AI takes the busywork out of call centers, but the hard problems still need people

AI Is Rewriting Call Center Work - But Some Problems Still Need People

Armen Kirakosian remembers the old grind: aggravated callers, endless menus, scribbled notes. Now, he starts calls with full context, suggested actions, and fewer clicks. His take sums it up well: "AI has taken the robot out of us."

That shift matters. Roughly 3 million Americans work in call centers, with millions more globally. Attrition is brutal - about half of agents leave within a year. The core job is "break/fix," and the only question now is who fixes it: a bot, a person, or a person with a smart copilot.

What's Actually Changing

AI already owns the simple stuff. Password resets, balance checks, order status, basic returns - faster, cheaper, fewer mistakes. That's good for the queue and for agents who'd rather not repeat the same task all day.

But full automation has limits. Klarna went all-in with chatbots after replacing a 700-person team. Costs dropped. So did customer satisfaction. They hired people back for issues AI struggled with - identity theft, higher-stakes problems, and messy edge cases.

The emerging model: AI handles the majority, and fewer, better-trained humans handle the hard stuff. As Gadi Shamia at Replicant put it, that mix is becoming real.

The IVR Era Is Fading

Interactive voice response (press 1 for this, 2 for that) made support feel like a maze. Customers "zero out" to reach a person - then get bounced anyway. Lawmakers have taken notice with proposals that push for clearer paths to humans.

The next step is smarter intent routing. Systems that understand, predict, and send customers to the right place without keypad gymnastics. Bank of America's "Erica" is a useful example: it resolves simple requests, predicts needs like repeat low balances, and passes complex issues to the right team without friction.

Playbook: Build an AI-First Support Operation That Still Feels Human

Automate These Now

• Status checks: orders, deliveries, appointments, balances, ticket updates.
• Account tasks: password resets, profile changes, subscription updates.
• Billing basics: due dates, invoice copies, fee explanations.
• Simple "how-to": device setup, app flows, common knowledge-base answers.
• Agent workflow: call notes, summaries, after-call work, suggested responses.

Keep These Human (AI-Assisted)

• Fraud, identity theft, chargebacks.
• High-emotion issues: outages, escalations, repeat failures.
• Multi-step problem solving that spans teams or systems.
• Policy gray areas where judgment and empathy matter.
• Anything with legal, compliance, or safety risk.

Routing Without Menus

• Use intent detection to skip IVR trees entirely.
• Pre-fetch context: account, recent actions, past tickets, likely intent.
• Always offer a clear path to a human for complex or sensitive issues.
• Warm-transfer with a crisp summary so customers never repeat themselves.

Team Structure That Works

• Tier 0: AI self-service and knowledge access.
• Tier 1: Agents with AI copilots, focused on mid-complexity cases.
• Tier 2: Specialists for technical, regulatory, or high-risk work.
• Coach role: reviews AI outputs, tunes prompts, improves playbooks.

Metrics That Matter

• Containment rate (self-service success) without hurting CSAT.
• First contact resolution by intent type.
• Average handle time for human-assisted cases (expect longer but higher value).
• Transfer rate and "say-it-twice" events after handoff.
• Agent effort: after-call work time, disposition accuracy, coaching wins.
• Risk flags: privacy incidents, bad escalations, policy misses.

Guardrails You Need

• Truth checks: ground AI on your knowledge base and ticket history. No free-form guessing.
• Clear fail-safes: thresholds for immediate human takeover.
• Privacy by default: data minimization, masking, retention controls, audit trails.
• Compliance baked-in: disclosures, scripts, and jurisdiction-aware workflows.
• Continuous tuning: sample transcripts weekly, tighten prompts, retrain on misses.

Agent Experience, Upgraded

• Live summaries, suggested next steps, and knowledge snippets in-call.
• Smart forms and autofill to cut dead time.
• Coaching insights: which phrases calm tense calls, which steps shorten resolution.
• Career paths toward specialist roles and AI operations.

Implementation Checklist

• Map your top 30 intents by volume and by risk. Automate the low-risk, high-volume first.
• Write decision trees in plain language. Then let AI suggest the flow - you approve.
• Pilot on one channel (chat or voice). Cap volume. Measure, then expand.
• Set a "human anytime" escape hatch - button, voice phrase, or agent request.
• Train agents on prompt use, data sensitivity, and judgment calls.
• Report weekly: intent mix, saves, escalations, CSAT shifts, and outliers.

Where This Is Headed

Old-school IVR is on borrowed time. AI will route callers to the right place and handle the simple work. Humans will take the complex cases, with better tools and better training.

That's good news if you lead support. Less grind. Fewer repeats. More time on problems that actually move customer trust.

Get Skilled for the New Support Stack

• Upskill your team on AI-copilot workflows and prompt habits that reduce rework. Explore role-based learning paths here: AI courses by job.
• Keep a pulse on practical tools and use cases: ChatGPT resources.

Real-World Signals

TTEC leaders expect AI to get callers to the right person without the old menu maze. Bank of America's virtual assistant has handled billions of interactions and now flags likely needs before the customer asks. The pattern is clear: simpler for the customer, smarter for the agent, and fewer dead ends.