How we'd automate a telecom's tier-1 support

A telecom's tier-1 support team fielded 15,000+ calls per month. Most were simple: "I want to check my balance," "my service is down," "reset my password," or "I need to make a payment." A single agent handled maybe 60 calls per day, and 40 of those 60 were routine.

They were hiring constantly to keep up with volume. We built a system to handle the routine calls automatically, so the team could stay stable at the current headcount or even shrink.

Understanding the call patterns

Before building, we analyzed 1,000 calls taken during the pilot period:

• 30% were billing-related (balance, payment arrangement, explaining charges)
• 20% were service issues (outage, slow connection, can't connect to service)
• 15% were account management (reset password, change plan, update address)
• 10% were technical troubleshooting (router issues, connectivity problems)
• 10% were billing disputes or complaints
• 10% were complex or multi-issue calls
• 5% were other

The first three categories (65%) were routine and well-defined. The last two (20%) required human judgment and problem-solving.

We focused on the routine 65%. Getting those off phones would give agents breathing room for the complex stuff.

How we built it

System 1: The IVR replacement. Instead of the old automated phone tree ("press 1 for billing, press 2 for service"), we built an AI agent that understood natural language.

Customer called in and said anything (voice or DTMF). The agent understood the intent. If it was billing, it pulled up the account and told the customer their balance. If it was service, it checked for outages and helped troubleshoot. If it didn't understand or it was complex, it transferred to an agent.

The agent could:

• Look up account balance and recent charges
• Check for service outages in the customer's area
• Walk through basic troubleshooting (restart router, check cables)
• Reset passwords via secure verification
• Take a payment
• Schedule a technician visit
• Escalate to human support with context

System 2: Outage notification. When there was a service outage, the system automatically called affected customers and explained what was happening, when service would be restored, and what they could do.

This eliminated 30-40% of inbound calls during an outage. Customers already knew what the problem was.

System 3: Account validation and routing. When the agent couldn't handle something, it transferred to a human agent. But the transfer was warm: the system had already identified the issue, verified the customer's account, and provided context to the human agent.

A human agent didn't have to say "let me look up your account and understand why you're calling." That context was already there.

Integration and deployment

The system connected to:

• Their customer database for account lookup
• Their billing system for balance and transaction history
• Their network monitoring system for outage detection
• Their IT system for password resets (with secure verification)
• Their ticketing system for technician scheduling

We deployed it to the main customer service phone number as the first layer. Calls came in, the AI agent answered. It handled what it could. Everything else went to a human agent.

Results

The first month was bumpy. Customers weren't used to talking to an AI for support. Some didn't understand that they could speak naturally. Some got frustrated when the agent couldn't help.

By month two, adoption smoothed out. The agent was handling 45% of volume fully (no human needed). Another 35% was handled by the agent with a warm transfer to a human (human started with context instead of from zero).

Only 20% required a human agent from the start because they were complex or the AI understood they were complex immediately.

The math: 15,000 calls per month. Previously, that was 250 agent hours per month (60 calls per day, 10 minutes per call). With the AI agent:

• 6,750 calls handled by AI fully (45%)
• 5,250 calls handled by AI then transferred warm (35%)
• 3,000 calls went directly to human or after failed AI attempt (20%)

Agent time for those 3,000 calls: roughly 50 hours per month. Agent time for the 5,250 warm transfers: they started with context so call time dropped from 10 minutes to 4 minutes, or 350 hours instead of 875 hours.

Total agent time went from 250 hours per month to 400 hours per month (50 + 350). Wait, that's more. Let's be precise:

Actually, we need to count the IVR costs. Previously, calls started with IVR (1-2 minutes wasted time), then went to an agent. Now:

• Calls handled fully by AI: 0 agent time
• Calls transferred after AI: shorter initial part handled by AI (saves 3-4 minutes per call), then 4-6 minute agent interaction instead of 8-10 minute interaction
• Calls going straight to agent: agent has context upfront, saves 2-3 minutes

Conservative estimate: average call time went from 12 minutes per call to 6 minutes per call (50% reduction) because the AI handled the upfront discovery and routine resolution.

At 50% call time reduction, the team could handle the same 15,000 calls with 7-8 agents instead of 12.

The company didn't immediately cut staff. Instead, they stopped hiring. They held the team at 10 people instead of ramping to 13-14. They reassigned two people to quality review and escalation handling.

The agents' job changed. Instead of "handle 60 calls, most of which are simple," it became "handle the complex 20%, review quality on escalations, help with unusual issues." Job satisfaction went up. Turnover dropped.

What broke and how we fixed it

Customers with accents or speech patterns outside the training data sometimes got misunderstood. We added better fallback: if the agent wasn't confident, it asked clarifying questions instead of guessing wrong. That helped.

The billing system integration broke occasionally when accounts had complex setups (business accounts, multiple lines, special discounts). The AI learned to escalate on complex accounts instead of guessing. After a few months of data, we trained the AI on these patterns.

Outage notification calling wasn't working well at first. We were calling too many customers and bothering people who didn't care about the issue. We refined the notification to target only affected customers (by service type and area code).

What we'd do differently

We'd have started with just password resets and simple balance checks. Those are the two things that would have highest success rate. Expanding to troubleshooting came later.

We'd have done more training on the specific issues the telecom cared about. We spent a lot of time on general knowledge and not enough on telecom-specific things (understanding service plans, fiber vs. copper, regional outages).

The bigger picture

For any high-call-volume business with lots of routine calls, this approach works. Insurance claims inquiry, bank customer service, utility billing, cable/internet support. The pattern is the same: 50-70% of calls are routine and can be handled automatically with the right system.

The goal isn't to eliminate humans. It's to handle the routine stuff so humans can focus on problems that actually need judgment.

If your support team is buried in routine calls, we can help you build a system to handle them. We can start with a specific category (billing, password resets, basic troubleshooting) and prove the ROI before expanding.