AI Fundamentals: 7 Exciting Use Cases in 2025

Artificial intelligence (AI) is developing at a rapid pace, becoming more versatile with each passing year. It’s become a holistic set of tools and applications that work together to make people’s lives easier. And in 2025, the speed of development and innovation shows no signs of slowing down.

In this beginner-friendly guide, we’ll go over a quick refresher on what AI is and how it works, before delving into exciting ways businesses and individuals are putting AI to work for them.

Artificial intelligence is the broad ability to have machines think and act like humans in processes like learning, reasoning, and problem-solving. Think of AI as a tool that can analyse data, make decisions and perform tasks that would normally require human input. By analysing vast datasets, recognising patterns, and making predictions based on that data, AI allows machines to improve performance over time and even generate new data and content.

AI systems are built with a range of capabilities that allow them to replicate human-like intelligence, including:

• Machine learning (ML): This is the most basic level of AI. Machine learning utilises millions of datasets to train algorithms designed to help solve specific problems and make predictions without explicit programming.
• Deep learning (DL): This is the next step in the progression from machine learning. Deep learning involves the creation and training of advanced algorithms (known as neural networks) to identify patterns in data to help make informed decisions.
• Natural learning processing (NLP): This is a third subset of AI; it’s dedicated to ‘teaching’ computers how to interpret human language and respond in a way that humans can understand. Large language models (LLMs) are a specific type of NLP.

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.

Regardless of which type of AI is in use, all forms mostly follow the same process during creation and subsequent implementation:

1. Data input: All AI applications begin with substantial data input related to the task that they’re intended to complete. For example, an email creation template might be presented with thousands of standard emails and spam emails to analyse initially.
2. Algorithm training: Once all of the data has been gathered, the machine is presented with a set of instructions (algorithms) that all data needs to be passed through. This is regardless of whether it’s machine learning or deep learning. As more data is interpreted, the AI will be able to recognise patterns that will lead to a determined outcome.
3. Model building: Eventually, the AI will have been exposed to a sufficient level of data that’s been interpreted and passed through the lens of AI algorithms. When this happens, developers can build AI models to facilitate the intended task. For instance, once an AI has seen enough examples of standard or spam emails, it can use its algorithms to determine which is which for any subsequent emails and remove the spam before it reaches the user.
4. Task completion and predictions: With the model in place, the AI will have a framework to deliver on its intended purpose. This will usually be a simple automation task or prediction; however, even complex AI applications such as self-driving cars will follow the same process, albeit at a much higher scale.
5. Feedback and improvement: AI developers must refine their AI knowledge and evaluate it regularly through feedback sessions. Data input must be current; otherwise, the models and algorithms risk becoming obsolete.

It’s important to remember that AI is still a relatively new phenomenon, and we’re learning more about its potential every day. That’s where Salesforce’s Trailhead can help; it provides additional in-depth learning plans and guidance across a whole range of topics, including AI for beginners and AI foundations.

Now that you have a basic understanding of what AI is and how it works, let’s take a look at some of the ways businesses and individuals are utilising AI applications and programs.

Individuals and organisations are using AI to:

1. Create smarter content beyond basic prompts
2. Harness intelligent summaries for real-time insights
3. Transform customer service with AI-powered conversations
4. Automate workflows to boost efficiency and scale
5. Boost lead engagement potential
6. Build trust with ethical and transparent AI practices
7. Orchestrate AI agents to work together

We’ll look at these in more detail and highlight practical examples of each use case where appropriate.

One of the most widely used AI applications is generative AI. This is when companies enter a prompt into an LLM system and receive a fully developed piece of creative digital content in mere moments. The content could be a text document for a blog or article, a piece of art, music, or a short-form video clip.

Generative AI can also help businesses create internal-facing content, such as emails, presentations or process and policy guides.

However, these are relatively basic use cases for generative AI. Companies can now use generative AI tools for more advanced tasks. For example, a company can use adaptive storytelling to personalise the customer experience based on user preferences and behaviours. Rather than sending out the same piece of content for every customer, such as in a newsletter, businesses will rely on these data points to send out alternative versions of the newsletter based on what that particular reader has shown interest in.

It’s easy to remember the days when business leaders or financial managers had to sift through reams of paperwork to find the information they needed. It was a laborious, time-consuming process.

Now, with the help of AI, it’s much easier to summarise reports, datasets and conversations. With concise information at their fingertips, business leaders can quickly get the insights they need for all aspects of their business operations, including finance, marketing and sales.

Advancements in AI have also made it easier for AI agents to examine and interpret these streamlined datasets and take appropriate action.

This is especially helpful when your business wants to better understand customer sentiment. You can quickly gather a summary of customer reviews, product reviews, and support tickets to develop a clear picture of how your customers feel about your products or services. You could simply train an AI program to pick out certain keywords and decide whether each one is positive or negative.

At Salesforce, we empower our customers to use Agentforce to create detailed summaries of datasets for deep research. As part of the Agentforce package, we also offer the Command Centre, where business leaders can track all of their AI agents simultaneously to make sure they’re optimised for the correct tasks at all times.

As businesses implemented more AI tools in their operations, AI chatbots became a regular fixture on websites. You’ll recognise them as the little pop-up boxes that sit in the corner of webpages and ask if you need assistance with something.

Like AI technology in general, AI chatbots have become far more powerful over time. These new tools, often referred to as autonomous conversational agents, are the cornerstone of conversational AI for businesses. The two tools essentially serve the same purpose, but conversational agents use agentic AI to take conversations to the next level.

For example, a chatbot could provide a list of flight times in response to a request. An autonomous agent would take that same prompt and respond with far more detail. It would provide the flight times, but it could also make recommendations based on budget or availability and offer hotel suggestions.

Essentially, with agentic AI, you get a virtual assistant capable of taking the initiative and delivering on more complex human requests.

With Agentforce, you can build custom autonomous AI agents that can respond to your customers’ needs. All information provided by our conversational assistants first goes through our Einstein Trust Layer, guaranteeing that any information provided to your customers is accurate and secure.

One of the lingering concerns about AI is that AI tools will eventually replace humans. Most businesses want human and AI agents to work together, with humans learning AI skills to help them prosper.

Businesses can now automate many manual tasks with AI. For example, tasks such as entering data, sending email reminders, and scheduling and posting content can now be done automatically, improving productivity.

Automation has also paved the way for many businesses to scale much quicker. For example, Audata utilises Agentforce to help resolve customer queries through automation, which allows them to provide 24/7 responsiveness and engage with customers on a global scale more effectively. Using Heroku software, they were able to build the Audata Promo app, which has now been white-labeled for other businesses to use.

It’s becoming clear to many businesses that using AI gives them the ability to provide customers with a more personalised experience, and more than 73% of consumers are expecting businesses to deliver that. It only makes sense that marketing teams would also want to tap into the power of AI.

AI is capable of lead scoring to help marketing and sales teams find those hot leads that are more likely to convert into a sale. AI applications can conduct a wide-scale analysis of customer behaviours on websites and interactions with social media posts. When it’s all connected, it can create a strong sales funnel.

Marketing teams can also leverage an AI application to highlight when customers are at risk of dropping off. The application could examine past behaviours and develop personalised campaigns that could persuade customers to keep shopping with them and help businesses minimise churn rates.

With Agentforce, our customers can develop predictive insights to enable higher conversion rates and shorter sales cycles and use generative AI to deliver personalised marketing emails.

How Agentforce is revolutionising Sales for Salesforce

This is more about AI implementation in general. While there’s no doubt AI adoption has been beneficial in most instances, issues still exist. Trust and transparency are two of the biggest concerns; customers must be able to trust that companies are using AI appropriately.

Businesses need to treat these concerns seriously. Seventy-two per cent of consumers say they trust businesses less than they did a year ago , and much of the growing mistrust is associated with the misuse of data, inconsistent quality, and pricing. Now that AI use is so prevalent, it’s more important than ever for companies to demonstrate a high level of trustworthiness.

Our Einstein Trust Layer is robust and reliable. And with Agentforce, our customers gain access to transparency controls and auditing to make sure everything they do remains compliant with expected standards and is trustworthy.

Many AI applications are very good at what they do. They’ve made the lives of business leaders, team members, and customers much easier. The next step, however, is finding ways to integrate these individual applications with one another.

This is what’s known as agentic AI, and it’s perhaps the area of AI that will see the most attention and development over the next few years. The idea is that the actions of one AI application will directly contribute to or activate the next, forming an entire, uninterrupted process with minimal human intervention.

For example, a research team might use individual AI agents for different aspects of their project that can work together to complete it. They might have one set up to research, one to gather data, one to organise the data, and one to bring everything together to produce the report. It can all be done quickly, allowing for a much higher level of efficiency.

If you’d like to get started on creating your first AI agent with a view to one day implementing agentic-AI into your own business, be sure to check out our Trailhead training module. It’s an excellent AI course that will talk you through the process.

The world of AI is constantly evolving; new applications become popular, and others quickly replace them. No two AI solutions are the same, so be sure to carefully consider what you want your AI to do before you dive into developing your own business AI infrastructure.

And take a tour of different options, like Agentforce. We’ve put a lot of time and work into it, ensuring that new systems and applications seamlessly integrate into the existing framework. We provide our customers with a holistic AI solution capable of taking their business to the next level while consistently keeping transparency and trust at the forefront.

You can watch a demo of our product in action, or if you’re ready now, try Agentforce for free.