What is an AI Model? Benefits, Types, & Industry Examples

An AI model is a trained software program that learns from vast amounts of data to identify patterns and predict outcomes, making decisions on various tasks with minimal or no human input.

The model drives the intelligent behaviour for artificial intelligence systems. It is trained on massive datasets, enabling the system to handle complex tasks and deliver useful insights for human users.

AI models may seem complicated, but when you break them down into their core components, they operate in a clear step-by-step way. In general, there are four stages to creating a successful AI model.

1. Obtaining as much high-quality data as possible
2. Building the AI algorithm to interpret this data
3. Training the AI using the data
4. Helping the AI model make predictions on its own

Let’s dive into each of these moving parts one at a time.

AI models learn from data—often large volumes of unstructured data sets—to identify patterns, recognise speech or images, and understand language.

As you might expect, the quality of this data is absolutely crucial. It needs to be accurate and plentiful enough to give the model enough information to identify patterns.

Data may be the building blocks of any successful AI model, but algorithms are the brains that allow models to interpret it.

An AI algorithm takes data and uses it to learn. For instance, it can analyse vast quantities of historical data to make predictions or identify patterns in complex information like text, speech, and images.

Deep neural networks and advanced architectures, such as generative pre-trained transformers (GPT) and diffusion models, further enhance the ability of AI models to tackle complex tasks. Complex learning algorithms drive these.

With the algorithm in place and the data ready to input, all that’s left is to feed information to the AI model to help it grow.

There’s no magic formula here; it’s more a process of trial and error. Think of an AI model as a trainee learning a skill. At first, it’s shown (by humans, the ‘trainers’) many examples (data) and then the correct answers. Over time, it compares its own attempts against the answers, adjusting its ‘knowledge’ with each round of feedback to get better.

• Supervised learning: where the model learns from labelled data.
• Unsupervised learning: where the model identifies hidden patterns in unlabelled data.
• Reinforcement learning: where the model improves through feedback loops and trial-and-error.

AI researchers use a combination of all three of these approaches to help the AI model improve with time.

After enough training, the AI model can begin to make predictions and detect patterns autonomously.

There’s no longer a need to instruct the model about the right answer each and every time. Just as a trainee gains experience with practice, the AI model refines its patterns and predictions in response to human monitoring until it can handle new tasks accurately on its own.

Businesses shouldn’t choose a hammer when they need a spanner. Every artificial intelligence model works similarly, but each is designed to handle specific tasks. To help you decide which is right for you, we’ll now break down five popular AI model types you should know:

• Supervised learning models
• Unsupervised learning models
• Reinforcement learning models
• Deep learning models
• Generative models

Let’s get into each of these types of AI models in more detail.

The ‘supervised’ here stems from the idea that these models work with labelled data. Each example includes both data inputs and the correct output. The model can learn from this clear-cut relationship to recognise what output is expected from given inputs.

Techniques like linear regression, logistic regression, and decision trees help predict outcomes or classify inputs. Supervised models are commonly used for tasks like credit scoring, sales forecasting, or spam detection.

As you might expect, unsupervised learning models rely on unlabelled data. This means they discover patterns and attempt to group items without predefined categories.

How does it do this?
Clustering algorithms and dimensionality reduction techniques are set to work in large data sets. For our purposes in this blog, all we need to know is that the model knows how to identify segments, detect anomalies, and find hidden relationships within the data we present to it.

For example, an online retailer could use an unsupervised model to analyse purchase histories. The model would naturally group customers into segments based on similar buying habits (that the human brain couldn’t have easily identified).

Ultimately, this helps the retailer tailor marketing messages, recommend products, and improve overall customer satisfaction without having predefined categories in mind.

Reinforcement learning models learn by trial and error. The team working on the model gives feedback (rewards or penalties) as the model completes tasks. The model is programmed to adjust its strategies to seek out rewards rather than penalties.

This approach is well-suited for dynamic tasks like optimising supply chains and managing inventory. It’s also the type of model used to try to perfect the latest autonomous vehicles.

Deep learning models learn in layers, each layer uncovering more detail and complexity from the data they encounter and process.

Imagine having a team of experts, each focusing on different elements of a problem, who then combine their insights to understand the bigger picture.

As an example, one type of DL model, a convolutional neural network, is like a “digital eye” scanning an image. It recognises patterns and objects without the need for pre-labelling.

Another type, known as a recurrent neural network, can read through information in a sequence, like following a sentence word-by-word. This aids in language understanding. It also means it can predict future trends based on an analysis of a sequence.

This type of DL model can enable various tasks:

• Accurate product recommendations based on images.
• Precise time series forecasting for sales or inventory.
• Voice assistants that understand spoken requests.

Deep learning helps businesses handle complex information more intuitively, making smarter predictions and enhancing customer experiences.

Generative AI models can create new content, such as images, text, or code, based on patterns learned from training datasets. They analyse large volumes of data to generate outputs that reflect the structure and style of what they’ve been trained on.

The most common types you’ll come across are generative adversarial networks (GANs) and large language models (LLMs).

A GAN can “imagine” realistic images or designs by understanding patterns from existing visuals in its training data. Large language models learn from vast amounts of written text, allowing them to generate coherent, context-aware responses. This foundation allows them to:

• Craft new sentences (text generation).
• Summarise long documents.
• Answer questions.
• Code generation.

Both these models do more than simply identify patterns in data. They use these patterns as a basis to deliver fresh content.

Businesses can use generative AI to:

• Deliver personalised marketing content: Easily and quickly generate customised content at scale, such as product descriptions, newsletters, and other promotional materials.
• Improve customer support: Power advanced chatbots/virtual assistants understand the context of a question to answer complex user queries and resolve issues.
• Creative ideation: Provide fresh ideas for creative teams and speed up the creative process with original concepts for branding or product prototypes.

AI agents fall under all four categories of AI models, and this can depend on how an AI agent is being used or trained. For example:

• Supervised learning: An AI agent can be trained by labelling data.
• Unsupervised learning: AI agents can discover patterns with unlabelled data.
• Reinforcement learning: AI agents can interact with a learning environment and receive feedback.
• Deep learning: AI agents can also use complex neural networks to process large amounts of data and leverage that data.

AI and machine learning models have a boundless number of use cases across industries - from complex, open-ended projects to simple day-to-day tasks. For example, here are some popular AI model use cases.

• Predictive modelling: AI models can use historical data to predict future outcomes, such as consumer demand, potential business risks, and maintenance scheduling.
• Image recognition: AI models can analyse images to identify objects, text, or faces. This is useful for everything from medical diagnostics to security surveillance.
• Recommender systems: Based on past behaviour, AI models can personalise product, movie, music, or content recommendations. Think Netflix recommending a TV show based on your previous viewing.
• Natural language processing (NLP): AI models can interpret human-written text and even translate languages in real-time. This enables chatbots, virtual assistants, sentiment analysis, and more.
• Anomaly detection: AI models scan incoming data to detect unusual patterns, which is groundbreaking for cybersecurity and fraud detection, as well as industrial fault monitoring.

To show you how AI models can benefit different sectors in practice, here are some examples from Australia that showcase how businesses across various industries are using AI models right now.

At the Peter MacCallum Cancer Centre in Melbourne, researchers use machine learning models to analyse medical images . They trained an ML model to detect patterns to help identify early-stage cancers.

The basic principle is that an AI model can examine vast amounts of medical data to elicit valuable information at a rate that would be impossible for any human to accomplish.

They are an ally within the research process thanks to their computing power. By applying deep learning techniques, they can identify subtle signs of diseases like melanoma. This technology can help doctors improve diagnostic speed and accuracy.

The Commonwealth Bank of Australia (CBA) uses AI-driven fraud detection models that monitor transactions in real-time.

CBA processes and analyses more than 20 million payments a day. Thanks to its investment in Gen AI, CBA now flags suspicious transactions and automatically sends proactive warning alerts to customers via its app.

The initiative has played a central role in reducing customer-reported fraud by 30%.

These models quickly recognise unusual activity (anything that doesn’t fit its predetermined ideals of ‘usual’ activity). This security upgrade protects client data while reducing any financial losses caused by fraudulent behaviour. The consequence is a boost to customer trust.

Retailers like Woolworths are leveraging AI models to improve customer service, predict sales, and optimise stock levels.

Woolworths implemented an AI-driven workforce management tool to optimise employee scheduling. The system uses AI to predict customer footfall across its stores.

Why? To allow managers to ensure enough staff are in stores to cope with customer demand effectively. Woolworths also uses AI models as part of its demand forecasting.

These models predict product demand to ensure shelves are stocked appropriately. Think of busy times like Christmas. In the past, regional managers would have to make an educated guess about how much stock was needed and order the stock. Now, they can lean on AI to analyse customer data and past Christmas periods to make better decisions regarding stock.

AI can also help to dynamically adjust prices based on customer behaviour and market conditions. There are even plans to roll out more ‘smart trolleys’ across its stores. These carts can track shoppers through stores and send targeted, personalised ads via a screen on the trolley. You’ll even see recommendations on screen based on what you’ve already put in the cart.

The driving force here is twofold:

1. The customer gets a better shopping experience — personalised, efficient, staff on hand, and enough stock in the store.
2. Woolworths boosts its profits through its improved customer experience.

Businesses across Australia will continue to implement AI models in innovative ways. Those who don’t risk being left behind by competitors who utilise AI models to lower costs and offer better customer experiences.

AI models offer huge potential and significant advantages for businesses of any size and across any sector.

At a high level, AI models automate repetitive tasks and analyse complex datasets. This leads to two key benefits for all business types:

1. Teams become more efficient.
2. Teams make better decisions.

But we can break this down further into more granular benefits. Here are some to consider.

• AI automation: AI-driven automation can handle routine tasks like updating client records, processing orders, or monitoring inventory. This frees up employee time to focus on higher-value activities.
• Accuracy: Humans can make errors in tedious day-to-day tasks. AI models are more reliable, giving businesses more accurate, consistent results.
• Analytics: Data analysis capabilities let businesses identify trends and anticipate customer needs. This offers a better idea of what makes their customers tick, letting them personalise their offerings. 
• Smarter decisions: The ability to analyse data at speed means businesses can make better-informed decisions. In turn, they can optimise everything from marketing campaigns to pricing strategy.

Combined, all these potential improvements mean better business operations and better customer experiences. In other words, a clear path to long-term growth.

However, implementing AI is not without its dangers. Organisations must be careful and considerate in how they use this technology that is now so publicly available. Here are four things to be aware of:

AI models learn from existing data, meaning they take on any flaws of that initial input. This data is likely to contain historical bias. These biases can influence the model’s predictions and decisions without careful oversight and responsible AI frameworks, resulting in unfair outcomes.

For example, if an AI model trained on past hiring data mostly saw successful candidates from a single demographic or psychographics, it might favour similar candidates in the future. This would exclude qualified applicants from other backgrounds.

As should be clear by now, AI models rely on data collection. Ensuring that we collect, store, and process this data securely is a key challenge that businesses must meet.

It’s essential to prevent harmful data breaches that will ultimately lead to a breakdown of trust.

It’s important to understand how models reach their conclusions. Many of us are naturally sceptical about the danger of outsourcing important decisions to programs run by algorithms rather than moral, sentient beings.

Clear explanations help build trust with customers and stakeholders, especially in sensitive applications like credit assessments or medical diagnoses.

While always a little behind technological advancements, governments around the world are working to define AI governance best practices. This collective drive aims to ensure the ethical use of technology.

In Australia, no official AI regulation exists. The government deems the ‘voluntary guardrails’ currently in place as “unfit for purpose,” and it is proposing to implement mandatory guidelines to address the distinct risks posed by AI.

All organisations need to comply with these developing regulatory standards to ensure customers are rightfully protected from harmful practices.

The point here is not to be afraid of implementing AI to improve business performance.

Instead, it’s about understanding that we need to address some potential issues to ensure that AI models can add value without compromising integrity.

As AI continues to evolve, we can assume that models will become more efficient, interpretable, and accessible. Pandora’s box is well and truly open, and the defining element of AI’s future will be how well we harness its power while curtailing and preventing the risks.

Emerging trends point to smaller, more energy-efficient models that can run on mobile devices. This progress would reduce reliance on massive computing resources .

New training techniques and improved architecture designs may help address ethical concerns, baking AI risk governance into the actual training process itself.

Backing this up, we can certainly expect governments to catch up and impose governance frameworks that will boost transparency and privacy protections, essentially aiming to address the risk factors we outlined above.

Ultimately, this should create a safe and responsible environment in which we can explore the possibilities of AI models growing aligned with human goals.

Right now, AI models will continue to enable a broader range of tasks for businesses to optimise their operations, allow for innovations in various industries, and improve the human experience while maintaining trust, safety, and responsible use.

AI models lie at the heart of transformations taking place in businesses all around the world.

They are helping to convert raw data into actionable insights for businesses that can streamline decision-making and improve customer experiences.

• Optimising sales pipelines.
• Predicting market trends.
• Improving customer service responsiveness.
• Creating new content or assisting the creative process.
• Innovating alongside professionals like doctors and research scientists.
• Improving efficiency among work teams.

Through a variety of AI applications, AI models are empowering organisations to compete, grow, and offer better services.

Explore how Salesforce Artificial Intelligence can help you integrate these intelligent capabilities into your workflows, support responsible practices, and drive better results no matter your industry or company size.