What is generative AI? Meaning and examples in business

For hundreds of years, revolutionary industrial inventions were bulky and formidable, forged of iron and steel. Today, however, the most transformative enterprise technologies are invisible – residing within the digital foundations that power modern business. Generative AI is just such a tool. And the secret to getting the most out of it is recognizing that it’s just that: a tool. It’s not a magic bullet – and like any good tool, it is ideally suited for some purposes and not others. So, while GenAI is no replacement for human creativity or strategy, its instant and awe-inspiring ability to deliver useful output from information, patterns, and disparate data sets make it a boundlessly powerful business asset. Especially in the hands of experienced teams and creatives who know how to leverage it with care and skill.

Generative AI is a subset of artificial intelligence that is specifically designed to produce new content – including text, images, videos, code, and audio – by learning patterns from vast amounts of existing data. Unlike traditional AI systems, which primarily focus on analyzing or interpreting existing information, generative AI actively creates new outputs based on its learned understanding of data structures and relationships. 

GenAI systems learn by studying enormous amounts of existing data such as text, images, audio, or code. This information is then stored within its neural network. And it’s no coincidence that the term “neural network” sounds a bit eerily human because in fact, this computational model was indeed inspired by our own brain structures. The AI neural network consists of interconnected layers of artificial “neurons” that process and input all the data that is taken in. 

Depending on the nature and purpose of the GenAI tool, there can be various algorithmic models used in this process. But in a nutshell, as the AI goes through repeated learning cycles, it increasingly recognizes various patterns and relationships. Throughout this iterative process, these networks learn to adjust and refine their connections, leading to their ability craft accurate better and better outputs over time. And when a GenAI model has training that includes a deep dive into your industry or company, it can be prompted to help you create things like product manuals or visual training simulations – the potential is practically endless.

Generative AI is an amalgamation of several sophisticated techniques and models. While there are many approaches, these three models are currently among the most influential and commonly used:

• Large language models (LLMs):
  These models, like ChatGPT, analyze extensive volumes of text data from diverse sources. They use a powerful form of neural network known as a transformer. This gives them the ability understand context and generate highly coherent writing. Businesses use LLMs for tasks such as drafting emails, summarizing reports, or developing product specs and manuals.
• Diffusion models:
  Diffusion models excel at tasks like creating realistic product images or automatically designing variations of existing visuals. These models work by refining and re-refining random noise until the AI can eliminate extraneous components and become as accurate as possible. This makes them very useful for generating visuals from text prompts.
• Multimodel transformer models:
  These models go beyond text and can process and generate across multiple types of input—like text, images, audio, and video. They form the basis of AI systems that can “see,” “listen,” and “talk.”