Marketers think consumers like AI. Consumers don't.
There's a clear gap between what teams believe and what buyers feel. According to Invoca's "B2C AI Marketing Impact" report, 86% of marketers say AI improves the customer experience, while only 35% of consumers agree.
The disconnect widens on high-stakes issues. 49% of marketers think buyers prefer AI help for complex problems, but just 30% of consumers trust AI to resolve them. That gap can drain trust, inflate costs, and quietly hurt brand equity.
Why the gap exists
- Task mismatch: Brands push AI into complex, emotional, or high-risk scenarios where it struggles.
- Poor handoffs: Bots trap customers in loops and delay escalation to a human.
- Generic answers: LLMs sound fluent but lack account context and policy nuance.
- Low transparency: People can't tell what's automated, what's logged, or how to reach a person fast.
- Wrong incentives: Teams optimize for containment and cost, not resolution and trust.
Where AI actually wins
- Simple, clear intent: order status, store hours, account balance, basic FAQs.
- Structured workflows: password resets, appointment booking, shipment changes, easy returns.
- Proactive alerts: delivery updates, outage notices, renewal reminders.
Outside of these, default to human-first or hybrid support. Use AI to speed discovery and drafting, while a person finalizes decisions.
Fix this in the next quarter
- Segment by complexity: Label every AI touchpoint by risk (low, medium, high). Route high-risk issues to humans by default.
- Set hard escalation rules: Escalate on repeat contact, negative sentiment, blocked intents, or time thresholds.
- Offer one-tap "talk to a person": Don't bury it. Make the path obvious on every bot and IVR screen.
- Be upfront: Clearly state what the bot can and cannot do, what's recorded, and expected resolution times.
- Limit scope: Launch with the top 10 intents that drive volume and are easy to solve. Expand only after hitting target CSAT.
- Ground the model: Feed policy, product, and account context. Block out-of-policy responses. Log and review all edge cases weekly.
- Retrain on real data: Use resolved tickets and call transcripts to improve prompts, flows, and guardrails.
- QA like a hawk: Red-team the bot. Test adversarial prompts. Run weekly failure reviews with CX, Legal, and Compliance.
- Change incentives: Tie team KPIs to first-contact resolution, CSAT, and complaint rate-not just containment and cost.
- A/B test paths: AI-only vs human-only vs hybrid. Publish results internally and iterate.
Messaging that rebuilds trust
- "Here's how we use AI to help you, and here's what a human handles."
- "You can switch to a person anytime-no penalty, no restart."
- "We keep your data secure. Here's our policy in plain English."
Metrics that matter
- Resolution rate by path (AI-only, human, hybrid)
- First-contact resolution and time-to-resolution
- CSAT/CES split by issue complexity
- Deflection vs false containment (bot "solved" but repeat contact within 7 days)
- Escalation speed and agent handle time after bot handoff
- Complaint rate and churn correlation for AI-initiated cases
One-week checklist
- Audit all AI touchpoints; tag by risk and intent.
- Add a visible human handoff in every bot flow and IVR.
- Turn off AI for high-risk categories until metrics prove parity with humans.
- Instrument CSAT/CES and false containment for AI vs human paths.
- Run a 50-case red-team test; fix the top 5 failure modes.
- Publish a simple AI-use statement on your support pages.
The play is simple: use AI where customers want speed, and people where they want certainty. Align your roadmap to that reality and the trust gap shrinks.
If your team needs focused upskilling on practical AI for marketing, explore this certification: AI Certification for Marketing Specialists.
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