Hyperautomation: the future of hyperautomation under the influence of generative artificial intelligence

Generative artificial intelligence has become the newest buzzword in all domains in 2023 due to its ability to enable the creation, exploration, and surpassing of boundaries like never before.

Hyperautomation: the future of hyperautomation under the influence of generative artificial intelligence

Written by Maria Irimias (Service Delivery Manager | RPA) and Iulia Prodan (RPA Solution Architect), in the August 2023 issue of Today Software Magazine.

Read the article in Romanian here

In our last year’s article on the topic, published in August 2022, hyperautomation was mentioned as the latest buzzword in the IT domain. For those new to this concept, hyperautomation has gained popularity relatively recently (as a term promoted by Gartner in 2020), riding on the success presented by Robotic Process Automation (RPA) in recent years. It emerged as a necessity for businesses to integrate more and more technologies, tools, and platforms to meet various market needs.

Under the umbrella of hyperautomation, one can find RPA (Robotic Process Automation), low-code platforms, process mining, artificial intelligence, machine learning, natural language processing, intelligent document processing, various business process orchestration methods, and advanced analytics and statistics. All these aim to support the acute need for global digitisation.

Generative artificial intelligence has become the newest buzzword in all domains in 2023 due to its ability to enable the creation, exploration, and surpassing of boundaries like never before. It is a fascinating subset of artificial intelligence. Not only does it interpret information, but it also creates original content.

Generative artificial intelligence combines the power of automated learning, deep learning, and artificial intelligence to produce text, video, audio, code, and images.


New trends in artificial intelligence

Until November 2022, the IT field was known for its fast but stable pace of developing new technologies that support digitalisation and beyond. However, this point has become revolutionary. The term hyperautomation had become popular, with a well-defined trajectory for the upcoming years. But since then, the focus has undergone a major shift.

ChatGPT made its debut at the end of November, and quickly became a viral sensation. Developed by OpenAI, the technology aims to generate text automatically based on written prompts in a much more advanced and creative manner than previous chatbots.

From that moment on, it became the central topic of discussions. Various messages, all with the same context of ChatGPT, flooded social media, with some even comparing its debut to the launch of the iPhone in 2007.

Just five days after OpenAI released ChatGPT, the chat tool, still in the research and development phase, “reached 1 million users!”.


With the first ChatGPT users demonstrating the technology’s ability to engage in multi-turn conversations and generate software code, the world of so-called natural language processing seems to be entering a new phase.

Significant investments are being made in the field of generative artificial intelligence, which refers to computers’ capacity to automatically create text, videos, images, and other media using state-of-the-art machine learning technologies.

Undoubtedly, this new technology directly influences the world of hyperautomation. The key question is how does it do so.

The influence of generative artificial intelligence on hyperautomation

Even before November 2022, hyperautomation has seen the pervasive integration of artificial intelligence into all its facets. Each technology integrated or was set to integrate artificial intelligence (AI) and machine learning (ML) to provide intelligent automation.

This progress allowed companies to automate more complex and sophisticated processes, such as decision-making and predictive analysis, generally by making use of specialised models, trained on project-specific data.

With the rising popularity of Large Language Models, already trained on huge datasets and multiple tasks, used in the development of generative artificial intelligence, an opportunity has emerged to save on resources required for training, development, and maintenance of specific models for each activity, and simply make use of the powerful capabilities that come out-of-the-box with these models.

Since then, major automation platforms have focused on integrating the new trend to remain competitive. A quick search yields results with various integrations and connectors that enhance traditional automation methods with the help of generative artificial intelligence.

The combination of generative artificial intelligence and hyperautomation will offer numerous possibilities for companies, enabling them to automate complex tasks and processes.

As automation scales, and generative artificial intelligence continues to evolve, more opportunities will arise for what companies can create – and they can scale rapidly, enhancing productivity and efficiency.

These two collaborative technologies can empower companies to surpass the boundaries of innovation.

Applying new technologies in hyperautomation

There are several ways in which the combination of generative artificial intelligence and automation can enhance the overall user experience:

Chatbots: Companies can use generative artificial intelligence to develop new capabilities for chatbots based on their interactions with customers. This would make chatbots more efficient and personalised over time, contributing to an improved customer experience. By automating chatbots, they can respond to customer queries without requiring human involvement and without the need for pre-defined scenarios or keywords.


Citizen developers: By utilising generative artificial intelligence, citizen developers can gain an advantage in creating their applications by generating code faster and exploring various options. This technology can also suggest improvements and optimisations for existing code, simplifying the development process.

Design: Using customer preferences and feedback, a company can employ generative artificial intelligence to develop new product models. These designs can later be automated and integrated into the manufacturing process, ultimately leading to a more efficient and individually tailored production process.

Image and video processing: Using generative artificial intelligence, automatic processing and analysis of visual data, such as static images and moving videos, became possible. This technology also allows generating new content based on this data. These applications can be valuable in industries like advertising and entertainment, where creating visually appealing content plays a crucial role, especially when displayed on screens.

Personalised marketing: Leveraging generative artificial intelligence enables the creation of personalised marketing strategies for individual customers based on their preferences and previous actions. The marketing process can also be automated, and campaigns can be integrated into it, leading to targeted marketing tailored to the specific needs of customers.

These are just a few examples of how automation and generative artificial intelligence can work together to develop new and innovative solutions in various industries. Countless other possibilities exist. As the development of generative artificial intelligence continues, we can look forward to a greater variety of fascinating use cases across diverse technologies.

Concrete example of combining generative artificial intelligence and hyperautomation

At this year’s edition of Accesa’s internal Hackathon, which that has already become a tradition, we witnessed some impressive concepts, each showcasing the use of artificial intelligence from a distinct angle.

Among these innovative ideas, one stood out by incorporating generative artificial intelligence, like ChatGPT, into eCommerce searches. This breakthrough had a profound effect, addressing the crucial challenge of precisely aligning users’ intentions with the most relevant product outcomes.

Currently, eCommerce searches primarily rely on keywords. For instance, if I search “What TV should I buy?” on a well-known Romanian online store, the top result is a book by Claire LaZebnik titled “What Should Be”.

The search bar cannot grasp my intention, as it is limited to keyword-based searches. ChatGPT fundamentally changes this approach to searching, thanks to its natural language processing system, which can understand user intentions, having been trained on nearly a trillion words.

Through ChatGPT, when I type “What TV should I buy?”, I receive a detailed five-point response, with top considerations for choosing a TV, such as budget, size, and features.

Additionally, I am provided with several ways to decide on the appropriate size, features, and price. ChatGPT comprehends what I am trying to find, making the search much more efficient and satisfying for users.


Once the selection process using the generative artificial intelligence model is completed, the next step is placing the order. This search and selection are the first stages, and what happens next is governed by hyperautomation.

Once the order is entered into the system, it is automatically processed by a software robot that carries it out step by step without any human intervention, ultimately resulting in the product being delivered to the recipient.

The combination of technologies involving generative artificial intelligence and hyperautomation has the potential to transform many different industries by increasing productivity, enhancing quality, and personalizing user experiences.

Hyperautomation alone has not made it possible to provide personalised experiences to customers in the past. However, combined with generative artificial intelligence, hyperautomation will enable the industry to offer customers a wide range of personalised experiences.

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