DEVELOPMENT OF THE HUMAN-AI COLLABORATION MATURITY MODEL (HAIC-MM) FOR SMALL AND MEDIUM-SIZED ENTERPRISES

Abstract

Most AI adoption guidance assumes an organization has a dedicated data science team, a seven-figure technology budget, and the capacity to sustain a multi-year transformation program. However, that describes a Fortune 500 company; it does not describe the 33.2 million small and medium-sized enterprises (SMEs) that make up 99% of U.S. businesses. SMEs face a different reality: limited budgets, employees performing multiple roles, and no clear roadmap for turning AI tools into business value. As generative and agentic AI lower the barrier to capable tools, the challenge has shifted from accessing technology to effectively integrating it into human workflows that produce real business results.Existing maturity frameworks fall short. An analysis of thirty popular models found that most models simply average human and AI capability scores together, concealing the imbalances that determine whether collaboration succeeds or fails. They also ignore the sequential nature of capability development and measure whether organizations possess AI tools rather than whether people use them effectively. This dissertation introduces the Human-AI Collaboration Maturity Model (HAIC-MM), a systems engineering framework built for SMEs. HAIC-MM assesses collaboration across seven dimensions and 32 capabilities, with each capability evaluated through questions that separately measure human readiness, AI capability, and the quality of interaction between the two. Its central innovation is a Balance Factor that rewards organizations developing both sides in tandem and penalizes lopsided investment, producing scores that reflect collaboration reality rather than a misleading composite. A dependency system reinforces this by reducing scores when prerequisite capabilities are underdeveloped. HAIC-MM was developed by synthesizing thirty existing frameworks and then refined and validated in three stages: a survey with 100 professionals confirming dimension and capability relevance; five focus group sessions with ten practitioners that revised 28 capabilities and added four new ones; and a pilot deployment in which ten SME practitioners completed the full 160-question assessment through the haicmm.com platform. Nine out of ten participants confirmed that the framework captures the collaboration challenges they face, the reports provide guidance they can act on, and the Balance Factor reflects how collaboration actually works in their organizations. This research contributes a framework that treats human-AI collaboration as a system-level property, a replicable methodology for building maturity models from diverse domains, and a self-service platform that delivers maturity reports without consultant support. The findings are published in two peer-reviewed journal articles and are available through a free assessment at haicmm.com.