GenAI after the Hype: Transforming Organizations with GenAI-based Agents
summary
A five-week pilot with 10 participants uses a dedicated cloud Jira and Confluence instance plus Atlassian Roo to auto-generate portfolio epics, break features into user stories, and produce acceptance criteria, claiming about 65% faster delivery and initial outputs at 70–75% quality. Proof enforces EU GenAI rules and security approvals, surfaces backlog health issues via proactive agents, and exposes limits: insufficient historical data reduces scoring accuracy and the rollout requires prompt-engineering training and change management to avoid people‑centred productivity dips.
AI-written summary · about
chapters (11)
- 0:00Introduction
- 1:16Economic value of GenAI
- 4:27Vodafone pilot with GenAI
- 8:46Improving epics with AI
- 11:55Enhancing backlogs with AI
- 15:27AI as a productivity multiplier for teams
- 17:12Change management in AI
- 21:06AI for leaders
- 24:15AI layers for orgs
- 28:50AI as coworker
- 30:19Q&A
related talks
36mGenAI governance in practice
34mOrganized chaos adopting GenAI at Enterprise
23mUnleashing the Power of Gen AI to Boost Engineering Engagement and Productivity
01h 06mAI in the Trenches: Engineering Real-World Systems
42m10x Engineering: How GenAI Tools Transformed My Team's Productivity
27mSoftware engineering with GenAI
29mIndustry generative AI in action
22mHow Can Engineering Leaders Measure the Impact of GenAI
