Neil Jacobstein gave a workshop on AI and Machine Learning Implementation at the 2019 Singularity Summit.

It turns out there is a fatal flaw in most companies approaches to machine learning, the analytical tool of the future. 85% of the projects do not get past the experimental phase and so never make production (Forbes, Enrique Dans, Jul 21, 2019.)

Run real projects and do not run pilot projects that cannot fail. Real experiments can fail. They will teach you something if they can fail.

Not all projects can be solved data.

AI Implementation Recommendations

1. Start with a problem, not the technology and improve the business case if solved
2. Determine the roles for AI and humans, AI alone, augmentation, hybrid
3. Identify data required, as well as acquisition and maintenance plans
4. Select the appropriate machine learning platform
5. Select a hardware schema that scales – mobile to cloud
6. Test real data from users and keep evolving test cases as things change
7. Design simple interfaces – minimize changes in behavior or existing workflow
8. Develop performance metrics for the problem and for the business
9. Design in AI safeguards, system security and exception cases.