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Business Value Assessment

The organization shall assess AI business value to evaluate the contribution of AI systems to organizational objectives and return on investment. Assessment shall include evaluation of business outcomes, assessment of cost-effectiveness, and identification of value improvement opportunities, conducted at appropriate intervals with results documented.

Requirement ID
AIG-006-R006
Parent Control
Domain Code

Requirement Details & Guidance

AIG-006-R006 · AIG-006
Why This Requirement Exists

This requirement exists to assess AI contribution to business objectives, outcomes, cost-effectiveness, and return on investment, ensuring AI systems are governed, secured, assessed, and operated in a controlled and accountable manner.

Implementation Guidance

Establish and document the process to assess AI contribution to business objectives, outcomes, cost-effectiveness, and return on investment. Define accountable roles, scope, required inputs and outputs, assessment or approval criteria, decision thresholds, review frequency, monitoring, reporting, exception handling, evidence requirements, and escalation paths. Apply the process across the AI lifecycle and integrate it with information security, privacy, risk, compliance, change management, incident management, and business governance as applicable.

Practical Application & Audit Readiness

AIG-006-R006 · Field Guidance

Business Examples

    Common Audit Findings

      Common Mistakes

        Best Practices & Ownership

        AIG-006 · Recommended Approach

        Recommended Practices

          Typical Owner

          Cross-Mapped Regulatory Frameworks

          Harmonized Control Mappings
          FRAMEWORK CODE FRAMEWORK NAME & DETAILS FAMILY / CATEGORY VERSION ACTION
          COBIT2019 COBIT COBIT is an enterprise governance and management f... Clause: 4.2 – Align, Plan and Organize (APO) > APO13 – Managed Security > APO13.02 – Define and manage an information security and privacy risk treatment plan.
          COBIT
          Governance Framework
          v2019 View →
          NIST-CSF NIST Cybersecurity Framework (CSF) The NIST Cybersecurity Framework provides high-lev... Clause: GV.RM – Risk Management Strategy | GV.RM-03 – Cybersecurity risk management activities and outcomes are included in enterprise risk management processes ID.RA – Risk Assessment | ID.RA-07 – Changes and exceptions are managed, assessed for risk impact, recorded, and tracked PR.PS – Platform Security | PR.PS-06 – Secure software development practices are integrated, and their performance is monitored throughout the software development life cycle
          NIST
          Framework
          v2 View →
          NIST80053 NIST SP 800-53 NIST SP 800-53 Rev. 5 presents control families, i... Clause: 3.13 – PROGRAM MANAGEMENT; PM-6 – MEASURES OF PERFORMANCE
          NIST
          Control Catalog
          vRev. 5 View →
          SOC2 SOC 2 Trust Services Criteria SOC 2 is an independent examination and reporting... Clause: CC4.1 – Monitoring of Controls – COSO Principle 16: The entity selects, develops, and performs ongoing and/or separate evaluations to ascertain whether the components of internal control are present and functioning. PI1.1 – Processing Integrity – The entity obtains or generates, uses, and communicates relevant, quality information regarding the objectives related to processing, including definitions of data processed and product and service specifications, to support the use of products and services.
          AICPA
          Audit Framework
          vLatest View →

          Parent Domain Context

          Governance Domain Baseline
          Domain Name:
          AI Governance & Model Security (AIG)
          Purpose:
          To ensure the trustworthy, secure, and ethical development and deployment of AI.
          Scope:
          Enterprise

          Parent Control Context

          Control Baseline
          Control Name:
          AI Performance Monitoring & Continuous Improvement (AIG-006)
          Summary:
          Establishes AI performance monitoring including performance metrics, drift detection, model validation, accuracy monitoring, reliability monitoring, business value assessment, performance reporting, model optimization, review, and continuous improvement.
          Business Description:
          Establish and maintain processes to monitor AI model performance, effectiveness, drift, reliability, business value, operational health, and continuous improvement throughout the AI lifecycle.