5 Things You Want to Know About AI Adoption in the Enterprise
summary
Claims five concrete executive questions that decide scale not hype and shows why prototypes usually stall: a Jupyter prototype that fails in production because of missing unit tests, architecture and productised data pipelines; the build is often a lighthouse that becomes silo; organisations need leadership engineering and data alignment, repeated workshops (10–20) and patience; surprising constraint is legacy systems 10–60 years old and tangled access; outcomes often lack measurement—no ROI tracking so working prototypes rarely prove long‑term value.
AI-written summary · about
chapters (12)
- 0:00Intro
- 0:23Who am I
- 1:32Key Terms
- 2:19Story Time
- 3:36What do we want to reach
- 4:54What is AI
- 6:24Why is everything taking so long
- 6:58How do we start
- 12:57Working Student
- 13:29Project
- 17:33Lighthouses
- 20:47Innovation is Complex
related talks
22mHow to operationalize AI
50mRealities & Misconceptions about Enterprise Applications of AI/ML and Data
39mLessons Learned About Data & AI at Enterprises and SMEs
24mAI Implementation: Navigating the Enterprise Lifecycle for Technical Leaders
05mPragmatic AI Adopton is Hard!
21mCutting through the hype. How enterprises can apply a strategic lens to leveraging AI to achieve ...
09mINTERVIEW
41mHow to Fail with AI - and How to Succeed?