Case studies

Every entry is a real, publicly documented AI implementation — a company deploying AI for a specific purpose, with reported outcomes, gathered by monitoring the open web. A use case's case study count is how many of these have been published about it in the last 24 months.

It is a proxy for real-world adoption — but a proxy, not a census: it reflects what companies and vendors have chosen to publish, not everything happening in the market. High volume with low proven impact indicates hype; high proven impact with low volume is anecdotal.

Growth (year over year)

Growth compares the case studies published in the last 12 months against the 12 months before that — a year-over-year read on whether a use case is gaining or losing momentum. It is always this 12-vs-prior-12 comparison, regardless of any date filter you apply elsewhere.

Treat it as a directional signal, not a precise figure: a positive number (shown in green) means more case studies were published than the year before; a negative number means fewer. On a small base a big percentage can be noise, so always weigh it against the case study count — a jump from 2 to 8 studies is “+300%,” directionally interesting but not robust.

Proven impact

Proven impact shows the quality of reported outcomes of the case studies within each use case. It is a composite score where financial results (ROI, revenue and cost savings) are weighted stronger than time savings and operational metrics.

It is a per-use-case average, not a per-company figure — a use case needs at least 5 case studies to earn one. Read it on this scale:

Under the hood, each type of reported result carries a weight, and the rare, hard financial outcomes deliberately count for far more than softer ones:

A single revenue or EBIT/ROI case therefore punches far above its count — intentional, because a hard financial outcome is the most concrete, tangible business result there is, and the rarest to find documented.

Build vs buy

How teams typically implement this use case — whether they bought or configured a vendor product, or built a custom solution in-house. Every case study is classified from its own story; the percentages are based on classified case studies only, and partner-built flags the share of custom builds a third-party integrator wrote rather than in-house.

We report it as the buy share — so “74% buy” means 74% bought or configured a vendor product and the remaining 26% built custom.

Agentic level

Agentic level captures how autonomous the deployed AI is — from L1 (a tool people operate) to L5 (self-directed agent teams). Each case study is classified individually, and a use case's level is the average across its classified case studies.

The scale, level by level:

KPIs tracked

We cluster every metric reported across a use case's case studies into distinct KPIs — so a use case that tracks “meetings booked,” “conversion rate,” and “pipeline generated” counts three. The KPIs tracked figure is how many distinct KPIs peers report for that use case — a measure of how broadly its results are quantified.

Only KPIs reported by at least three companies are counted, so it reflects genuinely shared measures rather than one-off metrics.