<commit_before>layout: post
title: AI Business Approach

---

Aspects of an AI Business approach. Intelligence is the benefit for your Client and what you want to sell. Artifical is the challenge, as models are wrong but sometimes useful.  

- **Business Plan / Canvas**
  - Either identified Problem
    - To Control
    - Facilitating Repetition
    - Instrumental to Communication
    - Acceleration towards automated Business
  - Or identified Technology
    - In search of a problem
    - In search of a market
    - Technology becomes a constraint in the solution space
  - Cost Case
    - Quality of Data, defines outcome, cost and risk
    - Frequent re-train of images, testing and distribution of updates
    - Team and People dependencies
      - Better organize Knowledge in the System and structured Data
      - Churn of People
    - Support(see Testing), SmartServices Potential and Necessity
    - Risk Management
  - Price
    - Standard or Project
    - AI implies disruptive flexible invests and payments
    - No underbidding, it’s about automation and control
    - No intelligence without automation, don’t forget appropriate infrastructure
- **Data**
  - Sourcing of Data
  - Control of sampling entities and management of diversity
  - Ownership of Data or Partaking agreements
  - Understanding of synthetic Data, documented modifications and fusion of sources
    - Derived and defined Probabilities
  - QUALITY Management of Data
  - Structure of Data like Graph or Semantic
  - Capability to conduct Updates of Datasets
- **Foundation for Algorithms**
  - Math, Engineering, Stats, Distributions
    - Own key developments 
    - Derive criteria for an industry platform

  - Modeling (Including Virtual Worlds for Testing)

  - Non- or directional Learning

  - Operational Pipes

  - Capacities for Problem Solving and Reasoning

  - Architecture (Central, Cloud, Edge, IoT…)

  - Updates, Release Management, DevOps
- **Testing**
  - Really understand the function intended
  - Behavior (AI is typically a black box)
  - Stress cases and fulfillment of QoS
  - Reproduction of behavior (Afterthought) including Legal Analysis in Virtual World after incidents
- **Communications, Dialogue**
  - Machine Man Communication
  - Machine Machine Communication, Signaling, APIs
  - Machine Machine Dialogue, understandable for and involving Man<commit_msg>_posts<commit_after>layout: post
title: AI Business Approach

---

Artificial Intelligence - Intelligence is the benefit for your Client and what you want to sell. Artifical is the challenge, as all models are wrong but sometimes useful.  

- **Business Plan / Canvas**
  - Either identified Problem
    - Instrumentation / Control
    - Repetition / Automation / Acceleration
    - Communication / Dialogue
    - Recognition
  - Or identified Technology
    - In search of a problem
    - In search of a market
    - Technology becomes a constraint in the solution space
  - Cost Case
    - Availability of quality Data, defines outcome, cost and risk
    - Constraints definition
    - Frequent re-train of images, testing and distribution of updates
    - Team and People dependencies
      - Better organize Knowledge in the System and structured Data
      - Churn of People
    - Support(see Testing), SmartServices Potential and Necessity
    - Risk Management
  - Price
    - Standard or Project
    - AI implies disruptive flexible invests and payments
    - It’s about automation and control, less discounts
    - No intelligence without automation, don’t forget appropriate infrastructure
- **Data**
  - Sourcing of Data
  - Control of instrumentation, sampling entities and diversity
  - Ownership of Data or Partaking agreements
  - Understanding of synthetic Data, modifications applied and state of sources
  - QUALITY Management of Data
  - Graph or semantic structure allow for alternative and re-use
  - Capability to conduct updates of Datasets
- **Foundation for Algorithms**
  - Math, Engineering, Stats, Distributions
    - Owned key developments 
    - Derive criteria for an industry platform

  - Modeling (Including Virtual Worlds for Testing)

  - Non- or directional Learning

  - Operational Pipes

  - Capacities for Problem Solving and Reasoning

  - Architecture (Central, Cloud, Edge, IoT…)

  - Updates, Release Management, DevOps
- **Testing**
  - Really understand the function intended
  - Behavior (AI is typically a black box)
  - Stress cases and fulfillment of QoS
  - Reproduction of behavior including Legal Analysis in Virtual World after incidents
- **Communications, Dialogue**
  - Machine Man Communication
  - Machine Machine Communication, Signaling, APIs
  - Machine Machine Dialogue, understandable for and involving Man
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