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Preparing and Architecting for Artificial Intelligence

As Artificial Intelligence gains traction in digital businesses, technical professionals must explore and embrace it as a tool for creating operational efficiencies.

What Is Artificial Intelligence?

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of its activities include: •Speech recognition •Learning •Planning •Problem solving

What Business Trends and Benefits Are Driving Artificial Intelligence?

Business use of AI is gaining momentum due to the increasing pervasiveness of the technology and the rising discovery of business benefits that can be derived from its use.

Trends and Benefits

Examples of How Artificial Intelligence Can Deliver Value to Organizations

Many types of organizations have gained business value from machine learning.

1: Customer Relationship Management
2: Failure Prediction for Preventive Maintenance
3: Finance and Hedge Fund Portfolio Pricing
4: Workforce Management
5: State Government and Litigation Case Management

Artificial Intelligence Can Also Provide Process Benefits for IT Organizations

1: Security Operations
2: IT Call Centers
3: DevOps
4: Project Management

How Should IT Prepare for Artificial Intelligence?

The benefits that AI can provide will likely drive interest from business leaders. If the IT organization is proactive about planning and is preparing the IT environment for AI now, it will be betterpositioned to deliver benefits. To be ready, technical professionals should start by planning in areas related to:

The AI process
AI technical architecture
Required skills



IT Processess in Artificial Intelligence

Learn the Stages of the Artificial Intelligence Process

Classify the problem
Acquire data
Process data
Model the problem
Validate and execute
Deploy

Understand the Model Development Life Cycle Needed for Artificial Intelligence

When planning to aggressively build custom AI algorithms and applications, organizations must develop a life cycle for machine learning to support the highly iterative building, testing and deployment of AI models

Development life cycles must support:

Collaboration for heterogeneous teams and technologies
Monitoring of AI models with statistical analysis capabilities
Reusability of AI models for rapid development
Interoperability between different analytic platforms and AI frameworks

A Comprehensive End-to-End Architecture

To support ML applications, technical professionals must envision a revitalized data and analytics end-to-end architecture that incorporates diverse data, models and algorithms and can deliver analytics anywhere.

Build a Use Case in the Cloud

Once you've identified a good first business challenge to solve with AI, build a small pilot project and conceptual architecture around that use case.

Recommendations

Business Insight Corporation recommends that technical professionals take the following steps to prepare and initiate AI capabilities.

Conclusion

Artificial Intelligence is about acquiring knowledge through data, and it differs from traditional applications or programs that generate statistics or engineering output. AI technologies offer the benefits of speed, power, efficiency and intelligence through learning without having to explicitly program these characteristics into an application.

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