Before you fund another AI pilot, it helps to know whether your organization can take one to production. Most AI projects that stall don’t stall on the technology. They stall because nobody agreed on the problem, the data wasn’t ready, nobody owned the risk, or the people meant to use the tool never changed how they worked.
This AI readiness assessment takes about fifteen minutes. Score your organization honestly on six areas, add up the total, and use the result to decide what to do next.
How to score your AI readiness assessment
For each area, pick the description that best matches your organization today, not where you plan to be. Score 1 to 4. If you’re unsure between two levels, choose the lower one. It’s more useful to find a gap now than after a pilot fails.
The six areas
1. Use case clarity
| Score | What it looks like |
|---|---|
| 1 | We want to “do AI” but haven’t named a specific business problem |
| 2 | We have a list of ideas, but none has a measurable goal or an owner |
| 3 | One or two use cases have a goal, an owner, and a rough value estimate |
| 4 | Use cases are ranked by value and feasibility, and we’ve decided which ones not to do |
2. Data readiness
| Score | What it looks like |
|---|---|
| 1 | We don’t know where the data for our top use case lives |
| 2 | We know where it is, but it’s scattered, inconsistent, or hard to access |
| 3 | The data is accessible and someone is responsible for its quality |
| 4 | The data is accessible, documented, and cleared for use with AI tools |
3. Governance and risk
| Score | What it looks like |
|---|---|
| 1 | No policy; people use whatever AI tools they like |
| 2 | We have a policy document, but nobody checks it in practice |
| 3 | Someone approves AI use cases and decides where a person must review AI output |
| 4 | Approval, human review, and monitoring are part of how projects get delivered |
4. Skills
| Score | What it looks like |
|---|---|
| 1 | Nobody internally could judge whether an AI proposal is sound |
| 2 | A few enthusiasts experiment, but leaders can’t evaluate their work |
| 3 | Leaders understand AI well enough to question proposals; some engineers can build |
| 4 | Roles that build, buy, and use AI have each been trained for their part |
5. Delivery
| Score | What it looks like |
|---|---|
| 1 | We’ve never taken an AI project past a demo |
| 2 | We’ve run pilots, but none has reached production |
| 3 | At least one AI system is in production, with someone accountable for it |
| 4 | We have clear gates from proof of concept to pilot to production, and we use them |
6. Adoption and change
| Score | What it looks like |
|---|---|
| 1 | We expect people to start using AI tools once they’re available |
| 2 | We announce tools and run a demo, but usage drops off |
| 3 | Each rollout has a plan for training, support, and feedback |
| 4 | Teams have AI champions, and we track whether new ways of working stick |
What your total means
Add your six scores. The total will be between 6 and 24.
| Total | Where you are | Best next step |
|---|---|---|
| 6 to 11 | Exploring | Pause new pilots. Pick one business problem, name an owner, and train the leaders who’ll make the decisions |
| 12 to 17 | Experimenting | Fix your lowest-scoring area before scaling. Set up approval and production gates |
| 18 to 21 | Delivering | Standardize what worked, measure results, and extend training to more roles |
| 22 to 24 | Scaling | Focus on portfolio decisions: which use cases to expand and which to retire |
Your lowest score matters more than your total
A total of 16 with a 1 in governance is riskier than a 14 with every area at 2 or 3. AI projects tend to fail at their weakest point, so look at your lowest-scoring area first.
| If your lowest area is… | Do this first |
|---|---|
| Use case clarity | Run a short workshop to rank use cases by value and feasibility, and drop the rest |
| Data readiness | Choose a use case whose data is already accessible, and start the data work for the next one in parallel |
| Governance and risk | Name one person to approve AI use cases, and write down which decisions need a human |
| Skills | Train decision-makers first, then the teams who build or buy |
| Delivery | Define what a pilot must show to earn production, before you start the next one |
| Adoption and change | Plan training and support for users as part of the project, not after launch |
Where training helps
AgileFever runs training on these topics, so weigh this suggestion accordingly.
AgileFever’s AI Transformation for Business Leaders covers all six areas in nine modules over 16 hours: use case prioritization, business cases and stage gates, governance, vendor evaluation, change management with ADKAR and AI champions, delivery, and why AI projects fail. You finish by presenting your own AI transformation playbook to a mock executive panel. No coding is involved. The current price is $1,000 in the US or ₹65,000 in India.
Additional discounts are available. Contact us to know more.
FAQ
Should one person fill this in, or a group?
A small group is better. Ask a business leader, someone from IT or data, and someone from risk or compliance to score separately, then compare. The disagreements are often the most useful part.
How often should we repeat it?
Every six months, or before any major AI investment.
What to read next
For deciding whether to build or buy once you’re ready, read Build vs Buy vs Upskill. For the course in detail, see AI Transformation for Business Leaders: The Complete Guide.
