Artificial intelligence explained

Understand the potential of AI and what to know before using it in your work

Artificial intelligence (AI) is technology that learns from data and recognises patterns. It uses those patterns to generate predictions, recommendations or content.

AI supports many tools people use every day. It helps software do tasks that usually need human thinking, such as:

  • understanding language
  • identifying images
  • making predictions.

AI systems rely on two core elements:

  • data to learn from
  • a model or algorithm that can analyse the data and produce a result or recommendation.

Unlike traditional software that follows fixed rules, AI learns patterns from data. This allows it to generate new results rather than repeat the same instructions each time.

What is AI? 

Artificial intelligence is core to how businesses operate today. It's the technology powering your phone, targeting your customers on social media and helping you recruit. Whether you know it or not, your organization relies on multiple AI systems every day. They might employ AI to help them be more efficient or to improve how they engage with customers. The Human Technology Institute's research reveals that almost every Australian business relies on multiple AI systems today. 

You may only be aware of a fraction of the AI applications that your employees use at work, often without any official sign off from management or IT. Some of these AI systems are valuable and low risk. For example, AI powered navigation systems, but others can introduce a range of risks and challenges from cyber security concerns to the threat of physical harm. As AI becomes an essential part of doing business, every business leader needs to cultivate what we call a minimum viable understanding around AI. And this starts with understanding how AI systems work. and why managing them carefully is critical to your organization's success. 

AI is challenging to define partly because what our understanding of AI is changes over time. When the field of AI began in the 1950s, AI systems tried to mimic how humans make decisions. These became known as expert systems. Thanks to massive increases in data and computing power, the last decade has seen the rise of machine learning. This is where digital systems apply algorithms to large historical data sets to learn deep patterns. 

This allows them to make predictions when applied to new situations. Most recently, generative AI has changed the way we think about the possibilities of AI systems. Applications like chat GPT and DLI rely on models trained on huge amounts of data to produce fluent text, novel images, and even video from simple text prompts. It's critical to remember that all AI systems are based on maths, not magic. 

Machine Learning

Machine learning is underpinned by statistics, linear algebra, probability theory, and calculus. While impressive AI systems are powered by complex algorithms and vast amounts of computing power, these systems do not possess common sense, interpersonal skills, or a true understanding of the world. They can and do fail in many different ways. As a business leader, you can think of AI as being a very broad collective term for digital computer systems that have three characteristics. 

First, AI systems do impressive things. AI systems combine algorithms and data to do things we have traditionally expected only of humans, such as predicting outcomes, classifying complex information, optimizing processes, or generating content. Many of the largest large language models are also remarkably flexible, able to do many of these tasks through the same interface. 

Second, AI systems tend not to be explicitly programmed. Neural networks in particular work by learning from data, often finding patterns and relationships that would be impossible for a human being to discern. They are therefore deeply influenced by the data on which they are trained, which can result in errors and biased outputs. 

Third, AI systems tend to be unpredictable and opaque. They produce different outputs depending on how they've been trained and the input they are given. It takes special effort to understand how and why they come to a particular conclusion or decision. All of this means that AI systems are more than just another IT application for your business. They offer huge promise but also require special attention to manage safely and responsibly.

Why use AI

Many organisations are beginning to use AI as part of everyday work. It’s used to support a range of tasks, such as:

  • writing or summarising documents
  • analysing information or data
  • automating routine work.

AI is built into many everyday tools and is also available as standalone products. Used well, AI can help you:

  • save time on manual tasks
  • reduce operating costs
  • support growth.

Learn more about why organisations use AI.

Common types of AI

You may already be using AI without realising it. Many AI features are built into everyday business tools, such as accounting, payroll and customer support software.

Different types of AI support different tasks.

GenerativeAI (GenAI)

Creates new content based on prompts or examples. It can create text, images, code, audio or video. Most people access GenAI through chat-based tools, where they type a question or instruction and get a response. Organisations use it to draft emails, create marketing content or generate ideas.

Machine learning (ML)

Analyses data to find patterns and make predictions. It can forecast trends, detect unusual activity or support decisions. ML systems usually provide recommendations or alerts, rather than acting on their own.

Automation with AI features

Automation tools are built into existing business software. They’re used to reduce manual data entry, with staff still responsible for reviewing the results.

Agentic AI

Refers to systems that work towards a goal rather than a single task or prompt. It doesn’t wait for instructions and can act on your behalf. Agentic AI can act independently, using other software tools or systems, and can complete multi-step tasks.

Computer vision AI

Analyses images or video. It can scan documents, track inventory and inspect product quality through cameras and sensors.

Natural language processing

Allows systems to understand and analyse written or spoken language. This includes summarising documents, analysing customer feedback or translating text.

Myths and limitations

AI is increasingly used in everyday business tools, but there are still many misunderstandings about what it can and can’t do.

Some people think AI will replace staff or is only for large organisations. But in practice, AI is usually used to support people with tasks – not replace them. Many everyday tools already include AI features, so organisations of any size can use AI.

AI can make mistakes and has practical limitations. For example:

  • AI responses can sometimes be wrong or misleading
  • outputs may reflect bias in training data
  • AI can’t apply judgement or context the way people do.

To use AI more safely and effectively, understand the myths and limitations