Articles
Practical insights on system improvement, AI integration, and building organizations where people and technology work well together.
Why Blame-First Investigations Bury the Truth About What Went Wrong
When leadership looks for who failed rather than why the system failed, the investigation itself destroys the evidence that could prevent the next event. This piece walks executives and chiefs through what a just culture incident investigation actually demands, what human factors analysis surfaces that a compliance audit never will, and why blame-first investigations are the most expensive mistake an organization can make after a near miss.
Your AI Tool Fails the Moment You Need It Most: Rethinking Connectivity Assumptions in Public Safety Technology
Cloud-dependent AI tools go silent exactly when disasters sever the infrastructure they rely on. This article examines the case for offline AI in emergency management using edge computing, the honest tradeoffs of local models, and the procurement decisions that determine whether your AI capability survives the incident.
Why Your After-Action Reports Keep Saying the Same Thing
Collecting after-action reports is not the same as organizational learning. This article explains why safety recommendations keep repeating across years of incident reviews, what the research says about closing the loop between a finding and a changed practice, and how structured learning teams build the institutional memory that prevents the next repeat failure.
Your Organization Does Not Need the Cloud to Run AI Privately
A capable private AI system can run on consumer-grade hardware for less than the cost of a conference booth, with no cloud subscription and no data leaving your building. This piece walks through what one practitioner built for his own consulting work, what it does every day, and what the architecture can teach any small organization thinking seriously about AI and data sovereignty.
The Do Not Touch List: AI Governance Framework for Public Sector Leaders
The most valuable document in any public sector AI governance framework is not the list of what AI will do. It is a clearly reasoned list of workflows AI should never touch. This article introduces The Human Factor's Do Not Touch list methodology and explains why any vendor who cannot tell you what their tool should not do is transferring risk to you.
ChatGPT Data Privacy in Government: What Actually Happens to Your Data
Consumer ChatGPT products can retain and use your inputs by default, creating real legal exposure for municipalities and health organizations. This article explains in plain language what actually happens to data in consumer AI tools, why it matters under public sector privacy legislation, and how private AI deployments offer a third path between banning AI and accepting the risk.
Why AI Pilots Fail: The Human Factors No One Diagnoses
Most AI pilots don't fail because the technology was wrong. They fail because no one assessed whether the organization was ready to change how people work. This article walks executives through the real culprits: eroded trust, workflow mismatch, and front-line exclusion from design.
Building Better AI: Why Systems Design Matters More Than Technology Alone
Most AI implementations fail not because the technology is flawed, but because organizations prioritize capabilities over human needs and existing workflows. The difference between transformative AI and expensive digital dust often comes down to whether the implementation follows proven systems design engineering principles that put humans at the center. Understanding how work actually happens—not how we imagine it should happen—is the key to building AI that people will actually use and that delivers real operational improvements.