How Does AI Work?

Artificial intelligence (AI) works by using computer systems that learn from data and past experiences to achieve various outcomes, such as recognising patterns, making predictions, generating new content and performing other specific tasks.

AI can accomplish these tasks with minimal human guidance. Through algorithms, models, and refinement, AI systems improve over time. In recent years, this has enabled machines to simulate human-like intelligence and solve increasingly complex problems.

AI systems improve via refinement over time. Sometimes, this refinement is primarily human-led through concepts such as supervised and reinforcement learning. However, sometimes, AI systems can ‘learn’ from input data without explicit programming.

For instance, rather than programming every rule, we feed the AI large amounts of data and let it identify patterns on its own, improving as it goes.

Whether there is significant human intervention or not, we call this process machine learning. Let’s break down the three main subsets of machine learning:

AI models learn from labelled data in supervised learning. This means the correct answers are known in advance, and the AI models learn to recognise what makes an answer correct or incorrect and can apply that to new data. By this method, we steer the AI towards our desired outcomes.

For example, a business might train a model to classify customer feedback as positive or negative. As part of the training process, AI developers would provide labelled data (a whole bunch of identified positive and negative reviews) that the model would analyse. The model would then be able to apply what it has learned from the data to classify new reviews into each positive or negative category.

Unsupervised learning models deal with unlabelled data. The complex AI algorithms these models are based on enable them to find hidden patterns within large datasets.

A small business could use this approach to group customers into segments based on buying habits. For example, the model might analyse patterns in purchase frequency, product categories, and average spending to naturally cluster customers who share similar shopping behaviours, all without predefined labels or categories.

Reinforcement learning AI models learn by trial and error. AI engineers set up the model to reward desirable actions while penalising inaccuracies.

As an example, a warehouse robot may learn to improve how it moves items by learning which actions lead to faster, more efficient handling. Since it is programmed to achieve greater efficiency, it knows to repeat any actions that prove to be time-efficient while avoiding actions that are time-consuming.

You may have heard the phrase ‘deep learning’ in relation to AI. Is it different to machine learning? Deep learning is an advanced branch of machine learning inspired by the structure of the human brain.

Deep learning uses artificial neural networks, layers of interconnected neurons that process information to understand complex patterns in data. Each layer refines its understanding step-by-step, enabling the system to tackle intricate tasks.

Deep learning is a significant step up from simpler forms of machine learning. That’s because it can handle vast amounts of unstructured data, such as images, audio, and text, unveiling insights that traditional methods might miss thanks to its complex neural network.

For small businesses, deep learning can simplify challenging operations. A neural network might learn to identify product images, recommend items to customers based on visual similarity, or even analyse time series data like stock levels over months.

These advanced, publicly available capabilities can help give small businesses a competitive advantage.

Natural language processing (NLP) focuses on helping machines understand and interact with human language.

Advances in natural language processing have enabled increasingly lifelike responses from systems such as virtual assistants or tools like ChatGPT.

NLP allows computers to interpret text or speech, extracting meaning, sentiment, and context. Through NLP, AI systems can translate languages, summarise documents, respond to questions, and even generate new content.

For small businesses, NLP-driven tools offer bags of potential. They can power AI programs like chatbots to easily handle repetitive customer queries, freeing up staff to handle more complex tasks (or devote time to cases where human empathy is more needed).

A restaurant might use NLP-based assistants to manage online bookings, answer menu questions, or update customers on delivery times.

A marketing agency might utilise an NLP tool to analyse customer feedback across social media and emails. The tool would have the capability to identify trends in the feedback and offer advice on how to refine future campaigns.

The key to NLP, and the major advance that has occurred in recent years, is the ability to understand language better.

This evolution now means AI systems can engage in more natural interactions than ever before. This is why we have noticed an improvement in chatbots like Alexa and Siri, while generative AI tools like ChatGPT and Gemini can create text that can sometimes pass as human-generated and understand context.

We should pause for a moment just to clarify one aspect. Nobody is claiming that we should replace human decision-making with AI. AI’s role is augmentation.

While human decision-making relies on intuition, personal experience, and emotions, AI uses data-driven analysis and algorithms to deliver insights.

Humans excel at creativity and empathy, making nuanced judgements that consider cultural context or moral values. AI, on the other hand, processes vast amounts of data quickly, identifying patterns and trends beyond human capacity.

There’s room for both in your business. In fact, an approach that combines both well is your best bet for success.

Until now, we’ve discussed the core methods and tools that power AI, such as machine learning, deep learning, and NLP. We’ve explored how these tools help AI learn, understand language, or identify patterns.

Another useful lens is to consider the different types of artificial intelligence based on their capabilities and complexity. This perspective focuses on what AI systems can do rather than just the techniques they use.

Let’s explore four different types of AI, from the widely accessible to the realm of science-fiction.

These are the simplest AI systems. They don't remember past events and react solely to current situations. An example is a basic chatbot that answers FAQs without learning from previous interactions.

This type builds upon past experiences to influence future decisions. Self-driving cars use limited memory AI to understand road conditions and patterns from past journeys. It’s how they adjust accordingly to new events.

This AI would understand that other beings have thoughts, feelings, and perspectives. If you’re thinking you haven’t come across this one, that’s because this is still largely theoretical. Progress in this area could lead to AI that comprehends human motivations more deeply, improving client interactions and negotiations.

Such an AI would possess a consciousness and a sense of self. We’re nowhere near here yet. No known system has achieved this level of complexity. If AI progresses to this stage, it will open up a whole pandora’s box of ethical considerations.

Small businesses typically encounter narrow or weak AI systems designed for specific tasks. These would fall in the reactive and limited memory AI categories. This is where you’ll find recommendation engines, customer support chatbots, or inventory forecasting tools.

These forms of AI focus on a single domain and excel at it rather than possessing a broad, human-like intelligence.

AI offers small businesses a wide range of benefits. Of course, how each business can use AI to its own benefit will vary, but we can break it down into four broad ideas:

With AI, businesses can simply do certain tasks quicker and with less human interaction. Handling basic customer queries, analysing data, and creating new content for a campaign. Automating some of these processes reduces human error and manual effort and ultimately speeds up the service you can provide.

When it comes to analysing data, it’s not just about the speed. AI can help uncover trends and patterns you may not have caught with your infallible human eye. Armed with this info, you can forecast demand and refine campaigns.

This should underpin pretty much all small business activity: Does it benefit the customer? AI gets a resounding thumbs up here. Personalised recommendations, voice assistants, and natural language understanding improve how customers engage with your brand.

NAB (National Australia Bank) uses advanced AI-driven risk assessment models to detect unusual credit card transactions in real-time. This protects customers from fraud and ensures a smoother banking experience.

These models quickly flag suspicious activity, allowing the bank to intervene early and maintain trust with its clients.

Ross McEwan, the bank’s Chief Executive, is optimistic yet cautious about AI at NAB, stressing that any progress needs to be built around improving customer experiences.

He has put in place various training schemes to help employees utilise AI to help offer better experiences while prioritising the need for responsible use.

The takeaway is that Mr McEwan’s approach reflects how many others in the finance industry feel: that AI has tremendous capabilities to improve experiences and that banks need to be prepared to embrace these potential benefits alongside regulatory compliance.

Kogan, a prominent e-commerce platform, utilises AI much in the same way as other retail giants around the world: Amazon, Lazada, and Taobao. They all use increasingly sophisticated AI-powered recommendation engines to personalise shopping experiences.

The key is to make visitors feel like their time with the website or app is a tailored experience. This personalisation builds a connection with the brand through customer centricity.

By analysing browsing history, purchase patterns, and customer ratings, Kogan can offer tailored product suggestions that it knows are likely to resonate with the individual user.

Kogan can also use AI to dynamically adjust pricing based on several factors. These calculations are something a human team simply couldn’t accomplish for thousands of products across the vast ecommerce store.

Dynamic pricing and its subcategory, personalised pricing, can result in a win-win situation for retailers. With dynamic pricing, retailers can maximise profits by adjusting prices to market conditions. In theory, this means an ecommerce store can maximise profit for each product it sells.

Personalised pricing is where you change a product’s value based on individual behaviours and past shopping experiences. It can feel like a ‘gift’ to a customer if they feel they are getting a better price as a reward for choosing your brand.

When done well, these strategies ultimately boost conversions and improve customer satisfaction.

This Sydney-based HealthTech startup utilises AI models to help medical professionals interpret diagnostic images.

By rapidly analysing patient scans, these tools help clinicians identify potential health issues, looking for cancers, signs of tumours, etc. The latest model, Harrison.rad.1, significantly outperforms human radiologists in standard exams and can, of course, arrive at its diagnostic decisions in seconds.

This speed and improved analytical capacity and accuracy bring forward treatment decisions. These kinds of HealthTech advances ultimately improve patient outcomes.

AI research makes doctors' lives easier and improves efficiency in their day-to-day work, but the foundational motive for implementing this technology is to improve patient outcomes. In healthcare, this is equivalent to improving the ‘customer experience.’

For more than 15 years, this Sydney-based technology company has been at the forefront of innovation in messaging. It provides solutions such as conversational AI messaging to help businesses deliver authentic engagement at scale.

This high-growth company uses Agentforce to assist with scaling sales and service, eliminating business silos.

It has also created its first agent, which autonomously engages with customers, using conversational language to reply to frequently asked questions, raise support tickets, escalate complex issues to team members, and serve up relevant knowledge base articles.

The agent can also quickly retrieve customer information from Data Cloud, summarising past interactions to help team members deliver more personalised and efficient support.

Due to the success of their first agent, Kudosity is now exploring additional agents to streamline routine processes like onboarding, price calculations, and appointment scheduling.

By incorporating additional agents to handle routine tasks, it will free up team members to focus on supporting Kudosity’s continued growth.

AI depends on accurate, well-structured data. Poor data management or publicly available but unreliable sources can undermine results for your business. If you make decisions based on poor data, your outcomes will be compromised. Then there’s the issue of data privacy.

Access to sensitive information raises significant security and privacy concerns. The world’s governments are still lagging behind when it comes to regulating data governance, meaning it’s still a Wild West scenario when it comes to the ‘fair use’ of data to train AI models.

As we move forward, business owners need to be aware of government regulations and even put up their own guardrails to protect customer data. Self-governance isn’t a long-term solution, but companies exposed as misusing customer data risk losing trust.

If training data includes biases, AI models may produce unfair outcomes. That’s because the AI output will reflect the biased input - the training data.

Consider a voice recognition system trained mostly on audio samples from individuals with a particular accent. If the training data underrepresents other accents or dialects, the model may struggle to accurately transcribe speech from those groups.

Ensuring responsible AI practices is essential to avoid this kind of unintended discrimination.

Deploying AI may require specialised knowledge, tools, or training, potentially increasing initial costs for small businesses.

This learning curve will likely always be there, yet tools like Salesforce offer comprehensive training materials to help guide new users. You don’t need a computer science degree to interact with these AI applications.

There are also a range of pricing options to suit your specific needs, further reducing the barriers to entry.

As with any new implementation, business owners have to balance the cost against the rewards. With the latest AI systems and their capabilities, the reward of increased productivity and better customer outcomes is certainly worth the initial hindrance of time, effort, and cost.

Training and running AI models, especially complex ones, consumes vast resources. This leads to higher costs — and a larger environmental footprint .

Balancing the need for powerful AI capabilities with responsible energy usage is an ongoing challenge for businesses looking to adopt AI sustainably.

We’re not in the business of crystal-ball gazing, but as a company at the forefront of AI implementation, we can see where AI for small businesses is heading.

The future of AI promises more intuitive, human-like interactions and broader integration into everyday business tasks.

We’re likely to see AI-driven project management tools that anticipate resource needs, unified platforms that coordinate entire supply chains, or voice assistants capable of even more complex negotiations.

Advancements in quantum computing, like Google’s Willow quantum computing chip, safer and more responsible AI frameworks, and a better understanding of human language will enable AI to tackle challenges that are yet to be imagined.

For small businesses, this means increased access to cutting-edge tools that level the playing field.

You are going to be able to compete with larger enterprises and deliver extraordinary experiences to your customers.

Artificial intelligence works by learning from data and adapting over time. The end goal is to facilitate smarter strategies to help businesses run more smoothly.

These AI technologies enhance decision-making through data analytics, boost efficiency, and provide deeper insights into customer behaviours.

Ready to explore AI for your business? Check out Salesforce’s AI solutions to discover how you can leverage AI-driven insights, automation, and intelligence to strengthen customer relationships and achieve sustainable growth.