Shruti GautamKey Points:
- The effective use of AI systems will be what separates the leaders from the laggards in the business world in 2024.
- Executives must have a working knowledge of AI technical language to make informed decisions and manage AI initiatives in their organizations.
Organizations are increasingly exploring how to deploy AI systems and move from strategy development to implementation. To effectively deploy AI systems, it is important for business leaders to have a working knowledge of AI technical language and concepts. This allows them to make informed decisions and manage AI projects within their organizations.
Business leaders should understand the basic terms and concepts related to AI, including algorithms, AI models, and AI datasets. Algorithms are the set of rules that machines follow to learn tasks. AI models are the output of training an algorithm, representing learned patterns and relationships within data. AI datasets provide the material from which an AI system learns patterns and makes predictions. Leaders should also have a basic understanding of terms like labeling, annotating, fine-tuning, and narrow AI.
Explainable AI (XAI) is another important concept for business executives. XAI aims to provide insights into how AI models arrive at specific predictions or decisions, making the decision-making process more transparent and accountable. To use AI most effectively, prompt engineering is emerging as a new skill set, allowing users to prompt AI systems to produce tailored results through specific queries.
There are also terms and concepts that may not have a direct impact on business leaders’ day-to-day activities, such as artificial general intelligence (AGI) and mathematical terms related to software engineering of AI models. Leaders should be aware of these terms, but they can leave the technical details to the experts and developers.
Overall, understanding the technical language and concepts of AI enables business leaders to make better decisions and effectively manage AI initiatives within their organizations. It also allows them to understand the limitations and potential biases of AI systems, ensuring they are used ethically and responsibly.
