Monthly to bi-monthly
Research Reports are the publication's flagship output. Each report is a definitive reference on a specific question enterprise technical leaders are trying to answer, typically 4,000 to 10,000 words, with sections, figures, and references.
Two disciplines determine whether enterprise AI earns operational trust: evaluation, the practice of measuring whether a system actually works in production; and governance, the delivery of policy as code, controls, and accountable workflows. Both remain underspecified. Evaluation in many organizat
Two enterprises with comparable AI ambition, similar vendor stacks, and similar talent pools routinely produce materially different results. The divergence almost never traces to the technology choice. It traces to the operating model, the design authority, build capacity, governance regime, run mo
By 2026, enterprise AI systems are no longer differentiated primarily by which large language model they use. The frontier models, Anthropic's Claude Opus 4.7, OpenAI's GPT-5.2, Google's Gemini 3 Pro, are converging on capability for the median enterprise workload. What separates production-grade s
The most consequential layer of the AI buildout is not the foundation models themselves but what sits between them and the organizations that deploy them: architecture, integration, evaluation, and governance. The public record has clarified the picture rather than settled it. The applied layer is