Marketers Love AI. Shoppers? Not So Much
AI is getting the spotlight in marketing meetings. But the customer's experience tells a different story. A new report from Invoca shows a clear gap between marketing optimism and what buyers actually feel.
Here's the headline: 85% of marketers believe consumers feel positive about AI interactions. Only 37% of consumers agree. And just 30% trust AI to solve complex problems.
The gap, by the numbers
- 85% of marketers say consumer sentiment on AI is positive
- 37% of consumers report positive experiences with brand AI
- 30% of consumers trust AI with complex issues
The findings come from interviews with marketers at 600+ B2C companies (100+ employees) across auto, financial services, health care, home services, insurance, telecom, and travel/leisure.
Why the disconnect exists
Most teams roll out AI to increase efficiency, then backfill the customer experience. That's backwards. Buyers judge your AI on one thing: did it make getting what they needed easier?
Common failure points: generic chatbots, no clear escalation to humans, poor handling of edge cases, and zero visibility into which AI interactions drive revenue vs. churn. Trust erodes quickly when someone feels stuck or misunderstood.
AI is becoming the new gatekeeper
As Acosta Group notes, "Generative AI tools are becoming the new gatekeepers of the shopper journey." That means your AI is often the first (and sometimes only) touchpoint. If it's clunky, you pay for it in lower conversion and higher support volume later.
What to do in the next 30 days
- Map your top 5 customer intents (by volume and revenue) and script AI flows for those only. Kill the rest for now.
- Add a visible "talk to a human" option within 2 steps for high-friction intents (billing, claims, healthcare, cancellations).
- Instrument every AI interaction: intent detected, resolution outcome, time to resolve, deflection rate, NPS/CSAT, and revenue attribution.
- Review 50 real conversations per week. Tag failure reasons. Fix one issue per week.
- Set confidence thresholds. If the model is unsure, route to a person. Don't fake certainty.
Build a practical AI CX playbook
- Guardrails: define what AI can say, what it can't, and when to escalate.
- Data hygiene: keep prompts and context up to date; remove stale offers and policies.
- Compliance: log decisions, mask PII, and document consent flows.
- Fallbacks: pre-write human-ready summaries so handoffs are smooth.
- QA loop: weekly failure postmortems with marketing, product, and CX in the same room.
Metrics that actually matter
- Customer: first-contact resolution, CSAT/NPS after AI, repeat contact rate within 7 days
- Revenue: conversion rate by intent, average order value, retention/save rate in service flows
- Efficiency: human handoff rate, handle time, cost per resolved interaction
If these numbers don't improve, the AI isn't helping customers - or your pipeline.
Strategy that tracks with reality
Invoca's takeaway is direct: marketing leaders need less ambition theater and more proof that AI improves the buyer's real experience. Anchor your roadmap to observed behavior, not internal hype. Ship smaller, measure faster, and promote features only after the data proves they work.
Next steps and resources
- Level up your team's AI skills for practical, revenue-facing use cases: AI Certification for Marketing Specialists
- Explore role-based training paths: Courses by Job
Bottom line: ship AI that reduces friction, proves value in the numbers, and gets out of the customer's way. Optimism is fine. Evidence is better.
Your membership also unlocks:
