Topic

AI rollout

Where to start with AI, what to automate, what to leave human, and how to measure impact.

Foundation

Revenue Per Worker

Headcount went up. Revenue per person did not. Find out whether you need more people, better systems, or AI before you hire again.

Self

Keeping Your Edge

AI is doing more of your thinking and your judgment is getting quieter. Keep the decisions that have to stay yours, yours.

AI vs Automation: The Difference That Matters

Most workflows the team thinks need AI actually need a deterministic automation. Some look automatable but require AI flexibility. Score the workflow before you spend.

Types of AI and Where They Pair

LLM, AI agent, autonomous agent. Different capability ceilings, different failure modes, different places they belong in the operation.

The AI Readiness Check

Before you buy another AI tool, check if the workflow is clear enough to automate. Most teams skip this step and waste the spend.

Data Discipline Before AI

AI gives plausible-but-wrong answers when the data underneath is fragmented. Fix the records before you spend on tools.

High-Impact Use Cases

Everyone is using AI badly. Pick the three or four use cases inside a service business that actually save the team real hours.

RAG: The AI That Reads Your Own Files

Retrieval-augmented generation lets the AI look something up in your own documents before it answers, instead of guessing. Here is when it earns the cost and when it does not.

Sales Leverage and Follow-Up Automation

Leads are leaking because nobody follows up fast enough. Use AI to keep outreach personal at the speed of the inbox.

The Cost of AI Getting Things Wrong

Direct cost, recovery cost, insurance cost. Most teams skip the second and third. Then the first one stops being the largest.

Bringing Your Team With You

The team is worried AI is here to replace them. Build the trust and skills they need so adoption actually sticks.

Measuring AI Impact

You spent on AI tools and cannot say whether they paid back. Measure hours saved, quality, and whether the team is actually using them.

Holding the Line on Quality

AI works until your team stops checking it. Hold the standard so output speed does not become quality drift the client catches first.

30-Day Quick Start

Thirty daily exercises across four weeks. Each day produces one written artifact. By Day 31 you have a working map, named dysfunctions, one repaired process, and a 90-day plan.