There are two types of companies: those who are great at AI - and everyone else

“There are two types of companies in the world: Those who are great at A.I. and everybody else.”

Mark Cuban • Fortune

"Using AI" is no longer a differentiator. The differentiator is whether AI is truly inside your workflow: does it shorten cycle time, reduce cost, and increase conversion?

What being "good at AI" means (not prompts, but processes + data)

Being good at AI does not mean "we have a ChatGPT account". It means you have 1-3 use cases where AI does real work and the impact is measurable.

Good AI = a process (input -> decision -> action -> log), data (price list, product catalog, policies, CRM), and quality control (who approves, which rules apply).

If AI can't access your "source of truth" (products, prices, terms), it can't make good decisions.

Five signs you're in the "other group"

• AI is for "trying", not a work standard - usage depends on the person, not the system.
• Support and sales still do copy-paste work (offers, recurring questions).
• Data is scattered and there is no single source of truth (prices, product info, policies).
• There is no owner and no KPI (e.g., response time, cost per ticket, conversion).
• You run many experiments, but none becomes a real workflow (POC stays a POC).

How to move from the "other group" to the first group in 60 days

• Days 1-7: choose one process with direct financial impact (customer support or sales offers) and define 1-2 KPIs.
• Days 8-21: consolidate your "truth" into one place (FAQ, products, prices, shipping/returns) and set simple routing rules (what must go to a human).
• Days 22-45: integrate into the workflow: email/chat -> AI triage -> draft -> approval -> log into CRM or Sheets.
• Days 46-60: standardize: templates, QA, training, and one-click usage. Then add the next use case.