Why Voice AI Is the Next Frontier for IT Support Automation

The 40% problem

IT service desks have spent the last five years automating chat and self-service. Password reset bots, Slack integrations, Teams chatbots — all designed to deflect tickets before they reach a human agent.

It worked. Chat deflection handles the employees who are comfortable typing their problems into a portal. But there is a stubborn 40%+ that still picks up the phone.

Who still calls — and why

Not everyone prefers text-based support. Three groups consistently default to phone calls:

Urgent issues. When a production system is down or a VPN blocks access to a client meeting in 10 minutes, employees call. They want a human voice (or something that sounds like one) confirming their issue is being handled right now.

Non-desk workers. Field technicians, warehouse staff, delivery drivers — employees who are not sitting at a computer. A phone call is faster than finding a portal on a mobile device.

Preference and habit. Some employees, particularly in organizations with an older workforce or in cultures where phone communication is the norm, simply prefer calling. Forcing them to a chatbot creates friction and frustration.

Why traditional IVR fails

Interactive Voice Response (IVR) systems — "Press 1 for password reset, press 2 for hardware issues" — have been the default for decades. They are universally disliked.

The problem is not technology. It is the interaction model. IVR forces callers into rigid decision trees. A caller who says "my laptop is slow and I also need to check on ticket 4523" has two separate problems that do not fit neatly into one menu branch.

Modern voice AI takes a different approach: natural conversation. The caller speaks freely. The AI understands intent, asks clarifying questions, and takes action — just like a human agent would.

What voice AI for IT support looks like

A modern voice AI agent for IT support does four things in a single phone call:

1. Authenticates the caller. Voice-based identity verification without requiring the caller to navigate a portal or remember a PIN.

2. Understands the problem. Natural language processing converts speech to text, then an LLM determines intent and extracts relevant details.

3. Takes action. The AI calls ITSM tools — searching the knowledge base, creating or updating tickets, checking asset information, processing approvals — all during the live conversation.

4. Resolves or escalates. If the issue can be resolved with information or a guided procedure, the AI handles it. If not, it creates a properly categorized ticket with full context and transcript.

The entire interaction happens in under a second of response latency. No hold music. No menu trees. No "your call is important to us."

The business case

The math is straightforward:

• Cost per L1 interaction: EUR 15-25 (industry average)
• Cost per voice AI interaction: EUR 0.05/minute (roughly EUR 0.15-0.20 per resolved call)
• L1 agent annual salary: EUR 35,000-50,000 (fully loaded)
• Voice AI cost for 200 minutes/month: EUR 199/month (EUR 2,388/year)

An AI voice agent handling 50-65 calls per month at EUR 199/month replaces a significant portion of L1 phone volume at less than 5% of the cost of a human agent.

What to look for in a voice AI solution

If you are evaluating voice AI for your IT support line, focus on these criteria:

• Latency. Sub-second response times are critical. Anything over 2 seconds creates awkward pauses.
• ITSM integration depth. Can the AI create tickets, search KB, manage assets, and handle approvals — or just answer questions?
• Language support. For multilingual workforces, can the AI detect and respond in the caller's language automatically?
• Authentication. How does the AI verify caller identity without a pre-authenticated session?
• Pricing transparency. Can you model costs before talking to sales?

The bottom line

Chat automation solved the easy part of IT support. Voice AI solves the hard part — the phone calls that still represent 40% of volume. The technology is here, the latency is fast enough, and the cost model works. The question is not whether voice AI will handle IT support calls. It is whether your organization will adopt it before your competitors do.