Last year, neural networks made a qualitative leap in development: the mass audience saw in them a tool that can write text, create a picture or speak in a human voice. That sparked talk of how many jobs AI would soon replace. However, everything is not so simple.
What does RPA have to do with it
RPA (Robotic process automation) technology appeared on the market relatively recently, about ten years ago, and quickly began to be introduced into the processes of large companies: from banks to telecom companies. RPA platforms allow using no-code tools to create robots – programs that solve certain work tasks for a person. In fact, these are virtual employees.
Until recently, experts, talking about RPA, emphasized that the technology helps to get rid of the routine. Indeed, robots are good at solving problems that can be decomposed into a sequence of simple actions – filling tables, checking acts, processing orders, and so on. For this reason, the main customers of RPA platforms were mainly large companies, where there is always a large volume of typical tasks – especially in the sales, marketing, logistics, HR and customer services departments. However, the introduction of neural networks in RPA platforms expands dramatically the range of areas where software robots can be used.
How neural networks are used today
Despite the boom in the development of AI and the general excitement, it is quite difficult to use neural networks in work today. To get a high-quality result from ChatGPT or Midjourney, you need to make an equally high-quality prompt, and not everyone can do it. On the contrary, comical situations arise. Thus, the American lawyer prepared for the trial with the help of a neural network – fictional precedents appeared in the defense strategy proposed by ChatGPT. The error was revealed when the judge and lawyers of the other party could not find the indicated cases.
Of course, this does not mean that neural networks work poorly, but this is primarily technology, and not a ready-made tool that will do everything for the user. However, AI can be integrated into other systems: this makes its use easier, and the result more stable. Therefore, neural networks have now begun to be introduced actively into RPA platforms. In fact, they were partially used before (for example, computer vision technologies helped to recognize text and tables on scans and speed up document flow), but now, with the growth in the quality of the neural network models, their influence has become much more noticeable. Thanks to this, software robots can solve not only typical routine tasks, but also more complex issues.
Here are some examples:
- Communication with users
ChatGPT generates text and RPA structures the rest of
the work. In fact, neural networks classify incoming messages and answer
popular questions automatically. If the task is difficult, the robot sends a
request to the employee. AI allows you not only to send the client standard
messages written in advance by a person, but to generate a personalized
Moreover, neural networks can access a prepared Q&A or even a database to prepare a message based on them. Thanks to this, for example, robotic lawyers are created. Unlike the example from the American court, they do not give out non-existent facts, but draw up an answer based on specialized sources such as Consultant and Garant.
- Data analysis
Before connecting neural networks, RPA technologies also accelerated the work of analysts: they could collect automatically data from the necessary sites, upload the necessary information from tables, and so on. Neural networks make the analysis deeper. For example, they can speed up work with purchases: automatically analyze the tender documentation, assess the chances of winning and even form an application.
We even had cases when the robot itself signed and sent an application for participation in the purchase. This is usually possible if the tender involves, for example, the supply of equipment (when you just need to specify goods and prices in the application). However, in the future, neural networks will also accelerate participation in more complex purchases – those where the conditions of the tender require creative work, drawing up a relevant portfolio, and so on.
• Content creation
So far, neural networks cannot write a deep expert article (in any case, without a detailed industrial training and a given texture), but they cope well with some tasks. For example, in conjunction with RPA, AI can create SEO materials and post them automatically on the site. Also, based on the company’s data, it formulates descriptions of goods, adds them to marketplaces and sites, saving major retailers from a large amount of work.
The generation of illustrations is not yet so effective in business (for example, real photos, not generated images, are more useful on the same marketplaces), but this may also change in the future.
What will happen next
The examples described are only part of a big trend. In fact, thanks to AI, software robots become more adaptive, learn to solve atypical problems and look for optimal solutions. This even applies to the very creation of such robots: neural networks simplify the process of generating a script for the user.
RPA is one of those areas for which neural networks have become a growth driver for years to come. This gives a synergistic effect: neural networks cease to be a tool for professionals and enthusiasts and begin to solve regular tasks of companies. At the same time, RPA solutions are becoming in demand for a wider range of companies. If earlier the main user of software robots was a large business with a large amount of routine, now RPA solutions can be useful for smaller companies – to replace some of the ordinary employees engaged in marketing, procurement and other areas.
By Konstantin Artemyev, founder of Sherpa PRA, the domestic business automation platform using software robots and AI