Why Most AI Adoption Stalls
Most AI adoption does not fail because the models are inaccessible. It stalls because organizations stop at access and never design an operating model around use.
Research & Analysis
Analytical reports, operational frameworks, and research notes from WBA's independent practice.
The Shift From One Model to a Model Stack
Serious AI use stops asking which model is best and starts asking which model is best for this step of the workflow.
How a Lab Sets Up AI Consumption
The real AI question is not which model is best. It is how a team routes, reviews, and operationalizes AI inside actual work.
AI Adoption vs AI Maturity: What Companies Get Wrong
Most organizations confuse AI adoption with AI maturity. Adoption is a procurement event — maturity is an institutional learning process that cannot be purchased or rushed. This analysis examines the five most common mistakes and what the journey from experimentation to operational capability actually requires.
When Agent Skills Lose to Simpler Guidance
A local Sigil benchmark found that, for summary and validation work, a small always-on guidance layer plus MCP outperformed manual invocation of a dedicated agent skill.
Why Most AI Projects Fail in Companies (7 Hidden Causes)
Global AI investment surpassed $200 billion in 2025, yet 60-80% of enterprise AI projects fail to deliver value. The bottleneck is not computational — it is organizational. This analysis examines seven structural patterns that prevent AI initiatives from succeeding, from the readiness illusion to absent feedback loops.
The Cognitive Infrastructure Gap: Why AI Adoption Doesn’t Equal AI Maturity
Based on WBA market observations, over 70% of organizational AI activity remains trapped in conversational interfaces. This analysis introduces a three-stage maturity framework, a self-assessment diagnostic, and an economic model for understanding the gap between AI adoption and AI orchestration.