The New Art of AI Engineering

Why the real impact of AI is yet to come and how to make that happen

1. Adopting AI at scale is a transformation challenge

The AI Productivity Paradox

“Any new technology tends to go through a 25 year adoption cycle”, Marc Andreessen

Dimension 1: developing and implementing AI solutions

Dimension 2: building enterprise AI capabilities

Figure 1. AI transformation paths.

2. The new art of AI engineering

“Specifically, the most impressive capabilities of AI — those based on machine learning — have not yet diffused widely. More importantly, like other general purpose technologies (GPT), their full effects won’t be realized until waves of complementary innovations are developed and implemented.” E. Brynjolfsson et al

AI as GPT requires new engineering discipline

Figure 2. The New Art of AI Engineering

AI Business Engineering

AI Solution Engineering

AI Platform Engineering

Figure 3. Solution-driven capability development.
Figure 4. Key differences in requirements for data architecture between BI and AI
Figure 5. Data Lake versus Data Warehouse
Figure 6. ML code (black box) is only fraction of complexity of entire ML system (source: D. Sculley et al, NIPS 2015)
Figure 7. High-level components of AI platform architecture

3. How to get started

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