Artificial Intelligence and the Changing Nature of New Ventures: Introducing the Virgen Framework for AI-Driven Entrepreneurship
- Miguel Virgen, PhD Student in Business
- 43 minutes ago
- 13 min read
The Virgen Framework: A framework for launching and operating a large company with a small team or a single founder by exploiting artificial intelligence (AI) at scale.
The Virgen Framework is an organizational and strategic framework introduced in 2026 by Miguel Virgen that explains how large scale enterprises can be launched, managed, and scaled by a very small team, or a single founder through the systematic exploitation of artificial intelligence (AI) at scale. The framework conceptualizes AI not merely as a productivity tool but as a core operational substitute for traditional human intensive functions, allowing businesses to gain disproportionate leverage, operational efficiency, and market reach.
Virgen, M. (2026). Artificial Intelligence and the Changing Nature of New Ventures: Introducing the Virgen Framework for AI-Driven Entrepreneurship. Available at, Doctors In Business Journal. https://doi.org/10.5281/zenodo.18475555
Abstract
The rapid diffusion of artificial intelligence (AI) is reshaping business limitations, labor structures, and competitive advantage. Traditional organizational and entrepreneurial theories assume that scaling a business requires proportional increases in human labor, managerial hierarchy, and fixed operational costs. My goal is to challenge that assumption and further academic literature and industry standards by introducing The Virgen Framework, a conceptual and operational framework that explains how large, scalable companies can be launched and operated by a single founder, or by small human teams through systematic exploitation of AI at scale. Grounded in the resource-based view, business economics, and entrepreneurial theories, the framework identifies essential business activities mechanisms, and validation pathways. The Virgen Framework advances theory by redefining scale, labor leverage, and coordination in the AI era, offering a lens that future empirical and conceptual research must engage with when examining AI driven startups. Within this paper I provide clear propositions, a formal model, and methodological guidance, in order to ensure replicability and scholarly scrutiny.
1. Introduction
The dominant logic of firm growth in management and entrepreneurship research has long equated scale with headcount, hierarchical complexity, and capital intensity. Classical and contemporary frameworks from Chandlerian organizational theory to scaling models assume that the size of the business and market reach grow alongside increases in human labor and managerial layers. However, the rise of artificial intelligence systems has changed and challenged this assumption.
AI systems can now perform cognitive, operational, analytical, and creative tasks that were once seen as inseparable from employee labor. Businesses that are able to effectively implement the proper use of AI can coordinate business operations, and serve global markets with minimal or no employees at all. Despite this shift, academic literature lacks a unifying framework that explains how businesses can intentionally design themselves to operating at large economic and market scale without the need of hiring a large number of employees or any employees at all.
This paper addresses that gap by proposing The Virgen Framework, a framework that explains the conditions, mechanisms, and processes through which AI enables small teams or a single founder to build and operate a large company.
2. Theoretical Gap
Even platform theories assume significant human involvement, large engineering teams, and managerial oversight as scale increases. I believe such literature should be challenged as AI continues to grow and be a major key player in innovation.
AI research has explored automation, decision support, and algorithmic management, and yet existing studies tend to analyze AI as a tool within traditional organizations rather than as the core organizing logic of the business itself.
I have found that current academic literature in AI in business is lacking a framework that conceptualizes AI as the primary coordinating and scaling resource of a company, instead of an assistant software. The Virgen Framework fills this gap by explaining how AI alters the relationship between labor, scale, and value creation.
3. Adding to and Extending Theoretical Foundations
3.1 Adding to and Extending Theoretical Foundations
The Virgen Framework advances scholarly understanding of firm formation, organization, and scaling in the AI era by identifying and remediating conceptual gaps across several established theoretical lenses. Below I summarize how the Framework interacts with, extends, and revises six influential traditions; Theory of Change, OKRs, conversion, AIDA models, Agile/Scrum, Design Thinking, VRIO, and the Entrepreneurial Process Model., Highlighting the core theoretical shortcoming each tradition exhibits when confronted with pervasive AI and showing how the Virgen Framework re-specifies central constructs, mechanisms, and research questions.
3.1 Theory of Change
Traditional Theory of Change (ToC) approaches model causal pathways from inputs and activities to outcomes under an assumption that humans are the primary agents of implementation and learning (Kayikci, et al., 2024). This anthropocentric formulation limits ToC’s explanatory reach when algorithmic systems, rather than human labor, perform substantive execution, experimentation, and adaptation. The Virgen Framework reconceptualizes the ToC inputs–activities–outputs chain by treating AI systems as autonomous operational agents whose learning dynamics, feedback loops, and deployment modalities constitute core mechanisms of change. Under this reconceptualization, causal mappings must account for (a) algorithmic learning rates and retraining cycles, (b) data provenance and representational bias as causal determinants, and (c) human–AI governance interfaces that mediate value capture and ethical risk.
Contribution: The Virgen Framework extends Theory of Change by (1) placing non-human actors into causal maps as endogenous drivers of outcomes, (2) making algorithmic adaptation a first-order explanatory variable, and (3) providing methodological guidance for combining realist evaluation with computational trace data. Empirical implication: program evaluators should incorporate operational metrics from AI pipelines into Theory of Change derived indicators to test whether algorithmic agents generate intended outcomes independent of, or in interaction with, human inputs (Shin et al., 2025).
3.2 Objectives and Key Results (OKRs)
OKRs are conventionally framed as a human-led performance and alignment mechanism. Objectives are set by leaders, interpreted by teams, and achieved through coordinated human effort (Rompho, 2024). This framing under-specifies how objective-setting, prioritization, and measurement operate when algorithmic systems increasingly perform operational and strategic tasks (Wowerath, 2026). The Virgen Framework augments OKR theory by (a) distinguishing human intent from algorithmic execution, (b) formalizing hybrid objective flows where AI agents autonomously propose, prioritize, and operationalize key results, and (c) defining new measurement primitives that capture AI contribution.
Contribution: By embedding AI into the OKR cycle, the Virgen Framework transforms Objective and Key Results Framework from a purely managerial coordination device into an integrated performance architecture that aligns human strategy with algorithmic capability. Studies should evaluate how AI-augmented OKRs affect strategic fidelity, resource allocation, and responsiveness, using experimental and longitudinal designs to observe cascaded effects when objectives are partially or wholly executed by AI agents (Gudigantala et al., 2023)
3.3 Conversion Models (AIDA and Funnel Theory)
Classic marketing funnel models such as AIDA assume human actors design persuasive messaging and consumers progress through relatively stable psychological stages (Sari et al., 2026). These models under-explain contexts in which AI autonomously generates, personalizes, and iteratively optimizes messaging in real time. The Virgen Framework reframes conversion as a dynamic, recursive learning system in which AI plays an active, adaptive role. AI agents continuously infer consumer intent, execute micro-experiments, and reshape upstream acquisition tactics (Gao et al., 2023; Shao et al., 2025). This reconception aligns marketing theory with systems theory and machine learning research by privileging feedback loops, online learning, and emergent segmentation as core mechanisms of conversion.
Contribution: The Framework extends funnel theory by (1) operationalizing conversion as algorithmically mediated measure–act cycles, (2) introducing temporal measures of adaptive persuasion, and (3) proposing methods to detect latent intent in-session. Empirical tests should combine session-level behavioral traces with A/B testing logs to evaluate how autonomous AI experimentation alters conversion elasticity across funnel stages.
3.4 Agile and Scrum
Agile and Scrum highlight human teams that self-organize, iterate, and coordinate through roles and ceremonies (Lawong et al., 2025). While suited to human collaboration, these frameworks under-theorize organizational forms in which AI systems perform routine analysis, synthesis, testing, and deployment tasks (Qureshi et al., 2024). The Virgen Framework reframes agility as a property emergent from human-AI ensembles rather than from team size or role design. It positions AI systems as primary execution agents that can sustain high-velocity iteration with minimal human oversight, thereby decoupling adaptability from large human headcounts and reconciling agility with scale.
Contribution: The Framework advances theory of scale in Agile research by showing how AI enables continuous delivery and rapid experiments without hierarchical scaling mechanisms (e.g., SAFe). It raises questions about transparency, trust, and governance in distributed socio-technical workflows. Methodologically, researchers should study hybrid Scrum processes by instrumenting pipelines (CI/CD, model training cycles) and mapping them to traditional Scrum artifacts and events to identify points of socio-technical friction (Gannar et al., 2025).
3.5 Design Thinking
Design Thinking’s human-centered emphasis on empathy, ideation, and prototyping has driven innovation practice across industries (Das, et al., 2024). However, its anthropocentric assumptions obscure how generative AI can contribute as an originator of insights, a large-scale observer of user behavior, and an automated prototyping engine (Patkar, et al., 2025). The Virgen Framework extends Design Thinking by treating AI both as a source of latent user insight and as a co-designer that augments, accelerates, and in some cases substitutes human ideation. AI-augmented design remain accountable and explicable—integrating explainable ML and iterative clinician or user engagement where relevant, to mitigate bias and preserve human values (Shulha, et al., 2024).
Contribution: The Framework expands Design Thinking theory to include algorithmic co-creation, specifying governance primitives (explainability, contestability, participatory oversight) necessary for responsible deployment. Research should evaluate hybrid human–AI ideation processes, paying particular attention to bias-amplification pathways and methods to surface and remediate them (Scharff et al., 2025).
3.6 VRIO and the Resource-Based View (RBV)
RBV and VRIO analyses predicate competitive advantage on firm-bound resources that are valuable, rare, inimitable, and well-organized (Amaya, et al., 2024). These frameworks presume that exploiting such resources typically requires hierarchical organization and human skill scarcity (Ferreira et al., 2022). The Virgen Framework modifies this account by demonstrating how AI transforms the economic properties of resources: models, datasets, and automated processes can be (a) highly valuable and scalable, (b) comparatively rare due to proprietary data or architectures, and (c) rapidly imitable unless protected by complementary governance and continuous learning regimes. Importantly, Virgen reframes “organization” in VRIO to include algorithmic orchestration layers—data pipelines, retraining governance, and monitoring systems—that substitute for traditional managerial structures.
Contribution: The Framework contributes to RBV by operationalizing how AI shifts marginal costs, imitability, and the organizational mechanisms need to capture rents. The VRIO model needs to be reanalyzed to incorporate technical affordances and institutional protections in data partnerships and privacy shields as resources (Gondim, et al., 2024). Empirical work should measure how AI investments alter the VRIO calculus over time, especially the dynamics of imitation and sustained advantage (Dastaki et al., 2026).
3.7 Entrepreneurial Process Model (EPM)
Classic EPMs emphasize human cognitive processes—opportunity recognition, judgment, resource mobilization, and learning—framed by bounded rationality (Freitas, et al., 2024). These models under-account for situations where algorithmic agents participate in, or drive, opportunity recognition, valuation, and execution (McSweeney, et al., 2025). The Virgen Framework embeds a theory of execution into the EPM: it articulates how AI can autonomously detect market signals, synthesize propositions, and execute growth experiments, thereby enabling venture creation and scaling under extreme human resource constraints. This does not displace the entrepreneur’s role but reconfigures it toward strategic intent, oversight, and meta-learning while AI handles operational leverage.
Contribution: By integrating AI into the Entrepreneurial Process Model (EPM), the Virgen Framework redefines the locus of entrepreneurial agency and enriches theories of venture sustainability under resource constraints. It also highlights performative and signaling dynamics in resource-acquisition contexts where founders leverage AI capabilities in pitches and governance to influence stakeholders (McSweeney, et al., 2025). Research should trace the causal chain from AI-enabled opportunity discovery to resource mobilization and long-term viability, employing mixed methods that combine computational trace data with qualitative founder narratives (Qiu, et al., 2026).
Cross-cutting theoretical contributions and future research directions
Across these traditions the Virgen Framework delivers three cross-cutting theoretical moves. First, it relocates agency: agency can be distributed across human and non-human actors, and theories must therefore model adaptive algorithmic agency explicitly. Second, it redefines scale: scale is no longer a linear function of human headcount but an emergent property of data, model architecture, and orchestration. Third, it operationalizes execution: the Framework supplies new primitives (automation elasticity, algorithmic learning velocity, human supervision intensity) that make execution testable within established theories.
To advance empirical scholarship, the Framework recommends (a) instrumenting algorithmic pipelines alongside traditional organizational metrics, (b) designing longitudinal studies to capture imitation dynamics and model drift, and (c) developing lab-in-field experiments that manipulate AI autonomy and governance to observe downstream effects on performance and legitimacy. By doing so, the Virgen Framework both extends theories in the world of business, management, and entrepreneurship. It also supplies concrete methodological directions that enable rigorous, replicable investigation of AI-driven businesses phenomena.
4. The Virgen Framework: Core Construct
4.1 AI Leverage
Implementing AI at a large scale can allow a small team or a single founder to control large executional activities that include, operations, marketing, customer interaction, analytics, and optimization, with minimal or no human intervention at all.
4.2 Scalable Intelligence Infrastructure
This refers to the data pipelines, models, APIs, and feedback loops that allow intelligence to scale without human labor and allow a company to maximize their revenue, and operations without increasing the number of employees.
6. The Virgen Framework Diagram
The Virgen Framework is structured as a non-hierarchical system that replaces traditional human managerial layers with AI-centered arrangements.
Framework Development
The Virgen Framework proposes that AI execution systems reduce coordination costs, while also replacing the need for human managers, enabling faster adaptation without large organizational delay.
7. Framework Development
The Virgen Framework was developed through a structured theory-building process. In the framework development I included synthesizing insights from established business theories, alongside emerging academic literature on artificial intelligence. In during this synthesis I was able to discover an inconsistency in existing theory. AI continues to reduce business costs, and prevailing frameworks continue to assume that human labor and managerial oversight scale proportionally with the size of a company.
8. Contributions to Theory and Practice
The Virgen Framework contributes to entrepreneurship and management theory by redefining scale, labor, and innovation in the world of advancing AI technology and capabilities. My goal is to provide entrepreneurs and scholars with a blueprint for designing AI driven corporations.
9. Future Research
I believe that future studies should evaluate the applications of AI in businesses that operate in different industries, along with the ethical implications of AI, and comparisons in company performance between AI driven businesses and traditional businesses.
10. Conclusion
This paper introduces The Virgen Framework as a theoretical contribution that explains how AI can be used by small to large businesses to operate with a small team or by a single founder. By addressing a critical gaps in academic literature and offering The Virgen Framework as an extensible model, the framework establishes a foundation that future academic research on AI-enabled businesses can build upon.
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Citation: Virgen, M. (2026). Artificial Intelligence and the Changing Nature of New Ventures: Introducing the Virgen Framework for AI-Driven Entrepreneurship. Available at, Doctors In Business Journal. https://doi.org/10.5281/zenodo.18475555
Keywords:
Artificial intelligence, organizational theory, entrepreneurship, business scalability, AI-first organizational framework, computational entrepreneurship, AI-enabled firm theory, autonomous organizational systems, AI-driven enterprise architecture, founder-led AI organizations, minimal-labor firm models, AI-based coordination mechanisms, post-human organizational theory, AI-powered business model, AI for scaling startups, solo founder AI business, running a company with AI
