AI sales agent statistics at a glance
| Category | Statistic | Source |
|---|---|---|
| Adoption | 54% of sales teams use AI agents today and another 34% expect to within two years | Salesforce, 2026 |
| Adoption | 34% of sales teams with agents point them at prospecting | Salesforce, 2026 |
| Adoption | "AI SDR" appeared as a distinct job category for the first time in 2025, at 1% of respondents | The Bridge Group, 2025 |
| Human baseline | Median SDR quota is 10 held meetings a month; 60% of SDRs hit it, the lowest in the study's history | The Bridge Group, 2025 |
| Human baseline | Median SDR on-target earnings: $80,000, unchanged since 2022 | The Bridge Group, 2025 |
| Cost | $667 of SDR pay behind every booked meeting at full quota, computed on the sibling inbound vs outbound page | The Bridge Group, February 2025 |
| Vendor data | Salesforce's own agents created 3,200 opportunities from 130,000 dormant leads in four months, a 2.5% lead-to-opportunity rate | Computed from Salesforce, 2026 |
| Performance | An average rep needs about 403 dials per booked meeting; a top-quartile rep needs 45 | Computed from Gong, 2024 |
| Performance | At 800 dials a month, the average rep books 2 meetings and the top-quartile rep books 18 | Gong, 2024 |
| Sentiment | 88% of reps who have agents say AI makes them more productive | Salesforce, 2026 |
| Forecast | Gartner predicts AI agents will outnumber sellers 10 to 1 by 2028, with fewer than 40% of sellers saying agents improved productivity | Gartner, July 2026 |
| Buyers | 67% of B2B buyers prefer a rep-free experience, and 45% used AI during a recent purchase | Gartner, March 2026 |
| Buyers | 69% of B2B buyers still turn to a sales rep to validate what AI told them | Gartner, May 2026 |
| Reality check | 95% of organisations get zero return from generative AI, and only 5% of integrated pilots extract measurable value | MIT Project NANDA, July 2025 |
| Reality check | Only 16% of enterprise AI deployments qualify as true agents | Menlo Ventures, December 2025 |
How many sales teams use AI sales agents today?
Salesforce found that 54% of sales teams use AI agents today, with another 34% expecting to inside two years, in a survey of 4,050 sales professionals across 22 countries fielded in August and September 2025.
| Statistic | Source |
|---|---|
| 54% of sales teams use AI agents now, 34% expect to within two years, 8% within five years, and 3% do not expect to use them. Survey of 4,050 sales professionals in 22 countries, August to September 2025. | Salesforce State of Sales, 7th edition, 2026 |
| 94% of sales leaders who have agents call them critical for meeting business demands. | Salesforce, 2026 |
| 88% of organisations used AI in 2025, against 78% in 2024 as reported by the previous edition of the same index. | Stanford HAI AI Index, 2026 and 2025 |
| Enterprises spent $37 billion on generative AI in 2025, up from $11.5 billion in 2024, from a survey of roughly 500 enterprise decision makers. | Menlo Ventures, December 2025 |
| 90% of sales professionals with agents say AI helps them understand customers better, 88% say it improves their odds of hitting targets, and 83% say it increases job satisfaction. Base: sales pros with AI agents. | Salesforce, 2026 |
| Only one in five companies has a mature governance model for autonomous AI agents. Survey of 3,235 leaders in 24 countries, August to September 2025. | Deloitte, 2026 |
| "AI SDR" appeared as a distinct respondent category for the first time in 2025, at 1% of the 351 B2B companies surveyed. | The Bridge Group, February 2025 |
Two of those numbers sit badly together and both are worth keeping. Salesforce says 54% of sales teams already run agents. The Bridge Group, asking sales development leaders about how their teams are structured, found AI SDRs at 1% of respondents. They are measuring different things: one counts any agent touching any part of the sales cycle, the other counts a named seat in the org chart. When a headline says "half of sales teams use AI agents", it almost always means the first definition.
I read the 54% as software adoption, not headcount replacement. Sales teams have been buying features called agents inside tools they already pay for, which is very different from handing a territory to a machine. The Bridge Group's 1% is the honest count of the second thing, and it is the number I would quote to a board that is being sold a restructuring.
What do AI sales agents do inside the sales process?
Prospecting is the most common job: 34% of sales teams with agents use them for prospecting, and 92% of sales professionals with agents say AI benefits their prospecting work (Salesforce, 2026).
| Statistic | Source |
|---|---|
| 34% of sales teams with agents use them for prospecting; 92% of sales pros with agents say AI benefits prospecting. | Salesforce, 2026 |
| High performers are 1.7 times more likely than underperformers to use prospecting agents. | Salesforce, 2026 |
| 47% of sales reps say cold outreach is one of the worst parts of the job, and 47% say their team lacks the bandwidth to do it. | Salesforce, 2026 |
| Sales professionals spend 16% of an average week on preparation and planning; 91% say AI benefits sales planning. | Salesforce, 2026 |
| Sales and marketing functions absorb the largest slice of generative AI budgets: the report's summary line says 50%, its body says about 70% of a hypothetical $100 allocation. Research period January to June 2025. | MIT Project NANDA, July 2025 |
| 10% of US workers use AI chatbots at work daily or several times a week; 55% rarely or never do. Survey of 5,273 employed US adults, October 2024. | Pew Research Center, February 2025 |
| Vendor data: Salesforce says its own agents contacted 130,000 previously untouched leads and created 3,200 opportunities in four months. | Salesforce State of Sales foreword, 2026 |
Salesforce's own disclosure is the most useful vendor number on this page because it states both sides of the ratio. 130,000 leads contacted produced 3,200 opportunities, a 2.5% lead-to-opportunity rate on leads the company admits it was previously ignoring, described in the report's foreword by its executive vice president of sales. That is a recovery play on dead inventory, not a replacement for a rep working a named account list, and the company describes it that way.
The budget split is worth sitting with too. MIT's Project NANDA asked executives to spread a hypothetical $100 of generative AI budget across functions and found sales and marketing taking the biggest share, then argued that back office automation returns more. The report contradicts itself on the size of that share, 50% in its summary line and about 70% in the body, which is a fair warning about how loosely these figures travel once they reach a slide.
Three different things get filed under "AI sales agent" and they behave nothing alike. There is AI assisting a human seller, which is the 88% satisfaction story. There is an autonomous agent running outbound on its own, which is where the evidence is thinnest. And there is a voice or chat agent answering inbound, which is the only one of the three with published operational numbers at scale. Mixing them is how a 2.5% lead-to-opportunity rate on dead leads turns into a slide claiming AI replaces SDRs.
AI SDR vs human SDR: what does each cost per meeting?
A human SDR at full quota costs about $667 in on-target earnings per booked meeting, a number I computed from The Bridge Group's 2025 medians of $80,000 on-target earnings and 10 stage 0 meetings a month, and first published on the inbound versus outbound statistics page. No AI SDR vendor publishes a comparable figure with a stated sample, so the second half of that comparison does not exist in public data.
| Statistic | Source |
|---|---|
| Median SDR on-target earnings: $80,000, split 68:32 between $55,000 base and $25,000 variable. | The Bridge Group, 2025 |
| Median monthly SDR quota: 10 stage 0 opportunities, which The Bridge Group counts as meetings that happen rather than meetings merely booked. | The Bridge Group, February 2025 |
| $667 in rep pay per booked meeting at 100% quota attainment, before tooling, benefits or management overhead. Computed on the inbound vs outbound page, repeated here for the comparison. | Computed from The Bridge Group, February 2025 |
| Only 60% of SDRs hit quota in 2025, the lowest share The Bridge Group has recorded in ten rounds of research. | The Bridge Group, 2025 |
| Average SDR ramp time is 3.0 months, the shortest since 2010; median tenure is 1.9 years and annual attrition ran 40% in 2024. | The Bridge Group, 2025 |
| Pipeline sourced per SDR rose to $3.78 million in 2025 from $2.83 million in 2022. | The Bridge Group, 2025 |
| SDR-to-AE ratio has held at 1 SDR per 2.4 AEs since 2018. | The Bridge Group, 2025 |
On reply rates the picture is worse. I could not find a single AI SDR reply-rate study that states a sample size, a date range and a control group on the same page. The numbers circulating in 2026 come from vendor blogs and aggregator sites reprinting each other, and the same figures appear with different attributions. Under the rule this page follows, none of them qualify. The honest answer to "do AI SDRs get better reply rates than humans" in September 2026 is that no public dataset can settle it.
What can be compared is ramp and risk. A human SDR takes 3.0 months to reach productivity, stays 1.9 years and has a 40% chance of leaving within the year. Those costs are real and recurring, and they are the strongest argument for automating the first touch. They are not an argument that the automated touch performs as well.
If a vendor quotes you a cost per meeting, ask three questions: over how many accounts, across what period, and what happened to show rate and meeting-to-opportunity conversion. I have yet to see a public answer to the third. A booked meeting is a cheap unit to manufacture and an expensive one to waste, and every honest comparison has to price the wasted ones.
What does a human sales rep achieve on outbound?
Gong's analysis of more than 300 million cold calls found the average rep connects with 5.4% of prospects and books a meeting on 4.6% of those conversations, against 13.3% and 16.7% for top-quartile reps.
| Statistic | Source |
|---|---|
| Average rep connect rate 5.4%; top-quartile rep 13.3%. Analysis of over 300 million cold calls, published July 2024. | Gong, 2024 |
| It takes 19 dials for an average rep to get one conversation and 8 for a top-quartile rep. | Gong, 2024 |
| Conversation-to-meeting set rate: 4.6% for the average rep, 16.7% for the top quartile. | Gong, 2024 |
| At 800 dials a month, the average rep books 2 meetings and the top-quartile rep books 18. | Gong, 2024 |
| Cold calling nearly doubles email reply rates: 3.44% for contacts who were also called, 1.81% for those who were not. | Gong, 2024 |
| Median daily quality conversations per SDR: 4.1, the first rebound in several years. | The Bridge Group, 2025 |
| 655,000 sales opportunities worth $48 billion and 240,000 minutes of discovery calls were analysed in Ebsta's 2025 GTM benchmark study of 349 high-performing companies. | Ebsta, 2025 |
Gong sells revenue intelligence software and these are its own customers' calls, so read it as vendor-published data with an unusually large sample. The figures are from July 2024, which makes them the oldest operational data on this page alongside Klarna's. I keep them because nothing comparable has been published since at that scale, and because the structure of the finding, a wide spread between average and excellent, is unlikely to have inverted in two years. Gong does not publish the dates its 300 million calls were placed, only the sample size, so treat the rates as a 2024 snapshot rather than a current benchmark.
Multiplying the two rates is Gong's own arithmetic, not mine. Gong states that 800 dials a month yields 2 meetings for an average rep and 18 for a top-quartile rep, which is exactly what 0.054 x 0.046 and 0.133 x 0.167 produce at that volume. That is the check that makes the 403 and 45 dial figures safe to quote.
Multiply those two rates and the average human rep converts a quarter of one percent of dials into a meeting. That is the real floor, and it explains why automated outbound sells so well: the thing being automated is already failing 99.75% of the time. It also explains why volume automation backfires. Nine times out of ten you are scaling the failure, and the prospect experiences all of it.
Do buyers want to deal with an AI sales agent?
Gartner found that 67% of B2B buyers prefer a rep-free experience while 69% still turn to a sales rep to validate AI-generated insights, from two surveys of about 645 B2B buyers each, fielded August to September 2025.
| Statistic | Source |
|---|---|
| 67% of B2B buyers prefer a sales rep-free experience and 70% prefer a completely digital, self-service buying experience. Survey of 646 B2B buyers, August to September 2025. | Gartner, March 2026 |
| 45% of B2B buyers used generative AI during a recent purchase, mainly to gather information on vendors and products. | Gartner, March 2026 |
| 69% of B2B buyers turn to sales reps to validate AI-generated insights. Survey of 645 B2B buyers, August to September 2025. | Gartner, May 2026 |
| Buyers used an average of seven information sources during a recent purchase. | Gartner, May 2026 |
| 51% of buyers say they are more likely to meet misleading information from generative AI; 49% say the same about a sales rep. | Gartner, May 2026 |
| 64% of consumers say they are more likely to trust AI agents that show traits like friendliness and empathy; half have already engaged with voice AI. Survey of 5,100 consumers and 5,400 CX leaders and agents across 22 countries. | Zendesk CX Trends, November 2024 |
The 51% against 49% split is the finding I would put in front of anyone arguing that AI outreach damages trust. Buyers rate a generative AI system and a salesperson as almost equally likely to mislead them. That is not a compliment to the machine.
Buyers want the rep out of the discovery stage and back in for the decision. Seven information sources and a 69% validation rate describe a purchase where AI does the reading and a human does the reassuring. Any agent strategy that automates the reassurance step is solving the wrong half of the job. If speed of response is your bottleneck rather than trust, the numbers on lead response time are the better place to start.
Can AI agents handle inbound conversations at scale?
Yes, at least on volume: Klarna reported in February 2024 that its AI assistant handled 2.3 million conversations in its first month, two-thirds of its customer service chats, doing the equivalent work of 700 full-time agents. Klarna then spent 2025 hiring human agents back, so read the volume and the staffing claim separately.
| Statistic | Source |
|---|---|
| Vendor data, DATED: Klarna's AI assistant handled 2.3 million conversations in its first month, two-thirds of all customer service chats, across 23 markets and more than 35 languages. | Klarna, February 2024 |
| Klarna reported average resolution time falling from 11 minutes to under 2 minutes, a 25% drop in repeat enquiries, and an expected $40 million profit improvement in 2024. | Klarna, February 2024 |
| Klarna reversed part of that move in 2025: Bloomberg reported in May 2025 that it was hiring human customer service staff again, its chief executive called human support a VIP service in June 2025, and total headcount fell from about 5,500 to about 3,000 over two years. | TechCrunch, June 2025 |
| 3,286 conversations per full-time-agent-month implied by Klarna's own comparison, roughly 156 per agent per working day. | Computed from Klarna, 2024 |
| 75% of customer experience leaders expect 80% of customer interactions to be resolved without human intervention. | Zendesk CX Trends, November 2024 |
| More than 90% of enterprises tested third-party applications for customer support rather than building their own. Survey of 100 CIOs across 15 industries, May 2025. | a16z, May 2025 |
| Customer support tools captured $630 million of enterprise generative AI application spend in 2025; marketing platforms took $660 million. | Menlo Ventures, December 2025 |
Divide Klarna's 2.3 million conversations by the 700 agents it says the assistant replaced and you get 3,286 conversations per agent per month. Over 21 working days that is 156 conversations a day, one every three minutes across an eight-hour shift with no breaks. Klarna's own claim therefore describes a capacity equivalence in a queue of short, repetitive enquiries, not 700 jobs of the kind a support team staffs.
Klarna also walked a good part of it back, which the 2024 press release obviously cannot tell you. Bloomberg reported in May 2025 that the company was hiring human customer service staff again, and by June 2025 its chief executive Sebastian Siemiatkowski was calling human support a VIP service, with headcount down from about 5,500 two years earlier to roughly 3,000 (TechCrunch, June 2025). The capacity numbers still stand. The conclusion people drew from them in 2024, that a support organisation can be swapped for a model, did not.
Inbound works because the customer arrived with a question and a deadline. Outbound has neither. That asymmetry, not model quality, is why the volume numbers in support are credible and the meeting numbers in outbound are not. Klarna's retreat does not undo the volume, it prices the last mile: the hard cases stayed human and got more expensive. If you are choosing where to put an agent first, put it where someone is already trying to reach you. The same logic runs through the AI workflow automation numbers.
Why is the evidence on AI sales agents so thin?
Most AI sales agent claims fail a basic test: MIT's Project NANDA found in 2025 that 95% of organisations get zero return from generative AI, from an analysis of more than 300 public deployments plus 52 executive interviews and a survey of 153 leaders.
| Statistic | Source |
|---|---|
| 95% of organisations get zero return from generative AI. Analysis of more than 300 public AI deployments, 52 executive interviews and 153 surveyed leaders, research period January to June 2025. | MIT Project NANDA, July 2025 |
| Only 5% of custom enterprise AI tools reach production. For task-specific generative AI tools the report charts 60% of organisations investigating, 20% piloting and 5% reaching production. | MIT Project NANDA, July 2025 |
| Only 16% of enterprise AI deployments qualify as true agents, against 27% of startup deployments. | Menlo Ventures, December 2025 |
| 74% of organisations hope to grow revenue through AI, against 20% already doing so. Survey of 3,235 leaders in 24 countries, August to September 2025. | Deloitte, 2026 |
| Gartner predicts AI agents will outnumber sellers 10 to 1 by 2028, yet fewer than 40% of sellers will say agents improved their productivity. Based on a survey of 210 chief sales officers and senior sales executives, January to February 2026. | Gartner, July 2026 |
| 51% of sales professionals say security concerns delayed their AI initiatives, and 46% of those with agents say data quality issues hurt their sales. | Salesforce, 2026 |
| 42% of sales reps say they are overwhelmed by too many tools. | Salesforce, 2026 |
| 35% of sales reps expected to hit 100% of quota or more in 2025, against 16% in 2024, computed from Salesforce's reported bands. | Computed from Salesforce, 2026 |
Here is what nobody has published, as of September 2026, with a sample and a method attached: show rates for AI-booked meetings against human-booked meetings, meeting-to-opportunity conversion for AI-sourced meetings, deliverability and domain reputation effects of agent-run outbound at volume, and any randomised or matched comparison of an AI SDR against a human SDR on the same target list. Four questions, no public answers. Anyone quoting a precise AI-versus-human uplift figure in 2026 is quoting a vendor's own funnel with no control.
Two numbers get put side by side a lot, and they should not be subtracted. Salesforce measured 88% of reps who already have agents saying AI makes them more productive, in August and September 2025. Gartner forecasts that fewer than 40% of sellers will say agents improved their productivity by 2028. One is a satisfaction reading today from a vendor selling agents, the other is a prediction about a different population three years out from a firm selling advice on buying them. My own bet is that Gartner is closer, because satisfaction surveys measure relief from admin work and a productivity claim has to show up in pipeline.
How we calculated the original numbers
Four figures on this page are computed here and do not appear anywhere else. Here is the arithmetic, so you can check it or swap the inputs.
- 2.5% lead-to-opportunity rate from Salesforce's own agents. The foreword to Salesforce's 2026 State of Sales report, written by its executive vice president of sales, says agents contacted 130,000 previously untouched leads and created 3,200 opportunities in four months. 3,200 divided by 130,000 is 2.46%, which rounds to 2.5%. It is a vendor figure about a vendor's own deployment, and it is still the most useful one published in 2026, because it prints both the numerator and the denominator. The comparison it invites is not with a rep's win rate but with whatever your own dormant leads convert at today, which for most teams is zero.
- 9x: the spread between an average and a top-quartile human rep. Gong's 300-million-call dataset gives the average rep a 5.4% connect rate and a 4.6% conversation-to-meeting set rate. Multiplied, 0.054 x 0.046 = 0.248% of dials become meetings, or one meeting per 403 dials. For the top quartile, 0.133 x 0.167 = 2.221%, or one meeting per 45 dials. 403 divided by 45 is 9.0. Gong's own published outcome at 800 dials a month, 2 meetings for an average rep and 18 for a top-quartile rep, matches that multiplication, which is why the two rates can be chained.
- 35% of reps expect to hit quota in 2025, against 16% in 2024. Salesforce publishes quota expectations in bands rather than as a single figure. Adding the 100% band (18%) to the 101% to 125%+ band (17%) gives 35% for 2025; the same two bands for 2024 were 10% and 6%, giving 16%. Expectations are not attainment, and the gap between those two is exactly what an agent story tends to be sold into.
- 3,286 conversations per agent-month implied by Klarna. Klarna stated that its AI assistant handled 2.3 million conversations in one month, the equivalent work of 700 full-time agents. 2,300,000 divided by 700 is 3,286 conversations per agent per month; divided by 21 working days that is 156 a day. Read it as a statement about queue volume in short enquiries, not about 700 replaceable roles, and read it next to Klarna's 2025 decision to hire human agents back.
One number on this page was computed elsewhere and is repeated for the comparison: the $667 of SDR pay behind a booked meeting, which I worked out from The Bridge Group's medians on the inbound versus outbound statistics page. It is the same arithmetic, $80,000 of on-target earnings divided by 120 stage 0 meetings a year, and it belongs here because it is the human price tag any agent claim gets measured against.
What these numbers say about AI sales agents in 2026
Adoption is real and shallow. 54% of sales teams touching an agent (Salesforce, 2026) sits next to 16% of enterprise AI deployments qualifying as true agents (Menlo Ventures, December 2025) and 1% of sales development teams having an AI SDR seat (The Bridge Group, 2025). Most of what is called an agent today is a feature inside software a team already bought.
The human baseline is worse than people think, which cuts both ways. A quarter of one percent of dials becomes a meeting for the average rep, only 60% of SDRs hit quota, and 40% leave within a year. Automating that is tempting for good reasons. It also means any vendor comparison against "the average rep" is competing with a very low bar and should be asked to compete with the top quartile instead.
Inbound has evidence, outbound has assertions. Klarna's volume numbers, Zendesk's expectations and the spend data all point the same way: agents that answer people who are already asking have a measurable job. Klarna's 2025 rehiring shows where that job ends, at the complicated conversations a queue metric never counted. The outbound claims, the ones about meetings booked and replies earned, have no published sample I could verify. Until somebody runs an AI SDR and a human SDR against the same list and publishes the show rates, the comparison stays a marketing exercise. If you are deciding where to spend next quarter, the inbound and outbound marketing numbers and the rest of the statistics library are a better guide than any vendor deck.
FAQ
How many sales teams use AI agents in 2026?
54% of sales teams use AI agents today and another 34% expect to within two years, according to Salesforce's seventh State of Sales report, a survey of 4,050 sales professionals in 22 countries fielded in August and September 2025. The same report finds 94% of sales leaders who have agents call them critical for meeting business demands. Note that this counts any agent anywhere in the sales cycle, not a dedicated AI SDR seat.
What does a human SDR cost per booked meeting?
$667 in rep pay per meeting at full quota, computed from The Bridge Group's February 2025 medians of $80,000 on-target earnings and 10 stage 0 meetings a month across 351 B2B companies, and first published on my inbound versus outbound statistics page. That figure excludes tooling, benefits, recruiting and management time, and only 60% of SDRs reach quota, so a team's realised cost per meeting runs higher.
Do AI SDRs get better reply rates than human SDRs?
No public dataset answers this as of September 2026. Every AI SDR reply-rate benchmark circulating this year comes from vendor blogs or aggregator sites without a stated sample, period or control group. For the human side, Gong's 300-million-call analysis (2024) puts the average rep at 5.4% connect and 4.6% conversation-to-meeting, and finds email reply rates of 3.44% for contacts who were also called against 1.81% for those who were not.
Will AI agents replace sales reps?
Gartner predicts AI agents will outnumber sellers 10 to 1 by 2028 while fewer than 40% of sellers will say agents improved their productivity, based on a survey of 210 chief sales officers conducted January to February 2026. Outnumbering is not replacing. Gartner also finds 69% of B2B buyers turn to a human rep to validate AI-generated insights (645 buyers, 2026).
Do B2B buyers prefer buying without a sales rep?
67% of B2B buyers prefer a rep-free experience and 70% prefer a completely digital, self-service purchase, from Gartner's survey of 646 B2B buyers fielded August to September 2025. The same research shows 45% used generative AI during a recent purchase, and 69% still ask a rep to check what the AI told them.
How well do AI agents handle inbound customer conversations?
Klarna reported in February 2024 that its AI assistant handled 2.3 million conversations in its first month, two-thirds of its customer service chats, with resolution time falling from 11 minutes to under 2. That works out to 3,286 conversations per full-time-agent-month on Klarna's own comparison. Klarna then reversed part of the move in 2025, hiring human customer service staff again and repositioning human support as a VIP service (TechCrunch, June 2025). Zendesk's 2025 CX Trends report found 75% of customer experience leaders expect 80% of interactions to be resolved without a human.
Sources
- The Bridge Group, SDR Metrics and Compensation Report, 10th edition (February 2025, 351 B2B companies)
- Salesforce, State of Sales Report, 7th edition (2026, 4,050 sales professionals in 22 countries, fielded August to September 2025)
- Gong, The hidden power of cold calling: insights from 300M calls (July 2024)
- Gartner, AI agents will outnumber sellers 10 to 1 by 2028 (July 2026, 210 chief sales officers)
- Gartner, 67% of B2B buyers prefer a rep-free experience (March 2026, 646 B2B buyers)
- Gartner, 69% of B2B buyers turn to sales reps to validate AI-generated insights (May 2026, 645 B2B buyers)
- Klarna, AI assistant handles two-thirds of customer service chats in its first month (February 2024)
- TechCrunch, Klarna CEO says company will use humans to offer VIP customer service (June 2025)
- Zendesk, 2025 CX Trends Report press release (November 2024, 5,100 consumers and 5,400 CX leaders in 22 countries)
- MIT Project NANDA, The GenAI Divide: State of AI in Business 2025 (dated July 2025, research period January to June 2025)
- Menlo Ventures, The State of Generative AI in the Enterprise (December 2025, about 500 US enterprise decision makers surveyed 7 to 25 November 2025)
- Deloitte, State of Generative AI in the Enterprise (2026, 3,235 leaders in 24 countries)
- Stanford HAI, AI Index Report 2026 (2026, organisational adoption reached 88% in 2025); the 2025 edition reported 78% for 2024
- a16z, The 2025 Enterprise AI Landscape survey (May 2025, 100 CIOs across 15 industries)
- Pew Research Center, US workers are more worried than hopeful about future AI use in the workplace (February 2025, 5,273 employed US adults)
- Ebsta, 2025 GTM Benchmarks (2025, 655,000 opportunities worth $48 billion)
Methodology. Numbers were collected in September 2026 and checked against the original publisher's page, report or press release, not against statistics roundups. Any vendor claim without a stated sample and period was excluded. Self-reported deployment figures from Salesforce and Klarna are labelled as vendor data in the row that carries them, and the other vendor-published datasets, Gong's call analysis and Zendesk's CX Trends survey, are named with their sample in the sentence. Two claims were dropped during review for failing that rule: HubSpot's cold-calling AI figures, which the source page publishes without a sample or a field date, and BCG's AI Radar percentages, whose page could not be retrieved for checking. Three categories are kept separate throughout: AI assisting a human seller, autonomous agents running outreach, and voice or chat agents handling inbound. Two sources are older than 24 months and marked as dated in the text: Gong's July 2024 cold-call analysis and Klarna's February 2024 press release, the second of which is read alongside Klarna's 2025 reversal. Gartner's press releases block automated fetching; their headline findings and survey details were taken from the releases themselves. Last updated 22 September 2026.