Why does enterprise AI adoption often stall, and how do companies successfully navigate continuous integration over time?

Host: Welcome to A.I.S. Insights — powered by Living Knowledge. I'm Anna Ivy Summers, and today we are looking into a study titled "Beyond TOE Dimensions: An Intersection-Based Spiral Model of AI Adoption," authored by Maria M. Smirnova and Dominik Alois Henkel, and presented at the International Conference on Information Systems in 2026. This study investigates how artificial intelligence adoption unfolds within business-to-business settings by examining how technology, organization, and environment intersect. Alex, welcome.

Expert: Thanks, Anna. Great to be here.

Host: Let's start with the central issue. Many companies invest heavily in artificial intelligence, yet so many B2B AI initiatives stall or fail to meet expectations. Why do traditional adoption models struggle to explain this?

Expert: Traditional frameworks—such as the Technology-Organization-Environment, or TOE, framework—typically view adoption as a linear, step-by-step process driven by separate factors. But in business-to-business markets, companies deal with complex supply chain interdependencies, conflicting customer expectations, and rapid software updates. The study points out that adoption does not stall simply because a single technical or organizational factor is missing. Instead, friction occurs where these domains interact with one another.

Host: How did the authors investigate these interactions?

Expert: They used an inductive qualitative approach, conducting 36 semi-structured interviews with professionals across diverse B2B industries, including manufacturing, consulting, energy, and information technology. Most respondents were from large European enterprises, with a concentration in Germany. Using a structured coding methodology, they analyzed how employees and managers actually experience and navigate AI implementations.

Host: What emerged from that analysis?

Expert: The authors identified 19 distinct mechanisms across three key intersections—nine constraining mechanisms and ten reinforcing ones. Rather than progressing along a straight line, AI adoption functions as a dynamic spiral through four recurring phases: external activation, internal alignment, operational integration, and strategic embedding. Because conditions continuously evolve, strategic embedding is provisional rather than permanent. New market pressures or model capabilities inevitably trigger renewed cycles of realignment.

Host: The study also highlights an interesting dilemma around trust in AI systems. What did the findings show there?

Expert: Trust emerged as a double-edged constraint. On one hand, employees exhibit under-trust, feeling skeptical of autonomous or agentic systems compared to traditional software interfaces. On the other hand, there is over-trust, where users accept outputs from tools like generative models without verification. Both extremes disrupt adoption—one causes delays, and the other introduces operational errors. To counter this, organizations maintain continuous human validation protocols and combine bottom-up employee experimentation with top-down governance.

Host: That brings us to the practical side. What should B2B managers do differently based on these findings?

Expert: First, managers should shift their focus from asking "Do we have the right technology?" to examining the intersections: "How do our technological choices interact with organizational trust and customer readiness?" Second, calibrate trust through clear human-in-the-loop guidelines. Low-risk administrative tasks may require less oversight, but high-stakes decisions need mandatory human verification. Third, treat AI adoption as an ongoing capability rather than a one-time project with a fixed finish line.

Host: So building feedback loops between pilot projects and strategic planning is critical when market conditions change.

Expert: Exactly. And fourth, balance the push and pull. Pair competitive urgency with proper digital infrastructure so you avoid an operationalization gap where ambitious AI goals cannot be supported by existing data systems.

Host: To summarize, successful AI adoption in B2B firms is not a linear checklist, but an ongoing spiral of alignment and reconfiguration across technical, organizational, and environmental boundaries. Thank you, Alex, for breaking down this study.

Expert: My pleasure, Anna.

Host: And thank you to our listeners for tuning in to A.I.S. Insights — powered by Living Knowledge. Join us next time as we translate research into practical business strategy.