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 an identified Problem
    • Instrumentation / Control
    • Repetition / Automation / Acceleration
    • Communication / Dialogue
    • Recognition
  • Or identified Technology
    • In search of a problem
    • In search of a market
    • Making sense of a morph dataset
    • Technology becomes a constraint in the solution space
  • Customer
    • Value
    • Control
    • Personalization
    • Closer to cognitive dialogue
  • Cost Case
    • Availability of quality Data, defined 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
  • Organization
    • Implications for the Process Layer
      • Internal automation
      • New labour
    • Responsive and Agile
    • IT Utilities and strategic Assets
  • Price
    • Standard or Project
    • AI implies disruptive flexible invests and payment scheme
    • It’s about automation and control, less discounts
    • No intelligence without automation, price for appropriate infrastructure
• Data
  • Sourcing of quality Data
  • Control of instrumentation, sampling entities and diversity
  • Ownership of Data and Partaking agreements
  • Understanding of synthetic Data, modifications applied and state of sources
  • Graph or semantic structure allow for alternative and re-use
  • A morph data with unsupervised - What does it tell you?
  • Capability to conduct updates of Datasets
• Foundation for Algorithms
  • Next Generation Capabilities
    • Visual Systems
    • Operational pipes
    • Generative Models
  • Math, Engineering, Stats, Distributions
    • Owned key developments
    • Derive criteria for an industry platform
    • Modeling (Including Virtual Worlds for Testing)
  • Capacities for Problem Solving and Reasoning
    • Architecture (Central, Cloud, Edge, IoT…)
  • Updates, Release Management, DevOps
• Testing
  • Really understand the function intended
  • Behavior (AI typically is a black box)
  • Stress cases and fulfillment of QoS
  • Reproduction of behavior including Legal Analysis in Virtual World after incidents
• Dialogue
  • Machine Man
  • Machine Machine, Signaling, APIs
  • Machine Machine Dialogue including Man, understanding yields trust