Artificial Intelligence: Implications and Impacts on Black Entrepreneurial Ecosystems

Abstract

Artificial intelligence (AI) is rapidly spreading across society. It is disrupting economies, traditional institutions, labor markets, and entrepreneurial ecosystems and will do so at an increasing pace over the next few years. In this conceptual paper, some of the emerging issues, implications and growing impacts of AI on Black entrepreneurial ecosystems—both positive and negative—are discussed. Prior research has demonstrated that Black entrepreneurs face significant challenges within the ecosystems in which they operate. While AI offers the potential for great improvements to productivity, efficiency, and profitability, it is not a foregone conclusion that Black entrepreneurs will equally benefit from these gains. Following a brief discussion of AI’s growing influence on society, this paper focuses attention on how AI is and will continue to improve Black entrepreneurial ecosystems as well as potential negative outcomes that must be considered. Nine formal research propositions are offered. While this paper is based on a review of the literature, it is not meant to be comprehensive. Rather, it is one of the first papers to identify emerging trends, open dialogue, outline practical implications and propose future research directions for further study with respect to AI and Black entrepreneurial ecosystems.

1. Introduction

Racial wealth inequality is a significant, ongoing, and growing issue in the U.S. (Bennett et al., 2022; Conley, 2009; McKernan et al., 2013). U.S. Census data figures show wealth levels of Black households at $14,100 vs. White households at $187,300 (Bennett et al., 2022). This wealth disparity represents a clear and growing danger to the overall U.S. economy due to the rapidly changing demographic nature of the country. According to 2020 U.S. Census data (see Jones et al., 2021), the Black population grew to 14.2 percent of the population, up 11.7 percent over the previous decade. By comparison, the White population declined by 8.6 percent from the 2010 Census and now represents 61.6 percent of the total U.S. population. As this demographic shift continues, the Black population will have an increasing impact on the nation’s economy and competitiveness, making it even more important to address the economic disparity.

Business ownership is directly tied to wealth creation as the 10 percent of American workers who are self-employed hold almost 40 percent of all wealth in the nation (Gorman, 2017). Consistent with this finding, Black entrepreneurs have been found to have greater upward wealth mobility than Black workers (Bradford, 2003, 2014). Unfortunately, Black and minority individuals remain under-represented among the ranks of the self-employed (Gorman, 2017; Singh & Nurse, 2024). In fact, Black venture creation has remained disproportionately lower than White venture creation for more than a century (Bates, 1997; Fairlie, 1999; Fairlie & Meyer, 2000). Today, just 5 percent of Black households own and operate a business vs. 12.4 percent of White households (Hagar & Kaymak, 2023). Making matters worse, Black entrepreneurs tend to be less successful than their White counterparts (Fairlie, 1999; Robb, 2002; Fairlie & Robb, 2007). Firms owned by Black entrepreneurs have lower profits, sales, and survival rates, as well as fewer employees compared to those owned by White entrepreneurs (Robb, 2002; Fairlie & Robb, 2007).

The lower rate of new venture creation and higher incidence of failure can be traced to inefficiencies and weakness within entrepreneurial ecosystems that Black entrepreneurs operate within (Singh & Nurse, 2024). Entrepreneurial ecosystems constitute the actors and social, political, economic and cultural factors within an infrastructure that supports entrepreneurs and enables productive entrepreneurship (Singh & Nurse, 2024; Spigel, 2017). Ultimately, the effectiveness of an ecosystem is determined by the ability of entrepreneurs to access resources within the ecosystem (Isenberg, 2010; Spigel & Harrison, 2018). Through the lens of systemic racism, Singh and Nurse (2024) develop and present a model illustrating how restrictions of resources within their ecosystems limit Black entrepreneurs.

Depending on one’s perspective, the most exciting or alarming development over the last several years is the spread and use of artificial intelligence (AI) across society. AI is certain to dramatically impact entrepreneurial ecosystems and the way in which entrepreneurs access resources. While there are many definitions of AI, in a broad sense it represents the creation of machines which can simulate intelligent human behavior with minimal human intervention (Benko & Lanyi, 2009; Haenlein & Kaplan, 2019; Wamba-Taguimdje et al., 2020). This is achieved through algorithms and programming which find ways to allow machines to emulate features of human intelligence such as learning, comprehension, and the ability to solve problems (Castelvecchi, 2016; McCarthy et al., 2006). The process of machine learning takes place as massive amounts of training data are used to hone the accuracy of the algorithms by giving AI systems the ability to detect patterns that are used for decision-making (Y. Wang, 2020).

The increasing wave of AI-based products and services based on new AI technologies represents what Tushman and Anderson (1986) refer to as technological discontinuities and Christensen (1997) describes as disruptive technologies. While it remains to be seen whether AI will have a net positive or net negative effect on humanity, we can be sure that it will increasingly impact humanity. These new products and technologies will continue to shift business environments, labor markets, and entrepreneurial ecosystems through a new cycle of creative destruction (Schumpeter, 1934) unlike any other in history in terms of the scope and breadth of its impacts and the speed with which new technologies are implemented.

For all its potential benefits and new types of business models, it is important to recognize that AI is not free from bias (Courtland, 2018; Y. Wang, 2021; Zou & Schiebinger, 2018), nor does it ensure that decisions are morally sound (Y. Wang, 2019, 2020). It is tempting to believe that because AI decisions are made by automated machines and algorithms, they must be based on purely objective decision-making processes, but there is much evidence to the contrary (e.g., Akselrod, 2021; Lifshitz, 2021; Y. Wang, 2021; Zou & Schiebinger, 2018). In fact, AI is likely to simply reflect the values and mirror the decision-making processes already in existence, even if these processes are systemically unfair and biased (Akselrod, 2021; Courtland, 2018; Murray, 2022; Whitfield-Anderson, 2023). The risks and implications of these biases are extremely serious, as they may further solidify systemically and structurally discriminatory processes which disadvantage Black and minority individuals relative to their White counterparts.

The purpose of this paper is to specifically focus on impacts and implications for Black entrepreneurs who have struggled relative to their White counterparts for decades. Following a brief discussion of AI’s growing impacts on society, this paper shifts to how AI can improve Black entrepreneurial ecosystems which are not as conducive to successful entrepreneurship as White entrepreneurial ecosystems. More specifically, Singh and Nurse (2024) describe five resources that are dampened within Black entrepreneurial ecosystems, and this paper describes how AI can improve access to each of these five resources. Toward this end, nine formal propositions are explicitly offered. At the same time, there continue to be significant risks to Black entrepreneurs from AI technologies if they are not mitigated. Automated and algorithmically based decision-making could be viewed as legitimate and race neutral when in fact it may very well be based on racially biased data. By discussing the positive and negative impacts of AI on Black entrepreneurs and their ecosystems, the goal of this conceptual paper is to identify emerging trends, open dialogue, outline practical implications and propose future research directions for further study.

2. AI’s Growing Impacts on Society

AI and AI-based systems are becoming ubiquitous, but we do not yet know what the full potential and impacts of AI will be (Bostrom, 2015; Hauer, 2018, 2019). However, there can be no doubt that AI is a disruptive innovation that has developed rapidly over the last decade and is now transforming every aspect of society (Barnhizer & Barnhizer, 2019; Brynjolfsson & McAfee, 2017; Y. S. Lee et al., 2022; Tegmark, 2017).

Tech giants such as Apple, Meta, Microsoft, and Google are all investing billions of dollars in a race for AI superiority and to incorporate AI in their products and services. As a result, AI is certainly no longer an exclusive tool for large corporations. To some extent, democratization of AI is taking place through widespread access to generative AI systems such as ChatGPT. Just as graphical user interface (GUI) operating systems such as Microsoft Windows and Apple Macintosh made home computers more useable to a broader audience and America Online (AOL), Netscape, and Microsoft’s Internet Explorer allowed for the expansion of web-based personal and business transactions to take place, we are now on the cusp of even more widespread personalized AI use. This is likely to lead to new venture startups and increased entrepreneurship just as we saw in the early 21st century with the explosion of Internet-based firms.

For entrepreneurs, perhaps the most important benefit of AI is its ability to reduce uncertainty in decision-making by expanding what Simon (1972, 1976) referred to as bounded rationality. Individual human beings are limited in their ability to process and store information which results in bounded rationality (Simon, 1976). Unfortunately, we cannot know everything personally. However, AI systems and technologies can help expand the boundaries of rationality by allowing access to knowledge that is revealed through automated processes which can sift through—and make sense of—enormous quantities of data.

Recognizing that early-stage entrepreneurial activity involves high levels of uncertainty and risk, information is the critical resource that can be used to reduce uncertainty and manage risk. Stinchcombe (1990, p. 7) noted that “what is precarious at one time becomes predictable at another time because of new information.” As no economic actor has perfect information with which to make rational choices and decisions due to bounded rationality limiting the ability to process and store information (Simon, 1972, 1976), AI is increasingly expanding the boundaries of rationality by creating and allowing greater access to knowledge/information. As the boundary is extended, more new venture ideas and opportunities and potential competitive advantages may be recognized, screened and assessed, and then, if appropriate, acted upon.

The vast amount of information and knowledge is continuing to increase and is becoming faster and easier to access through AI powered applications such as ChatGPT, Copilot, and Gemini. This is changing society and the entrepreneurial ecosystems in which the next generation of entrepreneurs will operate within.

3. AI’s Potential to Improve Black Entrepreneurial Ecosystems

Turning attention to Black entrepreneurs, there is great potential for AI to address historic and long-term deficiencies within the entrepreneurial ecosystems in which they operate. Singh and Nurse (2024) describe a range of issues and challenges that many Black entrepreneurs must deal with due to stunted entrepreneurial ecosystems and how this creates a vicious cycle of venture underperformance and failure. In their model, they identify five specific resource deficiencies that are common within these ecosystems: (1) lower access to financial capital, (2) limited access to human capital; (3) limited access to markets, (4) prohibitive cultural and societal support, and (5) inadequate policy infrastructure. In this section, how AI can help address and improve each of these resources for Black entrepreneurs are discussed. At the conclusion of each subsection describing each of the five deficiencies, formal research propositions are offered.

3.1. Lower Access to Financial Capital

The most serious long-term resource deficiency limiting Black entrepreneurs is inadequate access to financial capital (Bates, 1997; Cavalluzzo & Cavalluzzo, 1998; Cosgrove et al., 2023; Perry & Romer, 2020; Singh & Miller, 2024; Singh & Nurse, 2024). There are many reasons for this ongoing issue. Singh and Miller (2024) provide an expansive discussion of financial deficiencies that impede successful Black entrepreneurship including: financial illiteracy within the community, discrimination in lending, distrust in institutions, over-reliance on (inadequate) personal capital, and declining Black-owned banking and financial institutions, as well as community banking options in Black communities. AI has the potential to positively impact all these issues.

As Singh and Miller (2024) discuss, there are few community banks located within Black communities, and the number has diminished rapidly. At its peak, the number of Black-owned banks in 1934 stood at 134 (N. Lee, 2022). By 2001, the number had dropped to 48 (McKinney, 2019). According to the FDIC, there are now just 20 FDIC-insured institutions across the country (N. Lee, 2022). Given that there are about 4700 FDIC-insured banks, this represents less than 0.5 percent of all banks in the U.S. Historically, these banks were located within predominantly Black communities and provided special assistance to community members. The lack of physical access to community banks has made it more difficult for Black entrepreneurs to access formal financial capital.

It is also likely that the dearth of community banks has contributed to Black entrepreneurs lack confidence and comfort dealing with traditional banks. Black individuals are more likely to have their credit or bank loans denied than White individuals (Cosgrove et al., 2023; Palia, 2016; Perry & Romer, 2020). Palia (2016) found that Black small business borrowers have an approximately 30 percent higher rate of rejection than similarly situated White borrowers. In their audit study of small business owners seeking bank loans, Bone et al. (2014) found that, in the inquiry stage, in comparison to White applicants, minorities were consistently offered less assistance and subjected to greater scrutiny—another outcome of the loss of Black-owned community banks.

Online banking can address the issues of banks not being within, or in close geographic proximity to, many Black communities. In addition, AI systems have the potential to greatly improve how Black entrepreneurs interact with banks. The adoption of AI chatbots can help to provide effective communication that can resolve problems quickly and efficiently (Mogaji et al., 2021). Because it does not involve human interaction, many Black entrepreneurs may be more comfortable to take time and ask questions in a comfortable and safe environment in which they are not judged or feel time pressure.

Another benefit of AI-driven chatbots is that as they interact with customers, they also collect data on client queries, which allows them to learn and better address future client issues (Huang & Lee, 2022). Thus, AI can be used to increase financial literacy and access to high quality financial information while also reducing the stress of having to interact with human bank employees (Mistry, 2025).

As discussed above, Black entrepreneurs often have unsatisfactory experiences dealing with banks. While research has not specifically looked at whether chatbots have helped improve Black banking experiences, recent research has found evidence that they have improved overall customer experience by reducing wait times to have questions answered (Chen, 2025; Othayoth & Khanna, 2025). As AI systems continue to improve, they can better address needs and increase satisfaction levels of Black clients. This aspect of AI learning involves sentiment analyses of client communications (e.g., emails, social media interactions, chatbot queries, etc.) which helps the system understand the emotions of clients to predict client needs and improve service (Mogaji et al., 2021). By changing the quality and content of client services, AI technologies can change the historically cool relationship between financial institutions and Black entrepreneurs. This can help to improve overall access to financial capital. More formally, the following research propositions are offered:

Proposition 1.

AI chatbots available through existing and online banks will significantly increase Black entrepreneurs’ confidence in how to deal with those financial institutions.

Proposition 2.

As confidence in financial institutions increase as a result of AI technologies, Black entrepreneurs will be more successful in accessing formal financial capital.

3.2. Limited Access to Human Capital

Human capital theory posits that individuals or groups who possess greater levels of human capital will achieve greater performance outcomes than those who possess lower levels (Ployhart & Moliterno, 2011). Much of the benefit to entrepreneurs comes in the form of access to information and knowledge, as well as the ability to hire and retain high-quality talent. AI offers increased access to information and efficiencies resulting from AI-powered automation are making it increasingly possible to do more with fewer workers. As this trend continues, labor costs will shrink as jobs are eliminated, which can help resource-constrained Black entrepreneurs. Ernst & Young estimates that AI will replace almost 9% of human work over the next few years while at the same time improving product quality and production efficiency (Polak, 2021).

Again, consider chatbot technology which reduces the need to hire support staff such as customer service representatives, receptionists, copy editors, and administrative assistants. Entrepreneurs can now utilize AI to answer and screen phone calls, prepare memos or letters, and respond to customer queries and provide solutions through automated systems by phone or chat. This reduces the need for capital to hire such workers and can allow Black entrepreneurs to offer similar customer experiences as White entrepreneurs as human resource cost structures are reduced.

In addition, the use of AI in human resource management (HRM) has the potential to greatly enhance decision-making and improve the overall effectiveness of HRM processes (Ammer et al., 2023; Robbins & van Wynsberghe, 2022; M. Wang & Pan, 2022). Posting job advertisements, screening through resumes, and choosing the most qualified candidates to interview can now be carried out through AI systems. The need for a full HRM department or staff is being reduced and small, resource-constrained firms are less likely to struggle to find and recruit top talent for those positions that are not replaced by AI. The human capital cost savings are likely to greatly benefit Black entrepreneurs. This leads to the following proposition:

Proposition 3.

As AI technologies replace the need to hire human workers, Black entrepreneurs will be better able to overcome traditional human capital constraints and compete in the marketplace.

3.3. Limited Access to Markets

Because they are undercapitalized, Black-owned enterprises tend to be smaller, and are more likely to be in Black communities where they are limited to only serving a local Black customer base (Crump et al., 2018; Singh & Nurse, 2024). This geographic segregation results in having a customer base that has limited spending power. In addition, Black entrepreneurs are also more likely to operate within the informal economy (Crump, 2013; Crump et al., 2018, 2017; Gibbs et al., 2013). The informal economy consists of businesses engaged in otherwise legal trade activities, but that are not licensed or regulated. Examples may include childcare for a neighbor, selling baked items at church, or operating a lemonade stand. Businesses that operate in the informal economy typically stay small, are run by one person, do not attract investment or debt funding, and are usually borne out of economic necessity.

The inability to establish formal ventures and serve a broader, more diverse market limits revenues and reputational growth for Black-owned ventures. The resulting lower revenues may, in turn, limit expansion opportunities and growth as these firms are more likely to be viewed skeptically by banks and formal credit markets relative to those firms that had the resources to achieve early success and subsequent expansion. All of this creates a vicious cycle that keeps Black entrepreneurs from advancing.

As AI opens access to capital and reduces costs tied to human capital needs, the potential to serve a wider customer base also increases. AI can also help Black entrepreneurs who have been historically marginalized due to limited access to market data (Bates et al., 2018). AI can provide increased market knowledge and data, examples of successful promotional campaigns, and competitor analyses that may have been much more difficult to access in the past (Giuggioli & Pellegrini, 2023; Truong et al., 2023). This can greatly expand and improve the markets Black entrepreneurs serve.

Proposition 4.

As AI technologies continue to improve access to knowledge, historical information gaps between Black and other entrepreneurs will close, resulting in more formal Black-owned businesses.

Proposition 5.

As AI technologies continue to improve access to knowledge, historical information gaps between Black and other entrepreneurs will close, allowing Black entrepreneurs to increase their ability to compete in broader markets.

3.4. Prohibitive Cultural and Societal Support

An entrepreneurial culture is one that promotes the attributes, values, beliefs and behaviors that foster an entrepreneurial spirit and promotes collaboration within an ecosystem by instilling trust among stakeholders (Bischoff et al., 2018; Brownson, 2013). The limitations to financial and human capital are serious issues, but a related and equally important issue is the lack of social capital—the social resources available from group support networks—plays an integral role in generating such a culture of understanding, compassion, and trust needed to support the start-up and growth of business (Wilson, 1997). Entrepreneurs’ social networks include family, organizational, and community-based ties that help to provide a supportive environment (Singh, 2000).

AI systems now exist which can help match entrepreneurs with mentors (Haas & Hall, 2019). Matching algorithms can focus on communication styles, values, and personality traits. They can also reduce bias by finding matches that share skills and needs rather than simple demographic criteria such as race. This may be important as many Black entrepreneurs struggle to find appropriate mentors. Finally, as these matching algorithms learn from past mentor-mentee relationships, they can improve future matches.

At the same time, identifying actual mentors may not be as important as the past. Increasingly, generative AI systems will offer greater ease of access to reliable information and allow entrepreneurs to compete more effectively and efficiently. They can replace the need for network contacts and mentors—which have remained in short supply in Black entrepreneurial ecosystems due to the diminished rate of Black entrepreneurship in general (Gibbs et al., 2013; Singh & Nurse, 2024). As AI systems continue to evolve, they should increasingly be able to communicate, provide support, and help Black entrepreneurs operate within a more conducive culture for entrepreneurship. This leads to the following propositions:

Proposition 6.

As AI technologies continue to advance, there will be less need for Black entrepreneurs to find human mentors to provide the knowledge and social capital to help them grow their businesses.

Proposition 7.

AI technologies will increasingly provide the knowledge and information to build a culture for successful entrepreneurship with Black communities.

3.5. Inadequate Policy Infrastructure

Public policies play an important role in stimulating economies (Minniti, 2008), and entrepreneurship is an important driver of economic growth (Acs, 2006; Acs & Szerb, 2007). Unfortunately, public policies have left Black entrepreneurs behind and as a result, many Black entrepreneurs are forced to choose necessity-based entrepreneurship rather than opportunity-based entrepreneurship (Crump, 2013; Crump et al., 2017; Gibbs et al., 2013). Traditionally, developed economies such as in the U.S. give rise to entrepreneurs who pursue promising entrepreneurial opportunities that can lead to significant financial gain. Acs et al. (2004) found that the percentage of entrepreneurs pursuing opportunity-based entrepreneurship is positively related to per capita income. That is, as per capita income increases, opportunity-based entrepreneurship increases.

However, research has found that societies which struggle with significant economic inequality suffer dampening effects on economic growth and entrepreneurship over time (Alesina & Perotti, 1996; Berg et al., 2012; Easterly, 2007). Among other things, economic inequality creates social unrest that reduces investment (Alesina & Perotti, 1996). When inequality increases, necessity-based entrepreneurship increases as greater numbers of individuals seek to earn a living when facing similar challenging economic conditions similar to those in developing economies.

While political leaders regularly refer to small businesses and entrepreneurs as the engine of economic growth, the reality is that very little effort, legislation, or public policies have specifically focused on helping entrepreneurs in recent years (Singh, 2022). The federal policy response to the Great Recession of 2008 was the creation of the $700 billion Troubled Asset Relief Program (TARP). The TARP became the largest government bailout program for private sector firms in history. Taxpayers saved some of Americas largest private companies from demise, and “too big to fail” became established policy for the federal government (Suskind, 2011). This policy was largely applied again to address the economic shocks that resulted from the COVID-19 pandemic (Singh, 2022). While the $500 billion Payroll Protection Program (PPP) was created to help businesses deal with the pandemic, it failed Black-owned businesses (Fairlie, 2020). Only 12 percent of Black and Latino-owned businesses that sought assistance from the federal government received the amount they requested, and 41 percent were denied. As a result, 41 percent of Black entrepreneurs had to close permanently compared to a 17 percent drop in the number of White entrepreneurs (Fairlie, 2020).

While politicians have focused on large businesses over the last two decades, particular in times of crises, little has been done to address Black entrepreneurial ecosystems. There is a need for public policies which can help foster opportunity-based entrepreneurship in these communities as they are more likely to lead to greater economic growth. AI has the potential to level the competitive landscape by democratizing access to information and creating new efficiencies for all businesses—small or large and irrespective of who owns them. Firms will have to adjust to AI and any new regulatory requirements, legal frameworks, and policy regimes (Sloane & Wüllhorst, 2025). This will change the overall environment for Black entrepreneurs and any change may be welcome as this group of entrepreneurs has largely been left behind for decades. More formally, the following is suggested:

Proposition 5.

AI technologies will democratize information and access to resources, which will reduce negative outcomes of traditional public policy infrastructure gaps.

AI is changing entrepreneurial ecosystems in real time. Considering that Black entrepreneurship has struggled for decades and that the status quo for Black entrepreneurs needs change, AI’s disruptive effects on traditional economic and labor markets, regulatory standards, and the broader ecosystem could usher in welcome changes. Designed well, AI systems can directly address issues related resource constraints within Black entrepreneurial ecosystems (Singh & Nurse, 2024) and overall systemic racism (Murray, 2022). The end result is one final overarching proposition.

Proposition 6.

AI technologies will greatly improve entrepreneurial ecosystems, which will result in more successful Black entrepreneurship.

However, there are concerns about the fairness of AI and whether it is truly unbiased (Aitken et al., 2020). While broad-based theoretical advantages have been outlined above, there are very serious risks that must be recognized and mitigated to achieve these advantages. The development of AI systems is incredibly fraught with danger as it could be used to legitimate discriminatory decision-making and cement structural racism. This is explored further in the next section.

4. AI’s Potential Risks to Black Entrepreneurs

Pre-AI information technologies in recent years have been shown to be biased as a result of data mining that has resulted in system training data that has resulted in risk models and algorithms that have reinforced racism (Noble, 2018; Obermeyer et al., 2019) and disadvantaged poor people (Eubanks, 2018). Often the biases and issues are not a result of overt discrimination but rather the natural outcome of using historical data that may be biased (Obermeyer et al., 2019). Similar outcomes are likely with respect to AI-powered systems. Gerlich (2024) predicts that AI systems will greatly exacerbate inequalities as they also reduce critical thinking skills as a result of cognitive offloading (Gerlich, 2025). A recent study by Karunakaran et al. (2025) found that the development of AI technologies worsened workplace inequality as a result of cascading effects of different types of bias at each step of development. Much of this was driven by upper-level managerial assumptions and opinions that discounted or did not include the lived experiences of lower-level employees. Biases and the failure to properly account and adjust for different perspectives from various stakeholders both internal and external to organizations can have profoundly negative impacts. More specifically, the impacts may be much worse for minority stakeholders whose experiences and views are less likely to be captured accurately during AI system development (Karunakaran et al., 2025).

It is tempting to believe that AI offers purely objective solutions since decision-making is carried out by algorithms that are trained using massive amounts of real-world data. Ideally, that would be true, but what happens when the data is already biased? As Courtland (2018) points out, training data is rarely bias-free. Systemic biases within society are likely to be a part of the data that is used to train AI systems (Akselrod, 2021; Whitfield-Anderson, 2023). We can see direct examples of this in facial recognition software. A National Institute of Standards and Technology (NIST) study tested and found various facial recognition algorithms to be significantly more accurate for White male faces than for those of minorities, women, and infants (Grother et al., 2019; Meyer, 2020). The problem is primarily a result of biased or inadequate training data. If an AI algorithm to recognize faces is trained with one million pictures of faces, but 90 percent of those faces are White and male, then it is likely that the algorithm will be better at identifying White male faces. The NIST study found that the algorithms made the most errors with Black female faces (Grother et al., 2019). With law enforcement and government security systems starting to use such systems, there are growing risks of false arrest and detention of innocent individuals, particularly minority individuals. Unfortunately, this has happened to at least one Black man incorrectly identified as a suspected thief and detained in prison (Murray, 2022).

There are many other examples of bias that we can find in the AI systems we use every day. A widely used algorithm that guided healthcare decisions misdiagnosed the health levels of Black patients (Obermeyer et al., 2019). The racial bias was a result of the algorithm being programmed to equate health costs with health needs. The problem was that racial differences in access to healthcare were not factored in and the system was essentially trained to believe that Black patients were inherently more healthy than White patients. The result was that White patients were recommended treatments that were not recommended for equally sick Black patients.

AI is a tool, and it is a tool that is only as good as its programming and the data used to train it. The systemic biases that have been built into some AI tools are making decisions that are discriminatory toward minorities, women, and often poorer people (Grother et al., 2019; Murray, 2022; Obermeyer et al., 2019; Osoba & Welser, 2017; Y. Wang, 2021). As AI is implemented to automate processes within companies, industries, and over wider segments of the economy and society, there are significant risks that structural racism and discrimination may become more entrenched rather than reduced. We know that for decades, cannabis use and distribution were criminal offenses that led to the disproportionate incarceration of Black individuals compared to White individuals (see ACLU, 2020). If historical sentencing data were used to train an AI tasked with recommending jail time for lower-level drug offenses, one could easily imagine a scenario in which Black and minority individuals could be given unequal sentences to White individuals and unfairly punished.

As the examples above illustrate, the use of historical data to train AI systems can be problematic. As discussed earlier, there is evidence of significant discrimination in lending practices such that assistance with the loan process, loan decisions, and terms such as interest rates on those loans have favored White borrowers (Bates, 1997; Bates & Robb, 2013; Cavalluzzo & Cavalluzzo, 1998; Palia, 2016; Singh & Miller, 2024). It is likely that Black borrowers would continue to struggle to acquire fair and equal loans if an AI system to screen and recommend decisions on loan applications was trained with historical data without statistical controls and corrections to reduce eliminate bias. Black entrepreneurs would continue to face elevated levels of discrimination when seeking bank financing for their ventures. Instead of a decision being biased on unfair bank policies or an individual underwriter who knowing or unknowingly discriminates against minorities, it would be through an automated AI system that was trained with faulty data. Taking this a step further to larger equity financing deals, Black entrepreneurs are often shut out of the private venture capital (VC) market. In 2020, only three percent of the nearly $150 billion in formal VC financing went to Black-owned businesses (Joyner & McLymore, 2021). Again, if this was programmed into an AI system, there is no means by which positive change would happen in terms of improving access to capital for Black entrepreneurs.

Ironically, it is possible that AI systems and processes could result in maintaining the status quo if they are only trained with historical data. Black entrepreneurs must recognize the potential for bias and remain vigilant. The adoption of any AI tool or system should be carefully considered and whenever bias or potential bias is recognized, Black entrepreneurs will need to make developers aware and advocate for change. Thus, there is a special role that Black entrepreneurs must play in ensuring fairness in AI systems that impact their ecosystems.

In a broader sense, companies and AI developers must remain ever vigilant for errors, constantly testing and evaluating AI decision-making, and having fair and human-driven appeals processes to prevent or limit negative and unfair outcomes. AI’s programming should never remain in a “black box” that is never questioned or subject to any review. Companies should be able to maintain a certain level of propriety and security in their AI systems and processes; however, they must allow customers or clients to question AI decisions through a fair, transparent process. Transparency is a necessary condition to build acceptance in systems.

It should be recognized that review and understanding of how AI systems make decisions will become ever more difficult as neural network learning and future systems become more complex (Barnhizer & Barnhizer, 2019; Tegmark, 2017). How to proceed with oversight is already a challenge given the rapid changes already happening as a result of machine learning (Hauer, 2018, 2019). Further, as Lapuschkin et al. (2019) remind us, AI does not always result in the best or most reliable decision-making. This makes transparency and critical evaluation of AI an even more pressing issue (Obschonka & Audretsch, 2020).

One final issue that may be responsible for unintended bias within AI systems is the lack of diversity among AI researchers, computer scientists and software developers who are overwhelmingly male, and White or Asian. While about 12 percent of the population is Black, just eight percent of technology workers is Black (Gonzales, 2022). The percentages are even lower among the workforces at leading technology companies such as Google, which reports that just over four percent of their employees are Black (Gonzales, 2022). This presents a major challenge as concerns about certain racial biases within AI may not be a priority among developers. Having a diverse development team does not ensure freedom from racial biases, but having people from different backgrounds makes it more likely that different perspectives will be a part of the process. This can help identify and reduce the possibility of bias.

5. Discussion

AI has the potential to improve the human condition but is also eliminating jobs and fundamentally altering institutions that provide societal stability (Barnhizer & Barnhizer, 2019; Tegmark, 2017). This is why there is such a wide range of opinions about AI. That said, there is little question that the rapid advances of AI technology and its adoption across all aspects of society will increasingly continue to impact humanity. Many of these impacts will be in ways that we likely cannot yet see.

It is important for entrepreneurs to fully understand the potential pros and cons of AI to ensure fairness as AI systems are increasingly incorporated within businesses. Toward this end, there is a special need to understand sources of bias and their impacts on AI systems. Without careful consideration of how AI systems are developed, historical bias could further disadvantage minorities, women, and those lower on the socio-economic ladder.

This research, despite its conceptual nature, makes a valuable contribution to social science and the Black entrepreneurship literature more specifically. Two recent comprehensive literature reviews of AI and entrepreneurship peer reviewed articles (Giuggioli & Pellegrini, 2023; Uriarte et al., 2025) have been published. Combined they analyzed and categorized more than 400 such articles (Giuggioli & Pellegrini, 2023; Uriarte et al., 2025) and identified consensus findings, broad themes and overall future research needs. However, neither review paper explicitly identified any of the unique issues facing underrepresented minorities, and in particular Black entrepreneurs. This is not surprising as historical entrepreneurship research often overlooks the unique challenges and issues facing Black entrepreneurs (Gibbs et al., 2013; Singh & Nurse, 2024).

This paper is one of the first to specifically identify and discuss implications and significant impacts of AI on Black entrepreneurial ecosystems. As discussed in this paper, the significant changes wrought by AI offer potential benefits and pitfalls for Black entrepreneurs and the ecosystems in which they operate. The benefits of AI were discussed in the context of the model Singh and Nurse (2024) developed that showed resource constraints within Black entrepreneurial ecosystems. All five constraints can be alleviated by AI which offers the hope of greatly improving Black ecosystems. Nine specific propositions were explicitly offered, and should future research find support for some or all of them, we may finally find answers for how to address the vexing long-term dampened rates of founding and success among Black entrepreneurs. Ultimately, solutions may be found within the great potential of AI systems.

Addressing the racial wealth gap and the related diminished Black entrepreneurship rate of venture creation and success is critical. The fortunes of all sub-groups of citizens are vital to the health and strength of the U.S. economy, but the rapid growth of Black citizenry makes this sub-group even more critical for the future. The U.S. economy will struggle if the 14 percent of population made up of Black citizens (and growing) continues to struggle.

There are significant implications for practice and research because of AI and the issues discussed in this paper. These are expanded upon in the sections that follow.

5.1. Implications for Practice

The integration of AI technologies into entrepreneurship has practical implications for innovation and business operations. More specifically, AI can improve decision-making systems; automate processes, increase efficiency and productivity; reduce costs; or produce high-quality goods with high levels of customization (Giuggioli & Pellegrini, 2023). Further, AI solutions are now easily accessible to entrepreneurs at a relatively affordable cost. The democratization of AI has lowered the barriers to entry, making advanced tools accessible to small and medium-sized enterprises. This enables entrepreneurs to compete even with large companies as it has levelled the technological playing field (Truong et al., 2023).

While this is important to the broader economy, this paper focuses on Black entrepreneurs and how AI can lead to significant improvements within Black communities. Special emphasis is needed to ensure that economically challenged communities have access to AI tools and that they have the appropriate level of digital literacy to benefit from such tools (George & Mattathil, 2025). This may require additional resources and investments be made in community libraries, small business development centers, and other economic empowerment organizations in order to truly allow entrepreneurs in those communities to thrive.

While AI offers the great potential to dramatically improve long-struggling Black entrepreneurial ecosystems and improve Black entrepreneurship rates and success, this paper also points out that AI development must be managed carefully. As indicated earlier, AI is a tool and in and of itself is not moral or right or wrong, and it is certainly not inherently racist. However, if one considers history and examines AI through a systemic racism theory lens, it is not surprising that data used to train AI systems may include biases and issues that can disadvantage Black and other minority stakeholders. Black entrepreneurs, AI developers, and technology companies must ensure that AI decision-making and systems remain objective, fair, and free from bias.

To prevent the negative and potentially dangerous consequences of poorly designed AI systems discussed earlier, hyper-vigilance is needed by all stakeholders. Research is now emerging which is focused on finding ways to reduce and mitigate potential bias in AI systems (Afreen et al., 2025; Bansal et al., 2023; Nair et al., 2025). This important research will improve future AI decision-making, but unfortunately, bias still finds its way into current systems. Private companies and entrepreneurs must build in safety protocols, testing, and transparent appeals processes and public policy experts and lawmakers will need to consider new regulations and laws as AI technologies continue to advance. Without managing the potential for error, AI may only serve to solidify and further entrench structural racism within society.

In many ways, the use of properly trained AI systems may be significantly better than human interactions and may usher in an era of greater equity. With human interactions, disparate treatment may be the result of personal biases or just a result of a customer service representative having a bad day. The point is the reason for disparate treatment by humans may not be clear. It is likely to be highly debatable and could be disputed. Even if disparate treatment is the result of overt bias or some form of structural racism it is frequently unacknowledged and/or denied, making it problematic to resolve. AI systems that are trained properly offer the hope of equal treatment for all people.

Ultimately, as mentioned above, AI’s disruptive impacts have the potential to finally address long-term racial economic disparities that have plagued Black communities by helping to spur increased Black new venture creation.

5.2. Implications for Research

Much future research, particularly longitudinal research, is needed to further develop theories and to fully understand the effects to date and future impacts of AI. In Black communities, which often find themselves struggling economically, how will AI improve or worsen conditions? The answer to this question gets at the heart of future research needs specific to issues discussed in this paper.

The potential negative implications of AI notwithstanding, the nine propositions which suggest how resource limitations can be alleviated through AI should be empirically tested. The propositions offer important directions for research related to AI’s impact on Black entrepreneurs and the ecosystems in which they operate. Broadly, it is suggested that researchers primarily focus on how Black entrepreneurs specifically access and utilize AI systems and the specific information they draw from these systems. Comparisons against White and other racial groups could also illuminate whether democratization of information is indeed happening and whether there are improvements to Black entrepreneurial ecosystems. Studying Black entrepreneurs in urban and suburban settings, as well as those in different industries and with different levels of education and experience, can help to better understand the full potential of AI to help Black entrepreneurs. Specific outcomes such as increased financial literacy, comfort with loan application processes, and increased access to financial capital, whether knowledge acquired through AI mitigates the lack of mentors within Black entrepreneurial ecosystems, and relative improvements to success that labor cost savings offer are just some of the questions that can be explored and answered.

The challenge will also be keeping up with rapid changes and the likely accelerating pace of change that will almost surely result from greater advances and more widespread implementation of AI systems across society. Additionally, greater regulation to mitigate technological and security concerns as well as to protect personal data may change how AI systems develop and grow. Further, ethical and legal issues will need to be researched and resolved especially as deep learning systems train themselves (Castelvecchi, 2016). In many cases, the automated “black box” learning that will take place in some AI systems will be so complex that it may be impossible for humans to understand how AI decisions are made (Obschonka & Audretsch, 2020). This may require AI trained systems to assist researchers in their assessments.

There will be many new areas of research, and it is likely that there will be paradigm shifts that make existing entrepreneurship and business theories obsolete, or at least less relevant and weaker in their explanatory power. The changes to entrepreneurial ecosystems and impacts on entrepreneurs from all walks of life that result from AI processes are all areas that will need closer monitoring and new theory development. It will be up to entrepreneurship researchers, policy makers, and other thought leaders to help make sense of the changes and shape the new ventures that are sure to emerge.

5.3. Limitations

This research, despite its conceptual nature, makes a valuable contribution to the literature on Black entrepreneurs. It achieves this by discussing the implications and impacts of AI within the specific context of Black entrepreneurial ecosystems; however, several limitations must be acknowledged. First, this paper does not attempt to conduct a full literature review. There are two reasons for this: (1) there are few papers that specifically address the issue AI’s growing impacts on Black entrepreneurship and (2) the goal was to open dialogue and introduce researchers and readers to this important topic.

Second, the discussion of this paper is deliberately broad. Black entrepreneurs are not a monolithic group. As with all entrepreneurial groups, they operate different size businesses, work in different industries, have a wide range of goals, include necessity-based and opportunity-based ventures, etc. There are certainly some Black entrepreneurs who operate within highly productive entrepreneurial ecosystems. However, as a group, research on Black entrepreneurs has found that they struggle with such things as structural and systemic racism, unequal access to financial capital, and public policy challenges that result in a failure to achieve success on par with White counterparts. Focusing attention on the special issues facing Black entrepreneurs and the challenges from within their ecosystems can help to illuminate the issues and encourage further research.

Finally, the nine specific propositions are broad and not a comprehensive list of potential research areas. While they are developed around the five constraints that Singh and Nurse (2024) identified as problematic within Black entrepreneurial ecosystems, other issues are certain to also merit further theory development and study. For example, how is opportunity recognition impacted by AI in Black communities? Is there an impact on Black individuals who engage in informal entrepreneurship that improves outcomes for their ventures? For successful Black entrepreneurs, do they achieve even greater levels of success as a result of AI? These are just some of the other questions that need to be explored. Much further research is clearly needed to better understand the intricacies and finer points of how AI will impact Black entrepreneurs and their ecosystems. This is left to future researchers to decide how to narrow their analyses, their specific constructs and research variables of interest, and the industries, types of businesses, and overall samples of respondents they choose to study. This paper provides a more general discussion to provide a high-level view of issues.

6. Conclusions

Scholarly debate about the wisdom of adopting various AI algorithms will continue. That said, for existing firms and new venture startups alike, the increased efficacy with the promise of ever-increasing profits makes it unlikely that there will be any slowdown in the further development and adoption of these tools.

As AI-powered solutions become even more widespread, there will be significant changes to society and entrepreneurial ecosystems. In addition, the demographic makeup of the U.S. population is also changing rapidly, and the long-term economic viability and global competitiveness of the nation requires that all subgroups remain productive. As discussed in this paper, AI has the potential to address historical and ongoing economic inequity facing the Black population by improving Black entrepreneurial ecosystems and spurring increased new venture creation. This can put the nation on a better and more sustainable path to economic and social justice. However, as discussed in this conceptual paper, this can only happen if all AI stakeholders remain vigilant and constantly test and correct for common sources of bias that infect AI systems. Further findings and insights are needed, and researchers are encouraged to build on the propositions and discussion in this paper through qualitative and quantitative methods and longitudinal research.