Digital Innovation & Transformation
AI Readiness
Assessment
A structured, evidence-based tool to assess whether a specific problem or friction point is a strong candidate for an AI-based solution. Complete one readiness assessment per friction point identified in your process map.
Who is this for?
Teams in the early stages of exploring an AI-based solution who want a structured, evidence-based approach to discovery and planning.
Why use it?
Many AI projects fail not because the technology is wrong, but because it is applied to the wrong problem. This readiness assessment helps you de-risk your project with data-backed diagnosis.
Objective
Produce a clear profile of your AI opportunity and make a strategic decision on whether to proceed, pivot, or pause.
When?
During the discovery phase, when you are trying to understand the problem, before committing to a solution or conducting a full technical assessment.
How to use this readiness assessment
- Focus on one problem. If your AI solution performs multiple distinct tasks, complete a separate readiness assessment for each one.
- Complete all five sections. Sections A–D are scored; Section E is a mandatory compliance check.
- Be honest. Use the scoring guides in each section for each criterion.
- Use the results. Final scores and the interpretation guide facilitate a strategic discussion with your team.
Note: This readiness assessment is designed for initial operational discovery and does not replace a comprehensive technical deployment audit.
The Nature of the Task
This section assesses whether the task is a good fit for current AI capabilities. Enter 1 for Yes or 0 for No for each criterion.
| Criterion | Score (Yes = 1 / No = 0) |
|---|---|
1. Is the task highly repetitive? Examples suitable for AI: Recommendation systems · Anomaly detection · Forecasting Examples suitable for generative AI: Generating reports · Social media content · Summaries | |
2. Is the task data-intensive? Does it require analyzing large volumes of text, numbers, or images? | |
3. Does the task rely on finding patterns in data? For example: screening resumes, identifying trends | |
4. Is the task about generating standardized or templated content? For example: drafting first versions of job descriptions, emails | |
5. Have you already explored non-AI solutions and found them insufficient? | |
| Subtotal A — AI Fit Score | — / 5 |
The Impact of the Problem
This section assesses problem severity. Collect data rather than making assumptions. Rate each criterion from 1 (very low impact) to 5 (very high impact).
| Criterion | Impact Score (1–5) |
|---|---|
6. What is the resource impact of this friction point? For example: time, financial cost, physical resources | |
7. What is the psychological impact of this friction point on employees? For example: stress, frustration, burnout | |
8. How often does this friction point lead to errors? What is the error rate in terms of rework? | |
9. What is the impact of this friction point on the organization's goals? What is the strategic cost of doing nothing? | |
| Subtotal B — Pain Point Score | — / 20 |
Process Quality
Assesses whether the underlying process is stable and well-defined enough to support an AI intervention. Enter 1 for Yes or 0 for No.
| Criterion | Score (Yes = 1 / No = 0) |
|---|---|
10. Is the current process well defined and stable? In other words, not constantly changing or completely ad hoc | |
11. Are the desired outcomes of the process clear and agreed upon? Do we know what "good" looks like? | |
12. Has the process already been simplified as much as possible? | |
| Subtotal C — Process Quality Score | — / 3 |
Data Quality
Assesses whether the data required for an AI tool is readily available and of sufficient quality. Enter 1 for Yes or 0 for No.
| Criterion | Score (Yes = 1 / No = 0) |
|---|---|
13. Do we have access to the right data needed for the AI to learn or operate? For example: historical records, relevant documents that are fit for purpose | |
14. Is the available data of sufficient quality? Is the data accurate, complete, relatively clean and up to date? | |
15. Is the data structured and in a usable format? For example: in a database with appropriate governance, in a machine-readable format | |
| Subtotal D — Data Quality Score | — / 3 |
Compliance and Ethics
This is a preliminary check to identify mandatory policy requirements. A Yes to any question means you must engage the appropriate experts in your department.
| Compliance Question | Your Answer | Action Required |
|---|---|---|
16. Does the AI tool process any personally identifiable information? Personal information includes any recorded data about an identifiable individual — such as names, email addresses, employee IDs, performance reviews, financial data, government identifiers, addresses, or IP addresses. |
If Yes — A PIA is highly recommended. Contact our Risk & Compliance team to book a full Privacy Impact Assessment and secure your launch path. |
|
17. Does the AI tool assist or replace a human in making an administrative decision about or impacting an individual? For example: screening resumes, determining eligibility, assessing performance, automating approvals, facial recognition, generating a risk score |
If Yes — A NIST AI Risk Management Framework should be completed. Partner with NVCORE to run an automated risk mitigation audit across your core operational layers before the system goes live. |
|
18. Does the data used to train or operate the AI carry legal, compliance, or accountability implications if errors occur? For example: historical biases in training data, lack of data on marginalized groups |
If Yes — Significant ethical risk. Let NVCORE help you build a robust data mitigation plan to eliminate structural bias. |
Your Score Summary
Guide to Interpreting Your Scores
Use this guide to facilitate a strategic discussion with your team.
| Section | Score Range | Interpretation |
|---|---|---|
| A: AI Fit out of 5 | 4–5 High | Proceed. The task is an appropriate fit for an AI solution. |
| 2–3 Moderate | Pause. The task is a partial fit; a combined human and AI approach may be needed. | |
| 0–1 Low | Pivot. The task is not a good fit for AI; consider non-AI solutions. | |
| B: Pain Point out of 20 | 14–20 High | The problem is severe and justifies a significant investment. |
| 8–13 Moderate | The problem is a known inconvenience but may not be a top priority. | |
| 0–7 Low | The problem's impact is minimal. | |
| C + D Combined out of 6 | 5–6 High | Proceed. Your project has a solid foundation and is ready to advance. |
| 3–4 Moderate | Pause. Some foundational work (e.g. process cleanup) is needed. | |
| 0–2 Low | Pivot. Significant foundational work is required before an AI project can be deployed and succeed. |
Strategic Recommendation Profiles
Combine your scores and compliance checks to determine your overall recommendation.
This is an excellent project to move forward with, subject to completing any required PIA or AIA processes. Consult NVCORE to leverage existing marketplace architectures and accelerate your go-to-market timeline.
This is a potentially promising opportunity, but foundational work or compliance review is needed before proceeding. Consider an initial localized pilot, a hybrid human-in-the-loop approach, or workflow redesign first.
This problem is not currently a strong candidate for AI. Before any solution is considered, focus on standard process optimization, database hygiene, or proven software alternatives.