Feedback-Driven Training Could Save Your Gen AI Initiatives
Summary
Dr Gleb Tsipursky argues that Generative AI (Gen AI) learning programmes must be continuously improved using participant feedback and performance data. Static training quickly becomes irrelevant as technologies and business priorities shift; embedding systematic feedback loops and quantitative metrics keeps content practical, engaging and aligned with organisational goals. The article uses client examples — notably a legal firm and a software firm — to show how redesign informed by feedback and data drove large gains in engagement, satisfaction and productivity.
Key Points
- Gen AI training must be dynamic: static programmes risk becoming obsolete as tech and priorities move fast.
- Combine qualitative feedback (surveys, focus groups, interviews) with quantitative metrics (engagement, completion, assessment scores) to spot what works.
- Practical, role‑relevant content and hands‑on activities (simulations, case studies) significantly boost learning transfer and engagement.
- Case evidence: a mid‑sized legal firm reported completion up 56%, training satisfaction +49% and productivity +36% after a feedback‑led redesign.
- Five practical steps: establish feedback channels; analyse performance data; iterate and adapt; involve stakeholders; communicate changes.
Content Summary
The piece highlights the imperative of continuous improvement for Gen AI initiatives. Participant feedback provides qualitative insights into usability and relevance, while learning management system data offers measurable signals about which formats and topics succeed. Together they guide targeted fixes — for example, breaking complex modules into smaller units, adding video tutorials, simulations or peer study groups, and creating role‑specific case studies.
Through client case studies, the author shows how analysing engagement patterns and acting on feedback can transform programmes. A software firm increased engagement by switching from lecture formats to interactive simulations. A legal firm reworked curricula to include practical legal prompts and AI workflows, producing marked improvements in completion, satisfaction and productivity. The article concludes with a concise list of implementation steps to embed continuous improvement and foster a culture of learning.
Context and Relevance
This article is timely for organisations scaling Gen AI: many firms buy tools but underinvest in the evolving human side — training that actually sticks. With regulators and boards increasingly interested in AI governance and ethics, training that adapts by design helps manage risk and ensures staff can apply tools effectively. It ties into broader trends: skills gaps in AI, demand for measurable L&D outcomes, and the shift from one‑off courses to continuous workplace learning.
Author style
Punchy and practical. Dr Tsipursky mixes behavioural science with hands‑on consulting examples — no academic fluff. If your organisation is rolling out Gen AI, the article isn’t just interesting, it’s action‑oriented: you’ll find clear steps and quick wins to avoid wasted investment.
Why should I read this?
Quick take: if your company is spending on Gen AI tools or training, read this. It tells you how to stop training from going stale, get people actually using AI sensibly, and show measurable returns. Short, useful and full of no‑nonsense fixes you can start using straight away.
Source
Source: https://ceoworld.biz/2025/11/02/feedback-driven-training-could-save-your-gen-ai-initiatives/
