Most AI statistics count purchases. These measure whether the purchase worked, where teams have drawn the line, and how far ahead the companies that got it right already are.
There is no shortage of AI adoption statistics. Almost all of them count how many companies bought something.
Adoption is close to universal now, so that number has stopped being interesting. The useful questions are whether it worked, what is blocking it, and what teams refuse to hand over.
Ten pieces of research answer those directly. IBM asked 1,200 Salesforce customers whether their AI initiatives hit their targets. Gong surveyed 3,048 revenue leaders and analysed 7.1 million opportunities. McKinsey asked nearly 4,000 decision-makers across 13 countries.
The short version. A third of initiatives meet their ROI targets. What blocks the rest is the data and the systems underneath, not the model. And the companies already ahead are pulling away fast.
Each section is shaped to its data rather than flattened into one list.
Proportions are drawn as a hundred squares, ranked figures as bars, leader and laggard splits as opposing bars, and year-on-year movement as a shift.
Every figure carries a numbered link to the research it came from.
The ROI gap
of AI initiatives are meeting their ROI targets
Each square is one initiative in a hundred. The filled ones paid back.
Adoption
How deeply AI is embedded across sales and marketing workflows4
82% operate with AI moderately or deeply embedded. The interesting number is how few call it finished.
Maturity
Where roughly 200 companies actually are with AI in pricing and sales8
Only 8% have embedded it. The bulk of the market is running pilots, which is a very different thing from the adoption headlines.
By industry
The divide
Both bars come from the same survey. Leaders are those whose market share grew more than 10% against last year.
The blockers
Percentage citing each. Data quality is the leading barrier specifically for agentic AI, and not one of these is a problem with the model.
The boundary
Adoption has not meant handing over the relationship. The boundary is deliberate.
What it is used for
Prospecting is the only one falling. The work moved towards writing, enriching and automating rather than finding.
What sellers say
Reported by sellers already using AI, so it measures satisfaction among adopters rather than the whole population.
The buying side
The shift is real at mid-size order values and has stalled above them.
One in three, on IBM's survey of more than 1,200 Salesforce customers.
Separately, 72% failed to scale beyond the unit that piloted them and 20% stalled, failed or were abandoned. Only 17% of go-to-market leaders call their AI fully operational and measured.
Adoption itself is near saturation. Apollo puts it at 97% among go-to-market leaders and Seamless recorded 92.5% using AI daily as early as 2025.
Depth is the live question. G2 finds 82% moderately or deeply embedded, which leaves a substantial share still shallow.
Gong's analysis of 7.1 million opportunities found teams leaning heavily on AI generate 77% more revenue per rep and are 65% more likely to increase win rates.
That is a correlation across adopters, not proof that buying a tool causes the outcome.
The people running these teams do not think so. Only 6% of go-to-market leaders expect AI to replace their teams, and 28% of revenue leaders anticipate job eliminations against 21% expecting new roles.
G2 finds 85% automating top-of-funnel and administrative work while deliberately keeping relationship selling human-led.
Data and systems. Legacy modernisation at 64%, data availability and quality at 53%, disconnected systems at 51%.
None of those is an AI problem. All of them predate it.
Every figure was read at the publisher's own page or report rather than taken from a roundup.
Sample sizes are given for each source below and they vary a great deal. Gong's combines a 3,048 person survey with 7.1 million analysed opportunities; others are single surveys.
Several sources are vendors publishing research about their own category. That is stated, and their data is still the best available on these questions.
Paired figures such as 60% against 21% are leader and laggard splits from within one survey, not two separate studies.
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