Take out 'exhaustively', though, and everything parent comment said is correct. AI is just (big data + statistical inference) implemented in massively parallel ways using linear algebra.
- Distributed computing
- Feature selection
- Budgeting / accounting of experimental design (these TPU clusters are not cheap)
- ML architecture
- Involvement of domain experts (multidisciplinary teams)
- Storage
The whole list is much longer, but just some topics to think about. For me the term "AI", although overly broad, come to mean the engineering and organisation needed to pull these kind of projects.