Consumers are warming to AI - if it improves the experience
For years, marketers feared that showing AI would hurt authenticity. New data says the opposite. According to Optimove's 2025 AI Marketing Trust and Engagement Report, 57% of consumers trust brands more when AI is part of the experience.
Most people already assume AI is in the mix. The report says 87% believe they can tell when a company uses it. Only 5% report strong distrust. The message is simple: use AI, show your work, and make it useful.
What this means for marketers
- AI must add value: 32% of consumers value AI when it saves time; 28% say it signals the brand understands their needs.
- AI can drive revenue: 73% have purchased based on an AI recommendation, and more than half have done it more than once.
- Clarity beats secrecy: People aren't anti-AI. They're anti-waste, anti-creepy, and anti-confusion.
Where trust breaks down
- Data privacy: 34% are concerned about how their data is used.
- Over-personalization: 24% dislike experiences that feel too intimate or invasive.
- Bad recommendations: 18% say inaccuracies damage the experience.
This is the "creepy zone": intrusive, irrelevant, or overly familiar automation. The fix isn't less AI. It's better execution.
The rise of the "positionless marketer"
The report points to a new operator: the positionless marketer. This person blends analytics, creative, and ops to make AI useful and trustworthy.
- Keep humans in the loop: Review critical outputs, especially in regulated or brand-sensitive moments.
- Prioritize transparency: Label AI assistance and explain "why you're seeing this."
- Give customers control: Easy ways to set preferences, opt out, and correct data.
Playbook: build trust with AI, step by step
- Expose the value: "We use AI to speed support responses" or "to refine recommendations." Pair the claim with an outcome (faster, fewer steps, clearer choices).
- Disclose at the right moments: On chatbots, in product feeds, near recommendations, and in privacy settings.
- Offer control, not just consent: Preference centers that let users set topic depth, frequency, and channels. Let them dial personalization up or down.
- Guardrail personalization: Avoid sensitive inferences (health, finances, children). Respect context and recency.
- Explainability in UX: Add "Why this?" with a plain-language reason and a way to improve future results.
- Quality loops: Collect thumbs up/down, hide/replace actions, and allow quick profile edits. Retrain or re-rank frequently.
- Data hygiene: Minimize collection, set retention windows, and document vendors. Align with GDPR principles.
- Risk management: Use an internal rubric (impact, likelihood, mitigation) and map it to the NIST AI Risk Management Framework.
Practical use cases that hit the sweet spot
- Smarter recommendations: Blend collaborative and content-based signals. Cap repetition. Rotate exploration for discovery.
- Time-saving support: AI triage, instant summaries, and suggested replies with human approval for edge cases.
- On-site search that actually works: Semantic search, typo tolerance, and intent snippets. Always show filters and clear alternatives.
- Proactive service: Back-in-stock alerts, price-drop notifications, and order issue predictions with one-click fixes.
What to measure (and how to know it's working)
- Speed: Time to resolution, clicks to find, search success rate.
- Relevance: Rec CTR/CVR, dwell time on recommended items, hide/negative feedback rate.
- Trust: Privacy complaints, opt-out vs. opt-in rates, "Why this?" interactions, CSAT after AI-assisted moments.
- Revenue impact: Assisted conversions, AOV from AI-touched sessions, repeat purchase rate after AI recommendations.
A note on messaging: less story, more meaning
People don't need another brand bedtime story. They want meaning: save me time, reduce my risk, make my choice easier. Show the value in one line, then prove it in the experience.
Bottom line
Consumers are open to AI if it's useful, accurate, and respectful. Be transparent, keep humans close to high-impact decisions, and give customers control. That's how AI becomes a trust builder, not a liability.
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