Published recently in an IT blog, the author realised long way between ERP’s in 2000 years and now AI. It’s probably time to step back and share a few learnings from the initiatives that went exceedingly well and those that did not get anywhere.
He made some groupings and generalizations. While there is a lot of debate about AGI, potential job losses and so on – he doesn’t think that is what is hurting AI adoption at the moment in commercial companies. So I am ignoring a discussion on those kinds of topics here.
Here is the 12 lessons from AI adoptions
1. Adoption has not exactly matched what ERP did in the 2000s
2. “Fire and forget” does not work at all
3. Focus on business is key
4. Focus on IT is ALSO key
5. No Good Data, No Good AI
6. Quality of service
7. API, custom build, commercial vs open source platforms etc
8. Developers have a big role, along with data scientists
9. Ecosystem of talent
11. Change Management
To read the blog : https://www.enterpriseirregulars.com/134655/ai-adoption-challenges-12-lessons-from-the-trenches/
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