Is Your Business Ready for AI? A Practical Readiness Checklist

Most failed AI projects don't fail because the AI model wasn't good enough. They fail because the business wasn't actually ready for it yet. Before committing budget, it's worth honestly answering a few questions.

Do you have the data the AI actually needs?

AI systems, whether it's a chatbot trained on your documentation or an automation reading your invoices, need reasonably clean, accessible data to work from. If your product information, policies, or records are scattered, outdated, or inconsistent, that gets fixed first.

Is the process you want to automate actually well-defined?

Automating a process that nobody agrees on the steps for just automates the confusion faster. A process worth automating is one your team can already describe clearly and consistently.

Who owns this once it's live?

AI systems need monitoring and occasional adjustment. If nobody on your team is responsible for checking in on it after launch, quality tends to drift.

What happens if it's wrong sometimes?

Every AI system makes mistakes some percentage of the time. For low-stakes uses like answering an FAQ, that's fine. For higher-stakes uses involving financial or medical information, you need a human review step. Knowing which category your use case falls into changes the whole design.

The honest answer

If you answered "not really" to more than one of these, that's not a reason to avoid AI, it's a reason to fix the foundation first. That's a smaller, cheaper project than a failed AI rollout.