The Basics of How AI Works for Businesses

Artificial intelligence has quickly shifted from a “nice to have” to a core business tool. In our State of IT: AI and App Development report, 83% of developers already say that AI agents are fundamentally changing how their business operates, and 78% worry they’ll be left behind without them. Leaders also recognise that AI is only as good as the data behind it, with 86% of IT decision-makers saying data quality determines whether AI works effectively.

AI is already part of everyone’s day-to-day life, whether they lean into it or not. When you search on Google, AI shapes the overview you see. When you shop online, your recommendations are generated by AI models. Even when you call a business, an AI agent often handles the initial triage before passing you to the right team.

Understanding how these AI-powered systems work gives you an advantage by helping you choose how you want to interact with them and how you can use them for your own brand.

In this guide, you’ll learn the basics of how AI works, what powers it behind the scenes, and why businesses of all sizes are investing in it.

• How does AI work?
• The core building blocks of AI
• What are the different types of AI?
• The types of AI used today
• What the future of AI might be
• AI vs. human decision-making
• What are the benefits of AI, and how is it being used?
• Real examples of businesses using AI
• The challenges of using AI (and how to manage them)
• How to implement AI in your business
• Summing up
• FAQs

Before going into more detail, let's look at the basic steps behind how AI actually works.

When an AI system is trained, it’s fed millions of examples. For text, that might be sentences, articles, or conversations. For images, it could be photos labelled with what’s in them.

Despite what many people think, AI doesn’t do this alone. Teams of human reviewers prepare the data, label examples, and correct mistakes. Their input guides what a good output looks like.

The AI doesn’t “read” or “understand” the texts and images like a human. Instead, it looks for patterns in the data. It learns things like:

• Which words tend to appear together
• What shapes usually make up a face
• What customer actions normally lead to a purchase
• How tone changes the meaning of a message

After spotting enough patterns, the AI can be turned into a model. The model is a giant map of connections the system can use to predict what should come next, the next word, the next image detail, or the next best action. It’s not a set of written rules, but a huge network of learned patterns the AI refers to every time it generates an output.

The model keeps learning after it’s deployed. When humans correct an answer, rate a response, or supply better examples, the system uses that information to update its patterns. This new data also helps it adapt to how people actually speak, search, or behave.

To understand what AI can actually do for your business, it helps to break it down into its core components. Most modern AI systems rely on three foundational capabilities: machine learning, deep learning, and natural language processing. Each one plays a different role in how AI learns, improves, and interacts with the world.

At a glance, here are the key building blocks:

• Machine learning: Teaches systems to learn from data and improve over time
• Deep learning: Uses layered neural networks to understand complex patterns
• Natural language processing (NLP): Enables AI to interpret and generate human language

Let’s go into more detail for each one.

Machine learning is the foundation of most AI tools you use today. It’s the process that allows systems to learn from examples rather than being programmed step by step. Over time, the system becomes better at predicting outcomes, spotting patterns, and producing accurate outputs.

Machine learning usually works in one of three ways:

Supervised learning relies on labelled data, meaning humans have already marked the “correct” answers. The AI studies these examples, learns what makes them correct, and then uses that data to make predictions on new data.

Example: If a business wants to analyse customer feedback, a model can be trained on reviews labelled as positive or negative. Once trained, it can accurately categorise new reviews.

In unsupervised learning, the AI receives unlabelled data and is left to find patterns on its own. It identifies relationships, groupings, or clusters.

Example: A retailer could use unsupervised learning to discover natural customer segments. This could be high-frequency shoppers, seasonal buyers, or customers loyal to specific product categories.

Reinforcement learning teaches AI through trial and error. The AI tool is rewarded for good decisions and penalised for poor ones. Over time, it learns which actions lead to the best outcome.

Example: A warehouse robot can learn the fastest route for moving stock. When it completes tasks efficiently, it’s given positive reinforcement. If it takes longer routes or bumps into obstacles, it’s given negative reinforcement.

Deep learning is a more advanced branch of machine learning. Instead of simple rules or basic pattern recognition, it uses artificial neural networks , inspired by the human brain.

These networks contain multiple layers, and each one processes information at increasing levels of complexity. As data passes through the layers, the system builds a richer understanding of the data.

This is the technology behind many of today’s most impressive AI capabilities, including:

• Image recognition
• Speech-to-text and text-to-speech
• Real-time translation
• Advanced generative AI

Example: A small ecommerce business can use deep learning to automatically tag and classify product photos.

Deep learning stands out because it can handle massive volumes of unstructured data with a level of accuracy that wasn’t possible even five years ago.

That’s one reason businesses of all sizes are exploring how these models can automate their work and enhance personalisation.

Natural language processing (NLP) is the branch of AI that allows computers to understand, interpret and generate human language. It’s the reason you can speak to a virtual assistant, type a prompt into a chatbot, or ask an AI to summarise a long document.

NLP helps AI systems:

• Understand the meaning and emotion behind text
• Pick up on context and intent
• Extract key information from large amounts of writing
• Answer questions

This capability has improved dramatically in the last few years.

Tools like ChatGPT, Gemini and Salesforce’s AI assistants can now respond in full sentences, understand follow-up questions, and adapt to tone, something earlier chatbots struggled with.

Example: A retail business can use an NLP-powered chatbot to handle common questions, freeing up its staff to focus on more complex service needs. Below is an example of Agentforce answering a simple customer question and sending them to the correct resources.

There are many ways to categorise AI, but the most useful starting points are how businesses use it today and a conceptual, research-level view of where AI could go.

These are the main types of AI you’ll see in everyday business tools and how they’re being used right now.

These categories are more theoretical and describe the potential level of intelligence an AI could reach.

AI will move from guesswork to genuine strategic thinking. Models will be able to plan, justify decisions and check their own logic.

In practice, this means businesses could trust AI to run financial modelling, validate legal arguments or manage operational scenarios without the errors we see today.

Next-gen systems will combine pattern recognition with rule-based logic. That gives AI the ability to follow strict frameworks, such as tax law, visa conditions, safety regulations, or budget limits.

This makes AI a realistic tool for compliance-heavy industries like healthcare, finance, law and government.

AI will begin contributing actual research with new ideas. These systems will generate hypotheses, rank which experiments will work, and design new materials, drugs or models.

Whole research cycles could drop from years to months, especially in climate science, biotech and energy.

AI will increasingly operate through robots and machines that can see, move and manipulate objects.

This could lead to capable, task-driven systems in warehouses, aged-care facilities, hospitals and supply chains.

Most research points to a future where AI works alongside people. It handles the heavy analysis, while humans bring the context and judgment.

This will change how junior roles look and the skills new graduates will need to bring to the workforce. However, the younger generation will be uniquely skilled at working alongside AI compared to their older counterparts.

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 judgments 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.

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 faster and with less human interaction. Handling basic customer queries, analysing data, and creating new content for a campaign. If you want to automate these tasks at scale, Agentforce can handle routine queries and free your team for higher-value work.

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. If this could help your business, Data 360 can bring all your customer information into one place and share how you can act on insights in real time.

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. Agentforce Marketing can help you deliver these personalised moments consistently across all your channels.

AI gives small teams the ability to do work that once required specialist resources. It can help generate ideas, streamline product development, test scenarios and explore new directions with less cost. This helps businesses move faster, experiment more often and bring new offerings to market without major overhead.

AI is already improving accessibility and inclusion. Tools that convert text into accessible formats, translate in real time or support people with mobility and communication needs help more customers participate in digital services. Businesses that adopt these tools contribute to healthier, more equitable digital ecosystems.

As your small business grows into something larger, AI will stay for the ride. AI tools can handle increased workloads without compromising quality.

Let’s see these benefits in action with examples of how brands are utilising AI across four different industries.

Fisher & Paykel is a global luxury appliance brand operating in more than 50 countries. Since their products sit at the premium end of the market, they needed their service experience to feel just as high-end. Their challenge was fragmented data and manual processes that made it hard to personalise support, reduce service times, and operate at a global scale.

They introduced Salesforce to fix this. Using Data 360, they brought all their customer, product and service data into one place, and fed AI their 10,200-article knowledge base. Now, when a customer asks for help, Agentforce instantly pulls the right information, provides step-by-step troubleshooting, and resolves common issues without needing a human agent.

Since then, self-service rates are forecast to jump from 33% to up to 65%. On top of this, using the AI personalisation, their ecommerce site saw an increase in product views by 40% and a 33% order conversion rate.

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.

CEO Ross McEwan has made AI part of the bank’s long-term strategy, but with a strong focus on responsible use. Staff are being trained to use AI tools that improve customer experience while meeting strict regulatory and ethical standards.

As part of their digital transformation, they also consolidated 13 legacy systems with a unified Salesforce platform, providing their team with safe AI built for the financial sector.

This Sydney-based HealthTech startup, Harrison , 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, and other issues. Its speed, improved analytical capacity and accuracy bring forward treatment decisions. These kinds of HealthTech advances ultimately improve patient outcomes.

Kudosity is a Sydney-based messaging technology company helping businesses deliver conversational AI at scale. As the company grew, siloed data and processes made it difficult to scale sales and support.

This high-growth company started using Agentforce and Data 360 to build and deploy an AI agent to handle FAQs, raise support tickets, surface knowledge articles, and summarise past interactions for staff. The agent is able to communicate with customers in real time and route complex issues to the right team.

The results speak for themselves. The AI agent maintains a 97.36 NPS, and Kudosity is now expanding its use of Agentforce to automate pricing, onboarding, and scheduling. They also now use Agentforce Sales and Slack to keep their teams on one system, taking their collaboration to the next level.

While AI can be transformative, it’s not without challenges. Let’s examine those challenges more closely.

• Data quality: AI depends on accurate, well-structured data. Poor data management or publicly available but unreliable sources can undermine results for your business.
• Security concerns: Access to sensitive information raises significant security and privacy concerns. You can reduce this risk by using Salesforce’s secure AI, Agentforce, to keep customer data protected.
• Need for guardrails: As we move forward, business owners need to be aware of government regulations and even put up their own guardrails to protect customer data.
• Bias and fairness: Training data will include biases (we all have small biases without realising them). Unchecked, this will produce unfair outcomes. You can reduce this risk by reviewing your model outputs regularly with a critical eye.
• Implementation costs: Deploying AI may require specialised knowledge (unless you use Agentforce), tools, or training, potentially increasing initial costs for small businesses.
• Training and accessibility: This learning curve will likely always be there for new AI tools. However, Salesforce offers free comprehensive training materials to help guide new users.
• Balancing cost and reward: 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 almost always worth the initial time, effort, and cost.
• Energy efficiency: Training and running AI models, especially complex ones, consumes a lot of resources. This leads to a larger environmental footprint. You can minimise this by investing in verified carbon-offset programs. If you use Salesforce’s AI, we already match 100% of our global electricity use with renewable energy credits.

AI works best when it is connected to your real business needs. Start by being clear on what you want AI to do, then build the right mix of tools, process and training around it.

Some companies make the mistake of rapidly deploying AI models without clearly defining their purpose. This can lead to businesses that don’t fully understand the automated tools at their disposal. To maximise AI impact, start by identifying specific use cases that AI can help your business with and then research the right tools for the job.

Once you know your use cases, shortlist tools that fit your requirements and work with your existing systems. If you rely on custom-built software, you may need APIs or middleware to connect AI tools safely. Start with a small pilot in one part of the business, monitor the outputs closely and refine your prompts, workflows and guardrails before rolling out more broadly.

Bring your team in early so they know what the tool does, what data it uses and where their role sits in the workflow. Keep training practical; show people how to run tasks, check outputs and flag issues. As your team becomes more confident, you can introduce new use cases and refine how AI fits into everyday work.

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.