The thesis came before the product.
We were publishing on why bolted-on enterprise AI fails - the cost, the risk, the cloud trap and the missing operating foundation - before we built the answer. This library is the provenance of Quorum's work. We still say what the vendors won't.
The Signal Behind the Noise: How AI Hype is Obscuring the Risks Enterprises Cannot Afford to Ignore
The five most consequential risks enterprises are systematically underweighting - data exposure, accuracy failure, agentic autonomy, shadow usage, and the true cost of responsible governance - with a practical framework for leaders who need to move deliberately, not just fast.
Articles, opinions & views.
Was Cloud a Trap? - 15 Years of Enterprise Technology
Fifteen years into the cloud revolution, it is time to look back honestly - at what was promised, what was delivered, and what enterprises quietly surrendered along the way. A point of view on CapEx, OpEx, vendor lock-in, and why AI is heading down the same invoice.
Read the Opinion →The Monster in the Mirror: Anthropic's Glasswing and the New Architecture of Global Power
An analysis of Anthropic's Glasswing and Claude Mythos as an emerging power structure, where cyber capability, access control, and governance are being concentrated inside private frontier AI firms.
Read the Article →Is GenAI Failing Enterprise? The Real Question Is Whether Enterprise Is Ready.
A response to MIT research suggesting 95% of enterprise GenAI use cases are underdelivering. The technology is not the problem. Six reasons why most enterprises were never ready - and what doing it properly actually requires.
Read the Article →What our research addresses.
Six areas where enterprise AI governance conversations are most often incomplete.
The Accuracy Illusion
Why benchmark performance is a poor predictor of production reliability - and what to measure instead.
The Deprecation Trap
Enterprise upgrade cycles run 7–12 months. Frontier model lifespans run 6–12 months. These numbers are not compatible.
Agentic AI Readiness
Agency amplifies every reliability problem in generative AI. The commercial case is compelling. The technical readiness is not.
Shadow AI & Data Exposure
For most enterprises, employee data is already flowing to external AI systems. The breach may have already occurred.
Foundation Before Adoption
Data architecture, governance, and expertise gaps that no AI vendor will surface in their sales deck.
The True Cost of Responsible AI
Governance cost multipliers of 3–6× over raw inference cost - the number almost no enterprise calculates before deployment.
Follow the thesis from evidence to architecture.
Read the research that shaped our position, then see how governed evidence and authority become an operating foundation for agents.