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# Podcast Summary — “Redefining the CMS for the AI Era”

## Who’s talking

* **Host:** Barb Mosher Zinck (Content Matters podcast)
* **Guests:**

  * **Deane Barker** — Director of Strategic Engagement, Staffbase; author on content modeling and CMS.
  * **David Hillis** — CMO, Ingeniux; “tech optimist” focused on AI-powered web/customer experiences.

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## Core thesis

* **CMS is shifting from pages to *repository + context*.**
  AI lowers the cost of custom apps/interfaces, so the durable value of a CMS becomes the **content model** and the **rules/context** that govern how content is used.

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## Key ideas & arguments

* **AI as a magnifier:** It accelerates good practice—and bad—at scale.
* **Well-described, well-defended repo:**

  * *Described* = rich content model + human-readable intents/use rules.
  * *Defended* = constraints/permissions so AI (and editors) can’t corrupt core data.
* **Two-stage content model (proposed):**

  * **Systemic model** (inviolate): essentials that power site/app logic (e.g., title, date, slug).
  * **Editorial model** (flexible): fields AI/editors can add/modify safely (e.g., “author mood”).
* **Free-form vs structured:**
  Repeatable types (products, posts) need rigid structure; landing pages/marketing often require flexible, free-form blocks—hence governance tension.
* **UI is becoming a prompt:**
  Many interactions collapse to natural-language commands; differentiation shifts to the **logic/engine and training data**, not the chat box.
* **Context > prompt:**
  Effective AI work relies on persistent **context specs** (brand voice, templates, rules, taxonomies), not ad-hoc prompts.

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## Experiments & practices mentioned

* **SQLite “CMS” experiment (Deane):** Strict schema + AI-generated tools (UI, batch jobs, SSG) worked surprisingly well—*if* the schema and intent were explicit.
* **Schema “AI help text” (David/Ingeniux):** Attaching instructions/constraints to fields to guide AI for metadata and generation.
* **Orchestration via AI tools:** Using agents/MCP to read/write CRM/CMS via APIs, composing workflows outside the native UI.

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## Governance & risk

* **Model mutability risks:** Letting editors/AI change schemas can silently break downstream logic and invalidate content at scale.
* **Permissions & constraints:** Practical safeguard (even if you don’t adopt a formal two-stage model).

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## The future CMS/DXP

* **Separation emerging:**

  * **Context/Content layer:** repository, model, taxonomy, policies, governance.
  * **Experience/Generation layer:** AI-curated, personalized delivery and disposable UIs.
* **From “content management” to “context management”:** CMS as the organization’s **context memory** (voice, patterns, assets, models), sized to fit model context windows.
* **Unknown-unknowns:** Beyond Q&A, AI must **proactively curate** what users *don’t know to ask* (e.g., intranet alerts), reviving personalization with AI.

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## Skills for professionals

* **“Gist”/context engineering:** Encode intent, constraints, and usage stories so AI consistently produces on-brand, on-model output.
* **Brand/voice stewardship at scale:** Maintain a shared, living spec for tone, templates, taxonomies, and rules.
* **Design for “cognitive containers”:** Like responsive design for screens, ensure meaning survives across human and AI consumers.

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## Memorable lines & metaphors

* **Large sail, deep keel** (David): Embrace AI’s pace (sail) while anchoring in strong models, rules, and data governance (keel).
* **AI is non-deterministic:** If you can’t control output, **control the input** (context).
* **Prompt as UI; model as context:** The chat box is commodity—the **repository and model** are the moat.

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## Practical takeaways

* Treat your **content model as context**: augment fields with usage notes, limits, and intent.
* Enforce **permissions/constraints** on core fields; allow flexibility at the edges.
* Centralize **brand voice, templates, taxonomies** in a versioned, queryable store.
* Build **AI-curated experiences** for proactive delivery (unknown-unknowns), not just search/QA.
* Expect **disposable UIs**: generate task-specific interfaces on demand, anchored to the same governed repository.