AI Is Redefining CX: What Contact Centers Must Fix

Most companies still treat customer experience as a set of channels: phone, email, chat, social. That definition is already obsolete.

In 2026 planning season (and yes, December is when budgets harden and roadmaps stop being “drafts”), AI is forcing a tougher question: is CX still the experience customers have, or is it the experience your systems prevent you from forcing on them? If your customers must repeat themselves, wait for handoffs, or re-enter the same data, AI didn’t “modernize” anything—you just automated frustration.

This post is part of our AI in Customer Service & Contact Centers series, where we focus on the practical reality of chatbots, sentiment analysis, and automation at scale. Here’s the stance I’ll take: AI is not making CX more complicated. It’s making bad CX less defensible.

CX now means “unified context,” not “more channels”

Answer first: In the AI era, CX means your customer’s context follows them—across touchpoints, agents, and time—without them doing the work.

The old model optimized each channel independently. The result was predictable: each team hit its own KPIs while customers experienced the gaps between them.

The updated expectation is simpler and harsher: when a customer switches from chatbot to live agent, the conversation shouldn’t reset. When they call after a failed delivery, the agent should already see the order status, prior contacts, and likely next steps. AI raises the bar because it’s capable of stitching that context together—so customers now assume you can.

What “unified customer context” looks like in a contact center

Unified context isn’t just “we integrated systems.” It’s operational:

• One identity across phone, chat, email, and authenticated app/web
• One timeline of interactions (what happened, what was promised, what was resolved)
• One source of truth for policies and knowledge articles (so answers don’t vary)
• One handoff standard (so bots hand context to humans cleanly)

If you’re evaluating AI tools for customer service, ask a blunt question: Where does the truth live? If it’s scattered across CRM notes, ticketing tags, and tribal knowledge, your chatbot will be polite—and wrong.

The new CX “currency” is trust (and AI makes trust measurable)

Answer first: AI-powered personalization only works when customers trust your data practices, and when your organization can prove the system is behaving.

The RSS article stresses that customers increasingly understand the trade: data for convenience. What’s changed is how quickly that trade can backfire. With AI-driven personalization, one sloppy inference can feel creepy, unfair, or simply incorrect.

In customer support, trust shows up in small moments:

• Did you explain why you’re asking for an ID check?
• Did your chatbot disclose it’s automated?
• Did your agent escalate properly when sentiment turned negative?

Practical trust moves for AI in customer service

If you want AI to improve CX (instead of triggering complaints), build trust into the workflow:

1. Make the value exchange explicit
   • “We store your device and order history so we can troubleshoot faster next time.”
2. Give customers meaningful control
   • Preferences for channel, language, authentication method, and data retention.
3. Design for “graceful failure”
   • When confidence is low, the bot should say so and route to a human—fast.

Trust isn’t soft. It’s a KPI.

Automation isn’t the goal—predictive support is

Answer first: The highest-ROI AI use cases reduce inbound volume by preventing issues, not by deflecting conversations.

A lot of contact centers are stuck in “deflection mode”: push customers to self-service, contain calls, reduce handle time. Those goals aren’t wrong. They’re incomplete.

AI is best at pattern recognition and early warning signals. That’s why the more ambitious CX teams are shifting budget from reactive support to predictive intervention:

• Detect likely delivery delays → proactively message customers with options
• Identify repeat billing confusion → adjust invoice language and trigger micro-guides
• Flag churn risk after poor interactions → fast-track retention outreach

This is where sentiment analysis becomes more than a dashboard. Real-time sentiment and intent detection can trigger actions (callbacks, supervisor review, “save” offers, or simply a better handoff).

The “Ask less, predict more” redesign

Nao Anthony’s point (ask less, predict more) is a design rule contact centers should take seriously. Here are high-impact changes:

• Replace long forms with progressive disclosure (ask only when needed)
• Pre-fill known data with confirmation (“Is this still your address?”)
• Use authentication that matches risk (step-up only for sensitive actions)

Customers don’t want to be “verified.” They want the issue solved.

Chatbots and AI agents: where teams go wrong

Answer first: Chatbots fail when they’re built to handle “volume,” not to handle the right work with clean escalation.

The industry has enough battle scars to be honest about this. Many bots were deployed as a front-door cost-cutting layer and measured on containment. That creates two predictable failure modes:

1. The bot blocks customers from reaching humans.
2. The bot escalates—but loses context—so customers repeat everything.

The RSS article references a well-known cautionary example: Klarna publicly pushed aggressive automation, then had to reverse course and route more work to humans when service quality suffered. The lesson isn’t “don’t automate.” It’s this:

Handoff quality matters more than automation percentage.

A better division of labor: AI does routine complexity

Here’s what works in practice:

• AI handles routine complexity: order status, password resets, policy explanations, appointment changes
• Humans handle nuanced complexity: exceptions, negotiations, emotional situations, high-risk decisions
• AI supports humans during the conversation: suggested next steps, knowledge retrieval, after-call summaries

The most underrated AI win in contact centers right now is agent assistance that reduces cognitive load—without hiding behind a bot.

A practical CX reassessment checklist for 2026 planning

Answer first: If your CX strategy isn’t being rebuilt around unified context, predictive support, and continuous iteration, you’ll spend 2026 patching problems instead of improving outcomes.

The article argues for a “fundamental reassessment,” not another initiative. I agree—and I’d translate that into five concrete workshop questions you can run before the year ends.

1) Where does customer context break today?

Map the top 10 contact drivers and identify:

• where customers repeat information
• where agents switch tools mid-call
• where policy differences create inconsistent outcomes

2) Which journeys are most “preventable”?

Pick 2–3 journeys where proactive outreach can reduce inbound volume, like:

• shipping exceptions
• payment failures
• onboarding confusion

3) What should your chatbot refuse to do?

Define boundaries. Examples:

• anything involving threats, self-harm, or abuse
• complex complaints and legal disputes
• high-value customer retention moments

4) Can you prove your AI answers are correct?

If you can’t evaluate accuracy, you can’t scale responsibly. Put in place:

• grounded knowledge sources
• evaluation sets for top intents
• monitoring for hallucinations and outdated policy references

5) Do you have a continuous improvement loop?

“One-time transformations are dead” is the most important line in the source piece.

AI changes fast. Your workflows must change faster. That means:

• weekly review of failure conversations
• monthly tuning of intents, routing, and knowledge
• quarterly model and policy audits

The contact center operating model is shifting (and it’s overdue)

Answer first: AI is pushing contact centers toward an operating model where experience is designed end-to-end, not managed team-by-team.

Nao Anthony’s “value-chain view” is the antidote to a common CX trap: polishing the front end while the back end stays messy. Customers can’t tell the difference between “CX” and “operations.” They just know whether something worked.

If AI is in your stack, your weakest link becomes visible:

• An AI chatbot can’t fix a broken returns policy.
• Sentiment analysis can’t prevent churn if escalation rules are unclear.
• Agent copilots can’t shorten handle time if your tools are slow.

Here’s the better way to think about it: AI amplifies what you already are. If your processes are clean, AI makes them faster and more personal. If your processes are messy, AI makes the mess happen at scale.

What to do next

If your definition of CX still starts with “channels,” it’s time to update it. AI-driven customer experience is about unified context, trust, and anticipation—and contact centers are where those promises get tested.

If you’re planning your 2026 roadmap, start small but real: choose one high-volume journey, add unified context, deploy a chatbot with strict escalation rules, and layer in sentiment analysis that triggers action (not just reporting). Then iterate weekly.

The forward-looking question worth sitting with is simple: When AI makes it possible to prevent the next issue, will your organization still wait for the customer to complain first?