Before you invest in AI, you need to know if you're ready for it. Not every company is. And that's okay. This playbook gives you a clear-eyed framework for assessing where you are and what needs to happen first.
The four pillars of AI readiness
We evaluate AI readiness across four dimensions: data maturity, technical infrastructure, organizational alignment, and clear use cases. Weakness in any one area doesn't disqualify you, but it does tell you where to focus first.
1. Data maturity
AI runs on data. If your data is scattered across spreadsheets, locked in legacy systems, or simply doesn't exist in digital form, that's your first problem to solve. You don't need perfect data, but you need accessible, reasonably clean data for the specific problem you want to solve.
- Is the data you need captured digitally?
- Can you access it programmatically?
- Is it reasonably consistent and clean?
- Do you have enough of it? (This varies wildly by use case)
2. Technical infrastructure
You don't need a world-class engineering team to implement AI, but you do need basic technical foundations. Can you deploy and maintain software? Do you have APIs connecting your systems? Is there someone who can own the technical integration?
3. Organizational alignment
The biggest AI failures we've seen aren't technical. They're organizational. Someone built something cool that nobody wanted to use. Or leadership changed priorities mid-project. Or the team that was supposed to adopt it was never consulted.
The question isn't 'Can we build this?' It's 'Will this actually get used, and by whom?'
4. Clear use cases
The best AI projects start with a specific, measurable problem. Not 'We want to use AI' but 'We want to reduce customer response time from 4 hours to 15 minutes.' The clearer the problem, the better we can evaluate whether AI is the right solution.
What to do if you're not ready
If this assessment reveals gaps, that's valuable information. Most of our consulting engagements start here: helping companies understand what they need to do before they're ready to build. Sometimes that's a data cleanup project. Sometimes it's organizational work. Sometimes it's just getting clearer on the actual problem to solve.