Free resource

Is your company
actually ready for AI?

A practical, no-hype guide to figuring out where you stand — and the exact next step to take, whatever your answer is.

"AI-ready" doesn't mean having the fanciest model or a data science team. It means your business has the foundations in place to turn AI from an experiment into something that reliably saves time or makes money. This guide walks through what those foundations actually look like — based on the patterns we've seen across 40+ AI integrations.

1. Data foundations

Every useful AI system runs on data — and most companies underestimate how much cleanup is needed before that data is usable. You don't need "big data." You need data that's accessible, reasonably consistent, and connected to a real business process.

Ask yourself: can someone on your team pull last quarter's customer support tickets, sales records, or operational logs into a single place within a day? If the honest answer is "no, that would take weeks," that's your starting point — not a blocker, just the first thing to fix.

2. Process clarity

AI amplifies whatever process it's plugged into. A messy, undocumented process becomes a faster, messier process with AI attached. A clear, well-understood process becomes dramatically more efficient.

Before automating anything, write the current process down — even roughly. If you can't describe how a task is done today, an AI system can't reliably do it either. This single exercise resolves more "AI projects" than any model selection ever will.

3. Team & buy-in

The biggest reason AI projects stall after launch isn't technical — it's adoption. The people who'll use the system day to day need to trust it, understand its limits, and feel it makes their job easier rather than threatens it.

Bring them in early. Ask what frustrates them about the current process. The best AI use cases usually come from the people closest to the problem — not from a strategy deck.

4. Readiness checklist

Run through these honestly. You don't need a "yes" on everything to start — but the more you check, the faster and smoother your first AI project will go.

✓
We can describe our target process step by stepEven a rough flowchart on a whiteboard counts.
✓
Relevant data lives somewhere accessibleA spreadsheet, CRM or database — not just in people's heads or inboxes.
✓
We have one clear success metric in mind"Faster," "better," and "smarter" aren't metrics — hours saved or errors reduced are.
✓
At least one person who'll use it daily is involvedTheir early feedback will save you months of rework later.
✓
Leadership is fine starting smallThe best AI rollouts begin with one workflow, not a company-wide transformation.

5. Scoring your result

Count how many boxes you checked, then see where that puts you — and what we'd suggest as your very next step.

What your score means

0–2 checkedStart with a process-mapping workshop. We can run one with your team in under a week.
3–4 checkedYou're close. A focused discovery call will help you scope a pilot that proves value fast.
5 checkedYou're ready to move. Let's talk about your highest-impact use case and a realistic build timeline.
Next step

Want a second opinion
on your readiness score?

Bring us your checklist results — we'll walk through them with you on a free 30-minute call and suggest where to start.