Four types of AI and how they can be used in business

Artificial intelligence (AI) has been discussed since the 1950s, but it’s only in recent years that it’s really come to the forefront of everyday business operations. Instead of being a far-flung concept used in futuristic factories, many people are starting to discover how easily they can access and use AI technology for day-to-day tasks. 

It’s still a developing area, meaning that it’s far from being error-free, but technology is moving fast. When used correctly, and paired with appropriate human intervention or checks when needed, it can be wielded as a tool to speed up operations and handle large sets of data with ease.

Intrigued? Let’s explore how AI can be used in business today.

Natural language processing 

If you’ve ever interacted with a chatbot for customer service, then you’ve interacted with natural language processing (NLP) AI. This type of technology is able to comprehend and respond to human language in a ‘natural’ way, providing a real-time, personalized answer to simple questions. It analyzes the message the user sends, picks up key words or phrases, checks it against a database to look for answers, and then formulates this into a reply. 

It’s no surprise then that chatbot AI is valued at around $525.7 million, with an expected growth rate of 25.7% as we head towards 2030. This type of AI can be invaluable for saving customer service agent time, freeing them up to handle more complex queries. It also means that customers can receive 24/7 service for basic questions, improving customer satisfaction and helping your business go global. 

Deep learning

When it comes to decision making in business, data is key – often the more of it, the better. Data can help you understand what your customers want, what their buying habits are, potential areas of improvement on your website, new product ideas or even just customer satisfaction. However, collecting this data and analyzing it for patterns is extremely time-consuming, and it can be hard to spot similarities if they’re not glaringly obvious.

Deep learning AI is designed to identify images and text, and process it to understand it like a human would. However, it’s much faster, allowing you to access the benefit of data-driven insights in a fraction of the time. This can be particularly useful for businesses looking to scale up their operations, branch out into new markets or simply those who rely on data insights for day-to-day operations.

Generative AI

Most of us will have heard of ChatGPT, the free AI programme that has been capturing headlines in recent months. It’s simple to use, and will generate a response to almost anything, resulting in a flurry of AI content hitting the business world. This is an example of generative AI.

Whilst it’s vital to understand that generative AI is not a replacement for human creativity, it can be useful for sparking your imagination. Whether it’s generating a mood board for new branding, providing writing prompts to get you past writer’s block for social media posts, or summarizing complex documents, this type of tech can be useful especially for AI content writers, when used with appropriate caution.

Causal AI

Deep learning AI is a fantastic tool, but if you’re looking to understand why things are happening, not just what is happening, then look to causal AI. Able to explain cause and effect, this technology is invaluable for business decision makers, providing a wider context that is often missed when looking at the numbers in isolation. It is capable of simulating events to see what might happen, saving time and cost on physical experiments. 

From even a small business perspective, causal AI can be helpful when it comes to better understanding your customers. Using it allows you to not only see what your customer journey is like, for example, but also to identify pain points where you could do better based on data rather than gut feeling.

Boost your business

It’s natural to feel tentative about a new technology, especially one that is relatively unregulated. Whilst you should always take care to understand how the data you enter into your AI programme is used, there’s certainly a case for using it in regular business operations to support human knowledge and innovation. In the same way that we’re all used to using functions and formulas on spreadsheets, AI will likely become part of the norm in the future.

‍