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

Broadly speaking, we can separate the uses of AI models into two categories:

1. Automation of repetitive tasks.
2. Analysis of complex data sets.

And in turn, these two uses lead to two key benefits for all types of businesses:

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

But we can break these down into more granular chunks to explain exactly how businesses can benefit from AI models.

For example, AI-driven automation can handle routine tasks. Think of things like updating client records, processing orders, or monitoring inventory.

In turn, the time employees used to spend doing these things is now free; team members can focus on higher-value activities. Also, humans can make errors in these mundane tasks. AI models are more reliable.

Meanwhile, data analysis capabilities let businesses identify trends and anticipate customer needs. The ability of AI models to sift through data looking for nuggets of information is on a level we’ve never had access to before. The result is that businesses have a better idea of what makes their customers tick at the individual level and can personalise offerings as a result.

Greater access to information and the ability to analyse it at speed means businesses can make better-informed decisions.

• Optimise your pricing strategy.
• Tailor marketing campaigns to specific customer segments.
• Refine your sales funnels.

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

To wrap the benefits of AI models in a neat little cliche bow, they make your team work smarter, not harder, to achieve better outcomes.

Think of an AI model as a trainee learning a skill. At first, it’s shown (by humans, the ‘trainers’) many examples (data) and told the correct answers. Over time, it compares its own attempts against the answers, adjusting its ‘knowledge’ with each round of feedback to get better.

Just as a trainee improves 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.

The quality of the data is all-important in this training process.

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

There are three general categories for the ‘learning’ that takes place:

• Supervised learning (using labelled data).
• Unsupervised learning (Where the AI model is capable of finding its own hidden patterns).
• Reinforcement learning (improving through feedback loops).

AI researchers can use a mix of these as they train AI models and refine their abilities.

Deep neural networks and advanced architectures, such as generative pre-trained transformers (GPT) and diffusion models, further enhance their capacity to tackle complex tasks. These are driven by complex learning algorithms.

Over time, an AI model’s performance improves as it’s exposed to more data and fine-tuned parameters. Let’s unpack some of these models to identify their specific uses.

Businesses shouldn’t choose a hammer when they may need a spanner. Imagine you have a toolbox filled with different tools, each designed for a specific job. Artificial Intelligence models work in a similar way, each designed to handle specific tasks.

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? Clustering algorithms and dimensionality reduction techniques 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 deep learning 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.

These types of models 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 images, text, or code based on patterns learned from training datasets. They’re like skilled creators who have studied countless examples and can now produce ‘original’ works inspired by what they have seen before.

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. An LLM is like a writer who has read enormous amounts of text. This foundation allows it 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.

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 AI 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.

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, efficient, 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’s AI solutions can help you integrate these intelligent capabilities into your workflows, support responsible practices, and drive better results no matter your industry or company size.