Cost, Latency, and ROI of AI Systems
A practical framework for understanding the economic trade-offs of AI systems, including model cost, response speed, review effort, and business payoff.
Academy
Rules, RPA, ML, LLMs, and Agents: The Decision Ladder
A practical decision ladder for choosing between rules, RPA, traditional machine learning, LLM workflows, and agent-like systems.
The AI Stack in Plain English
A plain-English guide to the main layers of a modern AI system, from models and prompts to retrieval, tools, guardrails, and review.
What AI Gets Wrong
A practical guide to the most common ways AI systems fail in business settings, and how to design review controls before those failures become operational problems.
Where to Go Deeper Beyond This Academy
A curated guide to textbooks, authors, websites, and papers for readers who want to study transformer internals, attention math, fine-tuning, GPU optimization, and benchmarking in more depth.
AI Access Control, Logging, and Retention Policies
How to design access controls, prompt/output logging, and retention rules for AI systems so governance remains practical, auditable, and proportional to risk.
AI Agents vs Workflows
How to separate true agent-like systems from straightforward AI workflows, and why most business use cases should start simpler.
AI Evaluation, Monitoring, and Incident Response for Production Systems
How to evaluate, monitor, and respond to failures in production AI systems so quality, safety, and governance remain active after launch.
AI for Accounts Receivable, Collections, and Cash Application
How to use AI to support receivables operations, payment matching, collections communication, and dispute routing while keeping customer-sensitive decisions under control.
AI for Audit Requests, PBC, and Workpaper Support
How to use AI to manage audit requests, prepare PBC responses, and support workpaper assembly while preserving traceability, reviewer control, and defensible evidence.