AI Governance for Document Management: A Board-Level Brief

This brief is written for a Board Audit & Risk committee or equivalent governance body. It assumes minimal technical detail and focuses on the right questions to ask.

The five governance questions

1. What AI decisions are being made on our behalf?

The board should be able to enumerate the AI use cases in deployment:

Classification (what type of document is this?)
Extraction (what data does this document contain?)
Routing (which workflow should this trigger?)
Search (what documents answer this query?)
Synthesis (what's the answer to this question?)
Risk scoring (how risky is this counterparty / contract / loan?)
Anomaly detection (which user behaviour looks suspicious?)

For each, expect a documented answer to questions 2-5.

2. What's the accuracy and how do we know?

For each AI use case, management should report:

Current accuracy (against a labelled test set)
Accuracy trend over the last 4 quarters
How accuracy is measured (sampling methodology, sample size, refresh cadence)
What “accuracy” means in context (top-1 vs top-3, exact match vs semantic match)

Sample accuracies the board should expect:

Use case	Expected accuracy range
Document classification	85-95%
Field extraction (structured)	90-98%
Field extraction (unstructured)	70-85%
Semantic search relevance	80-92%
Risk scoring	varies; benchmark against historical outcomes

3. Where does the AI fail and what happens then?

For each use case:

Defined confidence threshold below which output is routed for human review
Failure mode catalogue: known failure patterns and mitigations
Recovery process when a failure is detected (correction, retraining, escalation)
Materiality threshold for board notification (e.g., classification accuracy drops below 85%)

4. How do we know the AI isn't biased?

For decisions affecting people (hiring, lending, customer service), bias is the most-loaded governance question. Management should articulate:

Whether the AI processes protected characteristics directly or indirectly
How bias is tested (fairness metrics, disparity ratios across groups)
Cadence of bias review
Remediation process when bias is detected

For document classification and extraction, bias is less critical but not zero. Even document classification can encode bias if training data was skewed.

5. What's our exit plan if the AI breaks?

If the AI vendor goes down, gets acquired, or the model becomes unfit for purpose:

Can we operate without AI for some period? (Yes — but humans replace AI's work)
Is our data exportable in non-AI formats? (Yes for Papyrus)
Can we switch to a different AI provider? (Yes — Papyrus's abstractions allow this)
What's the cost of operating without AI for, say, 30 days?

The controls the board should expect

Decision logging

Every material AI decision should be logged with:

Input reference (which document, which query)
Output
Confidence score
Model version and date
Timestamp
Subsequent human override (if any)

Papyrus's audit log captures all of this for AI decisions.

Periodic accuracy benchmarks

Quarterly minimum. Methodology:

Sample N=200+ documents (or interactions) from the period
Have humans label them independently
Compare AI output to human labels
Compute accuracy, precision, recall, F1
Track per-category breakdown (where does the AI fail most?)

Report to the audit committee.

Drift detection

Production data shifts over time. AI accuracy can degrade silently. Drift detection runs automated alerts:

Distribution of input data (is it changing?)
Distribution of output predictions (is the AI saying different things?)
Confidence score distribution (is the AI getting less certain?)
Human-correction rate (are users correcting AI more often?)

Material drift triggers retraining.

Human-in-the-loop

For decisions above a certain risk threshold, AI is advisory, not decisive. The decision logged is the human's; the AI's recommendation is captured for retrospective audit.

Explainability

For high-stakes decisions, plain-language explanations of why the AI produced its output. Not technical model weights — plain language. “This contract is scored high-risk because of unusual indemnity language and the counterparty's payment history.”

Model versioning

Every model deployed has a version, training date, training data summary, and validation results. Models are deployed via the same change-management process as any other production system. Rollback is possible within 24 hours.

The KPIs to track

Board dashboard for AI:

Metric	Target	Reviewed
Classification accuracy	>90%	Quarterly
Extraction accuracy	>85% on unstructured, >95% on structured	Quarterly
Human override rate	<10% on routine, <25% on judgement	Monthly
AI uptime	>99.9%	Monthly
Mean time to detect drift	<30 days	Quarterly
Mean time to retrain	<60 days from drift detection	Quarterly

The role of the audit committee

The audit committee should:

Receive quarterly AI accuracy reports
Review the AI decision log for material decisions in the period
Be informed of any material drift, retraining, or model changes
Approve material expansions of AI scope
Receive incident reports for any AI-related issues

This is the same posture the committee takes for any other material business system — and it should treat AI accordingly.

The conversation NOT to have

Don't have a board conversation about whether to use AI. That train left. The conversation to have is whether you're governing the AI you're using.