You, Me, and Your DAM's MCP: Optimizing Your DAM for the AI-Ready Brand

Observations

• Every brand is seeking to be AI-ready. Almost no one is answering the harder question underneath it: ready with what?
• The organizations seeing real returns from AI in content operations are not the ones who switched on the most features.They are the ones whose metadata and governance foundations were in good enough condition that AI had something trustworthy to work from. That is the whole game. 
• Governance is now the difference between AI that multiplies your team's output and AI that scales your mess at machine speed.

Jake Athey has spent 22 years in DAM, as a vendor and evangelist. In this session he will make the case that the unglamorous work DAM Managers have always done determines exactly how much value a brand gets from AI - and he will give you the language to prove it to the budget holders.

Delivering

• A capability model for deciding where AI belongs inside, outside, and alongside your library.
• The CRIT framework for putting any large language model to work on your governance backlog. 
• How to score your own metadata's AI-readiness, and which gaps to close first. 
• A plain, robust explanation of MCP, the Model Context Protocol, the open standard that lets AI agents query a governed library in context rather than guessing from whatever files they can reach.

Promises

No demo. No roadmap theater. Just a practitioner's argument for why good DAM practice is now a competitive position, and what to do about it before the agents arrive.

Key Takeaways

• Make the business case in your stakeholders' language. Connect governance work to speed to market, brand consistency, and rights exposure, so AI-readiness stops being an IT project and becomes a leadership priority.
• Know where AI belongs before you turn anything on. A capability model for sorting AI inside your DAM, outside directing it, and alongside it through MCP and agents, so investment goes where it generates the greatest returns.
• Put AI to work on governance immediately. The CRIT framework turns every LLM into a working partner on schema design, audits, and adoption plans, using data you can export today.
• Find out where you stand. How to assess your metadata's AI-readiness honestly, and which two or three gaps to close first for the largest gain.
• Understand what MCP changes, and what it does not. What AI agents can reliably do with a governed library right now, what still needs a human, and how to run a low-risk pilot before broader rollout.