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Marketing Operations Practical insights

Agree who approves AI work across agency teams

Define responsibility for AI drafts, factual review, campaign activation and corrections when an agency and an in-house marketing team share delivery.

01 / 03Key connections
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Shared delivery does not mean shared accountability for every decision. An agency and an in-house team need to know who can approve a claim, activate a campaign and authorize a correction. Write those decisions into the working agreement before AI increases production volume.

Map decisions rather than job titles

Follow one campaign from brief to live placement. For each consequential decision, identify who prepares the work, who has authority to approve it and who executes the approved action. Keep one accountable owner where ambiguity would delay a response. NIST’s risk framework offers a basis for considering responsibility across the AI lifecycle. [1]

02 / 03From insight to approach
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Define the approval evidence

An agency may draft copy using supplied product facts while the client retains authority over commercial claims. Specify the exact version and evidence the client reviews. Approval of a concept should not silently become approval of every later variant generated from it.

Cover changes and emergencies

In an illustrative campaign, a platform may reject an ad shortly before launch. Decide whether the agency can make a limited wording correction, which changes require renewed client approval and who can pause the campaign. Record a backup contact for times when the primary decision maker is unavailable.

03 / 03From evidence to decision
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Review the agreement through actual work

After the first delivery cycle, inspect delayed decisions, duplicate reviews and corrections with unclear ownership. Adjust the map to match the process and contractual responsibilities. Maintain it when the scope changes. A useful agreement makes the next action obvious to the people doing the work and preserves a clear record of who authorized what.

Sources and evidence
  1. NIST generative AI risk profile

Sources checked on 4 October 2026. Proposed workflows and hypothetical examples are editorial analysis.

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