Establishing accountability protocols

Clear accountability keeps quality high as AI does more work. Good protocols ensure humans stay responsible even when AI helps significantly.

• Start with clear ownership. Use RACI matrices: who's Responsible for accuracy? Accountable for results? Consulted on content? Informed of changes? This clarity prevents "the AI did it" excuses when problems happen.
• Keep quality standards consistent. Don't accept lower quality just because AI helped. Set clear criteria: accuracy levels, brand voice, legal compliance. These standards apply whether humans write alone or AI assists.
• Create clear paths for reporting AI problems. When someone spots bias or errors, they need to know where to report. Quick responses build trust.
• Document incidents to prevent repeats. Include AI issues in normal incident handling.
• Record why you made decisions, not just what you decided. For big choices AI influenced, document: why you trusted its analysis, other options considered, how humans shaped the outcome. This protects you and helps teams learn.