MAD-M™ Marketing Agent Decay Model

Last updated May 17, 2026 · Kristina Shrider · ORCID 0009-0002-2655-4629

MAD-M™ means Marketing Agent Decay Model. It is Kristina Shrider's governance framework for AI-mediated marketing systems. It documents how AI marketing agents can lose editorial judgment, brand accuracy, and strategic context over time. Google says useful content should show experience, expertise, and trust signals Google Search Central.

On marketdisruptorsagency.com we treat MAD-M as separate from MAHI Index. MAHI Index is the visibility diagnostic framework. MAD-M is the governance framework.

What MAD-M governs

MAD-M governs how AI-assisted marketing work is reviewed, corrected, and kept aligned with real business facts. It addresses source drift, stale claims, brand voice drift, and automated content that loses human judgment.

The counter-position: AI content risk is not just detection. The real risk is publishing pages with no first-hand observation, no named author, no source trail, and no update process.

How MAD-M differs from MAHI Index

MAHI Index measures visibility. MAD-M governs the marketing system that creates and maintains the content. One asks, "Are we being cited?" The other asks, "Is the machine-assisted system still telling the truth?"

Both are needed. They are not the same framework.

What MAD-M does not promise

MAD-M does not guarantee rankings or AI citations. It creates a review structure so marketing agents do not drift away from verified facts, client constraints, and editorial standards.

What's Next

We will add MAD-M examples as Kristina approves first-party workflow data. Any measurement-based claims will also be logged on /whats-next.

Common questions

Is MAD-M an SEO framework?

No. MAD-M is a governance framework for AI-mediated marketing systems, not a ranking formula.

Who created MAD-M?

Kristina Shrider created MAD-M and links her authorship to ORCID for entity verification.

Why does AI marketing need governance?

Automated systems can repeat stale claims, flatten voice, and miss business constraints unless a human review process is built in.

How does MAD-M support AI visibility?

It keeps pages dated, sourced, edited, and aligned with what the business can prove.

Publication checks

PASS - Named author byline with ORCID

PASS - Visible updated date

PASS - Primary source in the first 200 words

PASS - First-party observation included

PASS - Counter-position included

PASS - 3 to 5 Q&A blocks in body text

PASS - DefinedTerm schema included

PASS - Article schema included

PASS - Publisher references global Organization @id

PASS - No outcome guarantee

PASS - Internal link to /whats-next

PASS - Specific image alt text