Benefits & 5 Use Cases

Different industries are starting to integrate generative AI solutions to their business processes. According to an IBM survey, 35% of respondents identified generative AI as one of the most prominent emerging technologies that will have the greatest impact on their businesses within the next three to five years.¹

Generative AI is important to many industries as it indicates a big potential for automating various tasks. Unlike robotic process automation (RPA), as shown in Figure 1, the impacts of and predictions for automation with generative AI are yet to be seen.

Figure 1. RPA market size worldwide from 2020 to 2030

Source: Statista

In this article, we will explain how automation with generative AI can benefit businesses and describe top 5 automation use cases. Lastly, we will make some predictions regarding the future of automation through generative AI plus RPA.

Benefits of Generative AI Automation for Businesses

1- Faster product iterations

Content creation process can become immensely faster when automated with generative AI. Generative AI is a valuable tool to create content and design options in a short period, allowing content creators and designers to evaluate and refine products more quickly.

2- Improved efficiency

Automation with generative design can reduce time-consuming and repetitive tasks of mundane human labor, allowing employees to focus on more creative and high-value activities.

3- Cost savings

Generative AI automation can optimize material usage and reduce costs due to the waste generated during manufacturing, making certain business processes cost effective. Automation technologies like RPA, virtual assistants and artificial intelligence can reduce operational costs as much as 30% by 2024, according to Gartner’s predictions.²

4- Scalability

Automation using generative AI can help businesses scale up their operations to meet growing demands that are hard to catch up with traditional methods, without incurring additional costs.

5- Reduced errors and rework

Automation with generative design can help eliminate human error, reducing the likelihood of errors and the need for rework.

5 Generative AI Automation Use Cases

1- Content creation automation

Content creation or text generation can be automated with generative AI using natural language processing (NLP) algorithms and large language models like ChatGPT. These models are trained on large datasets of existing content examples to learn the patterns and styles of effective content.

Content creation with ChatGPT automation or generative AI automation in general can facilitate tasks such as:

• Blog post generation
• Product description generation
• Video script generation

2- Image generation automation

Image generation can be automated with generative AI using deep learning algorithms and generative models, such as generative adversarial networks (GANs). These models are trained on large datasets of images to learn the underlying patterns and features of the visual input data, and can then generate new images based on that learning.

Automation of image generation can facilitate tasks such as:

• Artistic style transfer
• Product design
• Logo design

3- Marketing automation

Automation is vital for marketing. 68% of B2B marketers implement automation in their marketing strategy.³ And artificial intelligence is playing an important role in marketing automation.

Marketing can be further automated with generative AI using a variety of techniques and applications to generate, personalize, and optimize marketing campaigns. Here are some ways in which generative AI can automate tasks for marketing:

• Personalization of marketing messages and content to specific target audiences, based on the data analysis of their demographics, interests, and behaviors
• Copywriting for generating ad copy, social media posts, email subject lines, and product descriptions
• A/B testing of marketing campaigns, by generating different variations of content and testing them against each other to determine which performs best

4- Customer service automation

Customer service can be automated with generative AI as conversational AI models can understand and respond to customer queries and requests. Generative AI can automate certain tasks in customer service in certain ways such as:

• Chatbots integrated with messaging apps, websites, and other customer service channels to provide 24/7 support
• Email automation for enabling responses to common customer queries and requests received via email
• Self-service portals to provide personalized recommendations and solutions to customers based on their query and history

5- Code-writing automation

Code writing can be automated with generative AI that can generate code based on natural language input. It can facilitate programmers and software developers in certain laborious tasks such as:

• Code optimization
• Bug detection
• Code completion

Figure 2. An example of ChatGPT generating code for the given prompt and explaining it

What is the future of automation with generative AI and robotic process automation (RPA)?

As another class of automation technologies, RPA consists of bots and software to automate the way most businesses operate. According to Gartner, 90% of businesses already have implemented RPA.⁴

Increase in productivity

Generative AI and RPA are both powerful tools for automating repetitive tasks and increasing productivity. The integration of these two technologies could lead to even greater efficiency and accuracy in a wide range of applications, from customer service to manufacturing.

Collaboration between humans and machines

While automation with generative AI and RPA can help to increase efficiency and productivity, it is important to remember that humans still play a vital role in many industries and applications. In the future, we can expect to see increased collaboration between humans and machines, as automation is used to augment human capabilities and improve overall performance. Plus, studies show that people are willing to interact with humans in the future rather than machines.

Figure 3. Percentage of people favoring human interaction rather than machine interaction

Source: PwC

Potential risks of automation

However, as automation with generative AI and RPA becomes more widespread, there will be important ethical considerations to take into account, such as the potential for job displacement (see Figure 4) and the need to ensure that automation is used in a responsible and ethical manner. 

Figure 4. Percentage of existing jobs at potential risk of automation, by gender and in total

Source: PwC

You can learn about other ethical risks from our article on the ethical considerations posed by generative AI.

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1. “Businesses Plan to Invest in Tech in 2023, Despite Economic Headwinds.” IBM Newsroom, 15 December 2022, https://newsroom.ibm.com/Businesses-Plan-to-Invest-in-Tech-in-2023,-Despite-Economic-Headwinds. Accessed 5 March 2023.
2. “Gartner Forecasts Worldwide Hyperautomation-Enabling Software Market to Reach Nearly $600 Billion by 2022.” Gartner, 28 April 2021, https://www.gartner.com/en/newsroom/press-releases/2021-04-28-gartner-forecasts-worldwide-hyperautomation-enabling-software-market-to-reach-nearly-600-billion-by-2022. Accessed 5 March 2023.
3. “2022 Marketing Statistics, Trends & Data — The Ultimate List of Digital Marketing Stats.” HubSpot, https://www.hubspot.com/marketing-statistics. Accessed 5 March 2023.
4. “Gartner Says Worldwide Robotic Process Automation Software Revenue to Reach Nearly $2 Billion in 2021.” Gartner, 21 September 2020, https://www.gartner.com/en/newsroom/press-releases/2020-09-21-gartner-says-worldwide-robotic-process-automation-software-revenue-to-reach-nearly-2-billion-in-2021. Accessed 5 March 2023.