When Customer Experience Collapses, AI Can Fix It

Customer experience didn’t “die” because customers stopped caring. It collapsed because support teams were asked to do the impossible: handle higher volumes, more channels, and angrier customers—often with the same headcount and outdated tools.

That’s why Ron Miller’s blunt framing—“It was a terrible year for customer service… customer experience… suffered.”—lands. When service breaks, “CX strategy” turns into a slide deck no one believes.

Here’s the upside: the failure of traditional customer service models is exactly why AI in customer service and contact centers has moved from “nice experiment” to “operational requirement.” The companies getting traction in 2025 aren’t using AI to dodge customers. They’re using it to answer faster, route smarter, assist agents in real time, and catch churn signals before they hit social media.

Why customer experience “died” (and why it felt sudden)

Customer experience didn’t disappear overnight—it accumulated debt, and the bill came due.

The pattern I keep seeing is simple: organizations invested heavily in acquisition and digital channels, then treated support as a cost center that could absorb infinite stress. When demand spikes (holiday surges, shipping delays, billing issues, product recalls, outage days), the system hits a wall.

The four forces that broke customer service

1. Volume inflation: Self-service portals and apps didn’t reduce contacts as much as promised. They often shifted contacts into more complex issues.
2. Channel sprawl: Email, chat, phone, in-app messaging, SMS, social DMs—many teams still run these like separate worlds.
3. Complexity creep: Subscriptions, bundles, returns, third-party logistics, fraud controls, and policy changes produce edge cases agents must decode.
4. Expectation gap: Customers expect “instant” because digital brands trained them to. A 48-hour email response now feels like abandonment.

When those forces combine, you get the modern symptom list: long hold times, robotic scripts, repeated identity checks, transfers, and “We’ll get back to you” that never closes the loop.

A simple truth: Customers don’t hate automation. They hate dead ends.

The real enemy: “contact center latency”

If you want a more practical label than “bad CX,” use this: contact center latency—the time between a customer needing help and the moment they feel meaningfully helped.

Latency shows up in obvious places (queue time) and hidden ones (three follow-up emails to clarify an address, a refund stuck in a manual approval queue, an agent searching five systems for order history).

AI earns its keep when it reduces latency across the whole service chain, not just at the front door.

Where latency hides (and how AI removes it)

• Before the contact: Customers can’t find the right answer → AI-powered search and knowledge retrieval makes help content actually usable.
• At the first message: Intake is vague → AI can ask the right clarifying questions and capture structured data (order number, SKU, reason codes).
• During the conversation: Agents hunt for policies → agent-assist surfaces the right steps, refund rules, and exception handling.
• After the conversation: Follow-ups fall through → automated summaries, next-step workflows, and reminders reduce reopen rates.

The goal isn’t “deflection at all costs.” The goal is resolution with fewer minutes wasted.

What AI can do right now in a customer service operation

AI is already strong in four areas that map directly to customer pain.

1) Better self-service (that doesn’t feel like a maze)

Answer-first self-service means the customer gets the correct solution fast, with context.

Modern customer service chatbots can:

• Pull answers from your knowledge base using retrieval (and keep answers consistent)
• Walk customers through step-by-step troubleshooting
• Handle transactional tasks: order status, password resets, address updates, subscription changes
• Escalate cleanly with full context when it’s not confident

The make-or-break detail: design the escalation like a handoff, not a restart. If a customer has to repeat themselves, the bot didn’t help—it just delayed.

2) Smarter routing and prioritization (so urgent issues don’t drown)

Most queues still treat contacts like identical tickets. They aren’t.

With intent detection and sentiment analysis, AI can:

• Classify reason for contact in real time
• Route billing disputes to the right skilled team
• Detect VIP/high-LTV customers and prioritize accordingly
• Flag likely fraud or account takeover patterns earlier

This matters most during seasonal spikes—like late November through year-end, when returns, shipping interruptions, and billing questions pile up. In December 2025, customers have even less patience for “we’re experiencing higher than normal volume.”

3) Real-time agent assist (the fastest path to measurable ROI)

If you want one AI investment that tends to show impact quickly, it’s agent assist.

Agent-assist tools can:

• Suggest responses aligned to policy and brand tone
• Summarize long histories (especially across channels)
• Provide next-best-action prompts (refund vs replacement vs escalation)
• Auto-generate after-call notes and wrap-up codes

I’m biased toward this approach because it avoids a common mistake: pushing everything to bots before the organization is ready. Agents are still your best problem-solvers—AI just helps them move faster and stay consistent.

4) Quality and coaching at scale

Traditional QA reviews a tiny sample of conversations. That’s like trying to manage a factory by inspecting 2% of the output.

AI-driven QA can evaluate a much larger share of interactions for:

• Compliance language
• Empathy markers
• Dead-air and hold behaviors
• Process adherence
• Escalation triggers

Done well, this changes coaching from “once a month, maybe” to continuous improvement with clear examples.

The fix isn’t “more AI.” It’s the right operating model.

Most companies get this wrong by treating AI like a widget you bolt onto a broken process.

If your policies are inconsistent, your CRM data is messy, and your escalation paths are unclear, AI won’t magically create clarity. It will amplify confusion at scale.

A practical rollout plan that doesn’t backfire

1. Start with one high-volume use case
   • Example: “Where is my order?” + “Change delivery address” + “Return label” as a bundled workflow.
2. Build a clean knowledge source of truth
   • One policy for refunds. One policy for exceptions. Version control. Owners.
3. Define confidence thresholds
   • If the bot isn’t confident, it escalates. No bluffing.
4. Design the human handoff
   • Pass transcript, customer profile, intent, and any collected fields.
5. Measure what customers feel, not just what finance sees
   • Track containment and CSAT, recontact rate, and time-to-resolution.

The metric that predicts long-term CX health: repeat contact rate within 7 days.

If AI reduces handle time but increases repeat contacts, you didn’t improve service—you sped up failure.

“People also ask” questions your team should answer internally

Will AI replace contact center agents?

For most businesses, no. AI reduces repetitive work and shrinks peaks, but complex cases still need humans. The best teams use AI to raise agent capacity and consistency, not eliminate empathy.

What’s the difference between a chatbot and an AI agent?

A traditional chatbot follows scripted flows. An AI agent can interpret intent, retrieve knowledge, take actions (like updating an address), and adapt to context—if it’s connected to the right systems and governed properly.

How do we keep AI answers accurate?

Accuracy comes from three controls: approved knowledge sources, confidence-based escalation, and continuous QA using conversation reviews and user feedback loops.

What should we automate first in customer support?

Automate the high-volume, low-risk tasks: order status, returns initiation, appointment changes, password resets, and policy explanations—paired with clean escalation.

The future of customer experience is a hybrid—and that’s good news

The “year customer experience died” narrative resonates because so many brands taught customers to expect instant gratification, then delivered slow, fragmented support when it mattered.

AI doesn’t fix customer experience by pretending humans aren’t needed. It fixes CX by removing the waste: repeated questions, manual tagging, searching for policies, and bouncing customers between teams.

If you’re building out your AI in Customer Service & Contact Centers roadmap for 2026, my advice is to pick one measurable outcome—like reducing repeat contacts, improving first contact resolution, or cutting time-to-resolution—and implement AI where it directly attacks that friction.

The brands that win aren’t the ones with the flashiest chatbot. They’re the ones that make help feel fast, fair, and consistent—especially during the moments customers are most stressed.

What would happen to your churn and referrals if your support latency dropped by 30% before your next seasonal surge?