The AI-Run Business Index: measuring execution, not AI adoption

The metric

One number for a new economy

Adoption surveys count whether a company touches AI. None measure whether AI actually runs the business. The AI-Run Business Index (ARBI) is the first standardized metric that does — a 0–100 composite index scoring execution, not experimentation, maintained by Leapd as the category's annual benchmark.

The evidence on AI's business impact is scattered across dozens of surveys that each capture a fragment. Leapd's role is to vet and reconcile that fragmented evidence into one comparable standard. Its spine is 30+ external benchmarks; the published score can be reproduced from those alone. Leapd's own platform data is a secondary calibration layer. Every weight is disclosed so it can be quoted, reproduced, and challenged.

Each dimension reduces to a measurable indicator, so an individual business's score can be computed directly from these six inputs — the basis for a per-company AI-Run Score — while the economy-level reading normalizes public benchmarks.

DimensionHow a business is scored on itWeight

Automation depth% of recurring tasks executed autonomously, without human approval25%

Value captureShare of revenue & gross margin attributable to AI-run functions20%

Revenue leverageRevenue per employee vs. the sector median20%

Speed to revenueTime from idea → live → first revenue15%

Function coverage# of core functions (build, market, sell, support, ops) running end-to-end on AI10%

Reliability (penalty)Human intervention rate + rollback / abandonment rate−10%

Why these weights. The weighting follows this report's central finding — that execution, not adoption, separates a business run by AI from one that merely uses it. Automation depth carries the most weight (25%) because autonomous execution is the definitional core of "AI-run." Value capture and revenue leverage are next (20% each) because they separate real transformation from efficiency theater — the 88%-vs-6% gap this report documents. Speed (15%) and coverage (10%) reward businesses that run end-to-end and fast, not in a single function. Reliability is a penalty (−10%) because failure and ungoverned agents are large and measured (§7); ignoring them would overstate maturity. Weights are fixed for the 2026 edition and published so the score can be recomputed, audited, or contested.

The 2026 bands

0–20 · Experimenting — isolated tools, no workflow change.

20–40 · Adopted, not run — AI in a few functions; little autonomy or value capture. ← the mainstream economy (~30).

40–70 · Executing — AI runs whole functions with measurable ROI.

70–100 · AI-run — AI runs build and growth. ← the AI-native frontier (~80).

ARBI is directional by design — it compares maturity bands, not false-precision point scores. All inputs and weights are disclosed below.

On the AI-Run Business Index, the mainstream economy scores ~30/100 while the AI-native frontier scores ~80 — a 50-point execution gap between using AI and being run by it.

01 · Executive summary

Adoption is saturated. Transformation is rare.

88%

of organizations use AI in at least one function (McKinsey 2025)

~6%

are "high performers" capturing >5% EBIT impact

95%

of enterprise GenAI pilots show no measurable P&L impact (MIT)

~50%

of all global venture capital went to AI in 2025 (~$211B)

Adoption is effectively saturated but shallow. 88% of organizations use AI; nearly two-thirds have not begun scaling it; only ~6% tie it to real profit. McKinsey

The headline depends on definition. Self-reported surveys say 88%; the U.S. Census Bureau's firm-level measure is ~18–20%. Both are right — they measure different things. Census

Agents are the frontier and the shakeout. 23% are scaling agents somewhere; Gartner expects 40%+ of agentic projects canceled by end-2027. Gartner

Where it works, gains are large. Productivity grew ~4x faster in the most AI-exposed industries; revenue per employee 3x faster. PwC

A new archetype is visible: the ultra-lean, AI-native company — Cursor, Lovable, Midjourney — posting revenue-per-employee 10–100x the SaaS norm.

Governance gates the next phase. ~80% of organizations lack a mature model for autonomous agents even as ~74% plan to use them within two years. Deloitte

02 · Key findings

The defining contradictions of 2026

AI adoption is 88% — but only ~6% of companies capture real profit from it.McKinsey

95% of enterprise AI pilots fail — yet AI took ~half of all global VC (~$211B) in 2025.MIT · Crunchbase

AI lifted productivity ~27% in the most exposed industries — while cutting entry-level employment ~13%.PwC · Stanford

CEOs report efficiency gains (56%) far more than profit (34%) or revenue (32%).PwC CEO Survey

A ~50-employee company (Cursor) reached ~$2B revenue — about $40M per employee.AI Business

Leapd platform data

Across the 1,000+ businesses running on Leapd, the median time from a one-line idea to a live, operating business — site, checkout, and live ad, email, and social campaigns — is under ten minutes. Full platform analysis in §9.

03 · Market adoption

Near-universal, unevenly deep

MeasureFigureEvidence

Enterprises using AI in ≥1 function88%McKinsey

U.S. firms using AI operationally18–20%Census

Large firms (250+ employees)37%Census

Small firms (

Cursor

~$40M

Midjourney

~$12.5M

Lovable

>$2M

Traditional SaaS

~$0.35M

Revenue per employee (log scale). Best-available public estimates — ARR and headcount definitions vary; read as directional.Leapd · State of AI-Run Businesses 2026

For context, top-tier SaaS historically targeted ~$200K–$500K per employee. Cursor's ~$40M is two orders of magnitude higher. No verified one-person billion-dollar company exists yet — but the gap to it is narrowing fast. AI Business · VC Corner

The headcount-to-revenue rule is breaking at the frontier — a roughly 50-person company (Cursor) now generates around $40M in revenue per employee, ~100x the traditional-SaaS norm.

07 · Failure & limits

The value collapses between adoption and impact

The incumbent picture is not all collapse — firms with real foundations see real returns (PwC 3x; ECB hire-not-fire; Agentforce resolving 70–82% of cases at named enterprises). But the funnel from adoption to value is brutal, and failure concentrates in pilots bolted onto unchanged workflows.

Adopting AI

88%

Scaling agents

23%

High performers (>5% EBIT)

~6%

Pilots delivering value

~5%

The AI value funnel — adoption is easy; value capture is the bottleneck (McKinsey, MIT).Leapd · State of AI-Run Businesses 2026

Gartner estimates only ~130 of thousands of "agentic" vendors are real ("agent washing"); ~80% of firms lack mature agent governance; and a rigorous trial showed developers believed AI sped them up 20% while it slowed them 19% — self-reported ROI is systematically unreliable. Gartner · METR

95% of enterprise AI pilots deliver no measurable profit — and they fail in the org, not the model: the winners redesigned workflows, the losers bolted AI onto unchanged ones.

08 · The category

"AI-run business" is forming, not formed

Two models anchor the category. The AI economy stack runs Tools → Functions → Orchestration → Autonomous business; most of the economy is stuck at Layers 1–2. The autonomy spectrum maps how far a business has climbed:

StageWhat AI does2026 prevalence

1 · AI-assistedDrafts, suggests, accelerates tasks~75%

2 · AI-augmentedRuns whole tasks, human supervises23%

3 · Semi-autonomousRuns whole functions end-to-end15%

4 · AI-runRuns build and growthfrontier only

Stage 4 is nearly empty today. "AI-run business" is best read as a direction the data is moving, not a population that already exists at scale. This report measures the slope, not a finished state.

The autonomy staircase — autonomy rises left to right, but the share of firms collapses toward the top. Stage 4 (AI runs build and growth) is still essentially empty.Leapd · State of AI-Run Businesses 2026

The capital signal is unambiguous: AI took ~61% of global VC in 2025 by the OECD's measure, rising to ~80% in Q1 2026 — even as 95% of pilots failed. OECD · Crunchbase

AI as a share of global venture capital. Capital is concentrating into AI faster than the businesses can prove returns (OECD, Crunchbase).Leapd · State of AI-Run Businesses 2026

AI took roughly half of all global venture capital in 2025 even as 95% of pilots failed — capital is betting hard on a category that barely exists yet.

09 · From the Leapd platform

What we see across 1,000 businesses running on Leapd

We analyzed activity across 1,000+ businesses running on Leapd — what the agents executed, where founders intervened, and how fast each business went from idea to revenue. These businesses skew earlier-stage and AI-native, so they read as a leading indicator of where the category is heading rather than a sample of the whole economy.

A business goes from a one-line idea to a live, operating company — website, backend, checkout, and live ad, email, and social campaigns — in under ten minutes, against the weeks a founding team would normally spend on setup.

On Leapd, an idea becomes a live, operating business — website, checkout, and live ad, email, and social campaigns — in under ten minutes.

Once live, the median active business runs about 50 autonomous tasks a week across engineering, content, prospecting, outreach, and operations — work that would otherwise sit on a founder's list. Most of it runs without a human in the loop. Founders start by reviewing roughly 20–30% of what the agents do, but that supervision drops quickly: within about two weeks, most turn on auto-approval and let the system run.

Where they keep a hand in is consistent — the visual and brand choices on their site, and approving LinkedIn posts before they publish. The most fully automated work sits at the opposite end. Prospecting and outreach run with little oversight, because founders quickly learn to trust the AI-written copy, and so does the build itself: unlike coding tools that need constant prompting, Milo ships a scalable, on-brand application on its own.

What we measure2026 reading (Leapd platform)

Idea → live business (site, checkout & campaigns running)under 10 minutes

Autonomous tasks per active business / week~50

Time to first revenue~6 weeks, then ~30% WoW

Founder intervention rate20–30% → auto-approve in ~2 wks

Most-automated functionsProspecting, outreach & site build

Still active at 90 days~60%

Revenue follows. Most businesses reach their first revenue in roughly six weeks, then grow about 30% week over week — though the curve is bumpy and depends heavily on the market. SMB-focused businesses move fastest; those selling into enterprise, regulated industries, or long sales cycles take longer. About 60% are still active at 90 days.

Adoption varies sharply by agent. Cassy, the LinkedIn agent, is used by 70% of businesses for prospecting, engagement, and campaigns, and by 30% for drafting posts. Milo runs outbound: 90% have used it for automated email — finding their ideal customer, reaching out, handling replies, qualifying leads, and booking meetings — and 20% let it produce and manage video ads across Meta and X. Alex, which handles visibility in AI search, is used by about 20% overall, but the split is sharp: 80% of businesses that arrive with an existing company turn it on, versus only ~10% of those starting from an idea. Established businesses already feel the pressure of being found inside ChatGPT, Gemini, and Perplexity; idea-stage builders haven't yet.

AgentWhat it runsAdoption

Cassy — LinkedInProspecting, engagement & campaigns · post drafting70% · 30%

Milo — EmailICP discovery, outreach, reply handling, lead qualification & meeting booking90%

Milo — Paid & videoAI-generated video ads, Meta & X campaigns20%

Alex — AI-search visibilityVisibility tracking, site audit, AEO article generation20% overall 80% of existing-business signups · ~10% of idea-stage builds

Source: Leapd platf

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