4 Use Cases For AI

Here's four essential AI use cases that business leaders should consider today.

By Maxwell Davish

May 8, 2023

4 min
Here's four essential AI use cases that business leaders should experiment with today.

The rise of generative AI — and especially large language models like GPT-3 and ChatGPT — has been the most important advancement in AI in recent years. Today, these models are much larger and more intelligent than their predecessors, and they can perform tasks simply by being prompted rather than requiring extensive training datasets.

This makes them a game changer for businesses and consumers alike, allowing for new types of automation and digital experiences that were previously impossible.

In other words, it makes possible things that would have been considered science fiction only a few years ago. And, used properly, it has the potential both to create enormous value for the economy and genuinely improve peoples' lives.

But to fully realize the potential of AI, it's essential to understand its strengths and its limitations, and to use it responsibly and effectively.

Here's a look at the four essential AI use cases that business leaders should be learning about today.

1. Use generative AI as a creative assistant

One of the most promising generative AI use cases is as a creative assistant, where it helps high-level creative employees with suggestions, information retrieval, and output support.

This is already happening with products like GitHub Copilot and ChatGPT, and we can expect more products to emerge in different industries. Conversational AI and generative AI will soon be used across dozens of industries, like financial institutions, retail, and even government and public sector, and even across functions and departments like marketing and support.

Importantly, the human is still in control and gets to accept, reject, or tweak the AI's suggestions.

2. Use AI for business process outsourcing

Many business processes will be candidates for automation in the coming years, such as classifying and routing support tickets, medical coding, and AML/KYC processes. LLMs (large language models) like GPT-3 raise the bar for what can be automated while lowering the technical barrier.

However, outsourcing a process completely to AI is challenging because there is no longer a human directly in the loop. The requirements for accuracy become much higher, and additional safeguards are needed to monitor and retrain the model over time if this option is pursued.

3. Use AI to improve user and customer experiences

A third use case of AI is to create or augment digital experiences.

This AI use case is already well-established. Most people have already been interacting with AI-enabled digital experiences for years now. We use an AI-powered search engine every time we interact with Google. We use AI-driven recommendation engines to browse movies, songs, and products on Netflix, Spotify, and Amazon. We use conversational AI in use cases with voice assistants like Siri or Alexa to look up information and perform tasks. AI is increasingly the medium through which we interact with digital businesses.

When it comes to AI-powered digital experiences, we usually aren't trying to replace a human but rather provide an experience that can only be delivered by machine learning algorithms and AI. The human's role — to the extent that there is one — is to monitor the AI's behavior and curate the content that it can access.

4. Use AI for prediction and forecasting

The final AI use case is one that you won't read about as often in the press, but it is extremely valuable: AI algorithms can be used to predict the future. Astute readers will point out that all AI is predictive: LLMs use natural language processing (NLP) in real-time to predict the next word in a sequence, diffusion models are predicting the ideal set of pixels for an image, recommendation engines are using sentiment analysis in customer interactions to predict whether a user will like a product.

In this case I'm referring specifically to predicting business outcomes in the real world. For example:

  • Predicting consumer demand for a specific product based on historical trends

  • Predicting what the stock market will do tomorrow based on a variety of factors

  • Predicting the weather tomorrow based on IOT data

You might think of these things more so as "statistics" than "machine learning" or "artificial intelligence (AI)," but, as it turns out, those are all the same thing. AI and machine learning are statistics, applied at an extraordinary scale and to problems that we don't traditionally think of as involving numbers.

Of course, this is just the tip of the iceberg when it comes to AI, and we're likely to see vast growth in these use cases — and more — in the coming months and years. Further, as with any new technology, there are risks and challenges that need to be addressed: bias, privacy, and job displacement among them.

But if used responsibly and effectively, AI has the potential to create enormous value for businesses and individuals alike, improving our lives in ways we can't even imagine yet.

Read Next: The Four Essential AI Use Cases

Learn about the strengths, limitations, and real-world applications of AI across industries.

Share this Article

Read Next

loading icon