Within the last few months, conversations around generative artificial intelligence (AI) have exploded. The groundbreaking technology is now a mainstream topic not only in technology circles, but among the broader business community. With the emergence of public models like ChatGPT, the conversation has intensified around the use cases of AI and the risks it might pose to human job functions.

While there are certainly a lot of opportunities to explore with generative AI to increase automation and productivity in the workplace, the hype can also be distracting. As with any emergent technology, the first step is to understand its potential and then define use cases so that practical applications can be explored. In this article, we’ll provide a brief history of generative AI, discuss some of its practical applications, and provide tips for exploring its use cases in your business.

What is Generative AI?

First let’s start with the basics. Generative AI refers to technology that can ingest raw source data and train itself to create new content (text, images, videos, audio) based on the sources provided. You may have heard of or even played around with ChatGPT, a model developed by the AI research company OpenAI. ChatGPT was trained using a large body of internet text (e.g. Wikipedia). With a user prompt, like asking it a question, ChatGPT produces a machine-generated text response, leveraging the body of data that it has already ingested and “learned from” in order to provide an informed answer.

A Very Brief History of Generative AI

ChatGPT may have brought conversation around generative AI to the forefront of technology and business discussions over the last few months, but the concept of generative AI is not new.

The history of natural language processing (NLP) and question answering dates back to the 1950s and 1960s when researchers began to experiment with computers processing human language. The name Noam Chomsky may be familiar from your school studies. Chomsky is a linguist who in 1957 published Syntactic Structures to expand upon his teacher Zellig Harris' model of transformational generative grammar. The work, which set up a model for linguistic units and analysis, brought the fields of linguistics and computer science closer together.

Early NLP systems defined a set of rules to understand and answer questions. Capabilities expanded in the 1980s and 1990s, when statistical models and machine learning algorithms became popular. In recent years, developments in large language models (LLMs) and the ability to process billions or even trillions of data points has allowed for significant progress in tasks such as text classification, sentiment analysis, language translation, and more.

ChatGPT, which was released to the general public for testing in November of 2022, has certainly increased public consciousness and conversation around generative AI. It’s interesting to note that the explosive growth of this technology may also be related to the decline of crypto. Throughout 2022, the value of many popular cryptocurrencies saw a steep decline, some even collapsing entirely. Blockchain and crypto mining companies who investigated significantly in their hardware and computing infrastructure are now pivoting to support AI applications. Chip maker Nvidia has pivoted to dominate the AI chip space.

Applications for Generative AI

So what can generative AI do for you and your business? At its core, generative AI can generate text, image, video, and audio outputs. Let’s consider the broader applications in 3 practical areas:

  1. Generative AI can increase productivity and reduce cost by automating tasks like:
    • Generating new ideas
    • Conducting research
    • Synthesizing information
    • Creating content
  2. Generative AI can create better experiences for your customers, potentially boosting acquisition and revenue, by delivering:
    • Personalized experiences (e.g. personalized email content)
    • Sophisticated chatbot interactions to aid in customer support, returns, reservation management, etc.
  3. Generative AI can deliver advanced business operational outputs, such as:
    • Writing code, analyzing code, detecting bugs
    • Synthesizing large datasets into streamlined reporting
    • Creating sophisticated models for predictive analytics

Where Can Your Business Get Started?

By no means is the above list exhaustive. Perhaps the most exciting aspect of generative AI is its potential. No matter where you or your business are at in your AI discovery and adoption journey, there is opportunity to leverage this technology to increase productivity, cut costs, increase revenue, and optimize your product or service offerings. But while many of the largest technology players in the market are offering AI-driven solutions, it can be difficult to figure out where and how to jump in.

The best place to start is in defining potential use cases for generative AI in your business, and this work should be informed by your business objectives and needs in order to achieve your business goals. In other words, what are the key problems you want to solve? Is task automation critically important for you? Or is customer experience the right place to start?

With these answers in mind, you can then focus on a technology or solutions provider that will help address these most critical needs. The AI industry is growing tremendously and with that growth there is a wide variety of companies ready to help, from AI consulting firms, to niche solution providers, to some of the largest technology companies in the market.

At DataLux, our focus is on applying generative AI technology to conversational text use cases like chatbot development. We specialize in the travel & hospitality, healthcare, and e-commerce fields, but welcome conversation with all professionals looking to optimize their business operations with generative AI.

What questions do you have about generative AI? Send us a message and we will get back to you shortly.