Reference

Frequently asked questions

Quick answers to the questions the Wave 3 benchmark most often settles — who was surveyed, how AI maturity is defined, and what separates the leaders from the laggards.

What is the Georgian + NewtonX Applied AI Benchmarks report?
It is a field study of how B2B software companies are turning AI ambition into operating reality. Wave 3 was fielded in March–April 2026 among 501 B2B software decision makers (n=501), including a longitudinal technical panel of 252 tracked from Wave 2, and reports changes at a 95% significance threshold.
Who conducted the research?
The benchmark was conducted by Georgian and NewtonX. It is the third wave (Wave 3) of an ongoing study; Wave 2 was fielded in June 2025.
How many B2B software companies now rank AI among their top priorities?
In Wave 3, 93% of B2B software decision makers ranked at least one AI initiative among their top-5 organizational priorities — the highest rate recorded in the benchmark series. AI rose 12 points in strategic importance from Wave 1 to Wave 3.
What AI maturity model does the report use?
Organizations are classified into four tiers by how far their AI practice has moved from experimentation to embedded production, using an athletics metaphor: Crawler (least mature), Walker, Jogger, and Runner (most mature).
What share of the market are Runners, the most AI-mature tier?
Runners are 22% of the market in Wave 3, roughly double their Wave 2 share. Runners run a qualitatively different AI stack, with agents embedded across engineering, instrumented models and infrastructure, higher agent autonomy, and committed budget.
How much more do Runners use parallel AI agents than less mature tiers?
Running multiple AI agents in parallel is a defining behavioural signature of Runners: 53% of Runners have engineers running parallel agents, versus 8% of Walkers. Agentic coding tools themselves sit near 95% adoption across tiers, so the gap is about embedding, not tool access.
What does "the gap between" refer to?
It refers to the distance the report quantifies between AI leaders and laggards across strategy, R&D, go-to-market efficiency, and governance — including a governance gap between how fast AI is deployed into production and the maturity of the security, guardrails, and oversight meant to manage it.