Roman Rodomansky

COO & Co-Founder at @ralabs_official

[Roman Rodomansky @rodomansky]

Klarna AI adoption needs a quality scorecard

Klarna AI adoption puts efficiency claims beside renewed attention to human support. Build a scorecard that pairs operating gains with customer quality.

https://t.co/DizEW4iUJb

[Roman Rodomansky @rodomansky]

OpenAI internal AI adoption: measure beyond volume

OpenAI internal AI adoption separates participation from token intensity. Borrow that distinction, then add evidence of accepted work before calling heavy use productive.

https://t.co/ZWDYKBxu4b

[Roman Rodomansky @rodomansky]

Microsoft token spending limits: budget for outcomes

Microsoft token spending limits put organizational budgets in focus. Use the reported ceilings as context for setting work objectives, spending rules and outcome reviews.

https://t.co/idf8un4jkc

[Roman Rodomansky @rodomansky]

Uber AI coding budget: fund work that lasts

The Uber AI coding budget story calls for more than spending caps. Pair accountable allowances with delivery signals and checks on work after release.

https://t.co/xFaK72NGqQ

[Roman Rodomansky @rodomansky]

Salesforce Agentic Work Units need acceptance checks

Salesforce Agentic Work Units count activity. Learn what to question in the totals and how to check acceptance, quality and cost separately.

https://t.co/UAUV0wjEkk

[Roman Rodomansky @rodomansky]

Shopify token leaderboard: reward lasting work

The Shopify token leaderboard became a usage dashboard to discourage competition. Learn how to investigate spending and review lasting work.

https://t.co/kKUVip8Z67

[Roman Rodomansky @rodomansky]

Meta Claudeonomics leaderboard: usage isn't impact

The Meta Claudeonomics leaderboard highlights why usage isn't impact. Separate adoption, spending and delivered work before deciding what deserves recognition.

https://t.co/8u6ShiroK3

[Roman Rodomansky @rodomansky]

Survey translation: check meaning before rollout

Survey translation needs more than fluent wording. Check question meaning, response choices and the questionnaire employees will see before rollout.

https://t.co/GecVWz5K0A

[Roman Rodomansky @rodomansky]

Interpreting survey results: choose what to check

Interpreting survey results gets harder when AI usage looks strong but skills or perceived impact lag. Follow a bounded workflow to choose your first investigation.

https://t.co/DHTF8Ik78S

[Roman Rodomansky @rodomansky]

DORA vs SPACE: investigate the review bottleneck

DORA vs SPACE offers complementary lenses when review work grows. Compare their scope and combine delivery records with developer feedback to choose what to investigate.

https://t.co/wcOKPvOqnm

[Roman Rodomansky @rodomansky]

Survey confidentiality statement example: check each promise

A survey confidentiality statement example needs verified promises. Check access, reporting, comments and retention before sending your employee AI survey.

https://t.co/WX9zsiCCad

[Roman Rodomansky @rodomansky]

Stakeholder mapping process: give AI barriers owners

Your stakeholder mapping process needs clear decision authority. Follow five steps to assign AI barrier owners, confirm approvals and agree escalation routes.

https://t.co/TJQZbTDSTA

[Roman Rodomansky @rodomansky]

Survey reminder email: invite without pressure

Your survey reminder email should invite, not pressure. Prepare two editable messages, accurate privacy wording and a manageable sending schedule.

https://t.co/QSe6rAcUYI

[Roman Rodomansky @rodomansky]

Skills matrix example: find single-person coverage

High skill ratings don't identify willing helpers. Use this skills matrix example to separate reported coverage from offers of help and plan a learning action.

https://t.co/qZtdCiYwC6

[Roman Rodomansky @rodomansky]

Employee survey results: share themes, protect people

Employee survey results can reveal respondents. Follow four steps to review groups, totals and comments before preparing an employee briefing.

https://t.co/30i2ee7RiY

[Roman Rodomansky @rodomansky]

AI board reporting: lead with use, retain access

AI board reporting should connect deployment to employee use. Compare two approaches and build an update that supports a concrete intervention decision.

https://t.co/PKjSG8JcPe

[Roman Rodomansky @rodomansky]

Employee resistance to AI adoption: investigate first

Employee resistance to AI adoption can signal objections or practical obstacles. Learn how to separate them and choose a response that addresses the actual problem.

https://t.co/wHvRooZ5Sp

[Roman Rodomansky @rodomansky]

AI maturity assessment model: compare teams fairly

An AI maturity assessment model needs context before department scores can guide decisions. Use these checks to compare tasks, access, respondents and assessment rounds.

https://t.co/MdSK6AeBMs

[Roman Rodomansky @rodomansky]

AI adoption survey: recruit champions who can commit

An AI adoption survey starts a conversation, not a commitment. Use suggested questions to separate volunteer capabilities, willingness, time and support needs.

https://t.co/WEgYdG2wWw

[Roman Rodomansky @rodomansky]

EU strategic sector AI adoption: evidence, not policy

EU strategic sector AI adoption rose, according to new JRC analysis. See what changed, which firms it covers and what the announcement leaves unspecified.

https://t.co/m4pww9kWk2