Two Key Elements of AI
Go back to the [[Main AI Page]]
See the [[Week 1 - Introduction]] AI contents page
#AIBusinessCase
Componentry
-
A unified, modern data fabric.
- Data must be prepared for the AI to use.
- Logical representation of all data assets.
-
A development environment and engine.
- A place to build, train, and run AI models.
- End-to-end, input-to-output.
- models help find patterns and strctures in data that are inferred rather than explicit.
-
Human features
-
HUman features.
- UI/UX attached to the AI’s IO with features like voice, language, vision, and reasoning.
-
AI management and exploitation.
- Enables you to insert AI into any application or business process.
- Make sure you can test, bench-test, improve, check what has changed, and measure variance.
- This is how you manage AI lifecycle, proof, and explain-ability of decisions.
Process
-
Identify the Right Business Opportunities for AI.
- Customer service (chatbots)
- Employee/company productivity << Reference for Proposal
- Manufacturing defects
- supply chain spending
- If it can be described >> It can be programmed >> Ai can make it better
-
Prepare the Organization for AI.
- [[The AI Job Replacement Axiom]]
- All technology is useless without the talent to put it to use
- Repetitive and manual tasks will be automated
-
Select Technology & Partners.
- Corporate culure should drive the choice of AI technology mix.
- Adopt many technologies and learn which ones work and which ones don’t.
- Pick and handful of partners that have the skills and tech to get the job done.
-
Accept Failures.
- [[AI’s Ratchet effect]]
- If you try 100 AI projects, 50 will probably fail.
- But, the 50 that work will be more than compensate for the failures.
- The culture you create must be ready and willing accept failures, learn from them, and move onto the next.
- Fail-fast, as they say.