State of AI-Run Businesses 2026 | AI-Run Business Index (ARBI)

Layer 1 · External grounding Every market figure is attributed to an independent source — McKinsey, Census, the Fed, Gartner, PwC, OECD, MIT, Stanford — that you can check.

Layer 2 · Synthesis The value added is reconciling fragmented, conflicting datasets into one picture — conflicts handled openly, not hidden.

Layer 3 · Owned metric Exactly one layer is Leapd's: the AI-Run Business Index, built transparently on the public data and reproducible from it.

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%<br>Value captureShare of revenue & gross margin attributable to AI-run functions20%<br>Revenue leverageRevenue per employee vs. the sector median20%<br>Speed to revenueTime from idea → live → first revenue15%<br>Function coverage# of core functions (build, market, sell, support, ops) running end-to-end on AI10%<br>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.

Copy<br>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%<br>of organizations use AI in at least one function (McKinsey 2025)

~6%<br>are "high performers" capturing >5% EBIT impact

95%<br>of enterprise GenAI pilots show no measurable P&L impact (MIT)

~50%<br>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 ·...