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.
Research & Analysis
Analytical reports, operational frameworks, and research notes from WBA's independent practice.
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.
48 Days of AI Memory: What the Productivity Data Actually Shows
Most organizations measure AI adoption by counting licenses, API calls, or chatbot sessions. These are activity metrics. They tell you tools are being used. They do not tell you whether…
Multi-Model AI Memory: What the Implementation Record Reveals
Most organizations deploying multiple AI models discover that each model operates in isolation — context evaporates between sessions, decisions are repeated, and institutional knowledge fails to accumulate. This analysis examines why multi-model memory is an organizational infrastructure problem and what the implementation record reveals about durable solutions.
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.