State of enterprise AI
The state of enterprise AI
What real enterprise AI deployments actually look like, in numbers — where they happen, what they deliver, whether they last, and which platforms run them. Computed from a continuously updated, source-linked corpus, not a survey.
- Data as of
- Aug 25, 2026
- Dataset revision
- dsr-d2824fe839d09681
- Canonical record count
- 3,811
3,811
18
85
6
As of Sep 2023 – Aug 2026, AI Use Cases Hub tracks 3,811 source-linked enterprise AI deployments across 18 industries and 85 countries. Where outcomes are quantified, the most-documented is time & speed — a median 50% reduction across 295 cases. Of the 154 deployments first documented in 2023 we can judge, 97% still have live public evidence today.
Expansion velocity
The second-deployment clock
Median time from an organization's first documented AI deployment to its second, by starting year · Lower is faster.
Median time from first to second documented deployment by starting-year cohort; lower is faster.
The latest observed median is 4.2 months while its observation window is still open, compared with 34.2 months for 2021 starters.
2021 median
34.2months
2025 medianwindow open
4.2months
Median time to second documented deployment
Months · lower is faster
Expanded within 12 months
Observed repeat deployers · complete windows
Recent cohort medians are right-censored. The 12-month comparison uses complete-window cohorts and measures the share among observed repeat deployers, not all starters.
Outcome claims
The round-number economy
Where 1,111 quantified outcome claims land, in 10-point ranges · Explicit % units only.
Vendors report improvements in suspiciously tidy numbers: 69.8% of quantified claims are multiples of five (49.8% are multiples of ten). The median claim is a 40% improvement, and the 20–29% range alone holds 18.6% of every claim in the catalog.
Reported improvement (%)
Only claims with an explicit percent unit count (41 claims above 100% are excluded from the histogram). Claims are vendor-reported and unaudited — the round-number clustering is the story, not a benchmark. The orange bar marks the most common range.
Delivery model
GenAI went direct
Share of deployments whose public evidence names an implementation partner, by publish year.
In the pre-GenAI corpus (2019–2022), 8.5% of documented deployments named an implementation partner. In the GenAI era it's 8.1% of 3,010 deployments — more organizations are shipping AI with the platform vendor alone.
Gray bars are pre-GenAI years. Column height is the year's full deployment count, so the thin early cohorts (under 100 deployments a year) are visibly thin — a single case moves those shares by more than a point. Counts deployments whose public evidence names at least one partner; first-party vendor posts under-report partner involvement, so read these shares as floors, not market sizing.
Momentum map
Which AI use cases are compounding?
Adoption volume vs. share of each use case's evidence published in the last 12 months · Select a point for exact values and its deep dive.
Cloud migration is the breakout: 55.1% of its evidence is under a year old (corpus average: 33.3%). At the other corner, customer service automation has 192 documented deployments but only 26% recent evidence — a large installed base that has stopped making news.
+1 labels hidden for clarity. Hover or focus a dot for details.
Showing the 20 highest-volume types of 42 with at least 25 documented deployments — the full taxonomy lives in the types directory. The momentum share is not size-adjusted — a small type reaches a high share far more easily than a 192-case workhorse. Volume split at the plotted median (78 deployments).
Agentification map
Which workloads are going agentic?
Adoption volume vs. share of each use case's deployments that are agentic · Select a point for exact values and its deep dive.
Agents are eating workflows, not perception: automotive operations automation is 84.3% agentic, while cloud migration sits at 5.1% (corpus average: 29.5%).
+1 labels hidden for clarity. Hover or focus a dot for details.
"Agentic" reflects our per-case classifier, not vendor self-description. Showing the 20 highest-volume types of 42 with at least 25 documented deployments.
Reach vs proof
Which use cases are both broad and measured?
How many industries each use case spans vs. the share of its cases that report a hard number · Select a point for exact values and its deep dive.
Document automation is the model horizontal — it spans 11 industries and puts a number on 61.4% of its cases. The bottom-left — narrow use cases that rarely quantify — is where adoption outruns evidence.
+3 labels hidden for clarity. Hover or focus a dot for details.
Breadth counts industries where a type has at least two documented cases; the proof axis is the share of a type's cases carrying an extracted quantified outcome (not the size of that outcome). Showing the 20 highest-volume types. Breadth split at the plotted median (9.5).
By industry
Where AI is deployed
Documented deployments by industry (top 10).
By platform
The platform landscape
Documented deployments by cloud platform.
Platform share here reflects which deployments we can document, not vendor market share — our source mix is weighted toward publishers with large public case-study libraries.
Compare providers →Outcomes
What it delivers
Median reported change by outcome category, among the 1,488 cases with a quantified result.
Time & speed
−50%
median reduction · 295 cases · p25–p75 30–76%
Cost savings
−40%
median reduction · 171 cases · p25–p75 25–70%
Productivity & throughput
+40%
median improvement · 100 cases · p25–p75 25–71%
Quality & accuracy
+40%
median improvement · 55 cases · p25–p75 21–95%
Durability
Do they last?
Of the 154 deployments first documented in 2023 that we can judge, 97% still have live public evidence today — 68% because the organization has since been documented doing more AI.
How persistence is measured →ⓘ How this is measured
Every figure is computed from the AI Use Cases Hub corpus — a continuously updated set of real, source-linked enterprise AI deployments — not a survey or estimate. Deployment counts are exact; outcome figures cover only the subset of cases that report a quantified result; persistence covers the 2023 cohort old enough to judge.
Expansion velocity pairs each named organization's first two dated cases (generic entity and platform-vendor names are excluded so unrelated organizations are never fused). Recent cohorts are right-censored, so each cohort also reports a fixed 12-month expansion share; cohorts with fewer than 10 qualifying organizations are omitted.