Long-form analysis on AI workflow operations, governance, and execution
Two tracks. The frameworks that gate AI adoption in regulated industries — statutes, rules, and bulletins with specific obligations and penalties. And the operational discipline that distinguishes the AI workflows that reach production from the ones that stall. The articles below cover both.
The two questions every operator is asking in 2026
One asks where the boundary holds when AI touches regulated data. The other asks which workflows actually reach production. Below, the flagship piece on each.
Sending Sensitive Data to ChatGPT, Claude, or Gemini: A US Compliance Analysis
A practical walkthrough of the legal landscape governing the transmission of protected health information, material non-public information, claimant records, privileged content, and student data to third-party AI providers. Covers HIPAA, HITECH, SEC Reg S-P, SEC Rule 17a-4, GDPR, ABA Model Rules, FERPA, the FTC COPPA Rule, and the EU AI Act — with concrete guidance on what the boundary actually requires.
Where 2026 AI Budgets Land: A Back-Office Automation Demand Map
An evidence-based 2026 map of where back-office AI automation budgets are actually landing, where pilots stall, and the five characteristics that distinguish the workflows that reach production. Sourced from Gartner, Deloitte, McKinsey, Hackett Group, Forrester, Menlo Ventures, and PYMNTS — every figure dated and traced to its primary.
Explainers and deep dives
The pieces below cover the topics CISOs, Chief Compliance Officers, General Counsel, and CFOs of regulated mid-market organizations regularly ask us about. New articles publish on a rolling cadence; the trust page’s regulatory calendar drives much of the publication schedule.
What is AI governance?
A plain-language definition of AI governance as the operating system of policies, ownership, controls, evidence, and review that lets an organization use AI responsibly in production.
What is the evidence layer for regulated AI?
The part of an AI system that produces independently verifiable proof of what the AI did with sensitive data and which controls applied — proof an auditor can check without trusting the vendor.
What is regulated AI?
A practical definition of regulated AI: AI used where the workflow touches regulated data, regulated decisions, regulated records, or regulated professional duties.
The Five Characteristics of AI Workflows That Reach Production
Most enterprise AI pilots never reach production, and the reason is rarely the model. A deep-dive on the five architecture and governance characteristics that separate the workflows that ship from the ones that stall — with verified 2025–2026 evidence from MIT, Gartner, Forrester, Menlo Ventures, McKinsey, and METR.
When the article raises a specific question, the discovery conversation is the next step
Every article is informed by client work. If a piece raises a question specific to your environment, the next step is a discovery conversation scoped to that question.