Becoming an Early Winner With AI by Optimizing the use of the AI Engine
In any application, whether it's artificial intelligence or something else, there are four critical divisions.
The first is the Procedure Division, which is the processing engine of the application where data manipulation occurs to produce results. Next is the Person-Machine Interface Division, which is the interface between an individual and the application they are using. The third is the Machine-to-Machine Interface Division, which involves the interfacing between one application and another. The fourth, which provides a competitive advantage in AI, is the Data Division.
The Procedure Division, or process division, is the engine where significant technological advancements in project processing speed have occurred over the decades since AI was first introduced in 1956. Focusing on the Data Division, there are four types of training data involved in AI: internet-related data, copyrighted data, balanced exchange of information, and best practice information.
Internet-related data is AI-accessible and free to use, but its quality is often questionable due to the lack of provenance. Copyrighted data is intellectual property owned by someone, and using it without permission is considered AI theft. This issue is being actively addressed in some countries, but not as aggressively in the United States. For instance, Taylor Swift famously opposed Apple Music's use of her album without compensation, leading Apple to change its policy overnight to compensate artists during their free trial period.
The third category is balanced exchange of information, where two parties exchange something of value, such as providing an email address to access an article. AI engines cannot access this data as it requires an exchange that validates the information. The fourth category, best practice information, is where significant competitive advantages lie. This information is not generally available on the internet and is protected as it provides measurable benefits to organizations.
To gain a competitive advantage with AI, it is crucial to harness a robust data architecture. This involves building a structured data environment with traceable and high-quality data that supports business activities and strategies. Your enterprise AI engine should focus on balanced exchange and best practice information, along with owned copyrighted data, while internet-related data can be used sparingly for general insights.
In conclusion, becoming an early winner with AI involves leveraging high-quality, well-structured data within a robust AI engine. By doing so, you can build an agile enterprise that continuously improves and maintains a competitive edge. For more information or to discuss this topic further, please contact us. Thank you for listening, and we look forward to helping you on your journey to AI excellence.
Categories
- Agility
- Architecture Models
- Architecture Views
- Artificial Intelligence
- Assemble to Order
- BTP
- Benefits
- Big Data
- Bill of Materials
- Book
- Business Architect
- Business Architecture
- Business Architecture Framework
- Business Architecture Participants
- Business Architecture Tools
- Business Capability
- Capabilities
- Capability Ability
- Certification
- Certification Levels
- Certification Mistakes
- Change Management
- Checklist
- Cloud
- Cloud Decommission
- Coding
- Communication
- Competition
- Complexity
- Confirmation Bias
- Consulting
- Cybersecurity
- Data
- Data Architecture
- Data Lake
- Data Modeling
- Data Sludge
- Data Swamp
- Differentiators
- Digital Transformation
- Distance Learning
- Enterprise Architect
- Enterprise Architecture
- Enterprise Architecture Framework
- Enterprise Architecture Participants
- Enterprise Architecture Tools
- Evaluation Checklist
- Evaluation Criteria
- Event Model
- Experiences needed
