Plenty of AI projects fail, not because the technology doesn't work, but because the business wasn't ready for it. A short readiness assessment up front saves time, money, and frustration.
The four dimensions of readiness
We assess readiness across four areas:
1. Data
AI runs on data. Ask:
- Do we have the data this use case needs?
- Is it accessible, reasonably clean, and reasonably structured?
- Are there privacy or compliance constraints?
2. Process
AI works best applied to a clear, well-understood process.
- Is the process we want to improve actually well-defined?
- Where exactly is the bottleneck?
- How will we measure improvement?
3. People
Adoption is everything.
- Who will use this, and are they bought in?
- Do we have a clear owner for the project?
- Is there appetite to change how work is done?
4. Goals
- What specific outcome are we targeting: revenue, cost, speed, quality?
- How will we know it worked?
The most common reason AI pilots stall isn't the model, it's an unclear goal or a process nobody actually wanted to change.
Scoring readiness
You don't need to be perfect in all four areas. A strong use case with clear goals and decent data can succeed even if other areas need work, as long as you know where the gaps are and plan around them.
What to do with the results
A good assessment produces a prioritised shortlist: the use cases where readiness is high and value is clear. Start there, prove ROI, build momentum, and reinvest in the areas that need strengthening.
If you're not sure where to begin, a structured readiness assessment is the lowest-risk first step you can take, and it's where most of our engagements start.