The ai-readiness blog
The Definition of Done for AI Drafts in Regulated Marketing
4:47 p.m., and the draft is “done”
On paper, it’s a good day.
The Director of Content Ops has an email draft ready for Monday. The PMM has already tweaked the subject line.
The brand lead left a 👍 in the thread.
AI helped the writer move faster than usual.
Then MLR returns it.
Not with a few nits. With a rewrite.
A phrase that sounded harmless now reads like an implied claim. A sentence that “felt on-brand” doesn’t match approved terminology. The risk context is technically present, but not where the reviewer expects to see it. Someone asks for a citation. Someone else asks which version they’re reviewing.
By 5:30, the team is back to the same old loop: more comments, more versions, more “FINAL_v7” energy—even though they “used AI.”
This is the moment most teams misdiagnose the problem. They think they need a better prompt.
They don’t.
They need a Definition of Done.
This is the moment most teams misdiagnose the problem. They think they need a better prompt.
They don’t.
They need a Definition of Done.
What changed: AI didn’t break MLR. It exposed your handoff contract.
A Definition of Done (DoD) is old news in software. It’s simply the shared criteria that make work “complete.”
But regulated marketing has a twist: “complete” doesn’t mean written. It means reviewable—bounded, substantiated, traceable, and packaged so reviewers can validate rather than rewrite.
That matters more now for three reasons:
Review is becoming the highest-value AI battleground
Life sciences platforms are increasingly positioning AI’s biggest impact around MLR workflows, not just drafting.
If your drafts arrive “almost ready,” you don’t get speed. You get a more congested bottleneck.
Enforcement is drifting toward a simple theme: don’t mislead
Even amid shifts in posture, the FTC has been consistent about targeting deceptive claims—especially false claims about what AI does.
For marketing teams, that translates into a practical operating rule: your process has to prevent overclaiming—about your product and about your AI-enabled workflow.
Discovery is changing, and “sourceworthy” structure is now a business requirement
Google’s guidance for AI features (AI Overviews / AI Mode) is clear about approaching inclusion thoughtfully as these experiences evolve.
At the same time, Google warns that generating pages “without adding value” can violate spam policy.
And recent reporting has raised concerns about AI-generated health summaries producing misleading information—exactly the kind of environment where clarity and evidence packaging matter.
So the problem isn’t that AI drafts are “bad.”
The problem is that your org lacks a shared answer to: What must be true before a draft enters review?
But regulated marketing has a twist: “complete” doesn’t mean written. It means reviewable—bounded, substantiated, traceable, and packaged so reviewers can validate rather than rewrite.
That matters more now for three reasons:
Review is becoming the highest-value AI battleground
Life sciences platforms are increasingly positioning AI’s biggest impact around MLR workflows, not just drafting.
If your drafts arrive “almost ready,” you don’t get speed. You get a more congested bottleneck.
Enforcement is drifting toward a simple theme: don’t mislead
Even amid shifts in posture, the FTC has been consistent about targeting deceptive claims—especially false claims about what AI does.
For marketing teams, that translates into a practical operating rule: your process has to prevent overclaiming—about your product and about your AI-enabled workflow.
Discovery is changing, and “sourceworthy” structure is now a business requirement
Google’s guidance for AI features (AI Overviews / AI Mode) is clear about approaching inclusion thoughtfully as these experiences evolve.
At the same time, Google warns that generating pages “without adding value” can violate spam policy.
And recent reporting has raised concerns about AI-generated health summaries producing misleading information—exactly the kind of environment where clarity and evidence packaging matter.
So the problem isn’t that AI drafts are “bad.”
The problem is that your org lacks a shared answer to: What must be true before a draft enters review?
Our careful review process.
The five failure modes that show up right before “final” stops meaning anything
Back to our 4:47 p.m. team. The rewrite isn’t random. It’s predictable. It comes from the same set of failure modes that appear across regulated workflows.
1) Review becomes a rewrite cycle
When the draft doesn’t declare what it’s trying to do—and what it’s not trying to do—reviewers fill the gap. That’s how MLR becomes the place where messaging gets decided.
Practitioners describe this in plain language: approvals slow down when feedback is scattered and decision rights aren’t clear.
2) Voice drift at scale
AI can draft in your “tone.” It cannot enforce your voice system unless you’ve made one explicit: approved terminology, do/don’t patterns, and examples that constrain output.
You can see people wrestling with this in marketing forums: “brand consistency” becomes a moving target once AI enters the drafting loop.
3) Claims creep (the adjective that triggers a reset)
Most review churn isn’t caused by headline claims. It’s caused by implied meaning: “best,” “proven,” “only,” “prevents,” “ensures,” “eliminates.” Small words, big consequences.
Even outside formal regulatory settings, pharma/food marketers talk about how claim substantiation gates everything—and how slow things get when a claim crosses a line.
4) Template friction turns “minor edits” into major risk
Character limits and modular reuse encourage “tiny” rewrites. In regulated marketing, tiny rewrites can be new meaning.
This is why tier-based review keeps showing up as a throughput lever: it’s a way to focus experts on what truly changed.
5) Version confusion makes trust impossible
MLR asks a reasonable question: Which version is this? What changed? Where did this language come from?
When nobody can answer crisply, reviewers default to caution—because that’s their job.
If you’ve ever seen “final approval is a short ‘okay’… but it’s not clear which version they meant,” you’ve seen the problem.
Back to our 4:47 p.m. team. The rewrite isn’t random. It’s predictable. It comes from the same set of failure modes that appear across regulated workflows.
1) Review becomes a rewrite cycle
When the draft doesn’t declare what it’s trying to do—and what it’s not trying to do—reviewers fill the gap. That’s how MLR becomes the place where messaging gets decided.
Practitioners describe this in plain language: approvals slow down when feedback is scattered and decision rights aren’t clear.
2) Voice drift at scale
AI can draft in your “tone.” It cannot enforce your voice system unless you’ve made one explicit: approved terminology, do/don’t patterns, and examples that constrain output.
You can see people wrestling with this in marketing forums: “brand consistency” becomes a moving target once AI enters the drafting loop.
3) Claims creep (the adjective that triggers a reset)
Most review churn isn’t caused by headline claims. It’s caused by implied meaning: “best,” “proven,” “only,” “prevents,” “ensures,” “eliminates.” Small words, big consequences.
Even outside formal regulatory settings, pharma/food marketers talk about how claim substantiation gates everything—and how slow things get when a claim crosses a line.
4) Template friction turns “minor edits” into major risk
Character limits and modular reuse encourage “tiny” rewrites. In regulated marketing, tiny rewrites can be new meaning.
This is why tier-based review keeps showing up as a throughput lever: it’s a way to focus experts on what truly changed.
5) Version confusion makes trust impossible
MLR asks a reasonable question: Which version is this? What changed? Where did this language come from?
When nobody can answer crisply, reviewers default to caution—because that’s their job.
If you’ve ever seen “final approval is a short ‘okay’… but it’s not clear which version they meant,” you’ve seen the problem.
The turning point: the day the team stopped asking for “a better prompt”
In our story, the Director of Content Ops does something boring—and it works.
Instead of tweaking prompts, they schedule a 30-minute retro with three people:
They bring the last 10 review returns and sort every comment into one of four buckets:
That’s when they write their DoD. Not as a policy. As a preflight.
The framework: S-C-T-H
A regulated “Definition of Done” needs to do one thing: turn drafting into a controlled input for review.
Here’s the simplest frame that holds up under pressure:
S — Spine is locked
The asset has a clear message hierarchy (what’s in, what’s out), tied to audience and channel.
C — Claims are bounded
Every claim is tagged and supported (or explicitly marked as needing review).
T — Traceability exists
A reviewer can see what AI produced, what a human changed, and what sources support the language.
H — Handoff is packaged
Reviewers get a single submission that answers “what changed?” and “what do you want validated?”
This maps cleanly to how mature governance frameworks think: risk management as lifecycle work, not a one-time memo.
In our story, the Director of Content Ops does something boring—and it works.
Instead of tweaking prompts, they schedule a 30-minute retro with three people:
- one content lead,
- one brand stakeholder,
- one MLR reviewer.
They bring the last 10 review returns and sort every comment into one of four buckets:
- Spine (what the asset is trying to accomplish)
- Claims (what it’s allowed to say, and what needs evidence)
- Voice/terminology (how it must say it)
- Traceability/handoff (what changed, where it came from)
That’s when they write their DoD. Not as a policy. As a preflight.
The framework: S-C-T-H
A regulated “Definition of Done” needs to do one thing: turn drafting into a controlled input for review.
Here’s the simplest frame that holds up under pressure:
S — Spine is locked
The asset has a clear message hierarchy (what’s in, what’s out), tied to audience and channel.
C — Claims are bounded
Every claim is tagged and supported (or explicitly marked as needing review).
T — Traceability exists
A reviewer can see what AI produced, what a human changed, and what sources support the language.
H — Handoff is packaged
Reviewers get a single submission that answers “what changed?” and “what do you want validated?”
This maps cleanly to how mature governance frameworks think: risk management as lifecycle work, not a one-time memo.
Fly, little deliverable—you're free!
The preflight checklist (the artifact that makes the story end differently)
This is what our Director of Content Ops prints and puts above the “Submit to MLR” button.
A) Spine (2 checks)
☐ Audience + channel are explicit (HCP vs patient; email vs web vs social).
☐ The draft states the one sentence it’s trying to accomplish (“what we want them to believe/do”).
B) Claims boundaries (4 checks)
☐ A claims list is extracted (bulleted, plain language).
☐ Each claim is tagged: Safe / Risky / Needs-review (using your terms/claims sheet).
☐ Every non-obvious claim includes an evidence pointer (citation, approved source, internal reference).
☐ “Claims creep” phrases are removed unless explicitly approved (comparatives, absolutes, implied guarantees).
C) Voice + terminology (3 checks)
☐ Approved terminology is used consistently (especially outcomes language).
☐ The draft was constrained by examples, not adjectives (2–3 “on-voice” snippets).
☐ A quick voice rubric is passed (tone, reading level band, sentence length norms).
D) Traceability + handoff (3 checks)
☐ A change log says what changed since the last approved version.
☐ Reuse vs new language is marked (what’s inherited, what’s novel).
☐ The submission includes a short review intent: “Here’s what to validate; here’s what’s fixed.”
E) Tier suggestion (1 check)
This is where the team stops flooding MLR with “almost-ready” drafts—and starts delivering reviewable packages.
And it aligns with where the MLR world is already headed: risk-based tiering, similarity comparisons, and workflows that reserve expert attention for the highest-risk changes.
This is what our Director of Content Ops prints and puts above the “Submit to MLR” button.
A) Spine (2 checks)
☐ Audience + channel are explicit (HCP vs patient; email vs web vs social).
☐ The draft states the one sentence it’s trying to accomplish (“what we want them to believe/do”).
B) Claims boundaries (4 checks)
☐ A claims list is extracted (bulleted, plain language).
☐ Each claim is tagged: Safe / Risky / Needs-review (using your terms/claims sheet).
☐ Every non-obvious claim includes an evidence pointer (citation, approved source, internal reference).
☐ “Claims creep” phrases are removed unless explicitly approved (comparatives, absolutes, implied guarantees).
C) Voice + terminology (3 checks)
☐ Approved terminology is used consistently (especially outcomes language).
☐ The draft was constrained by examples, not adjectives (2–3 “on-voice” snippets).
☐ A quick voice rubric is passed (tone, reading level band, sentence length norms).
D) Traceability + handoff (3 checks)
☐ A change log says what changed since the last approved version.
☐ Reuse vs new language is marked (what’s inherited, what’s novel).
☐ The submission includes a short review intent: “Here’s what to validate; here’s what’s fixed.”
E) Tier suggestion (1 check)
- ☐ A proposed review tier is included with rationale (reuse/derivative vs net-new, claim novelty, audience risk).
This is where the team stops flooding MLR with “almost-ready” drafts—and starts delivering reviewable packages.
And it aligns with where the MLR world is already headed: risk-based tiering, similarity comparisons, and workflows that reserve expert attention for the highest-risk changes.
The second ending: what happens when “done” actually means done
Two weeks later, the same team ships a similar asset.
The difference isn’t that AI wrote better.
The difference is that MLR receives a package that is:
The comment count drops. More importantly, the type of comment changes.
Less: “Rewrite this section.”
More: “Confirm this interpretation.”
That is what healthy review looks like.
If you’re a Director of Content Ops, this is the win: predictable handoffs, fewer cycles, less rework.
If you’re a VP, this is the win: reduced risk from drift, and a system that scales across teams and agencies.
Two weeks later, the same team ships a similar asset.
The difference isn’t that AI wrote better.
The difference is that MLR receives a package that is:
- explicit about intent,
- bounded on claims,
- supported with pointers,
- traceable by version,
- clear on what’s being asked.
The comment count drops. More importantly, the type of comment changes.
Less: “Rewrite this section.”
More: “Confirm this interpretation.”
That is what healthy review looks like.
If you’re a Director of Content Ops, this is the win: predictable handoffs, fewer cycles, less rework.
If you’re a VP, this is the win: reduced risk from drift, and a system that scales across teams and agencies.
VP lens: what to fund, how to measure, what to de-risk
What to fund
Metrics that make it real
Track weekly, by asset type:
What to fund
- Narrative spine / message map (reusable backbone)
- Voice + terminology guardrails with examples (not “friendly,” not “bold”)
- Terms/claims sheet (safe/risky/needs-review + evidence pointers)
- Review checklist + intake gate (the DoD preflight)
- Optional: a prompt pack that references these assets so AI drafts can’t wander
Metrics that make it real
Track weekly, by asset type:
- Median time-to-approval
- # review cycles per asset
- Substantive rework rate (spine/claims/terminology rewrites)
- Post-approval change rate (should trend toward near-zero)
- Reuse rate (modules reused without claim change)
How to start in 2 weeks
Week 1: Write the DoD and pilot it on one asset type
Week 2: Turn it into a workflow gate and iterate with MLR
Week 1: Write the DoD and pilot it on one asset type
- Pick one asset type (e.g., HCP email, unbranded web page section).
- Build the DoD preflight above (keep it to ~12 checks).
- Create a starter terms/claims sheet (15–30 entries) for that asset type.
- Pilot on 5–10 drafts: no DoD, no submission.
Week 2: Turn it into a workflow gate and iterate with MLR
- Make the checklist a required intake step (checkboxes + required fields).
- Standardize the change log and “review intent” note.
- Add a tier suggestion rule (reuse + novelty + audience risk).
- Hold a 30-minute retro with MLR: update the DoD based on real misses.
FAQs
A definition of done is a shared checklist that states when an AI-assisted draft is reviewable and releasable—including claims boundaries, evidence pointers, approved terminology, and a clear change log.
It prevents review from becoming rewrite. When drafts arrive with claims tagged, evidence pointed, and changes declared, reviewers can validate risk and accuracy instead of reconstructing intent.
At minimum: audience/channel intent, claims list + risk tags, substantiation pointers, approved terminology checks, reuse vs new marking, a change log, and a “review intent” note.
Replace vague guidance (“on-brand”) with enforceable guardrails: approved terminology, do/don’t patterns, and examples that constrain the draft. Practitioner discussions consistently point to drift when guidelines aren’t explicit.
It raises the value of structure and clarity. Google’s site-owner guidance for AI features and its genAI content guidance both reinforce that content should add value and be understandable in these new experiences.
AI doesn’t need to slow you down. If you’re seeing review churn, voice drift, or “final” that isn’t final, CopyRx can help you put the guardrails in place—so drafts move faster and approvals stay predictable.
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