AI ownership is the next PR headache-here's a workable path
Olivier Laouchez, co-founder of the transnational media group Trace, put it simply: AI learns from existing content and transforms it into new outputs. The friction point isn't the tech-it's ownership. Who holds rights to what the model produces? His proposal: assign a share of ownership or value from AI-generated outputs back to original creators or relevant stakeholders.
For PR and Communications teams, this isn't abstract. AI is already touching press materials, campaign assets, social content, and pitches. If ownership is unclear, you invite takedowns, disputes, and public pushback-often at the worst possible moment.
Why this matters now
Regulators and courts are circling, and platforms are changing rules on AI content disclosure and labeling. Two good benchmark resources: the U.S. Copyright Office's AI guidance and case tracker, and WIPO's work on AI and IP policy.
U.S. Copyright Office: AI Guidance
WIPO: AI and IP
What Laouchez's approach looks like in practice
- Share model: Allocate a portion of value from AI outputs to rights holders whose works informed training or prompts, where identifiable and appropriate.
- Attribution: Include credits in metadata and visible captions when creator signals are clear.
- Licensing: Prefer models, datasets, and stock sources with rights cleared; pay for enterprise terms that cover commercial use.
- Provenance: Keep a lightweight log of prompts, sources, and assets used so you can evidence responsible creation if challenged.
- Watermarking and labels: Use content credentials where supported and label AI-assisted assets to reduce confusion.
Action plan for PR and Communications teams
- Audit your AI use across press offices, social teams, and agencies. Identify where outputs could be considered "original creative."
- Update contracts and SOWs: Require agencies and freelancers to disclose AI use, confirm rights, and indemnify for IP claims.
- Pick approved tools: Use models and media libraries with clear commercial licenses and opt-outs respected.
- Set disclosure rules: Decide when and how to label AI-assisted content, then apply it consistently.
- Keep creation logs: Prompts, references, stock IDs, and human edits. It's your safety net in a dispute.
- Review checkpoints: Add legal/brand checks for high-visibility assets or sensitive topics.
- Plan for claims: Draft a fast-response playbook for takedowns or public creator complaints.
- Support creators: Build a policy for revenue shares or licensing when campaigns lean heavily on identifiable styles or datasets.
- Train the team: Short sessions on rights, acceptable prompts, and disclosure standards.
Messaging you can use
- "We use AI as a tool. People lead, review, and own the final decisions."
- "We respect creators. We license content, credit where appropriate, and pay for the media and models we use."
- "We keep records of our process to ensure accountability and transparency."
- "If we receive a credible claim, we act quickly to investigate and resolve it."
Risk signals to watch
- New court rulings on AI authorship and training datasets that shift what's considered permissible.
- Platform policy updates on AI labels, watermarking, and monetization.
- Industry standards for content credentials gaining traction with media partners.
- Spike in DMCA notices or creator coalitions targeting brands using AI-generated visuals or copy.
Tools and training worth considering
If your team needs a practical primer on safe, useful AI adoption for comms work, explore structured learning paths by job role.
AI courses by job role
AI certification for marketing and comms
Bottom line
Ownership will define how far brands can push AI in media and communications. Laouchez's shared-value idea offers a workable middle ground: use the tech, respect contributors, and document what you do. Get your policy, contracts, and messaging in place now-before your next launch puts it to the test.
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