Voice AI for Utilities and Public Services: 3 Real Cases

The short version: Voice AI for utilities works on the calls that have a known answer. A Swiss electricity supplier fully automated more than 25% of its calls, a waste company reports about $4M saved a year, and a road traffic office built its bot on public information only.

Nobody calls a utility to chat. They call because the pickup was missed, the bill looks wrong, or the office closed an hour ago and something needs an answer tonight.

Many of those calls have a known answer, and the people who take them can only work the hours the office is open. That is the arithmetic every utility and public office lives with.

Three published examples follow, from vendors' and organizations' own pages. The organizations are anonymized and the sources stay named, so every number can be checked: a regional Swiss electricity supplier, a waste services company in the United States and Canada, and a Swiss cantonal road traffic office. A wider set of results across industries sits in a roundup of real voice and answering outcomes.

Does voice AI for utilities work for routine customer calls?

Yes, for the routine ones. In the published cases, a voice assistant fully automated over 25% of a Swiss utility's calls, and an after-hours voice bot took a waste company's routine requests when agents were off. People still take the complex calls. The figures come from the organizations and their vendors, not from audits.

That is a narrower claim than "voice AI replaces the call center," and it is the one these cases support. Each organization automated the repeatable question and kept a person behind it.

The cases also differ in a useful way. One utility cares about hours. One cares about volume across several channels. One public office cares about not touching personal data at all. Same technology, different problem each time.

How a voice assistant fully automated 25% of calls for a Swiss electricity supplier

Situation

A regional Swiss electricity supplier that serves nearly 90,000 clients. It received about 75,000 phone calls a year on its 0800 and 0848 numbers, with employees answering only during business hours. Customer support and billing questions also came in by email in large volumes. Customers often contacted the company for basic information, which is exactly the kind of answer that can be automated.

What changed

The supplier deployed a virtual assistant that answers frequently asked questions around the clock in Italian and German. After each call, it sends an automated text message with the relevant follow-up information. It runs as a managed cloud service, so the supplier made no upfront IT investment. A chatbot on the supplier's website, built on a large language model, runs alongside the phone assistant so customers can pick their channel.

This is an AI voice assistant plus a website chat, working together.

Results

More than 25% of calls are fully automated, and inbound phone calls are down 40%. The supplier now offers 24/7 customer service on a phone line that used to close at the end of the business day.

What the published account reports	Detail
Calls fully automated	More than 25%
Inbound phone calls	Down 40%
Calls a year, before	About 75,000
Hours	24/7, up from business hours only
Languages	Italian and German
Channels	Voice assistant plus website chat

Do not subtract 25% from 40% and call the gap a mystery. The two numbers measure different things. The 25% is the share of calls the voice assistant finishes on its own. The 40% is a drop in inbound phone call volume, which the supplier credits to the voice assistant and the chat platform together. Both are the supplier's own figures.

How a waste company's AI agent automated service for 250K+ users a month

Situation

A North American waste services company serving residential, commercial, and industrial customers across the United States and Canada. It receives more than 850,000 customer calls and website inquiries every month. Many are routine: scheduling a pickup, reporting a missed collection, placing a vacation hold, tracking a driver, asking about billing. Those repetitive requests used up agent capacity, and customers expected help outside standard hours, in both English and French.

What changed

The company deployed an AI agent that gives customers 24/7 self-service across its website, email, voice, and WhatsApp in English and French. Customers can schedule pickups, pay bills, report missed collections, and place vacation holds through natural conversation. An after-hours voice bot uses speech-to-text and text-to-speech to take calls when live agents are off, filtering, prioritizing, and resolving routine requests and passing the rest to people. The agent also collects feedback on service quality, pickup punctuality, and driver experience. The deployment took weeks.

This is an AI agent across voice and digital channels, with the voice bot handling after-hours calls.

Results

The company reports about $4M in annual savings and 250K+ users served every month.

What the published account reports	Detail
Annual savings	About $4M
Users served every month	250K+, across all channels
Customer calls and web inquiries a month	850,000+
Channels	Website, email, voice, WhatsApp
Languages	English, French
Deployment time	Weeks

Read the 250K+ as users across every channel, not voice callers. The dollar figure is annual and the company's own, and a company director describes saving "millions of dollars over the past year" on routine queries with high customer satisfaction. The page does not break the $4M down, so you cannot tell how much came from the voice bot and how much from the digital channels.

What a voice bot did for a Swiss cantonal road traffic office, and why its numbers disagree

Situation

A Swiss cantonal road traffic office that receives a large number of calls about licenses and vehicle registration, many of them about recurring questions on opening hours, procedures, and forms. Staff at the counter and on the phones were stretched, the lines were busy at peak times, and live service was limited to opening hours.

What changed

The office has used a voice bot for first-level phone information since 2022, and renewed and expanded it in February 2026 after a long test and optimization phase. The bot answers questions about licenses and vehicle registration using only the public content of the office's website. It processes no personal data. When it cannot answer, it forwards the caller to the right specialist. The new version is no longer limited to a fixed list of questions, and mobile callers can optionally get a text message with links and the right online forms.

This is an AI voice bot answering first-level questions on the office's own phone line.

Results

Here the published figures do not agree, which is worth seeing. The office's own page says it receives roughly 250,000 calls a year and gives no automation percentage. It describes the goal as stable, data-protection-compliant support for phone information, "not its complete automation." A 2025 conference write-up says over 300,000 calls a year. A September 2026 post by the integration partner says the bot handles about half of those 300,000 calls automatically. An earlier vendor figure put it at 20 to 25% of calls, and no public page for that figure was found.

What the published sources report	Detail
Calls a year	About 250,000 (the office, February 2026) or 300,000+ (2025 write-up, 2026 partner post)
Share handled by the bot	About half (partner post, September 2026) or 20 to 25% (earlier vendor figure, no public page found)
Data processed	Public website information only, no personal data
Fallback	Forwarded to the responsible specialist
Language	Understands Swiss German with an accent (2025 write-up)

The sensible reading is that the share grew as the bot grew, but nothing published says so. The only claim that does not wobble comes from the office itself: it uses a bot, it keeps people in the loop, and it is not trying to automate every call. The more useful fact is the design choice. A bot that only uses public information needs no identity check and sits on much lower data-protection risk.

What the three cases prove about utilities and public services

Read them together and a shape appears. A supplier fully automated over 25% of its calls by covering the hours it was closed. A waste company reports about $4M in annual savings after adding an after-hours voice bot and digital self-service. A public office built a bot that touches no personal data at all. In each, the machine took the repeatable call and a person kept the rest.

None of them pretends otherwise. The road traffic office says out loud that complete automation is not the goal.

The three headline numbers do not measure the same thing. The supplier's 25% is calls its assistant closed on its own; its 40% is a fall in inbound volume credited to voice and chat together. The waste company's 250K+ counts users across four channels, not phone calls. The road traffic office's published figures disagree across the sources that report them. Keep that in mind the next time one of these figures is used to sell you a different product.

Photo by stephan de MARANTHI on Unsplash.

What to copy first

Start with what you can already answer in public. If your website explains your hours, forms, and procedures, a bot that reads only that content can take calls without ever asking who the caller is.

Copy in this order:

1. Start with the call that needs no identity check. The road traffic office's bot uses only public information and processes no personal data. That is the simplest first deployment, and it removes the hardest compliance question.
2. Cover the hours you are closed. The supplier answered only in business hours and now runs 24/7. The waste company's after-hours voice bot works the same way.
3. Match languages to the callers you actually have. The supplier chose Italian and German, and the waste company chose English and French. Pick yours from your own call log, not from a feature list.
4. Count your calls by type for one month. The supplier started with about 75,000 calls and a pile of basic-information questions. Yours has a pattern too.
5. Measure time per call and calls avoided as separate numbers. The supplier's 25% and 40% measure different things, and mixing them hides what actually changed.
6. Keep a person path and a text follow-up. Every case hands off to staff, and the supplier and the road traffic office both send a text with links after the call.

The shape of the fix does not change with size: answer the routine call, use public information first, and hand off with context.

If you want to build this

The cheap first version is a public-information test. Take the answers your website already gives, list the ten calls they cover, and hand the list to one person to keep accurate. If every answer is already public, you never have to identify a caller to help them, and the hardest compliance question disappears.

That list is also the wiring plan. Make.com can drop a call's details into the system your team already opens, so a caller who reaches a person never has to start over.

To answer the phone itself, teams usually begin with Retell AI or Vapi. Callers judge the voice before they judge the answer: a November 2025 Twilio blind test found 90% of consumers could not correctly identify an AI voice, while 72% believed they could, and the neural voices from ElevenLabs are a big part of that gap.

Public offices carry one extra rule the private cases do not. Keep personal data out of the bot entirely. The road traffic office answers only from its own public pages and forwards anything personal to a specialist, which is both the safest and the simplest first deployment.

An AI receptionist is one of the digital employees we build. How it compares with an answering service covers the same front-desk job at small-business scale.

Still deciding which calls are safe to automate? Book a free discovery call and we will separate your billing questions from your license questions instead of treating them as one bucket.

The numbers in this article come from each provider's published case study or public page: Spitch's electricity supplier case study and Yellow.ai's waste company case study, and, for the road traffic office, the office's own page, a 2025 conference write-up, and a 2026 post from the integration partner. The road traffic figures disagree across sources, as shown above. These are real anonymized case studies that show what voice AI can do in practice. Some links in this post are affiliate links: if you sign up through them, we may earn a commission at no extra cost to you.