Why this is a leader's job, not a legal footnote
Your team already has the tools to write a hundred posts, generate a hundred images, and personalize a hundred emails before lunch. The bottleneck used to be production; now it is trust. When something goes out under your brand that is fabricated, undisclosed, or built on someone else's IP, the speed that felt like a win becomes the reason you are explaining yourself to a regulator, a client, or your CEO. This session gives you the vocabulary and the controls to lead that conversation - not to become a lawyer, but to know exactly what to insist on.
The brand risks AI creates 13 min live
Most AI-marketing damage is not exotic. It is six ordinary shortcuts, each one made cheaper and faster by AI, and each one landing under your brand name. The point of naming them is not fear - it is to build a single stack of controls that catches all six before anything ships.
LiveSix ways AI can quietly damage your brand7 min▶
None of these needs bad intent. They happen when a busy team lets a fast tool publish without a control in the way.
- Voice inconsistency at scale. One prompt, ten writers, ten slightly different brand voices - multiplied across every channel until the brand sounds like no one in particular.
- Hallucinated stats and claims. A confident, invented "73% of buyers..." dropped into an ad or landing page. Fabricated, citable-looking, and now a public claim you have to defend.
- Fabricated reviews and testimonials. AI-written "customer" quotes or incentivized reviews presented as genuine - now finable, not just embarrassing.
- Deepfakes and likeness. Synthetic voices or faces of real people (a founder, a customer, a celebrity) used without rights or consent.
- IP and copyright of AI images. Generated visuals that echo a protected style, character, or logo, or were trained on inputs you have no right to.
- Content-sludge diluting the brand. Volume for its own sake - thin, generic, everywhere - that trains your audience to scroll past your name.
Self-studyThe risk register - which risk bites which channel3 min read▶
A quick map from risk to where it usually surfaces, so you know where to look first.
| Risk | Where it usually shows up |
|---|---|
| Voice inconsistency | Social, email, and any channel with many hands on the keyboard |
| Hallucinated stats / claims | Ads, landing pages, sales decks, PR |
| Fabricated reviews | Review sites, testimonials, case studies, app-store copy |
| Deepfakes / likeness | Video, voiceover, influencer and spokesperson creative |
| IP / copyright of AI images | Ad creative, thumbnails, social graphics, packaging mockups |
| Content-sludge | Blog, SEO pages, high-volume social calendars |
The disclosure + regulatory map 9 min live
You do not need to cite chapter and verse. You need to know which bodies of rules apply to AI marketing and, in one line each, what they demand of your team. This is the plain-English leader view - enough to know when to say "we cannot ship that yet" and when to call your legal partner.
LiveFour regimes, one line each5 min▶
| Regime | What it demands of marketing |
|---|---|
| FTC (US) | Disclose material connections and AI-generated endorsements. No deceptive or fake reviews - now finable. Do not "AI-wash" (overstate the AI in your own product). |
| ASA (UK) | Ads must be legal, decent, honest, and truthful. Substantiate claims, make ads identifiable as ads, and do not mislead - the same bar applies to AI-made creative. |
| GDPR / PDPA | Personalization needs consent and a lawful basis. Use first-party data for the purpose people agreed to; never paste customer PII into consumer AI tools. |
| IP + copyright / likeness | AI images and voices can infringe copyright or a person's likeness. Use rights-clear inputs, review outputs, and get consent for any real face or voice. |
Notice the through-line: every regime is really asking the same thing - be honest, be permissioned, and own what you publish. Your controls satisfy all four at once.
Self-studyWhy the map keeps moving - and how to stay current2 min read▶
AI regulation is one of the fastest-moving areas in marketing law. Specifics shift quarter to quarter - what counts as adequate disclosure, which practices draw fines, how AI-generated content must be labeled.
- Treat this map as a snapshot, not scripture. Re-verify the disclosure specifics before any major campaign.
- Assign an owner. Someone on your team should track FTC/ASA and privacy guidance the way they track platform ad-policy changes.
- When in doubt, disclose and substantiate. Over-disclosure rarely hurts the brand; under-disclosure is where the fines live.
The brand-safety stack you need 13 min live
This is the leader's version of what the builder track wires into every workflow (session b10). Six controls, stacked so that nothing your brand publishes skips a layer. You do not run these yourself - you insist they exist and you know when one is missing.
LiveThe six controls, bottom to top6 min▶
- Brand-voice guide the AI must follow. Not a nice-to-have - the actual prompt input. If the AI does not have your voice, it will invent one.
- Claim-verification step. Every stat, comparison, and proof point checked against a real, named source before it ships. No source, no claim.
- Disclosure policy. A simple rule for when and how you label AI-generated endorsements, testimonials, and material connections.
- Approved-tools list. Which tools are cleared for which data, so nobody has to guess whether a customer list can go into a free chatbot.
- Human sign-off. A named person approves before publish. The buck stops with a person, not a model.
- Review cadence. A standing check that the controls are actually being used - and updated as the rules move.
The fake-review crackdown. Regulators have moved from warning letters to fines for AI-generated and incentivized reviews presented as genuine. The teams that stayed clear were not lucky - they had a claim-verification step and a disclosure policy in the way, so the shortcut never reached publish.
LiveMake brand-review a badge of honor, not a bottleneck3 min▶
The failure mode is a stack everyone routes around because it feels like the "no" department. Your job as a leader is to make passing review feel like a mark of quality, not a punishment.
- Make the controls fast. A one-page checklist beats a committee. Speed is what earns compliance.
- Celebrate the catch. When someone flags an invented stat before it ships, that is a win to name in the team meeting.
- Own it visibly. When you sign off, you are staking your credibility - say so, so the team sees the standard is real.
Brand-safety + disclosure gap-scan ◐ 30-40 min
Turn the stack into a to-do list for your own team and your agency. Do the scan first, then take the questions into your next vendor conversation.
- Score the six risks. For each of the six brand risks in Part 1, mark it green (controlled), amber (partly controlled), or red (exposed) for your team today. Be honest - amber and red are where the work is.
- Check the six controls. Walk the brand-safety stack top to bottom. Which controls exist in writing? Which are "in someone's head"? Which are missing entirely?
- Name the owners. For every missing or amber control, write one name next to it. A control without an owner is a wish.
- Draft the one-pager. Combine the above into a single brand-safety + disclosure checklist - short enough that a busy marketer will actually use it.
Official sources covered
Taught from public regulatory guidance and brand-canon material. This is a leader's plain-English digest, not legal advice - re-verify specifics with your legal partner before a major campaign. This session covers:
Three questions before you go 🎯 ◐ 90 seconds
1 · What do the FTC and ASA broadly require of AI-assisted marketing?
Both regimes demand honesty: disclose material connections and AI-generated endorsements, and do not deceive. Fake and incentivized reviews presented as genuine are now finable, not just frowned upon.
2 · Why do AI-generated images carry IP and likeness risk?
AI visuals can reproduce copyrighted styles, characters, or logos, and can render a real person's face or voice without permission. Use rights-clear inputs, review outputs, and get consent for any real likeness.
3 · Why does a claim-verification step matter in the brand-safety stack?
AI produces plausible-sounding numbers with no lookup behind them. Checking every stat and comparison against a real, named source is what stops a fabricated claim from reaching thousands of people under your brand.