What you’ll learn

Explain the predictive AI and insights control purpose behind ai governance

Configure predictive algorithms, forecasting variables, and module settings

Handle stockout risk warnings, smart procurement, and collection actions

Provide audit-ready logic explanations and verify fallback configurations

Lesson 1 of 3 Reading 12 min

Set access permissions

AI Governance focuses on AI governance rules, managing access permissions, plan parameters, and defining fallback behavior rules. In AWRA, predictive AI turns historical data trends into clear, actionable operational decisions.

The primary objective is risk avoidance and optimization. Teams should understand AI forecasts without blindly trusting suggestions, maintaining human oversight.

In practice, an admin updates AI permissions, locks algorithm configs, and configures fallback calculations.

AI governance path

Evaluate

Check security policies, user roles, and plan limits.

Configure

Define AI edit permissions and fallback rules.

Lock

Secure configuration using admin credentials.

Audit

Run compliance checks showing settings remain locked.

Predictive model

Forecasts combine historical averages with current transaction velocity.
Predictions provide confidence levels and risk warnings.
Smart suggestions must connect to manual review check gates.
Always verify baseline metrics before committing AI outputs.

Lesson 2 of 3 Workshop 14 min

Enforce plan parameters

The operating routine is to administer access permissions, verify plan thresholds, configure fallback rules, and audit configurations. This keeps AI tools aligned with actual inventory, sales, or accounting status.

Before executing suggestions, check user permission rules, plan limits, fallback equations, configuration locks, and compliance logs. These safety reviews protect budgets, supply levels, and financial statements.

A certified operator can interpret logic values, read model explainability logs, or change algorithm parameters.

Governance action guide

Signal	Check	Action
Plan limit reached	Check plan definitions	Disable model recalculations or upgrade
Unauthorized edit attempt	User lacks settings permissions	Block action and alert system owner
Data source missing	Model fails to execute	Trigger fallback baseline calculation
Audit check completed	Settings remain locked	Save compliance validation report

Predictive decisions

Translate AI signals into reorder requests or hold commands.
Triage anomalies using historical transaction histories.
Set strict policy rules for auto-approving minor requests.
Ensure explainability reports are saved for audit purposes.

Lesson 3 of 3 Practice 14 min

Lock fallback behaviors

AI predictions and manual actions should leave proof. Useful evidence includes governance configs, permission change logs, fallback validation logs, and audit files, which is required to confirm that human approval occurred.

Management should review forecast accuracy monthly: wide variances or stale recommendations indicate settings adjustments are needed.

In practice, closure means AI access rules are active, settings remain locked against edits, and fallbacks are verified.

AI governance checklist

Permissions are configured

Plan limits are active

Fallback rules are locked

Audit checks are complete

Change history is saved

Insights validation

Check model outputs against actual historical records.
Validate that fallback settings remain locked.
Confirm user permissions restrict editing algorithm fields.
Ensure explainability files are linked to system changes.

| AWRA OpsHub

AI Governance

What you’ll learn

Course content

Set access permissions

Enforce plan parameters

Lock fallback behaviors

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More in this path

Workflow Builder Basics

Approval Workflows

Security & Data Protection

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