Artificial intelligence (AI)

Definition

Artificial intelligence is the branch of computer science focused on creating systems capable of performing tasks that normally require human intelligence. These tasks include learning, reasoning, problem solving, pattern recognition, natural language processing, and decision-making. AI systems can be narrow, designed for specific tasks, or general, intended to perform a broad range of cognitive activities.

AI is now a core driver of innovation in business and technology, powering applications from voice assistants to predictive analytics, automation, and robotics.

Advanced

At an advanced level, AI relies on algorithms, statistical models, and large datasets to simulate intelligence. Machine learning, a key subset, enables systems to improve performance over time without explicit programming. Deep learning, using neural networks with many layers, powers image recognition, speech understanding, and generative models.

AI is integrated into enterprise systems, cloud platforms, and edge devices. Advanced deployments involve reinforcement learning, explainable AI, and hybrid architectures combining symbolic reasoning with neural models.

Why it matters

• Automates complex tasks to improve efficiency and reduce costs.
• Enhances decision-making through predictive insights.
• Powers customer-facing tools such as chatbots and recommendation engines.
• Creates competitive advantages in innovation and scalability.

Use cases

• Predictive analytics for finance, healthcare, and marketing.
• Intelligent chatbots for customer service.
• Image and speech recognition in security and accessibility tools.
• Autonomous vehicles and robotics in logistics and manufacturing.

Metrics

• Accuracy and precision of AI models.
• Model training time and inference speed.
• ROI from automation and efficiency gains.
• User adoption and trust in AI-driven systems.

Issues

• Ethical concerns around bias, transparency, and accountability.
• High resource and data requirements for advanced AI models.
• Security risks if AI systems are manipulated or exploited.
• Regulatory compliance challenges in sensitive industries.

Example

A retail company uses AI to analyse customer purchase history and predict future buying behaviour. The system personalises product recommendations, increases sales, and improves customer satisfaction.