8 min read

What Is The Difference Between Generative AI And Automation?

Generative AI and automation are two terms people oftentimes use interchangeably. That shouldn't be the case. Learn about the important differences between the two and how use cases for these technologies can vary.
Written by
Jonathan Mitchell
Published on
June 14, 2024

For the uninitiated or unfamiliar, the interchangeable usage of terms like Generative Artificial Intelligence (Gen-AI) and automation makes sense. However, any piece of marketing that lacks differentiation between the two is essentially manipulation. Fundamental differences exist between these newfound technologies' capabilities, training processes, and usage. While both concepts involve AI, they differ significantly in their approach and applications. Explaining that difference is necessary, and that’s what needs to be cleared up. Let’s explore the fundamental differences between generative AI and automation and shed light on their respective roles in shaping the future.

Generative AI

In simple terms, generative AI is the technology that enables machines to generate new content. Unlike traditional automation, which follows predefined rules and patterns, generative AI leverages complex algorithms and neural networks to create something entirely new. However, it’s important to remember that this “new” creation is only based on the data used to train the AI’s model, which is the foundational cornerstone of generative AI. This is not an actual conception in the sense of a unique offering but something created to represent a specific ask of a user to the best of the model’s ability.

Generative AI algorithms are designed to learn from existing data and patterns. For example, generative AI can be used to create stunning visual artwork by learning from a vast collection of examples and then generating new, never-before-seen pieces. Similarly, in music, generative AI can compose original melodies and harmonies that evoke emotions and captivate listeners. In software development, code gen tools are already becoming commonplace, where the usage of generative AI leads to lines of code being written rapidly. Is it always correct? Absolutely not, but the capability exists and is being innovated upon. Understanding that the underlying root of all these creations lies in what the algorithm is fed is essential. There is no spontaneous burst of creativity that leads to a unique creation. Anything constructed by generative AI is based on data utilized in training. That is the foundation used to fulfill the request of the user.

Automation

Automation isn’t about creation, at least not in the same sense as generative AI. Automation is really about the execution of processes and tasks without the need for human interaction. There are a variety of positives associated with the usage of automation systems. It’s easier to scale, data collection becomes streamlined, and efficiency improves. Also, because there’s no need for human intervention, the systems can work constantly. Machines have no need for coffee breaks. The scope of usage for automation is wide. It serves many functions, whether that be quality assurance, testing, or infrastructure as code (IaC). And that’s just to name a couple.

Automation reduces busy work and eliminates frivolous tasks that would otherwise take valuable time away from developers. By getting rid of busy work, developers can spend more time problem-solving and focus on value creation. Automation serves as a way to enable great work while minimizing taxing tasks that an intelligent system can easily do. It’s important to note that not everything can be automated, nor should it be. Having the available expertise of an internal or external tech team is still the best way to go about creating impactful products. Mainly, automation should be viewed as a process to minimize the time suck certain tasks have while enabling developers to have comprehensive ownership over the projects they’re working on.

The Difference Matters

Language matters. What words are used and how they’re framed is incredibly important. Certain organizations might try to use automation and generative AI interchangeably in sales collateral or website content. If that’s the positioning, run for the hills. We’re already seeing the impact great marketing can have, even when the product itself can’t deliver on promises made. Red flags exist everywhere within the software development space, but they’re hard to find if you don’t know what to look for.

It boils down to this. Automation is used for processes and tasks. The purpose is to require less human intervention for frivolous assignments and free up time for more important asks. Generative AI involves a creative output, whether that be code, an image, or a 1,000-word blog. While some underlying technology and processes exist in generative AI and automated systems, the purpose and final product are vastly different. Understanding that difference is imperative for organizations trying to decipher what should be used, where it should be used, and how it should be used within their business.

Next Steps

Even with all the complexity that goes into this decision making process, there's a simple way to start. Define your problem statements. That'll help the organization get a good grasp of lay of the land. For each problem statement apply the 5-whys. At this point, your answers will lead you to automation, gen-ai, or somewhere else entirely. Simple yet effective. Overcomplicating a situation will lead to more harm than good. This is an easy way to get going down the right path and identify what your business truly needs.

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