πŸ“• Node [[two key elements of ai componentry and process]]
πŸ“„ Two Key Elements Of AI - Componentry and Process.md by @KGBicheno

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&#8217;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.

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