Responsible AI in the Enterprise
Accountability, oversight, and controlled use
Outcome: Maintain human accountability for AI-assisted outcomes with practical oversight mechanisms.
Oversight expectations, decision authority boundaries, and bias-risk control framing.
Implementation Outcome
This course establishes responsibility and oversight for AI use without relying on abstract ethics. It clarifies decision authority, human accountability, bias risk controls, and escalation mechanisms in enterprise settings.
- Accountability mapping template for AI-assisted decisions
- Bias and fairness risk control checklist
- Escalation and exception-handling playbook
Controls & Evidence
- Designed for records retention, version control, and documented review cadence
- Supports internal control alignment and defensible oversight practices
- Produces an audit-reviewable evidence set suitable for internal audit request workflows
Data handling: No submission of sensitive or proprietary data is required to complete the program.
Risk Exposure
Lack of defensible policies or controls can result in significant, unmitigated enterprise risk exposure. This program addresses the risk areas most relevant to this capability.
- Unclear responsibility for AI-assisted outcomes
- Bias or fairness issues without detection controls
- Opaque decision logic in material workflows
- No escalation path when outputs are suspect
Deliverables
Accountability & Decision Authority Map
Defines who owns outcomes, approvals, and escalations.
Bias/Fairness Controls Checklist
Practical controls and documentation expectations for material decisions.
Oversight & Reporting Workbook
Cadence, KPIs, and evidence capture for executive review.
Exception Handling Playbook
How to pause use, escalate, and remediate issues.
Governance Lifecycle Integration
- Baseline: Establish policy-aligned use patterns and minimum control expectations across affected teams.
- Oversight: Assign accountable owners, decision rights, and escalation paths for AI-assisted activities.
- Monitoring: Define review cadence, metrics, and control checks aligned to operational reality.
- Documentation: Maintain version-controlled artifacts and evidence suitable for records retention and review.
- Audit Review: Enable internal audit and leadership review with traceable controls, decisions, and evidence.
Buyer Questions
Does this require sharing confidential data with the provider?
No. The program is designed for policy, controls, and safe-use practices. Participants can complete the program without submitting sensitive or proprietary data.
Who should attend?
Executives, risk, compliance, product owners
What evidence is produced for audit review?
Version-controlled artifacts (policy templates, oversight workbook outputs, and control-aligned documentation) suitable for internal audit requests and governance reviews.
How is it deployed?
On-demand delivery with enterprise licensing options. LMS and SSO integration can be included in rollout scoping.
How are artifacts maintained over time?
Artifacts are designed for version control and periodic review. Organizations can align updates to internal change management and records retention requirements.
Request Enterprise Pricing
For rollout scoping (seat counts, deployment model, LMS/SSO integration, and licensing options), request enterprise pricing and deployment scope.