---
id: "lib-2026-10-15-llm-adoption-without-rose-tinted-glasses-500"
type: "library"
title: "LLM adoption without rose-tinted glasses: 500 engineers"
title_original: "Внедрение LLM без розовых очков: честный разбор гипотез, ошибок, метрик на масштабе 500 инженеров"
lang: "RU"
org: "Raiffeisenbank ({speach!})"
source_type: "Talk"
date: "2026-10-15"
week: "2026-W42"
url: "https://nosov.ai/library/lib-2026-10-15-llm-adoption-without-rose-tinted-glasses-500/"
original_url: "https://www.youtube.com/watch?v=DeGfabhMu4c"
tier: "A"
tier_name: "Strong evidence"
tier_reason: "First-hand report from Raiffeisenbank's domain agile coach on a year of AI adoption across about 50 teams, with a measured usage funnel by intervention, flow metrics as the goal, and negative results reported plainly."
summary_basis: "transcript"
original_minutes: "40"
tags: ["ai technology", "enablement", "adoption funnel", "developer productivity"]
frames: ["solow-paradox"]
---

# LLM adoption without rose-tinted glasses: 500 engineers

Original title: Внедрение LLM без розовых очков: честный разбор гипотез, ошибок, метрик на масштабе 500 инженеров

Tier A (Strong evidence): First-hand report from Raiffeisenbank's domain agile coach on a year of AI adoption across about 50 teams, with a measured usage funnel by intervention, flow metrics as the goal, and negative results reported plainly.

Summary based on the full transcript.

## In short

A conference talk (uploaded 15 October 2026) by Marat Kinyabulatov, domain agile coach at Raiffeisenbank Russia, on about a year of AI adoption across roughly 50 teams and 500 people in the bank's operational core. His alternative title: three hypotheses about AI were tested and none reduced development time. The value is the funnel: which interventions turned occasional chat use into regular tool use, and the finding that lead time, the goal metric, did not move.

## Key numbers

- 50% vs 3%: share of people using any LLM chat monthly versus share using the API (agents, autocomplete) after awareness campaigns
- 88%: workshop attendees who went on to use the API at least monthly after a 90-minute hands-on session
- 95%: six-month retention of people who started using the API
- 35-40%: share of a team pulled into API use when a tech lead attended, versus 10-20% for an ordinary engineer
- 86% / ~65%: current share using any LLM / using the API

## What it reports

- Request and token counts were dropped as adoption metrics because agents and autocomplete inflate them. The bank tracks people: monthly use, use on 50%+ of working days (W50) and on about 80% (W80).
- Hypothesis 1, give people LLMs: awareness campaigns took monthly use from about 5% to about 50%, with a self-reported gain of 15-20%. Only 3% used the API; the rest used chat. Lead time did not change.
- Hypothesis 2, coding agents: the main barrier was not knowing how to install and apply the tools. 90-minute hands-on workshops in small groups, on real tickets, converted 88% of attendees to monthly API use, 62% to use on more than half of working days and 35% to daily use. Six-month retention was 95%.
- Spread: an ordinary engineer who attended pulled in 1-2 colleagues (10-20% of a team), a tech lead 35-40%, and a tech lead who ran regular sharing sessions about 55%.
- Flow metrics stayed flat: defect rate and throughput unchanged; lead time fell in some teams and rose in others. His reading: lead time is set by the slowest stage and the hand-offs, so faster coding alone changes nothing end to end.
- Hypothesis 3, AI-first teams on a greenfield service: two developers delivered in a month roughly what five had, but lead time still did not fall. Humans became the review bottleneck; small end-to-end teams carry high cognitive load and burnout risk.
- What did not work: webinars, mailings and documentation (5-10% uptake), mandates. What worked: workshops, hackathons, small groups, scripted environment setup. Current state: 86% use some LLM, about 65% the API; chat use has plateaued.
- The three people from the five-person team moved to another feature team; no one was laid off.

## How they got it

Usage measured through corporate logins and tool telemetry, tracked per person over time; flow metrics (lead time, throughput, defect rate) from the delivery system. Change framework: ADKAR. Results are presented as a sequence of hypotheses with outcomes, including the failures.

## Limits

- Internal figures from one bank, shown on slides only; no external audit.
- Lead-time effects are not tested statistically, and team-level changes have many causes.
- The AI-first result is a single pilot on a non-critical greenfield service.
- He leaves open whether any of it pays back.

## Why it matters for companies

This is one of the few adoption reports that separates chat tourism from working use and shows which intervention moved which number. Hands-on workshops and tech leads did most of the work; broadcasts and mandates did little. The flat lead time is the warning for anyone promising productivity: speeding up one stage does not shorten delivery unless the hand-offs, review and neighbouring teams change too.

## Key passages in translation

> мы проверили три гипотезы про AI, ни одна из них не уменьшила время разработки

We tested three hypotheses about AI, and none of them reduced development time.

> Чат это как туризм. Просто пришёл, что-то сделал, но системно в свою работу не встроил.

Chat is like tourism. You just came, did something, but did not build it into your work in any systematic way.

> Никто ощутимо быстрее пока не стал.

Nobody has become noticeably faster yet.

Original: https://www.youtube.com/watch?v=DeGfabhMu4c