Is Your Business Ready for AI? A 12-Point Readiness Checklist
A business is ready for AI when it has at least one clearly defined, repetitive process worth automating, access to the data that process needs, systems that can be connected, a named owner for the project, and basic rules for how AI may use company and customer data. Score yourself on the 12 questions below. You don't need a perfect score to start, but low scores show what to fix first.
How to score
For each statement, give your business: 0 = not true · 1 = partly true · 2 = fully true
Processes
- We can name at least three tasks our team repeats every week that follow roughly the same steps.
- We know roughly how much time those tasks take: hours per week, people involved.
- Those processes are documented, or at least one person can explain every step and exception.
Data
- The information those tasks need is digital: emails, PDFs, spreadsheets or software, not mainly paper.
- Our key data lives in identifiable systems (CRM, accounting, inbox), not scattered across personal files.
- Our data is reasonably accurate and up to date.
Systems
- Our main software tools have APIs or integrations. Most modern cloud tools do.
- We have someone who manages our systems and accounts, in-house or external.
People
- A senior person will own the AI project, with the time and authority to make decisions.
- Our team is open to changing how work is done, and understands the goal is to remove repetitive work, not people.
Governance
- We have rules about what company and customer data can be put into AI tools.
- We understand our data-protection obligations (e.g. the Nigeria Data Protection Act 2023 or UK GDPR) for the data involved.
What your score means
| Score | Readiness | Next step |
|---|---|---|
| 18–24 | Ready to start | Pick your highest-value process and run a pilot |
| 10–17 | Nearly ready | Fix the 0-scored items, often data access and ownership, then pilot |
| 0–9 | Foundations first | Document key processes, centralise data and set AI usage rules before building |
The most common gaps, and how to close them
- No one owns it. AI projects without an owner stall. Assign one person with decision rights.
- Data in the wrong place. Move critical information out of personal inboxes and spreadsheets into shared systems first.
- No AI usage policy. Staff are already pasting company data into public AI tools. A one-page policy on what's allowed is an urgent, cheap fix.
- Starting too big. Start with one workflow, prove it, then expand.
Frequently asked questions
Do we need a data scientist to start using AI?
No. Most business AI automation uses existing AI models connected to your systems. You need a clear process, accessible data and a capable implementation partner, not an in-house research team.
What should an AI usage policy include?
Which AI tools are approved, what data may never be entered into them (customer personal data, financials, credentials), who approves new uses, and how AI-generated work is checked before it's used.
How long does it take to become AI-ready?
Businesses scoring in the middle band can usually close their gaps in a few weeks. Those starting from the foundations may need two to three months of process and data work first.
Want a second opinion on your score?
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