FIELD INTELLIGENCE
Four positions.
Four positions.
One operating thesis.
I write about where AI, dashboards, data signals, and leadership fail in real operations. Each position is a lens — backed by articles, field experience, and uncomfortable trade-offs.
01
ai-operations
AI exposes weak operations
AI does not repair unclear ownership, slow escalation, or broken workflows. It exposes them faster and at scale.
02 decision-authoritySignals need authority
A risk signal has no value if nobody can act on it. The best model loses when approval chains move slower than the threat.
03 risk-intelligenceReporting is not intelligence
Reporting explains what happened. Intelligence changes what happens next. If metrics do not trigger action, they are decoration.
04 ai-and-workAI is changing the skill floor
AI exposes people who only operate tools. The safer skillset is judgment: setting thresholds, owning trade-offs, and knowing when to escalate.
ALL ARTICLES
20 posts
Why Your AI Project Fails When Nobody Owns the Escalation Path
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AI Models Don't Repair Operations; They Expose Unclear Escalation Paths
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AI Exposure Reveals If Your Senior Operators Are Just Advanced Tool Users
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AI Job Risk Is Not About Your Job Title
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If You Only Build Dashboards, AI Will Take Your Job
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Analytics Jobs Will Split in Two. AI Will Automate Tool Operators; Decision Shapers Will Control Outcomes.
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AI Won't Save a Sloppy Process: Fix Your Workflow Before Deploying Models
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Escalate or Eat the Loss: Why Risk Signals Fail Without Decision Authority
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Build Controls, Not Charts: Designing Loss Prevention That Acts
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Green Metrics, Real Losses: Why AI Freezes When Risk Moves Fast
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Pre-Wired Controls: How to Stop Fraud Before the Meeting Starts
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The Blind Spots Your Dashboard Hides: Where Fraud Actually Lives
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Ship Decisions, Not Dashboards: A Playbook for Risk Teams
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From Fraud Signal to Action: Why Decision Latency Costs More Than Bad Models
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Why Good Data Fails Under Real Risk: The Decision Authority Gap
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How AI Is Redefining Expertise: From Having Answers to Creating Meaning
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Leading vs. Lagging Indicators: Choosing Metrics That Predict, Not Just Report
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12 Key Metrics Every Security and Loss Prevention Team Should Track
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Predictive Analytics for Security: From Reactive to Proactive Loss Prevention
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How Data Teams Should Prioritize: Saying No to Low-Impact Work
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