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Research · Cross-Industry Analysis

Does size predict AI savings?

Analysis of 6,727 US companies with joint revenue + AI-addressable savings data. Headline finding: median Tier-1 savings is 15% of annual revenue, with cross-industry Pearson r = -0.01 between revenue scale and savings-%.

By Meo Advisors Editorial, Editorial Team

Companies analyzed

6,727

of 31,602 indexed

Median savings %

15%

tier-1 ÷ annual revenue

Cross-industry r

-0.01

revenue × savings-%

Industries (NAICS-2)

8

cohorts ≥ 30 companies

Revenue × savings-% — full sample

Each dot is one company. The regression line (dashed blue) is the least-squares fit over log-transformed revenue. A flat or shallow line means savings-% is roughly constant across the size spectrum; a steep negative slope would mean larger companies see proportionally less waste.

$1M$10M$100M$1BAnnual revenue (log scale)0%25%50%100%150%200%Savings as % of revenue

Across the full sample the slope is mildly negative (r = -0.01). Industry-level dynamics dominate — the heatmap below shows where size effects are strongest.

Industry × size-band heatmap

Median savings-% by 2-digit NAICS sector and size band. Darker cells = larger addressable waste relative to revenue. Top 10 industries shown by sample size.

Industry (NAICS-2)mid-size regionallarge regionalnational operatorenterprise
Healthcare(609)23%4%
Finance & Insurance(548)25%3%
Professional Svcs(336)23%4%
Transportation(143)22%
Manufacturing (Chem/Plastic)(38)40%
Manufacturing (Metal/Comp)(30)26%
Arts/Entertainment(19)43%
Postal/Warehouse(11)29%
Admin & Support(6)5%

Cell value = median savings_estimate_tier1 ÷ annual_revenue, expressed as a percent. Cells with fewer than 5 companies are blanked. Numbers in parentheses = total companies in that NAICS-2 cohort.

Top 5 industries by median savings-%

Cohorts with the highest median Tier-1 savings as a fraction of revenue. Read these as "where AI deployment is currently underexploited relative to the operating efficiency it could deliver."

  1. 1

    Arts, Entertainment & Recreation

    Median savings: 20% of revenue · 70 companies · within-cohort r = -0.13

  2. 2

    Finance & Insurance

    Median savings: 17% of revenue · 2,212 companies · within-cohort r = -0.01

  3. 3

    Manufacturing — Paper, Chemical, Plastic

    Median savings: 17% of revenue · 199 companies · within-cohort r = -0.07

  4. 4

    Professional, Scientific & Technical Services

    Median savings: 16% of revenue · 1,143 companies · within-cohort r = -0.09

  5. 5

    Health Care & Social Assistance

    Median savings: 16% of revenue · 2,327 companies · within-cohort r = -0.01

Methodology

Source. The dataset is content.ai_opportunity_companies, filtered to rows with both annual_revenue > 0 and savings_estimate_tier1 > 0. That yields 6,727 companies of the 31,602 indexed in the AI Adoption Index.

Savings estimate. Tier-1 savings is a per-company estimate of addressable labor waste that AI agents could eliminate within 12-24 months given current model capabilities. It's bottom-up: we identify high-confidence automatable tasks (Tier 1 = production-ready agent solutions today) using the company's industry, headcount, and tech-stack signals, then apply industry-standard cost loadings.

Statistics. Median rather than mean for cohort summaries — the distribution has long tails. Pearson r is computed in-sample on log-transformed revenue; values close to 0 indicate no monotonic relationship at the global level, even when individual cohorts show structure (Simpson's paradox is a real risk here, hence the per-industry heatmap).

Caveats. Companies without published revenue are excluded; this biases the sample toward larger, public-facing firms. Tier-1 savings only counts workloads that are already deployable today — Tier-2 (12-36 month roadmap) and Tier-3 (research-stage) workloads are excluded from this analysis. Healthcare (NAICS-62) is over-represented because Apollo enrichment coverage is denser in that vertical.