AI readiness audit
Get your AI readiness score
R2 tests whether your existing taxonomy gives AI the context to decide applicability, and scores it.
How the audit works
From your taxonomy to your score
R2 works from your existing data. Most organizations export the fields from their GRC platform.
01Structure evaluation
R2 evaluates each exported field against the form generative AI can use reliably to evaluate obligations.
02Obligation evaluation
R2 normalizes the exported data for the evaluation, leaving your records unchanged, and uses that structure to determine whether the obligations in the selected regulatory sources are applicable.
03AI Readiness Score
The structure evaluation produces the structure score and the obligation evaluation the applicability score; together they show how your taxonomy should be updated for AI to scale applicability.
04Suggested modifications
R2 presents your AI Readiness Score and suggested modifications to your team, for each field that generated errors and could be limiting AI’s ability to scale applicability.*
Pricing depends on the number and size of the regulatory sources evaluated and on the complexity of your taxonomy and mappings.
*Updates are handled under a separate engagement.
Example report
AI Readiness Scorecard
R2 tracks how each taxonomy item affects AI’s ability to scale compliance work. The audit tests whether your taxonomy gives AI enough context to decide applicability accurately and reliably, the decision on which four internal flows depend: regulatory change triage, impact assessment, risk assessment and testing scope.
AI readiness audit
Example report
AI Readiness Scorecard
Prepared by R2 Compliance Data Services
Assessed October 14, 2026 · Evaluation set v1 · AI model as set in the engagement
- Overall score
- 38.5
- Applicability
- 32.0
- Structure
- 51.5
Scope: 800 requirements · 13 taxonomy items
Applicability, by requirement
Whether an obligation applies depends on the businesses, legal entities, locations and products it reaches. This test checks whether each requirement’s record, read in full, gives AI enough context to decide applicability accurately and reliably: whether each obligation it cites applies, and to which parts of the business. No single item provides that context on its own. The pass rate is the percentage of the 800 requirements whose record passes. Select the row to see the reasons for failure and an example.
| Measure | Result | Finding |
|---|---|---|
| 32.0% |
Structure, by taxonomy item
Whether each taxonomy item, a field in each requirement’s record such as its citation, holds the base information AI needs to scale. Each pass rate is the percentage of the 800 requirement records whose value for the item passes. This example shows 13 of the potential taxonomy items R2 currently tracks. Select an item to see the reasons for failure.
| Taxonomy item | Result | Finding |
|---|---|---|
| 56.0% | ||
| 55.0% | ||
| 65.0% | ||
| 30.0% | ||
| 62.0% | ||
| 38.5% | ||
| 40.0% | ||
| 23.5% | ||
| 60.0% | ||
| 70.5% | ||
| 49.0% | ||
| 62.5% | ||
| 57.5% |
How the scores are calculated: A requirement passes applicability when AI, working from its record, reaches the decision your experts approved; the applicability score is the percentage of the 800 requirement records that pass. The structure score is the average pass rate of the 13 taxonomy items shown. The overall score weights applicability twice as much as structure; it is a composite of the two tests, not a measure of AI accuracy. Each failing requirement is counted once, under its primary reason. The checks follow the data standards agreed with you before the audit; this example uses common ones.
Questions
What to expect
- What is a requirement topic?
- An entry in your regulatory taxonomy: the record your GRC platform keeps for a group of related regulatory requirements. It usually has a name and description, the requirements and citations it covers, an oversight function, the business lines, legal entities, locations and products it applies to, risk ratings, and links to policies, procedures and controls. Your platform may call it a requirement group.
- How common are these problems?
- Common. In the taxonomies R2 has worked with, citation formatting error rates have run as high as 60%. In the case study on the home page, the taxonomy’s requirement topics had no scope notes and its requirement records did not state who must act or what they must do, and AI reached wrong applicability decisions as a result.
- What do we need to provide?
- An export of your existing taxonomies and mappings from your GRC platform. They usually already cite their regulatory sources, so you choose which ones to audit.
- How long does an audit take?
- It depends on the number and size of the sources you choose. R2’s proposal sets out the timeline before work starts.
- Who makes the changes?
- Making the changes is a separate engagement. After the audit, R2 can update your taxonomy on your behalf or alongside your team, or your team can make the changes itself.
- How does the audit decide that a requirement passes applicability?
- Your experts approve a set of applicability decisions for your taxonomy. A requirement passes when AI, working from its record, reaches the approved decision. Records pass or fail as units.
- Which AI does the audit test?
- AI models: During the initial engagement and project scoping, the team will determine which generative AI model should be used for the audit. *Model selection might affect the cost of the audit.
- What happens to our taxonomy?
- R2 uses it only for your engagement and keeps it for the length of the engagement: the audit, and the ongoing services if you continue. When the engagement ends, R2 deletes it.
- How is the score calculated?
- The audit applies two tests, and each check passes or fails. Applicability tests whether each requirement’s record, read in full, gives AI enough context to decide accurately and reliably whether each cited obligation applies, and to which parts of the business; its score is the percentage of requirement records that pass. Structure tests whether each taxonomy item, a field in each record, holds the base information AI needs; its score is the average pass rate of the items. The overall score weights applicability twice as much as structure, because applicability is the decision the other work depends on.
- Does the score assess our compliance program?
- No. The score measures how well your taxonomy supports AI in determining applicability and in the internal flows that follow it. It does not assess whether your controls are effective.
Start with an AI readiness audit