Govern Well, Move Fast: Consent, Compliance and Trust in AI Marketing

Consent, compliance and customer trust in an AI world

AI can create real value for marketing teams - but only if your data is clean, consented, and handled with care. That was the clear message from a September session at The MarTech Conference featuring enterprise AI consultant Anthony Coppedge, Alex Cash of OneTrust, and Adam Eisler from the IAB.

The takeaway: trust beats tactics. If customers can see how their data fuels your AI, they'll engage more. If they can't, they'll leave.

The data shift marketers must plan for

Marketing data won't live only inside browsers and apps. As Cash noted, AI will interact through voice, wearables, and new interfaces. That changes how consent is captured and how context is honored.

Eisler framed it simply: it's not just how companies use AI - it's how AI uses companies. Coppedge added that customers want the "why" and the "how" of data use made visible. Show them, don't just tell them.

AI will also put data work directly into marketers' hands. As Melissa Reeve pointed out, teams that relied on analysts will get tools that collapse collection, processing, and analysis into the workflow.

Agile governance beats waiting for rules

Traditional governance is too slow. Coppedge pushed for living systems - think user-facing dashboards that show what data you collect, how it's used, and where it flows. Update them as models and use cases change.

Eisler cautioned against waiting for lawmakers. Companies that moved early on privacy earned a head start. Also, current laws already apply: states include opt-outs for targeted ads and require consent for sensitive personal data.

If you need a reference framework, the NIST AI Risk Management Framework is a strong starting point for building practical safeguards without stalling deployment. See NIST AI RMF.

Consent and the "unbaked cake" problem

Not all AI use is equal. There's a big difference between using a tool with an embedded LLM and training your own model. Cash urged teams to define which mode they're in for each use case and apply the right controls.

Consent breaks down once data trains a model. If a user withdraws, do you roll back the model? You can't un-bake a cake. That's why you need clear boundaries around what data is included in training vs. what's used only for inference.

Who owns governance? Everyone, with marketing at the table

Cash pushed for marketing to sit beside privacy, risk, and legal: govern well, move fast. Coppedge argued it can't be siloed. It needs a cross-functional group with clear accountability.

Reeve suggested a layered model: a top-level council, a middle layer to translate policy into practice, and AI leads on the front line to make decisions in real time.

The next 18 months: what to expect

• Legal and IP: Copyright and fair use debates will influence your tool choices. Eisler urged teams to define use cases and measure actual ROI versus hype.
• New marketer skill sets: Coppedge sees a hybrid pro - part data scientist, part ethicist, part storyteller - able to build trust and personalization at the same time.
• Agent-to-agent ecosystems: Cash flagged AI agents that will negotiate data access and automate workflows, reshaping how marketers acquire and use data.

Key actions for marketers

• Act now: Use existing privacy frameworks to guide AI programs.
• Make transparency tangible: Dashboards and plain-language explanations foster trust.
• Distinguish AI use cases: Deploying AI tools vs. training models requires different safeguards.
• Share responsibility: Governance is cross-functional, with marketing a core stakeholder.
• Skill up: Future marketers must blend data fluency, ethics, and storytelling.

Operational checklist to put this into practice

• Create an AI use-case inventory: tool-based inference vs. in-house training. Assign owners and risks.
• Stand up a privacy-by-design review for every new model or feature. Document data sources and consent basis.
• Implement a customer-facing data use page or dashboard that explains collection, use, sharing, and model involvement.
• Segment training data from activation data to reduce the "unbaked cake" risk. Where possible, use synthetic or aggregated data for training.
• Honor state privacy rights with a consistent signal approach. The IAB Tech Lab's GPP can help standardize consent signals across channels. Explore GPP.
• Measure lift with guardrails: track ROI, bias checks, data drift, and error rates alongside conversion and LTV.

Bottom line

AI success in marketing won't be decided by features. It will be decided by trust - earned through clear consent, fast governance, and visible value to the customer. Move now, keep it transparent, and evolve your playbook as quickly as your models learn.

If your team needs a structured path to build these skills, explore the AI Certification for Marketing Specialists from Complete AI Training. View the certification