AI models are software systems designed and trained to perform specific tasks. This includes analyzing data to identify trends and patterns, or executing tasks without continuous human intervention. They are built using artificial intelligence (AI), which enables the model to make decisions and generate outputs based on learned patterns.

There are three main ways to train an AI model:

  • Supervised learning: The model is trained on a labeled dataset, where the correct outputs are provided.
  • Unsupervised learning: The model is trained on an unlabeled dataset and learns to identify hidden patterns or groupings.
  • Reinforcement learning: The model learns optimal behaviors by receiving rewards or penalties in response to its actions.

Different types of models are better suited to specific tasks depending on their training method. For example, supervised models are well-suited for predictive tasks, such as forecasting patient readmission rates in healthcare or credit risk in finance. Unsupervised models are often used to power retail recommendation engines or to detect anomalies in financial transactions or medical imaging.

AI models are typically developed using two primary approaches:

  • Machine learning: Algorithms that identify patterns in data and make predictions or decisions based on them.
  • Deep learning: A subset of machine learning that uses neural networks (structures inspired by how the human brain processes information) to process large volumes of complex data, such as images, text, or time series.

How do AI models work?

AI models function by processing data through structured techniques, including training parameters, applying algorithms, and refining outputs, to deliver accurate and actionable results.

Input data

Input data refers to the information used to build and train a model. It is also used after training to generate predictions. The model processes this data to identify trends and patterns, which it then uses to produce outcomes. The type and quality of input data depend on the task the model is designed to perform — for example, transaction logs in finance, patient records in healthcare, or purchase histories in retail.

Parameters

Parameters are variables within a model that are learned during training. These include weights — which represent the strength of connections between nodes in a neural network — and biases, which are constants added to inputs to help the model adjust and generalize better. These parameters influence how well the model performs tasks such as fraud detection in banking or predicting inventory needs in retail.

There are also hyperparameters — settings defined before training that guide how learning occurs. Examples include the learning rate (which controls how quickly a model adapts during training) and LLM temperature (which affects the randomness of text generation). Unlike parameters, hyperparameters are not learned from data but are set manually to influence performance and behavior.

Algorithms

Algorithms are sets of instructions that the model follows to learn from input data. They enable the model to recognize patterns, make classifications, and support decision-making. These algorithms power a wide range of cognitive functions, such as identifying fraudulent transactions, recommending personalized treatments, or segmenting customer behavior in retail.

Optimization

Optimization is the process of improving a model’s performance, typically by enhancing accuracy, efficiency, or both. This involves adjusting parameters or hyperparameters and refining the model to better suit its task. For enterprises, well-optimized AI models can deliver measurable gains, such as reduced operational costs, faster decision-making, or more accurate forecasting.

What are the different types of AI models?

There are two primary architectures of AI models, each with different learning methods suited to specific tasks.

Machine Learning Models

Machine learning models can be developed in various forms. They learn from training data to make predictions and generate outputs — and generally improve in accuracy as they process more data.

Common machine learning types:

  • Linear regression: Identifies linear relationships between input and output variables to form predictions. It is commonly used in financial institutions for risk analysis and forecasting.
  • Logistic regression: Estimates the probability of an event and is used for classification tasks. It is often applied in medical research, for example, to model how the presence of certain variables affects disease likelihood.
  • Decision trees: Use a flowchart-like structure of ‘if-else’ logic to make decisions based on input variables. These are useful for retail organizations creating personalized product recommendations.
  • Random forests: An ensemble of decision trees, where each tree contributes to a final decision. This aggregation typically results in higher accuracy. Random forests are often used to predict buyer behavior or identify fraudulent transactions.
  • Support vector machines (SVMs): Solve classification problems by finding the optimal boundary that separates different data classes. SVMs are suitable for tasks such as classifying customer feedback or diagnostic imaging data.
  • K-nearest neighbors (KNN): Classifies new data points based on similarity to nearby examples in the training data. This method is useful for customer segmentation or medical diagnosis support.

Deep Learning Models

Deep learning is a subset of machine learning based on neural networks with multiple interconnected layers. These models are particularly effective for unstructured data — such as text, images, or audio — which lacks a predefined format. Each layer processes and refines the input, improving the model’s performance as it learns.

  • Neural networks: Used to identify patterns within complex datasets and solve tasks such as image recognition, speech processing, or trend forecasting.
  • Large language models (LLMs): Specialized deep learning models trained on vast amounts of text. They excel at tasks involving text understanding, summarization, or human-like language generation — for example, automating responses in healthcare support or analyzing customer inquiries in retail.

What are common applications of AI models?

AI models are commonly used for various enterprise applications. Thanks to their ability to automate and enhance a wide range of business functions, research shows that 71% of global organizations now regularly use generative AI as part of their daily operations.

Finance

In finance, AI is used for its analytical and predictive capabilities. Financial institutions will use AI to detect fraudulent transactions on a bank account, or to predict market forecasts such as credit risk.

Healthcare

AI models are used in healthcare for analyzing medical images to predict patient outcomes. Using machine learning methods, the model analyzes X-rays and CT imagery to learn to detect anomalies and abnormalities. They are becoming increasingly accurate with their diagnoses due to learning through experience. 

Retail

Retail organizations use AI models to support and simplify customer service activities. This includes chatbots to provide a personalized experience, product recommendations based on previous purchasing behavior, and targeted adverts. 

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