AI Customer Engagement Playbook for Uncertain Times

Long wait times. Multiple contacts for the same issue. Getting transferred and re-explaining everything.

Customers are still running into the same problems in 2025—and they’re less patient about it. Recent consumer research found that only 42% of people are “very satisfied” when communicating with businesses, while 75% say they’re likely to switch providers after a bad experience. That’s not a CX problem. That’s a revenue problem.

This is happening at a moment when many leaders are planning for volatility: tariff-driven pricing uncertainty, tighter household budgets, and more scrutiny on “Is this brand worth it?” If you run a customer service operation, this is the uncomfortable truth: uncertainty doesn’t lower customer expectations—it raises them.

In our “AI in Customer Service & Contact Centers” series, I keep coming back to one theme: AI isn’t valuable because it’s impressive. It’s valuable when it removes friction—especially across channels, moments, and handoffs. This post turns the latest customer engagement data into a practical playbook for contact center and CX leaders who need better outcomes fast.

Customers still hate the same three things (and AI can fix them)

The clearest signal from 2025 customer engagement research is blunt: the top frustrations haven’t changed, and the numbers are trending the wrong way.

Customers report these pain points most often:

• Long wait times to reach an agent (65%)
• Needing to contact support multiple times (65%)
• Having to repeat themselves across transfers (63%)

Those three problems share a root cause: the contact center loses context. Either the business can’t recognize who the customer is, can’t keep the full story attached to the interaction, or can’t route the issue to someone who can actually solve it.

Where AI actually helps (not the “AI everything” fantasy)

AI in customer service works best when it’s used as a systems glue—connecting identity, intent, and next-best action across channels.

Here are practical, high-ROI applications that map directly to those frustrations:

1. Intent detection + smarter routing

   • Classify the customer’s issue in the first 10–20 seconds (voice) or first 1–2 messages (digital)
   • Route to the right queue with the right priority
   • Reduce transfers, reduce re-explanations

2. Agent-assist summaries and “reason for contact” capture

   • Auto-summarize the conversation so the next agent starts with context
   • Log disposition, sentiment, and promised follow-ups automatically
   • Prevent the “I have to call back again” loop

3. Automation for the repetitive 30–60%

   • Password resets, appointment changes, order status, address updates, billing copies
   • These aren’t “easy” to customers when they’re time-pressured; they’re just repetitive to you

My stance: if your AI roadmap starts with “fully automate the contact center,” you’re going to ship a frustrating bot and then call it a “customer adoption problem.” Start with removing wait time and repetition. Customers will reward you for that.

Omnichannel expectations are real—and customers notice the gaps

Customers don’t think in channels. They think in outcomes.

In the last year, 75% of consumers reported using multiple channels with a business about the same topic. That’s normal behavior now: they might start in web chat, switch to email while commuting, and then call when it gets urgent.

The failure pattern is also predictable: each channel behaves like a separate island.

The new baseline: “Pick up where I left off”

When customers switch channels, they expect:

• The business remembers what happened
• The next agent sees the full history
• They don’t need to resend screenshots, order numbers, or error messages

If you want a simple internal metric that reflects this expectation, track:

• Repeat-contact rate (within 7 days)
• Transfer rate per case
• Customer effort score (CES) by channel and by journey

Then tie AI initiatives to one of those metrics. If you can’t, it’s probably not a priority.

AI’s job in omnichannel customer service: continuity

AI supports omnichannel customer service when it provides continuity in three ways:

1. Conversation memory (with guardrails)

   • Persist case context across chat, SMS, email, voice, and in-app
   • Keep it scoped to the customer’s issue and privacy permissions

2. Channel translation

   • Convert a messy call transcript into a clean case summary
   • Turn chat history into a voice-ready briefing for the agent

3. Next-best action suggestions

   • Recommend a policy step, troubleshooting flow, or credit/refund option
   • Ground suggestions in your knowledge base and policy rules

The reality? You don’t get “omnichannel” by adding more channels. You get it by making the channels share the same brain.

What customers want from AI support (and what they don’t)

Customer attitudes toward AI are more practical than philosophical. They don’t care about your model architecture. They care about speed, accuracy, and being treated like a person.

Two data points worth anchoring your strategy:

• 66% of consumers are likely to use AI-powered support if it means faster responses and shorter wait times.
• 72% are more likely to buy online if they can ask questions in real time.

That’s the business case for AI in the contact center: it protects conversions and reduces service cost at the same time.

The “safe zone” use cases: where AI wins trust

Customers say AI is most useful for:

• Resolving simple issues (54%)
• Answering FAQs (50%)

So don’t start AI with edge cases. Start with the high-frequency flows that already have clear answers.

A practical shortlist:

• Login and password recovery
• Order status and delivery changes
• Subscription pause/cancel flows with clear policy
• Appointment scheduling and rescheduling
• Simple billing explanations and invoice retrieval

The “trust builders” customers actually asked for

When consumers describe the AI support they want next, they consistently pick capabilities that reduce friction:

• Proactive issue resolution (47%)
• More personalized experiences (45%)
• Smarter cross-channel performance (42%)
• Real-time voice recognition and response (39%)
• Multi-language support (31%)

Here’s how I interpret that list: customers are asking brands to be less reactive and less repetitive.

Where customers prefer AI to show up

Customers are most comfortable with AI-powered support in:

• Messaging apps (44%)
• Website chat (39%)
• Email (30%)
• Mobile apps (28%)
• SMS/text (26%)
• Voice calls (26%)

This is a useful sequencing hint. If you’re building your 2026 roadmap right now, it often makes sense to:

1. Mature chat/messaging automation first
2. Add agent-assist and summaries for voice
3. Expand to voice automation only when you can prove containment and satisfaction

A practical blueprint: AI layers you can ship in 90 days

Most companies get stuck because they treat “AI in customer service” like a single project. It’s not. It’s a stack.

Below is a simple layering approach that works well under budget pressure, staffing volatility, and peak-season spikes.

Layer 1: Deflect the obvious (without harming CSAT)

Goal: reduce wait times by removing repetitive contacts.

Implement:

• A virtual agent for top 10 contact drivers
• Smart FAQ that answers with policy-approved text
• Authentication and account lookups in the flow

Success metrics:

• Containment rate (but don’t worship it)
• CSAT for bot-handled interactions
• Drop-off rate at key steps

Layer 2: Make agents faster and more consistent

Goal: reduce handle time while improving quality.

Implement:

• Real-time agent assist (knowledge suggestions)
• Auto-summaries and after-call work reduction
• Tone/sentiment detection prompts (used for coaching, not punishment)

Success metrics:

• Average handle time (AHT) + quality scores (together)
• After-call work minutes per contact
• First contact resolution (FCR)

Layer 3: Fix the handoffs (the hidden CX tax)

Goal: stop repeat contacts and repeats of the same story.

Implement:

• Unified case timeline across channels
• “Customer context card” that follows the interaction
• Intent-based routing using CRM + recent journey behavior

Success metrics:

• Transfer rate
• Repeat-contact rate
• Customer effort score (CES)

Layer 4: Get proactive (where loyalty is made)

Goal: prevent contacts, not just handle them.

Implement:

• Outage and delivery delay notifications with two-way messaging
• Proactive billing anomaly alerts
• “You might be stuck” triggers (rage clicks, repeated failed logins)

Success metrics:

• Contact rate per 1,000 customers
• Cost per resolution
• Retention and save rates for at-risk segments

Guardrails matter more in uncertain times

When budgets tighten, leaders tend to push automation harder. That’s exactly when you need stronger guardrails.

A bad bot doesn’t just cause a bad moment—it creates distrust that bleeds into every channel. And with 48% of consumers saying it only takes one to two bad experiences to move on, you don’t get many retries.

The guardrails I’d insist on before scaling AI

• Grounded answers: AI responses must be anchored to your approved knowledge base and policies.
• Clear escalation paths: customers need an obvious route to a human, especially for billing disputes, cancellations, and safety issues.
• Data minimization: collect only what you need; store only what you must.
• Auditability: you should be able to review what the system told the customer and why.
• Bias and accessibility checks: ensure equitable outcomes across language, accent, and ability.

One line to keep your team aligned: automation is only “successful” when it reduces effort and preserves trust.

What to do next: a simple starting plan for contact centers

If you’re planning for 2026 and you want AI customer engagement to produce leads, retention, and lower service cost, start with a tight 30-day assessment:

1. Identify your top 10 reasons for contact by volume and by cost
2. Map where conversations break (handoffs, transfers, channel switches)
3. Pick two “safe zone” automations and one agent-assist use case
4. Define success metrics up front (FCR, CES, repeat-contact, bot CSAT)
5. Run a controlled pilot with weekly QA reviews and fast iteration

Customers are already telling us what they want: faster help, fewer repeats, and real continuity across channels. They’re also telling us they’ll punish brands that don’t deliver.

If you’re building your AI in customer service roadmap right now, here’s the question I’d use to pressure-test every initiative: Will this make the customer repeat themselves less—across channels, across agents, across days?

Because in uncertain times, loyalty isn’t earned by big promises. It’s earned by removing friction when it counts.