What Qualtrics Found

In Q3 2025, Qualtrics XM Institute surveyed more than 20,000 consumers across 14 countries as part of their 2026 Consumer Experience Trends Report. The scope was broad: eighteen industries, every major economy, consumers across every age and income group.

The headline finding on AI was brutal.

Nearly one in five consumers who had used AI for customer service reported no benefit from the experience at all. That failure rate is almost four times higher than for AI use in general. For context, consumers ranked AI-powered customer support as the worst AI application for convenience, time savings, and usefulness across every category tested. Only "building an AI assistant" ranked lower.

Isabelle Zdatny, head of thought leadership at Qualtrics XM Institute, was direct in the report: "Too many companies are deploying AI to cut costs, not solve problems, and customers can tell the difference."

That single sentence captures the entire failure mode.

Why This Matters More in B2C Than B2B

The Qualtrics numbers represent averages across all industries. But for B2C brands handling high ticket volumes in fintech, e-commerce, and digital subscriptions, the failure cost is asymmetric.

In B2C, a broken support interaction is not just a service failure. It is a loyalty event.

Qualtrics found that consumers who choose a brand for great customer service report 92% satisfaction and 89% trust. Those who choose on price alone sit at 87% and 83%. That 5-point gap in trust compounds across hundreds of thousands of interactions per year.

Meanwhile, only 29% of consumers now share negative feedback directly with companies after a bad experience, an all-time low, down 7.5 points since 2021. Another 30% say nothing at all. That means when AI fails a customer today, you are almost certainly not hearing about it. You are just watching retention decay.

For B2C CX leaders operating at scale, the failure modes are specific:

• A refund mishandled by an AI agent that fabricates confirmation leaves a customer disputing a charge they never received.

• A subscription cancellation handled without policy enforcement exposes the brand to consumer rights violations.

• An order inquiry that loses context after three turns leaves a customer with a delayed shipment and no answer, at exactly the moment they are deciding whether to reorder.

Each of these destroys the consumer relationship faster than no AI at all.

The Real Problem: Deployment Philosophy, Not Technology

The Qualtrics report lands on a diagnosis that aligns exactly with what the Salesforce CRMArena-Pro benchmark found earlier this year: the technology itself is not the primary failure. The deployment philosophy is.

Most AI support agents are deployed as cost-reduction tools. They are measured on deflection rate, not resolution rate. They are built to handle volume, not to complete tasks. And they are deployed without the memory, policy enforcement, or audit infrastructure to function reliably in regulated B2C workflows.

The result is a familiar pattern. The agent answers questions until the conversation gets complicated, then either loops, escalates poorly, or fabricates a response. The customer leaves the interaction worse off than if they had emailed a human, and they rarely complain directly, so the brand never connects the AI deployment to the churn signal weeks later.

Here are the specific failure modes driving the numbers:

Context Loss in Multi-Turn Conversations

Most AI agents reset context after a few turns. A customer verifying an account, asking about a charge, and then requesting a partial refund across four messages is a completely standard support flow. An agent without persistent memory treats each message as a new conversation. The interaction fails, but the deflection metric still records it as "handled."

Hallucinated Actions and False Confirmations

Without a deterministic execution layer, LLM-based agents will generate plausible-sounding confirmations for actions they did not perform. In fintech environments, this is not a quality issue. It is a compliance and fraud risk. A customer told their dispute was filed when it was not has legal standing in most markets.

Privacy and Data Exposure

The Qualtrics report found that 53% of consumers fear misuse of personal data in AI interactions, up 8 points year over year. This fear is well-founded. Agents without proper tokenization and output scrubbing routinely surface masked account numbers, internal record IDs, and escalation notes during standard conversations. In GDPR and PCI-regulated environments, this is a violation, not just a bad experience.

No Audit Trail

When a regulator asks why an AI agent told a customer they were eligible for a refund they were not owed, or why a cancellation was processed without a required notice period, a flat conversation log does not answer the question. Without SHA-256 hash-chained action logs, every AI-involved support incident becomes a liability.

What B2C CX Leaders Should Actually Be Asking Vendors

The Qualtrics findings establish a new bar. The question for 2026 is no longer "can your AI handle our FAQ volume?" The question is:

• Does the agent maintain verified context across a full resolution lifecycle, not just a single session?

• Can it execute policy-aware actions through typed API integrations, not just generate text about those actions?

• Is every decision logged with a traceable audit chain that satisfies GDPR, PCI-DSS, and internal compliance review?

• Can you cap inference costs and define hard fallback behaviors at the workflow level?

• Can the system run within your VPC to maintain data sovereignty?

If the answer to any of those is no or "we're working on it," the agent is not enterprise-ready for B2C support at scale.

One Alternative: How Fini Is Built for This Environment

Fini was designed from the ground up for the exact environment the Qualtrics report describes. High volume. Regulated data. Customers who will not tell you when something goes wrong, but will quietly churn.

The architecture is different at a structural level.

Fini does not replace your support agents. It runs inside your existing Salesforce, Zendesk, or Intercom environment with scoped permissions and defined fallback paths. Your human agents handle what the AI cannot, with full context transferred automatically.

A Real-World Result: 600,000+ Tickets in Fintech

A US fintech used Fini to automate refund processing, plan change flows, and KYC verification across their support operation.

The result on trust metrics was notable. Post-deployment sentiment data showed a 2x increase in positive phrases like "felt cared for" in follow-up surveys. Retargeting click-through rates on the same customers improved 17% in the quarter following deployment.

This aligns directly with what Qualtrics identified: customers who experience genuinely good service, not deflection masquerading as support, become the highest-trust, highest-loyalty cohort in your base.

What a Deterministic Refund Flow Looks Like

To make this concrete, here is what a structured cancel-and-refund workflow looks like in Fini's Action Graph:

trigger: user_requests_cancel
steps:
  - RAG_QUERY: subscription_status
  - IF active:
      - CREATE_CASE: type=cancel_refund
      - ISSUE_REFUND: provider=stripe amount="{{subscription.amount}}"
      - UPDATE_SUBSCRIPTION: status=canceled
      - INFORM_USER: "Refund issued. You're all set."

No generative guesswork. No prompt chain that can hallucinate a confirmation. Every step is logged, policy-bounded, and auditable. This is what "resolving the issue" actually means, versus deflecting it.

The Bottom Line for 2026

The Qualtrics report is the second major third-party study this year, following Salesforce's CRMArena-Pro benchmark, to confirm the same structural problem: AI support as it is commonly deployed is not working, and customers are paying attention.

The companies that will build durable B2C loyalty in 2026 are the ones who stop measuring AI on deflection and start measuring it on verified resolution, trust scores, and audit compliance. That requires a different kind of architecture.

Fini deploys inside your existing stack in under 10 days. No replatforming. No hallucinations on critical workflows. And a per-ticket cost that runs a fraction of what generic LLM agents currently cost.

If you want to see what structured, auditable AI support looks like in practice, across Salesforce, Zendesk, or Intercom, book a 30-minute walkthrough. Real flows, real data, real resolutions.