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The Essential Components Of AI

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By Rob Thomas, General Manager, IBM Data and Watson AI

Today, artificial intelligence (AI) is optimizing the way business is conducted, enabling predictions with supreme accuracy and automating business processes and decision making. The outcomes range from greater customer experiences to more intelligent products and more efficient services for enterprises. Just as the auto industry suddenly flourished in the early 20th century after many years of incremental developments and experimentation, AI has reached this point in the 21st century with many of the key technologies and building blocks firmly in place.

AI’s value proposition is now generally understood: It has the potential to make virtually any task or process more efficient and yield powerful new business insights.

These days, organizations with no AI strategy are like businesses in 2000 that had no Internet strategy, or those in 2010 that had no mobile strategy. And yet, for many organizations, AI is still uncharted territory.

In my view, there are six key components that are essential to AI. While they may not all fit in the classical definition of AI, the following represent the core building blocks that are needed:

  1. AI Applications: Packaged applications that solve a business problem (i.e., virtual agents, financial planning)
  2. Data Prep and Cleansing: Make your data ready for AI
  3. Model, Build, Train and Run: The studio of a data science artist to build, train and run models (machine learning)
  4. Consumer Features: Speech, images and vision, primarily used in consumer use cases
  5. Natural Language Processing: The nervous system of enterprise AI
  6. Lifecycle Management: Managing the lifecycle of AI models and understanding how they perform

As companies progress on their AI journey, incorporating all or most of these components into their businesses, trust is becoming paramount. Helping users understand how the AI is working and being able to explain decisions is becoming essential to fostering trust and confidence in AI systems. In fact, 68% of business leaders believe that customers will demand more explainability from AI in the next three years, according to an IBM Institute for Business Value survey.

The truth is, AI is still in the experimentation phase for many industries. But, at this particular moment, the opportunity for the technology ecosystem to drive new use cases and new innovations in a thoughtful and ethical manner is profound.

A prudent approach to AI means making data simple and accessible. It means creating a foundation of business-ready data analytics, building and scaling AI with trust and transparency, and having a coherent step-by-step plan for rolling out AI throughout the organization and governing its use. With that shared sense of mission, we will all benefit from the remarkable economic and societal benefits that AI will bring to companies, countries and citizens.

This article was produced in partnership with The AI Summit and is part of Forbes’ AI In Action series.

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