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AInora Report · April 2026

Voice AI Adoption: What Is Actually Measured

Voice AI adoption rates by industry and by region are not measured by any first-party dataset we could reach. This report keeps the one controlled study of AI assistance in live customer conversations, states plainly what it did and did not measure, lists the adoption percentages an earlier version of this page carried and where they came from, and sets out what you can measure in your own operation instead.

14%
Uplift in issues resolved per hour for support agents given an AI assistant (NBER, 5,179 agents)
34%
The same uplift for the least experienced agents. Minimal for the most experienced (NBER)
No figure
Voice AI penetration by industry or by region. No reachable dataset measures it
2023
Year of the only large controlled study on this question we could verify

What a controlled study actually found

One large study measures what happens when a generative AI assistant is put into live customer conversations at scale. It is the strongest evidence available on this question and it is narrower than the way it is usually quoted, so its limits are stated next to its result rather than below it.

14%
Average increase in issues resolved per hour among customer support agents given access to an AI conversational assistant
Brynjolfsson, Li and Raymond, Generative AI at Work, NBER working paper 31161, April 2023 (revised November 2023). 5,179 customer support agents. Academic and independent of any voice AI vendor.
34% / ~0
The same uplift split by experience: large for novice and low-skilled agents, minimal for experienced ones. The average hides most of the effect
Same paper. This is the finding that matters operationally, and it is the half most often dropped when the 14% is quoted.
Not stated
The channel. The paper reports conversations handled by support agents and does not state whether they were phone, chat or email, and publishes no separate voice result
Read directly from the paper. Applying this figure to a phone deployment is an extrapolation, and this report labels it as one rather than presenting it as a voice measurement.
One firm
The agents all worked for a single company. A single-firm result is evidence, not a market rate, and it should not be read as an industry benchmark
Same paper. We carry it because it is measured and independent, not because it generalises.

What this report cannot tell you

Every adoption percentage this page used to carry has been removed. They were not weak sources; they were attributions to documents that do not appear to exist. No published first-party dataset we could reach measures voice AI penetration by segment or by region, so this report carries no number for it rather than an estimate dressed as one.

No figure
Share of dental, veterinary, legal or collections firms running an AI-answered line
Vendor surveys on this question are self-selected samples of that vendor customers, and none we found publishes a sampling frame. Count it inside your own sector association instead.
No figure
Voice AI penetration in North America, Western Europe, the Nordics and Baltics, or Southern Europe
Region-level penetration requires a denominator of all businesses in the region. No published study we could reach establishes one.
No figure
A ranked list of why buyers adopt voice AI, by share of respondents
A ranking requires a named survey with a stated population. The ranking this page previously carried named four analyst firms and no survey.
No figure
Time from contract to first production call. We have published no deployment data because we have no deployment data to publish
Any timeline figure here would be a claim about our own delivery record. Ask us on a call and we will tell you what a build actually involves.

What you can measure in your own operation

The questions this report cannot answer from public data are all answerable from your own call log inside a fortnight. None of these require a vendor.

Step 1
Answer rate. Count calls that rang out or hit voicemail, divide by total inbound. Do it per weekday and per hour, not as a blended monthly figure
This is the number every published missed-call statistic is a poor proxy for. See our companion report at /reports/missed-call-statistics-2026 for what the public figures do and do not measure.
Step 2
Off-hours share. Count what arrives outside your staffed window. Call timing is category-specific to the point where an industry average is misleading
The one large first-party dataset on call timing reports a spread of roughly five to one across categories, which is why we give you the method rather than a number.
Step 3
Repeat-caller rate. Count distinct numbers that called more than once in 48 hours. This is the closest thing to a direct measure of unmet demand
Available from any telephony provider call detail record export.
Step 4
Handle-time distribution by agent tenure. The controlled study above found the gain concentrated in the least experienced staff, so this tells you where automation would land in your team
Derived from the NBER finding in the first section, applied to your own staffing rather than to a market average.

Figures this report no longer uses

These appeared on earlier versions of this page. Each was checked and each failed. They are listed with their former attribution so the trace does not have to be repeated, and so none of them is quietly re-added.

42% / 61% / 29% / 18%
Adoption rates for dental and veterinary practices, collection agencies, EU mid-market BPOs and law firms. Formerly attributed to "Henry Schein + AAHA 2026", "ACA International 2026", "Everest Group 2026" and "Clio 2026".
Four future-dated reports, each cited only to the organisation home page rather than to a document. No publication carrying any of these figures could be located. Removed with their attributions rather than re-sourced.
48% / 31% / 22% / 14%
Regional SMB voice AI penetration for North America, Western Europe, Nordics and Baltics, and Southern Europe. Formerly attributed to "BIA 2026" and "IDC 2026".
Same shape: future-dated analyst reports cited to a home page. The four figures also form a suspiciously smooth descending series, which is what an invented distribution looks like.
#1 to #4
A ranked list of adoption drivers, formerly attributed to "Everest Group 2026", "Gartner 2026", "Forrester 2026" and "ACA International 2026".
No survey named, no population stated, no document reachable. Forrester and BIA Advisory failed the same trace on our missed-call report and were removed there for the same reason.
$47B
"Forecast voice AI market by 2030", formerly attributed to Juniper Research.
Juniper publishes conversational AI and commerce forecasts, but no reachable Juniper document carrying a 47 billion dollar voice AI figure for 2030 could be found, and the deep URLs previously used for it return 404. Removed as unverifiable rather than replaced with a different market forecast.
~30%
"Average reduction in service cost from AI deployment", attributed to McKinsey State of AI 2024. A late correction replaced an earlier unsourced 70% with this, and stopped there.
The referent is wrong, not just the number. McKinsey State of AI reports the share of respondents who saw costs decrease within a business function, not an average percentage cost reduction, so a figure of this shape cannot come from it. We could not reach the document from here to quote it, and a figure we cannot quote does not go on the page. Removed rather than restated.
3-5x
"Increase in first-attempt answer rate", formerly attributed to Invoca 2026.
Invoca attributions failed the same trace on our missed-call report, where two cited Invoca URLs returned 404 with no Internet Archive capture at any date. Removed on the same basis.
38%
"SMBs using AI voice in at least one call workflow." Carried no source at all.
An unattributed percentage is not a weak citation, it is no citation. Removed.
5 days
"Median time to first production call, managed deployments." Carried no source, and asserted a delivery record.
This is a production receipt about our own work. We have no closed deployments to draw a median from, so the figure could only have been invented. Removed, and no substitute offered.

Methodology

Every figure on this page was checked against a reachable primary document before it was kept, and the sample size and independence of the source are stated on the figure rather than left implied. An earlier version attributed adoption percentages to Juniper Research, Everest Group, IDC, Gartner, Forrester, BIA, ACA International, Clio, Henry Schein, AAHA, Invoca and McKinsey. With one exception those attributions named a future-dated report and linked only to an organisation home page, and no document carrying the figures could be located; they have been removed with their attributions rather than re-sourced to something else. The McKinsey figure is a separate failure worth naming: it was correctly attributed to a real publication but described a quantity that publication does not report, which is how a sourced figure can still be wrong about what it measured. Only one study survived the check, and this report says so rather than padding the page back to its former length. No figure here is our own measurement, and this report claims no deployment data of any kind.

Sources

Turn these numbers into operations

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