Artificial intelligence in business

Artificial intelligence (AI) enables computers and algorithms to mimic intelligent human behavior. Since its establishment as an academic discipline in 1956, AI has gone through multiple cycles of optimism and skepticism. Today, it is a rapidly evolving field with applications across industries, offering businesses new ways to enhance efficiency, automate processes, and make data-driven decisions.

The Many Faces of AI

Human intelligence is complex and multifaceted, and AI attempts to replicate different aspects of it. This has led to the development of specialized AI models:

• Computer vision enables machines to analyze and identify objects in images.
• Natural language processing (NLP) allows AI to understand and generate text.
• Generative AI can create images, audio, and video.

While there are aspirations to develop Artificial General Intelligence (AGI)—a system capable of human-like reasoning across multiple domains—its practical value remains uncertain. Current AI models excel in specific tasks but struggle with adaptability beyond their training scope.

AI vs. Human Efficiency

Unlike humans, who must be adaptable to a wide range of situations, AI systems can be optimized for specific tasks. Just as industrial robots are designed for precision without unnecessary features, AI models can be tailored to run efficiently on small, low-power devices. Some AI applications can operate on hardware the size of a credit card, making them well-suited for embedded systems and edge computing. This contrasts sharply with the energy demands of a human, averaging 2.2–2.9 kWh per day.

AI in Business: Strengths and Limitations

To integrate AI effectively into business operations, it is crucial to recognize its strengths and limitations:

• AI can process vast amounts of data quickly, identifying patterns that humans might miss.
• It can automate repetitive tasks, freeing up human workers for more complex decision-making.
• However, AI models, like humans, can make mistakes. They rely on training data, which may contain biases or inaccuracies.
• In high-stakes environments (e.g., medical diagnosis, financial risk assessment), traditional algorithms or human oversight may still be preferable to AI-driven automation.

The Role of AI in Workforce Automation

While AI enhances productivity, full automation remains rare. Businesses still rely on human workers alongside AI-powered tools:

• Factories use robotics and AI-driven automation, yet still require human oversight.
• Retail stores have self-checkout kiosks, but customer service representatives remain essential.
• Border security employs biometric AI scanners, yet manual checks are still in place.

AI is most effective when it augments human work rather than replaces it. The key to leveraging AI in business is to identify areas where automation provides the greatest value while maintaining a balance between efficiency and human expertise.