Debunking Myths and Misconceptions About Artificial Intelligence

The hype around artificial intelligence has led to many myths. Enterprise architecture and technology innovation leaders implementing AI projects must separate reality from the myths to devise future strategies, or enhance existing ones, that will deliver business value outcomes.

Key Findings
  • AI has massive potential, but enterprise architecture and technology innovation leaders often struggle to identify where and how to realize its business value.
  • Hype around explainable AI and black-box AI models may result in unintended consequences that will inhibit AI adoption in certain business contexts.
  • Operationalizing AI as part of an automated decision-making system is the greatest barrier to AI success.
  • Enterprise architecture and technology innovation leaders are overwhelmed by the sheer number of available AI technologies; they struggle to decide which ones to select and when. 
Gartner recommends that enterprise architecture and technology innovation leaders engaged in areas using AI must:
  • Maximize business value by pairing business priorities with near-term opportunities, especially those that leverage AI’s power to augment human work, decisions and interactions.
  • Document explainability needs by consulting with lines of business or legal departments. Put AI models in context and define how AI fits in overall business solution design and operations.
  • Improve organizational appetite to accept AI by securing deployment and infrastructure budgets approved in advance and securing the help of domain experts while operationalizing AI.
  • Leverage AI technology from your preferred cloud vendor; these have improved capabilities, such as integration, deployment and infrastructure scalability, required for AI-based systems.
  • Create a flexible machine learning pipeline by leveraging traditional algorithms first. If they are not good enough, utilize deep learning approaches with all their known complexities.

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