The barriers are well-documented. The solutions haven't scaled.
For decades, researchers and practitioners have understood why entrepreneurs from underserved communities struggle to build sustainable businesses. The barriers are well-documented: limited access to capital (Money), restricted networks and customer bases (Markets), and gaps in business knowledge and coaching (Management). This framework, known as the 3Ms (Bates et al., 2007), has guided billions of dollars in philanthropic and public investment aimed at leveling the entrepreneurial playing field.
Yet the barriers persist. Despite an extensive ecosystem of small business development centers, community lenders, accelerators, and technical assistance programs, underserved entrepreneurs continue to face the same fundamental challenges their predecessors faced a generation ago.
This paper argues that agentic AI (artificial intelligence that can take autonomous action on behalf of users) offers a genuinely new approach to this persistent problem. Unlike previous interventions that struggled to scale due to cost, geography, and human resource constraints, agentic AI can deliver personalized business coaching to any entrepreneur, in any language, at any hour, at near-zero marginal cost.
Ena Intelligence represents a live laboratory for testing this thesis. Currently in private beta with Chicago-area entrepreneurs, Ena provides 24/7 multilingual AI coaching via a simple phone hotline. Early validation from ecosystem leaders suggests the approach merits rigorous study.
This paper is an invitation. We are seeking research partners (foundations, universities, and corporate social responsibility programs) to help answer the questions that matter: Does AI-delivered business support actually improve outcomes? What's the right balance between human and AI assistance? And can this approach finally break down barriers that have proven stubbornly resistant to traditional interventions?
Why the 3Ms Still Matter
The Framework
In their landmark 2007 study published in The ANNALS of the American Academy of Political and Social Science, researchers Timothy Bates, William Jackson, and James Johnson identified three fundamental barriers that explain why minority and underserved entrepreneurs struggle to build viable businesses. They called it the 3Ms framework:
Money
Access to financial capital: both internal (personal savings, family wealth) and external (loans, investment, grants)
Markets
Access to customers, suppliers, and business networks that create revenue opportunities
Management
Access to business knowledge, skilled advisors, and the human capital needed to operate effectively
The framework built on decades of entrepreneurship research, but its power lay in its simplicity. Bates and colleagues argued that these three barriers are interconnected and mutually reinforcing. Without capital, entrepreneurs can't access markets. Without market access, they can't generate the revenue to attract capital. Without management knowledge, they struggle to navigate either. Of the three, they considered management—"the skilled and capable entrepreneur, or the management team"—the most critical.
Why These Barriers Persist
The business support ecosystem has grown substantially since the 3Ms framework was first articulated. America's Small Business Development Centers serve hundreds of thousands of entrepreneurs annually. Community Development Financial Institutions (CDFIs) have deployed billions in capital to underserved markets. Corporate supplier diversity programs have opened doors to new customers. Accelerators and incubators have proliferated in cities large and small.
Yet the data tells a sobering story. According to the Federal Reserve's Small Business Credit Survey (2024), Black-owned firms are approved for financing at roughly half the rate of white-owned firms, even when controlling for creditworthiness. Immigrant entrepreneurs still struggle to access mainstream banking. Rural small businesses still face geographic isolation from markets and services. Women entrepreneurs still receive a fraction of venture capital despite starting businesses at record rates.
The barriers persist for structural reasons that traditional interventions struggle to address:
Cost
High-quality business coaching is expensive. A seasoned business advisor might cost $150-300 per hour (prohibitive for an entrepreneur whose business generates $50,000 annually).
Geography
Technical assistance resources concentrate in major metropolitan areas. An entrepreneur in Gary, Indiana or rural Wisconsin has far fewer options than one in Chicago.
Capacity
Even well-funded programs face waitlists. The demand for quality support vastly exceeds the supply of qualified advisors.
Language and Culture
Immigrant entrepreneurs (a significant and growing segment of small business owners) face additional barriers when programs operate only in English or lack cultural competency.
Time
Business challenges don't respect business hours. An entrepreneur facing a crisis at 9pm on a Saturday can't wait until Monday morning for advice.
Why These Barriers Don't Affect Everyone Equally
These barriers don't affect all entrepreneurs equally. As researchers Candida Brush, Anne de Bruin, and Friederike Welter noted in their extension of the 3Ms framework, entrepreneurs face additional contextual barriers (including household responsibilities and the broader policy environment) that compound the core challenges. The entrepreneurs with the fewest resources (those who could benefit most from support) are precisely the ones least likely to access it.
Consider the compounding disadvantages facing a first-generation immigrant entrepreneur in a mid-sized city. She may speak English as a second language, making it harder to navigate complex financial products or legal requirements. She lacks the family wealth that provides a financial cushion for entrepreneurs from more affluent backgrounds. Her professional network may not include people with business expertise. The local SBDC has a six-week wait for appointments. The nearest CDFI is an hour away.
For her, the 3Ms aren't just abstract barriers: they're the difference between a viable business and another statistic. As Nobel laureate Amartya Sen argued, development is fundamentally about expanding human capabilities and freedoms, not just economic metrics (Sen, 1999). This framing applies directly to entrepreneurship: the goal isn't just business survival, but enabling entrepreneurs to pursue opportunities that matter to them.
As Morris, Kuratko, and colleagues describe in their research on the "liability of poorness," entrepreneurs in poverty face compounding disadvantages: gaps in financial, business, and technological literacy; a scarcity mindset driven by constant resource constraints; and non-business distractions like housing instability and transportation challenges that compete for attention (Morris et al., 2020). Recent research confirms these dynamics persist, showing how entrepreneurs can move from poverty traps into "commodity traps" where they remain stuck in low-margin businesses despite initial success (Morris et al., 2025).
What Is Agentic AI?
Artificial intelligence has evolved rapidly over the past several years, but most applications remain relatively passive. Traditional AI tools wait for users to ask questions and provide answers, think of a search engine or a customer service chatbot.
Agentic AI represents something different. These systems can take autonomous action on behalf of users, remember context across interactions, and adapt their behavior based on each user's specific situation. Rather than simply answering questions, agentic AI can guide users through complex processes, proactively surface relevant information, and coordinate multiple steps toward a goal.
The distinction matters for business support. An entrepreneur doesn't just need answers: she needs guidance. She needs someone (or something) that understands her specific situation, remembers what she's working on, and can help her navigate from where she is to where she wants to be.
How Agentic AI Differs from Generative AI
The distinction is fundamental: generative AI creates content in response to prompts; agentic AI takes autonomous action to achieve goals.
No Prompting Expertise Required
Generative AI rewards users who craft sophisticated prompts. Voice-based agentic AI removes this barrier entirely. There's no text box, no cursor waiting for the perfect prompt. Entrepreneurs simply call and talk, explaining their situation in their own words.
Action, Not Just Information
Generative AI tells you about resources. Agentic AI connects you to resources. When an entrepreneur needs an accountant, agentic AI can actually make the connection, routing the entrepreneur to vetted service providers in their community.
Coaching, Not Just Answering
Generative AI responds to whatever you ask. Agentic AI coaches. It pushes back when an idea needs refinement, asks probing questions to clarify goals, and helps entrepreneurs think through problems rather than simply providing answers.
Proactive, Not Reactive
Generative AI tools with memory still wait for users to return. Agentic AI can proactively reach out, checking in on progress, following up on action items, and re-engaging entrepreneurs who may have stalled.
Why Agentic AI Suits Business Support
The characteristics that make agentic AI powerful are precisely what's been missing from scaled business support:
Availability
AI doesn't sleep, take vacations, or have scheduling constraints. Support is available at 2am on a Sunday when an entrepreneur is preparing for a Monday morning pitch.
Patience
AI can answer the same question multiple times without frustration. It can explain concepts at whatever pace the user needs. It never makes an entrepreneur feel stupid for asking.
Language
Modern AI can operate in dozens of languages, automatically detecting which language a user prefers and responding accordingly. This removes a barrier that traditional programs struggle to address.
Consistency
Every entrepreneur gets the same quality of support, regardless of which advisor happens to be available or how busy the program is.
Personalization
Paradoxically, AI can be more personalized than human advisors at scale, because it can maintain detailed context about each entrepreneur's situation across interactions.
Cost
The marginal cost of an additional AI coaching session approaches zero, making universal access economically feasible for the first time.
AI in Adjacent Fields
Business support isn't the first field to explore AI coaching. Mental health platforms like Woebot have demonstrated that AI can provide meaningful therapeutic support. Educational applications like Khan Academy's Khanmigo are personalizing learning at scale. Financial planning tools are helping consumers navigate complex decisions.
These applications share a common insight: AI works best not as a replacement for human expertise, but as a way to extend that expertise to people who would otherwise go without. Business support faces the same fundamental constraint: There aren't enough advisors to serve every entrepreneur who needs help. AI can fill the gap.
How AI Can Address Each Barrier
How might agentic AI actually address the three fundamental barriers identified in the 3Ms framework? This section explores the mechanisms: both the traditional approaches and how AI might augment or transform them.
Access to Management Knowledge
The Traditional Model
Technical assistance programs have historically delivered management knowledge through workshops, one-on-one advising, and cohort-based programs. These approaches work for entrepreneurs who can access them. But the constraints are significant: programs have limited capacity, advisors have limited hours, and entrepreneurs have limited time to travel to where services are offered.
The Agentic AI Model
Agentic AI can deliver management knowledge on-demand, personalized to each entrepreneur's specific situation. Rather than attending a general workshop on cash flow management, an entrepreneur can ask questions about her cash flow: with an AI that understands her business model, her industry, and her specific challenges.
This isn't about replacing human advisors. It's about ensuring that every entrepreneur has access to baseline support, regardless of whether they can get on an advisor's calendar.
What Ena Does
Ena Intelligence provides 24/7 business coaching via a phone hotline (+1-888-681-9938). Entrepreneurs call, explain their situation, and receive guidance: in any of 32 languages, at any hour. The AI remembers previous conversations, building context over time. After each call, entrepreneurs receive an email summary with action items and relevant resources.
Early users have used Ena for everything from pricing strategy to handling difficult customer conversations to understanding business structures. Kelly Evans, VP of Entrepreneur and Economic Development at Chicago Urban League, has validated the approach with her network. Tony Wilkins, a well-established angel investor and startup figure in Chicago, has found value even as an experienced entrepreneur. And Dr. Alex DeNoble, Professor Emeritus at San Diego State University, praised Ena for "her" business acumen and high quality advice.
Access to Markets
The Traditional Model
Connecting entrepreneurs to customers and business opportunities traditionally happens through networking events, supplier diversity programs, industry associations, and matchmaking initiatives. These approaches depend on getting the right people in the same room—physically or virtually—and hoping connections form.
The challenges are obvious. Networking events favor extroverts and those who already have professional networks. Supplier diversity programs work for businesses pursuing corporate contracts but not for those serving local markets. And none of these approaches scales: each connection requires coordination and follow-up.
The Agentic AI Model
Agentic AI can serve as an intelligent matchmaker within entrepreneurial ecosystems. Rather than waiting for entrepreneurs to find the right programs or connections, AI can proactively surface opportunities based on each entrepreneur's profile and needs.
More importantly, AI can connect entrepreneurs with each other. Many of the services small businesses need (accounting, legal, marketing, printing) are provided by other small businesses. AI can facilitate these B2B connections at scale.
What Ena Does
Ena is built to route referrals intelligently. When an entrepreneur needs an accountant, Ena can connect them with small business accountants in the network. When they need legal help, Ena can route them to attorneys who serve small businesses. This creates a network effect: every entrepreneur who uses Ena becomes both a potential customer and a potential service provider for other entrepreneurs in the ecosystem.
The AI doesn't just connect entrepreneurs to programs: it connects them to each other.
Access to Money (Capital)
The Traditional Model
Helping entrepreneurs access capital has traditionally meant connecting them to CDFIs, preparing them for loan applications, and providing financial literacy education. These services are valuable but labor-intensive. Loan readiness programs might work with an entrepreneur for months before they're prepared to apply.
The challenges compound for entrepreneurs who lack banking relationships or have damaged credit. They may not know what capital products exist, which ones they might qualify for, or how to position their business for approval.
The Agentic AI Model
Agentic AI can provide ongoing financial coaching that meets entrepreneurs where they are. Rather than a one-time loan readiness program, AI can work with entrepreneurs continuously: explaining credit concepts, helping them understand their financials, and preparing them for capital conversations over time.
AI can also help entrepreneurs understand the landscape of capital options. Many underserved entrepreneurs don't know that CDFIs exist, or that they might qualify for grants and microloans. AI can surface options that entrepreneurs wouldn't otherwise discover.
What Ena Does
Ena provides guidance on credit, capital readiness, and financial operations. When entrepreneurs have questions about improving their credit score, understanding their business financials, or preparing for a loan application, Ena can help. When they're ready to pursue capital, Ena can connect them with responsible lenders: banks and CDFIs that serve small businesses without predatory terms.
The key word is responsible. The AI connects entrepreneurs with vetted capital providers, not predatory lenders who target vulnerable businesses.
What We Don't Yet Know
Agentic AI for business support is largely uncharted territory. While the theoretical case is compelling, rigorous research is needed to answer critical questions:
Does AI-delivered technical assistance actually improve business outcomes?
Early indicators are promising, but we need systematic measurement of whether entrepreneurs who use AI coaching show better survival rates, revenue growth, or other markers of business success.
What's the optimal balance between human and AI support?
AI shouldn't replace human advisors entirely. But where should the handoff happen? What kinds of support are best delivered by AI, and what requires human connection?
How do engagement patterns differ across demographics?
Do immigrant entrepreneurs engage differently than native-born entrepreneurs? Do women entrepreneurs use AI coaching differently than men? Understanding these patterns is essential for ensuring equitable access.
What are the limitations and risks?
AI isn't perfect. It can make mistakes, miss nuances, and fail to recognize situations that require human intervention. We need to understand these limitations and build appropriate safeguards.
How does trust develop (or fail to develop) in AI coaching relationships?
Trust is essential for effective coaching. Do entrepreneurs come to trust AI advisors? What builds that trust? What undermines it?
What Rigorous Research Would Look Like
A research partnership might pursue several complementary studies:
Quantitative Outcome Analysis
Track entrepreneurs who use Ena over time, measuring business survival, revenue growth, employment, and other outcomes. Compare to matched entrepreneurs who don't use the platform.
Engagement Pattern Analysis
Analyze how different demographic groups use the platform: call frequency, topics discussed, referral acceptance rates, repeat usage patterns.
Qualitative User Research
Conduct interviews with entrepreneurs to understand their experience: what worked, what didn't, how they perceived the AI coaching relationship.
Ecosystem Intelligence Validation
Compare insights generated from Ena conversations to traditional ecosystem data sources (surveys, census data, BSO intake records). Assess whether conversational AI can provide faster, more accurate intelligence about entrepreneurial needs.
A Unique Research Opportunity
Ena Intelligence represents a unique research opportunity for several reasons:
Live Users
Ena is operational, with real entrepreneurs using the platform for real business challenges. This isn't a hypothetical: it's generating data now.
Diverse Population
Ena's Chicago pilot includes partners serving Black, Hispanic, Asian, and women entrepreneurs across multiple languages. This diversity enables comparative analysis across demographics.
Privacy-First Architecture
Ena doesn't store identifying information or raw transcripts. Phone numbers are hashed, and only anonymized, categorized data is retained. This privacy-first approach enables research while protecting entrepreneur confidentiality.
Ecosystem Perspective
Because Ena aggregates data across the ecosystem, it can generate insights about patterns and needs that no single organization could see.
Who Should Be at the Table
This research opportunity calls for a coalition of partners:
Foundations
With focus areas in entrepreneurship, economic development, or technology for good. The potential to transform how business support is delivered should interest funders who have invested in this space for years.
Universities
With research programs in entrepreneurship, organizational behavior, or AI applications. The academic questions here are novel—this is genuinely new territory for peer-reviewed research.
Corporate Social Responsibility Programs
Seeking impactful ways to support small businesses in their communities or supply chains. Funding research that could reshape the field creates lasting impact.
CDFIs and Community Lenders
Interested in understanding how AI might prepare more entrepreneurs to successfully access capital products.
What Partnership Could Look Like
Partnership might take several forms:
- Research Funding: Support a dedicated research track to systematically study Ena's impact and engagement patterns. Funding would enable robust data collection, academic partnerships, and publication of peer-reviewed findings.
- Academic Collaboration: University partners could serve as co-investigators, bringing methodological rigor, IRB oversight, and academic credibility to the research. Co-authored publications would contribute to the emerging literature on AI and entrepreneurship.
- Pilot Expansion: Partners could fund Ena deployment in new markets (their communities, their grantee networks, their supplier ecosystems) generating data across diverse contexts.
What Partners Would Receive
Research partners would benefit from:
- Thought Leadership: Co-branded research positions partners at the forefront of understanding AI's role in economic development.
- Data Access: Partners would receive access to anonymized, aggregated insights about entrepreneurial needs and patterns.
- First-Mover Advantage: As AI transforms business support, partners who invest in understanding it now will be better positioned to shape its development.
- Impact Documentation: Rigorous research provides the evidence base to demonstrate program impact to stakeholders and boards.
An Opportunity to Shape the Future
The 3Ms framework, first articulated by Bates, Jackson, and Johnson in 2007, has guided our understanding of entrepreneurial barriers for nearly two decades. The barriers it identifies: Money, Markets, and Management, remain as relevant today as when the framework was first articulated. But our tools for addressing those barriers have largely failed to scale.
Agentic AI offers something genuinely new: the possibility of personalized, always-available business support that doesn't depend on advisor capacity, program funding, or geographic proximity. For the first time, we might be able to ensure that every entrepreneur (regardless of language, location, or resources) has access to quality guidance.
Ena Intelligence represents an early test of this possibility. The platform is live, real entrepreneurs are using it, and early signals from ecosystem leaders are promising. But promising isn't proof. Rigorous research is needed to understand whether this approach actually works and, if so, how to optimize it.
The business support sector stands at an inflection point. AI is advancing rapidly. The question isn't whether AI will transform how entrepreneurs receive support; it's whether the sector will shape that transformation or simply react to it.
We have an opportunity to be intentional about this. To study what works and what doesn't. To build AI systems that genuinely serve underserved entrepreneurs rather than creating new disparities. To ensure that this technological shift advances equity rather than undermining it.
That work requires partnership. It requires foundations willing to fund research without guaranteed outcomes. It requires academics willing to study emerging phenomena. It requires practitioners willing to test new approaches alongside proven ones.
The invitation is open.
About Ena Intelligence
Ena Intelligence, developed by SoundStrategies, is a 24/7 multilingual AI business coaching platform that delivers on-demand entrepreneurial support while generating ecosystem insights for cities, foundations, and corporate partners. Currently in private beta with Chicago-area business owners, Ena serves entrepreneurs via a simple phone call: no apps, no internet required, available in 32 languages.
Bates, T., Jackson, W. E., & Johnson, J. H. (2007). Introduction: Advancing research on minority entrepreneurship. The ANNALS of the American Academy of Political and Social Science, 613(1), 10-17.
Brush, C. G., de Bruin, A., & Welter, F. (2009). A gender-aware framework for women's entrepreneurship. International Journal of Gender and Entrepreneurship, 1(1), 8-24.
Federal Reserve Banks. (2024). Small Business Credit Survey: 2024 Report on Employer Firms.
IBM. (2025). What is agentic AI? IBM Think.
Morris, M. H., Kuratko, D. F., Audretsch, D. B., & Santos, S. (2020). Overcoming the liability of poorness: Disadvantage, fragility, and the poverty entrepreneur. Small Business Economics, 58, 425-446.
Morris, M. H., Santos, S., & Kuratko, D. F. (2025). From poverty trap to commodity trap: How entrepreneurs escape one and get stuck in another. Journal of Business Venturing, 40(1), 106445.
Sen, A. (1999). Development as Freedom. Oxford University Press.
Published: January 2026 | Version: 1.0
For research partnership inquiries, contact Jason William Johnson, Ph.D. at hello@drjasonwilliamjohnson.com