Notes on AI, automation, and operations
Practical writing on implementing AI, building with low-code, and optimizing how your business runs.
How to Measure ROI on an AI Implementation
A practical framework for measuring the return on an AI implementation — baseline first, isolate the AI-driven delta, then track cost, time, and quality against a clear payback period.
Why AI Pilots Fail to Reach Production (and How to Fix It)
Most AI pilots stall before production due to missing business metrics, data gaps, and no owner — here are the six failure modes and the fix for each.
Read moreHow to Choose Your First AI Use Case
Score your first AI use case on five criteria — clear outcome, data quality, volume, error tolerance, readiness — then rank by value vs effort.
Read moreLow-Code vs Custom Development: When Each Wins
A decision framework for choosing low-code or custom development — weigh speed-to-market, total cost of ownership, the customization ceiling, integrations, team skills, and lock-in.
Read moreA Practical Process-Optimization Playbook
A step-by-step process-optimization playbook — map the process, find bottlenecks and rework, quantify cost and cycle time, prioritize, redesign, and automate.
Read more5 Automation Quick Wins for Operations Teams
Five concrete, high-ROI automations operations teams can ship in weeks — data reconciliation, approvals routing, report generation, alerts, and document handling, with typical time saved.
Read more