The Mid-Market Al Adoption Journey

From First Use Case to Operational Scale
A research report on why mid-market companies get stuck - and a structured blueprint for moving forward

• AI adoption is widespread, but operationalization remains limited. 88% of organizations use AI in at least one business function, yet many are still focused on experimentation rather than embedding AI into workflows at scale.
• AI maturity is not determined by company size or industry alone. Organizations with similar headcounts can be at very different stages of AI adoption. Factors such as AI proximity, leadership orientation, talent and culture, process and data readiness, and ownership and governance all shape an organization's ability to scale AI.
• Agentic ambition is moving faster than agentic readiness. 85% of businesses want to become “agentic enterprises” within the next three years, but only 19% currently run multi-agent systems. The gap between experimenting with agents and operating them in production, with governance, monitoring, and escalation in place, is significant.
• The biggest barriers to scaling AI are increasingly organizational. Tooling alone isn't enough. Ownership, governance, process design, and a clear path to ROI are becoming critical to moving AI from experimentation into everyday business operations.

The report introduces an AI Adoption Profile and an AI Operational Scaling Model to help organizations understand where they are today, identify the barriers holding them back, and determine what it takes to scale AI successfully.

19