AI without governance is like driving without a steering wheel: speed increases, but direction and control do not. AI-specific governance directs and oversees how artificial-intelligence systems are adopted and used through policies, processes, and controls. section 9.3.4
For generative marketing, governance must address several forms of organizational risk:
- Legal risk: determine whether models may use company datasets and whether the organization accepts intellectual property generated by AI. section 9.3.4
- Regulatory and privacy risk: assess whether providers handle personally identifiable information and qualify as subprocessors under GDPR or similar rules. section 9.3.4
- Ethical risk: examine whether AI behavior introduces bias that conflicts with organizational values. section 9.3.4
- Operational risk: plan for model costs at scale, employee training, and concerns about job loss or redundancy. section 9.3.4
A practical governance approach begins before adoption:
- Consult compliance and security teams before sending company data to third-party providers such as OpenAI or Google Cloud. section 9.3.4
- Consult legal teams about generated intellectual property and document whether providers may process personally identifiable information. section 9.3.4
- Add human oversight as marketing tools become more autonomous, with explicit attention to the human-AI user experience. section 9.3
- Keep teams current through training and address workforce concerns as part of adoption planning. section 9.3.4
A governed marketing deployment
Before using a generative AI provider to personalize campaigns, a marketing team checks privacy and security requirements, determines whether the provider is a PII subprocessor, reviews intellectual-property implications, and establishes human review for autonomous decisions. section 9.3.4 section 9.3
