Contact center automation: from IVR menus to AI agents
Gartner expects AI agents to resolve 80% of common customer service issues without a human by 2029. Getting there depends less on the AI than on the systems it can reach.
Contact center automation has gone through three stages. Interactive voice response (IVR) menus routed callers by keypad. Chatbots answered simple questions from scripts. AI agents, built on large language models, can now understand a request in plain language, look up the customer's account, take action and confirm the result.
In March 2025, Gartner predicted that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, leading to a 30% reduction in operational costs.
What changes with AI agents
| Scripted chatbot | AI agent | |
|---|---|---|
| Understanding | Keywords and fixed flows | Free-form language, voice or text |
| Actions | Answers from a knowledge base | Checks orders, changes bookings, issues refunds through connected systems |
| Handoff | Starts over with a human | Passes a summary and context to the human agent |
| Failure mode | "I didn't understand that" | Confident wrong answers if not grounded and tested |
What to automate first
Start with high-volume, low-risk requests that follow clear rules: order status, password resets, appointment changes, address updates and simple billing questions. These make up a large share of contacts in most centers and have clear right answers.
Keep humans for complaints, cancellations where retention matters, vulnerable customers, and anything involving judgment or exceptions.
AI also helps human agents
Agent-assist tools transcribe calls, suggest answers, pull up relevant articles and write the after-call summary. They cut handling time without customers talking to a bot, which makes them a lower-risk first project.
The work is in integration
An AI agent can only resolve what it can reach. If order, billing and account data sit in systems without APIs, the agent can answer questions but not fix problems. Most of the effort in contact center automation goes into connecting those systems, setting limits on what the agent can do, and testing it against real conversations.
What IT teams should do
- Rank contact reasons by volume and pick three simple ones to automate first.
- Check which back-end systems an AI agent would need, and whether they have APIs.
- Pilot agent-assist for human agents alongside any customer-facing AI.
- Set rules for when the AI must hand off, and make the handoff keep the context.