiable organizational data.
• Respondents unwilling to participate in follow-up qualitative interviews.

The inclusion and exclusion criteria made the study population relevant, credible, and focused to consider only those social enterprises that genuinely incorporate AI to benefit the society and not just use technology to make their work more convenient.

1. Overview of the Study Sample

A total of one hundred and fifty respondents were invited to fill out the survey; and they consisted of AI-integrated social enterprise founders (n = 60), project employees (n = 50) and beneficiaries (n = 40) in the areas of education, health, and financial inclusion in Asia and Africa.

The aim was to assess the role of AI applications in social enterprises in bridging social and economic disparities, without prejudice or unfairness.

Table 1. Demographic Profile of Participants (N = 150)

Interpretation
The surveyed organizations work mostly in the fields of education and financial inclusion proving where AI is used to solve social differences. Moderate AI adoption (44.7%) implies that adoption is growing but there are obstacles to complete deployment such as funding and technical expertise.

2. Impact of AI Integration on Social Outcomes

Five primary dimensions of social impact were measured using a 5-point Likert scale (1 = Very Low, 5 = Very High):

• Access to services
• Operational efficiency
• Community engagement
• Transparency and accountability
• Employment creation

Table 2. Impact of AI on Social Entrepreneurship Outcomes

Interpretation:
Access to services has the greatest mean score (M = 4.43), which proves that AI tools, i.e. chatbots in the educational or health diagnostic field, increase the coverage of underserved groups. Operational efficiency (M = 4.36) demonstrates the contribution of AI to the optimization of workflow, the decrease in their administration overhead, and the improvement of the accuracy of the decisions. Nevertheless, the creation of jobs (M = 3.89) is a bit lower, which suggests that there are fears that automation, in fields with high automation, will lead to job losses.

Figure 1. Average Impact Scores of AI Integration on Social Outcomes

|███████████████████████████████████  4.43 | Access to Services

|█████████████████████████████████    4.36 | Operational Efficiency

|█████████████████████████████        4.12 | Community Engagement

|███████████████████████████████   4.28 | Transparency & Accountability

|██████████████████████████           3.89 | Employment Creation

(Each bar represents mean scores; longer bars denote higher impact)

3. Relationship Between AI Integration and Inclusive Growth

A Pearson correlation test was used to determine the interdependence between AI integration level, social impact index and the inclusive growth index (a combination of income growth, access to basic services, and participation rates).

Table 3. Correlation Matrix

p < 0.01 (**) indicates statistical significance

Interpretation:
The findings demonstrate that the correlation between the AI integration and social impact (r = 0.63) and inclusive growth (r = 0.58) is strong and positive. Moreover, the correlations between the social impact and inclusive growth (r = 0.72) imply that there is a direct impact of AI-based social programs on the fair development and community resilience.

Figure 2. Correlation Plot Between AI Integration and Inclusive Growth

Inclusive Growth ↑

|                          • • •

|                    • • •

|              • •

|         •

|    •

|_________________________________→ AI Integration Level

(Scatter shows positive upward trend — higher AI integration correlates with higher inclusive growth.)

4. Qualitative Findings

The interviews were intensive based on 20 social entrepreneurs and 10 AI developers demonstrating three prominent themes:

1. AI as a Bridge for Accessibility

Women-owned businesses and businesses based in rural areas suggested that AI chatbots, translation applications and predictive analytics were not able to reach marginalized customers who could not access information before.

1. Enhanced Transparency and Accountability

The AI systems operated on blockchains enhancing financial accountability and reducing corruptions and allocation of resources in a fair manner.

1. The Human-AI Collaboration Model

According to the respondents, AI increases the empathy of human beings, and does not replace it. The best outcomes were the social ones in cases when the AI was not imposed on the community but was developed in terms of its needs.

5. Challenges and Risks

Although these were very helpful, a number of limitations were detected:

Table 4. Key Barriers to AI-Driven Social Entrepreneurship

Interpretation:
The biggest hindrance especially in developing regions is the financial strain (54.7%). In addition, the control of data and ethics was also a concern, which demanded strong privacy models and local policy support.

Figure 3. Frequency of Reported Barriers

High Cost of Implementation     ██████████████████████████ 54.7%

Lack of Technical Expertise     ██████████████████████     48.7%

Data Privacy Concerns           █████████████████████      46.0%

Resistance to Change            ████████████████           36.7%

Infrastructure Limitations      ██████████████             33.3%

The findings suggest that social enterprises that have recently been improved with AI are re-architecturing the world of inclusive innovation. This hypothesis concerning the use of AI as one of the methods to achieve operational effectiveness and service accessibility is proved by the quantitative data, but on the other hand, the qualitative data supports the social nature of AI implementation, i.e., trust, empowerment, and local problem-solving.

• Bridging the social gap: AI facilitates the provision of services to the marginalized in a personalized manner, resulting in equity and inclusion.
• Reframing entrepreneurship: Social enterprises are moving beyond the charity-focused and towards the data-focused impact framework, combining sympathy with algorithmic judgment.
• Challenges remain: Justice of access, fair access, and ethical design should be emphasized to avert the AI divide.

This can be aligned with the previous literature related to the democratizing effect of technology on social innovation (Portales, 2019; George et al., 2022) but expounds on it by showing the impact of AI as an amplifier of the systemic impact of social entrepreneurship in a more empirical manner built in by design.

Limitations of the study

1. Limited Sample Representation: The study sample was then reduced to 100 social enterprises whose operation was primarily in the developing economies. This limitation cannot be used to apply the findings to bigger, more international environments where the implementation of AI and infrastructure may vary significantly across the board.
2. Data Reliability: Some of the data was self-reported and hence could have been subjective. The optimism of respondents in the use of AI applications might increase perceived impact scores.
3. Evolving Technological Context: Since the rapid progress of AI, the results are a moment in time. The role of technology in social entrepreneurship may be changed by new AI models or new regulations in the near future.
4. Ethical and Cultural Variations: The ethical aspects of AI change in different regions and cultures. The research was unable to account entirely on the cultural perceptions of AI-driven interventions, in particular to privacy and bias.
5. Measurement Constraints: It is a cross-sectional study that employed perception-based impact measures instead of longitudinal data. Hence, the causal associations between social inclusion outcomes and AI implementation are not conclusive, but only indicative.
6. Sectoral Imbalance: The majority of the participants reflected education and healthcare businesses, and therefore, there were few details available on the use of AI in agricultural or environmental social business.

Chart 1: Major Challenges in AI-driven Social Entrepreneurship

Future Scope

The crosspoint of Artificial Intelligence (AI) and social entrepreneurship remains a new field, and it has a vast potential of investigating, using, and developing it. Although this paper has given preliminary insights on the application of AI in closing social divides by creating socially-driven businesses, there are a number of future avenues that can be explored further to help bring about innovation and creativity.

1. Expanding Empirical Research Across Regions

Further research is required on comparative studies in the various socio-economic and cultural settings to determine the performance of the AI-based social solutions in the advanced and underdeveloped economies. This will assist with determining the contextual determinants, which may be digital literacy, infrastructure, and governance, which condition the success or constraints of AI-based social ventures.

2. Longitudinal Assessment of Impact

The majority of the existing studies such as this one consider the outcomes over a short period of time. The next generation of research must be based on longitudinal designs to investigate the social and economic effects of AI-integrated social entrepreneurship in the long run. This would provide an idea of long-term scalability, adaptability, and society advantages.

3. Development of Ethical and Responsible AI Frameworks

The use of AI in the social inequality issue is associated with the privacy of data, bias, and even openness. A focus by future scholars must be on setting up ethical systems and accountability systems that are distinct to social enterprises. It will involve the formulation of the defensible algorithmic decision models that will be appealing to the ethics of social justice and the community acceptance.

4. Integration of Human-Centered Design

The research on ways of using AI-based services that are human-centric should be increased to foster inclusivity in the use of technology. Research might examine how participatory design might be used to make sure that social innovation is equitable, contextually specific, and culturally sensitive, as in the form of a local community co-creating AI solutions.

5. AI for Marginalized and Underrepresented Communities

Further studies are needed to explore how AI can be more useful to marginalized populations, such as women, rural entrepreneurs, people with disabilities, and so on, by developing specialized platforms to deliver education, microfinance, and jobs. By complicating social accessibility divides in AI ecosystems, social entrepreneurs will be in a position to design less discriminatory digital interventions.

6. Measuring Social Return on AI-Driven Innovation

It is necessary to develop strong approaches that would measure the social value of AI-enhanced social innovations (SRAI). It is possible that the in the future, work may consider integrating impact measurement models where social metrics (e.g., empowerment, inclusion, wellbeing) are taken into consideration alongside technological performance metrics (e.g., accuracy, scalability, sustainability).

7. Public–Private–Social Sector Collaborations

The potential of collaboration between governments, social enterprises, and the private sector is enormous when it comes to applying AI-based solutions to the problem of societal problems. The research needs to explore in the future how the models of multi-stakeholder governance can be used to promote responsible AI-driven innovation and remain inclusive and affordable.

8. Skill Development and Digital Capacity Building

Digital skill gaps could be a barrier to the uptake of AI tools as AI tools are introduced in the operation of social enterprises. Future efforts must evaluate the ability of capacity-building programs to equip social entrepreneurs, particularly in low-resource environments, to be responsible and effective in the use of AI.

The implementation of the Artificial Intelligence (AI) in social entrepreneurship is a paradigm shift towards an inclusive and fair growth. This paper has determined that AI could be applied in a way that could lead to elimination of social and economic divide since it will allow the entrepreneur to address multifaceted challenges in the society more precisely, expandably and sustainably. With the help of AI, the social enterprises will be capable of offering services to underserved groups by means of predictive modelling, operational efficiency, and data-driven decision-making.

The findings indicate that socially-oriented AI-driven ventures are much more than a technological breakthrough; they are a novel empathy-driven technology, with the human values and digital intelligence colliding against one another to create a social effect. The usage of AI in education, healthcare and the environment suggests that the projects present tangible beneficial impacts, in the context of accessibility, affordability and optimization of resources. This confirms the fact that digital inclusion, in that it is balanced with social purpose, can play a key role in bridging the disparities that exist in gaps.

However, the ethical and structural problems associated with the combination of AI and social entrepreneurship are also mentioned in the study. The issue of the data privacy, the prejudice of the algorithms, and the unequal access to the digital instruments should be addressed with the assistance of the transparent systems of governance, the principles of inclusive design, and the capacity-building efforts. Without these measures, even those technologies that attempt to prevent gaps could end up propagating them.

Regarding the re-definition of the role of social entrepreneurship, AI does not replace human empathy or the ability to be in contact with the community, it just extends it to a new level and makes it more effective. The future of socially motivated innovation lies in the creation of hybrid ecosystems, or more accurately in the combination of technological expertise and local knowledge, and profit motives and purpose. Enhanced intersectoral interactions among governments, technology firms, and social enterprises will be important in order to triple sustainable impact.

One of the solutions may be the AI-driven social entrepreneurship that will result in a more equitable digital nation where technology is employed to the overall advantage of humanity and not personal gain.

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