In our first edition of Insights Roundtable, we invite AI-driven production workflow expert Steve Intrabartola,  Head of Production Russell Sharpe, CMO/CINO Edmond Handwerker to break down exactly what’s happening across the agency ecosystem, as they respond to current media and trends. From auditing bundled media deals to mastering true token economics, get the full picture on how to keep costs in check in our first off-the-cuff discussion exclusively for clients.

👉 [Listen to the 10-Minute Behind-The-Scenes Recording Here]

OR EMBED THE VIDEO HERE RACHEL

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CAN WE INSERT A LINE BREAK HERE, RACHEL?

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In the audio, you can hear the team talking a lot about "good governance," but they never really crack into what good governance looks like. We've got that here for you, as a fast resource for you and your teams.

What does good governance actually look like in practice?

To implement "Good AI Governance" effectively, we must move beyond abstract platitudes and treat AI as a core business service. This requires a balanced strategic framework: you must decide what to build in-house based on cost-effectiveness, privacy, and security, while simultaneously determining which components are better suited for external partners. 

Furthermore, your governance model should prioritize outcomes—focusing on end-result specifications rather than micromanaging the specific tools or processes used to get there.

To bridge the gap between high-level strategy and tactical execution, organizations should consider adopting three concrete rules for day-to-day operations:

Three Rules for "Good AI Governance"

1. Implement Comparative Bidding: Evaluate whether AI is the optimal solution for a specific task by running "dual bids"—comparing traditional production against AI-based approaches—to determine true cost-effectiveness and efficiency before commencing a project.
2. Enforce Revision Guardrails: To avoid the "token burn" caused by unstructured technology deployment, stick strictly to agreed-upon rounds of review. This prevents the endless, unstructured revision loops that rapidly inflate costs.
3. Mandate Contractual Transparency: Require full disclosure of all AI usage in contracts, including the specific platforms and versions employed. Governance should also demand a clear breakdown of AI versus human labor and explicitly define IP ownership for any generated assets.

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Need a checklist for your agency agreements?

With these principles established, the next step is ensuring your agency partnerships align with these standards. To help you translate these operational rules into enforceable contract language, use the following topline audit checklist for your agency agreements.

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Agency Agreement Audit Checklist

• Comparative Bidding Protocol
  
  • Does the contract mandate a "dual-bidding" process—comparing traditional production against AI-based approaches—to confirm cost-effectiveness before commencing a project?
• Revision Loop Guardrails
  
  • Are there strictly defined, capped limits on revision rounds for AI-generated work to prevent unstructured "token burn"?
• Contractual Transparency & Disclosure
  
  • Does the agreement require full disclosure of all AI platforms and software versions employed?
  • Is there a clear, required breakdown of AI labor versus human labor?
  • Is IP ownership for all AI-generated assets explicitly defined in the contract?

While adoption speed is top-of-mind for every brand, true efficiency comes from transparency and clear guardrails. Don't let hidden compute bills, unpriced AI packages, or uncontrolled prompt burn undermine your ROI.

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