IV. The Proof: Check Our Work
AI Investment and Exposure: The Certified Data
AI Monetization and Investment Signals
This section is the investment-signal half of the AI story: how companies price AI, where they expect it to pay off, and how fast they plan to spend on it. Two things stand out in the table. Monetization is already common, with 67% of AI-Native and AI-Enabled respondents charging for AI capability, most of it folded into subscription rather than usage pricing. And the spend intentions all point one direction.
| Metric | Value | Source |
|---|---|---|
| Respondent classification: AI-Enabled / AI-Interested / AI-Native / Non-AI | 31 / 19 / 5 / 0 | 2025 Survey, p. 8 |
| AI monetization: actively monetizing | 67% | 2025 Survey, p. 24 (n=36; AI-Native/Enabled only) |
| AI monetization: testing / not monetizing | 19% / 14% | 2025 Survey, p. 24 |
| AI monetization strategy: subscription / hybrid / usage-based | 58% / 25% / 17% | 2025 Survey, p. 24 |
| Top AI opportunity areas (% of respondents) | new products/services 82%; back-office automation 56%; customer service & support 55%; quality control 45%; sales/GTM enablement 40%; workforce training 29%; workforce reduction 15% | 2025 Survey, p. 25 |
| Change in AI spend, next year (counts, forecast) ᴱ | increase 21%+: 24; increase 1–10%: 16; increase 11–20%: 10; no change: 4; any decrease: 0 | 2025 Survey, p. 23 |
| Timeline to material AI impact (counts) | already material: 30; within 1 year: 16 ᴱ; 2 years: 7 ᴱ; 3 years: 2 ᴱ; 4+ years: 0 ᴱ | 2025 Survey, p. 23 |
| Foundational LLMs in use (counts) | ChatGPT 25; Gemini 16; Claude 11; Copilot 9; Llama 1; Grok 0 | 2025 Survey, p. 23 |
| Internal AI use cases (counts) | 1–4 cases: 18; 10+: 16; 5–9: 14; zero: 7 | 2025 Survey, p. 23 |
View data table
| change | Respondents (n=54) |
|---|---|
| Any decrease | 0 |
| No change | 4 |
| Increase 1-10% | 16 |
| Increase 11-20% | 10 |
| Increase 21%+ | 24 |
Source: 2025 Survey, p. 23 (forecast change in AI spend, next year; n=54)
The spend distribution has no left tail: intentions run from flat to sharply higher, with the largest single group planning the steepest increase. Whether that commitment is earning its return is the question the Reading the Reckoning section takes up, against a retention benefit that has not yet appeared in the actuals.
Retention by AI Classification
View data table
| year | NDR, AI-Interested | NDR, AI-Native/Enabled |
|---|---|---|
| 2022 | 100 | 106 |
| 2023 | 101 | 102 |
| 2024 | 102 | 100 |
| 2025E | 100 | 104 |
| 2026E | 103 | 104 |
| Metric | 2022 | 2023 | 2024 | 2025E | 2026E |
|---|---|---|---|---|---|
| NDR, AI-Native/Enabled | 106% | 102% | 100% | 104% ᴱ | 104% ᴱ |
| NDR, AI-Interested | 100% | 101% | 102% | 100% ᴱ | 103% ᴱ |
| GDR, AI-Native/Enabled | 88% | 85% | 87% | 89% ᴱ | 91% ᴱ |
| GDR, AI-Interested | 83% | 83% | 85% | 86% ᴱ | 88% ᴱ |
Source: 2025 Survey, p. 21
Reading the Reckoning
The monetization data says the industry has moved fast on packaging: 67% of AI-Native/Enabled respondents already monetize AI capability, and only 17% of those chose usage-based pricing; 58% folded AI into subscription, which means most AI monetization inherits the seat-and-subscription exposure documented in Pricing and Contract Posture. Under the certified 2024 values, AI-Native/Enabled companies fell to exactly 100% NDR, the contraction boundary, in 2024, while AI-Interested companies edged to 102%; across the three actual years the two cohorts are separated by one to four points with a crossing, not a gap. Inference
Two structural cautions on this entire cut: the cohorts are small (36 AI-Native/Enabled respondents against 19 AI-Interested), and the zero count of Non-AI respondents means the survey cannot observe the counterfactual, so every AI comparison here is between shades of adoption, not adoption versus abstention. One asymmetry survives those caveats: on gross retention, AI-Native/Enabled companies have led AI-Interested companies in all three actual years (88/85/87 vs. 83/83/85), a consistent two-to-five-point gap that the NDR series does not show. Through 2024, AI adoption showed up in keeping customers, not in expanding them. Inference
Not one respondent plans to cut AI spend; twenty-four of fifty-four plan increases above 21%. Thirty respondents say AI impact on their business is already material, and among those who say it is coming, nearly all expect it within two years. The industry is committing capital to a technology whose retention benefit, per the certified NDR cut above, has not yet appeared in the actuals, and whose seat-compression risk falls on the 40% seat-priced revenue documented in Pricing and Contract Posture. See AI and Seat-Pricing Exposure for the full argument. Inference
This section underwrites AI and Seat-Pricing Exposure primarily, and also feeds The NDR Crisis.
Frequently asked questions
What does it mean that AI-Native NDR hit 100%?
In the certified 2024 data, AI-Native and AI-Enabled companies posted net dollar retention of exactly 100%, the contraction boundary. That means the average dollar earned from existing customers matched the dollar lost to churn and downsell, with zero net expansion.
How many companies plan to cut AI spending?
Zero. Not one of 54 respondents plans to decrease AI investment next year, and 24 of them plan increases above 21%. The industry is unanimously committing capital to AI regardless of whether retention gains have shown up yet.
What share of AI-Native companies already monetize AI?
67% of AI-Native and AI-Enabled respondents are already actively monetizing AI capability, out of 36 companies in that classification. Most folded it into subscription pricing, at 58%, rather than charging separately by usage.
Why does AI adoption not yet lift SaaS retention?
AI-Native and AI-Enabled companies lead on gross retention every year, 88%, 85%, and 87% versus 83%, 83%, and 85% for AI-Interested peers, but that edge disappears in net retention, where 2024 fell to exactly 100%. AI is helping companies keep customers, not yet grow revenue from them.
Last reviewed: July 2026