1. INTRODUCTION

2. MATERIALS AND METHODS

For this research project, a literature review will be conducted using the PRISMA 2020 methodology, which provides the essential guidance through instructions and guidelines widely recognised by the academic community for conducting high-quality systematic reviews, seeking to reduce bias in the selection and synthesis of studies [13]. It also reflects advances in systematic review to report the results of meta-analyses or systematic reviews, as it uses flowcharts that have been developed based on various authors, contributing to improved quality and transparency [14]. This provides us with scientific support that guarantees greater validity of the results obtained.

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology establishes detailed guidelines for conducting systematic reviews, ensuring rigorous study analysis. This methodology establishes a search for various articles according to the line of research to provide an interpretation of the results [15].

For this research, the following question was posed: How does the integration of automated solutions contribute to improving efficiency and increasing productivity in organisations?

2.1 INCLUSION AND EXCLUSION CRITERIA:

The inclusion criteria considered for this research were as follows: articles published between 2020 and 2025 were included. The following search terms were also considered: “Robotic Process Automation (RPA) in make (integromat)”, “Robotic Process Automation (RPA) in n8n”, “implement integromat automation workflow”, “implement n8n automation workflow”, as shown in Figure 1. On the other hand, the exclusion criteria considered only freely accessible items, excluding sources of dubious reliability.

2.2 INFORMATION COLLECTION PROCESS

As we can see in Table 1 Search terminology in databases, we can demonstrate our search terminology by focusing on the repository of articles and publications in Google Scholar.

Table 1 Search terminology in databases.

Source: own elaboration

3. 3. RESULTS

Figure 1 Inclusion and exclusion criteria shows the PRISMA flow chart with the process of identifying, evaluating, and selecting articles that are relevant to our research. Initially, 3,282 articles were obtained through Google Scholar, from which duplicate and unrecovered articles were excluded. Their relevance, quality, and content were then evaluated, and 11 articles were selected.

Figure 1 Inclusion and exclusion criteria.

Source: own elaboration.

Table 2 Analysis of academic articles shows a review of the selected works, detailing their main contributions and the areas in which they can be applied.

Table 2 Analysis of academic articles.

Source: own elaboration.

The results of the content analysis of the 11 selected articles reveal a clear distribution of RPA applications across six main areas, as shown in Figure 2 Distribution of RPA application sub-areas.

Figure 2 Distribution of RPA application sub-areas.

Source: own elaboration.

The data presented in this figure reveals that process optimisation represents the area of greatest application with 33.3% of the cases studied, followed by notifications and alerts with 27.8%. Data processing ranks third with 22.2%, while the areas of automation with GPT, voice agents, and customer service each represent 5.6% of the applications identified. This indicates that organisations are prioritising the automation of fundamental operational processes and communication and alert systems.

We will analyse each of the six areas identified, addressing the main characteristics and their benefits for the organisation:

Notifications and alerts:

RPA allows notifications and alerts to be generated in real time in different scenarios. For example, in the case of research applied to a clinic, a robot could monitor databases or manage systems to find errors, generate results, and automatically send emails, text messages, and internal reports to employees. This reduces risks and ensures that actions are decided at the right time, saving time and improving responsiveness. RPA tools could also be integrated into dashboards to provide monitoring and issue alerts, thereby improving key processes [24].

Automation with GPT:

We can also see how the remarkable advances in artificial intelligence have led to integration in automation, using models such as ChatGPT to enable the creation of intelligent models. This powerful combination not only allows key scheduled tasks to be executed, but also makes it possible to understand actions and generate natural language [3], opening the door to a vast world of automation applications in various administrative, medical and scientific contexts. For example, using GPT, you can automate mailings, summarise clinical reports, or interact with customers, all within an automated workflow. This combination of RPA and GPT significantly reduces the operational burden by automating key processes, improving accuracy and speed of communication.

Process optimisation:

Automation with RPA generally improves processes by reducing execution times for key tasks, such as administrative and repetitive tasks [6]. RPA facilitates communication between different systems, which facilitates improvements in development and also improves the quality of work by automating processes to minimise manual errors. This is known as resource optimisation, allowing human resources to be reallocated to other activities, which enables the digital transformation of organisations. In other words, RPA not only automates, but also optimises processes to make them more scalable and autonomous.

Voice agents:

Voice agents are a form of automation that personally improves communication between customers and businesses. According to the article, Robotic Process Automation (RPA) allows these agents to perform repetitive, rule-based tasks such as customer service and query management. The implementation of an RPA-based voice agent, such as Stacy. This system uses AI to conduct verbal interviews instead of written forms, which streamlines data collection. Stacy adapts its questions based on previous answers and the type of business. The results show a reduction in processing time and an increase in customer satisfaction, which tells us that process automation using voice agents optimises processes and improves customer satisfaction [4].

Customer service:

Customer service is also being transformed as companies automate repetitive and complicated tasks. Automation agents can interact with communication systems, such as chats and messaging platforms, to provide more efficient and personalised customer service. This improves response times and reduces human error, saving time that can be used for other activities [25].

Data processing:

We can also take advantage of the rise of RPA tools for data processing in companies, as they allow large volumes of data to be captured for processing. By automating tasks such as extracting data from databases and entering information into systems, errors are greatly minimised. RPA agents can be integrated with various systems, allowing them to play a key role in processes. This automation capability improves efficiency and allows employees to focus on other activities such as decision-making [25].

This objective was successfully achieved by clearly identifying the most widely used RPA tools, confirming that n8n and Make are the predominant low-code/no-code platforms in the implementations analysed, appearing in all the selected studies. The results obtained revealed tangible improvements in operational efficiency, documenting reductions of 20-40 hours per month in administrative tasks, elimination of human error in repetitive processes, and significant economic savings. It was particularly valuable to identify how the integration of artificial intelligence, especially GPT models, amplifies traditional automation capabilities, enabling natural language processing and more sophisticated decision-making.

4. DISCUSSION

After conducting a thorough analysis of the research projects obtained, we noted that the integration of automation technologies contributes decisively to operational efficiency by eliminating repetitive and error-prone tasks, freeing up human and technological resources that can be directed towards strategic functions with greater added value, generating higher productivity, as demonstrated by various authors in different contexts and applications.

This research makes a significant contribution to the field of business information systems, as we provide a systematic review that specifically categorises the applications of low-code/no-code RPA tools such as n8n and Make. Unlike previous studies that focused on traditional RPA tools, this work identified six specific areas of application with accurate quantitative data, such as process optimisation (33.3%), notifications and alerts (27.8%), and data processing (22.2%), among others.

The differential value of this study lies in its focus on accessible platforms that democratise business automation, allowing organisations of various sizes to implement RPA solutions without requiring significant investments in licences or specialised programming personnel.

[16] implemented n8n to process security alerts issued by Wazuh, which managed to extract key information such as IP addresses and determine whether they were new threats or recurrences. This automation optimised response times and allowed analysts to focus on tasks of greater analytical value. Similarly, [12] demonstrates how these systems can detect irregularities in real time in industrial environments, enabling better decision-making, especially in situations where response speed is critical to preventing failures.

[9] with its “ProAgent” proposal, managed to transform the traditional paradigm by implementing LLM-based agents that not only execute programmed tasks but also design complete workflows based on human instructions. Similarly, [19] demonstrated how the integration of Make.com with ChatGPT allows for the automation of email communications tailored to the language of each customer, improving retention rates and eliminating the need for multilingual staff to expand into new markets.

[4] developed “Stacy”, a voice agent for insurance risk assessment that extracts customer information from Google Sheets and connects it to CRM systems to improve tracking. This approach automates processes and improves the customer experience through more natural interactions and more efficient workflows.

They can also be implemented in administrative systems, which are useful for controlling documentation and processes, as was done in [4], where n8n was implemented in administrative systems, reducing time and errors in standardised procedures. Similarly, in T. Eliaš's research project [22], Make.com was used to automate billing, domain management, and reminders to debtors.

Our findings coincide with those of [3], which also highlighted the advantages of business digitisation through RPA. However, our study broadens its perspective by including specific integration with artificial intelligence models such as GPT, an aspect that was not addressed in depth in previous research.

In contrast to [6], which focused primarily on rule-based automation, our results demonstrate that low-code/no-code tools enable more agile and flexible implementations. It reported implementation times of several months, while the cases analysed in our study show successful deployments in a matter of weeks.

Several of the studies analysed show positive impacts on customer service through automation. The study of [4] with voice agents improves the customer experience in the insurance sector, while [19] demonstrates how the automation of personalised communications in different languages increases customer satisfaction in e-commerce environment. These advances enable faster and more personalised responses without increasing the human resources required.

In the field of data management, information from different systems can be processed and merged into a single database, as demonstrated by [18], who implemented a system for the University of Chile that allows the validation and updating of personal data through a form integrated with the Civil Registry API, keeping all parties informed through automatic notifications. This demonstrates that data integration problems can be solved in complex environments.

This research has certain limitations that should be considered when interpreting the results. First, the search was limited to Google Scholar, which excludes relevant studies from other specialised databases such as IEEE Xplore, ACM Digital Library, or ScienceDirect. Additionally, most of the studies analysed correspond to specific implementation cases in particular organisations, which limits the generalisation of the results to different industrial or geographical contexts.

The results we obtained suggest that organisations should prioritise automation in process optimisation and alert systems, as there was a 61.1% success rate. Low-code platforms offer cost-effective alternatives to traditional RPA solutions, especially for SMEs, while their integration with AI exponentially enhances their capabilities. Strategically, RPA should be considered as a facilitator of digital transformation that is capable of freeing up human resources and allowing them to focus on activities with greater added value. However, it is important to consider the warnings in [21] about how these technologies, while improving efficiency, can reduce work autonomy and increase surveillance in the professional environment, especially with tools such as “Task Mining”, raising ethical considerations about the balance between optimisation and workplace well-being.

5. CONCLUSIONS

In conclusion, our systematic review has demonstrated the relevance and transformative impact of RPA tools in various organisational environments. The integration of RPA with AI, in this case Chat GPT, constitutes an advance in process automation, as it has been possible to incorporate greater textual understanding. This has led to the creation of hybrid systems that merge the productivity of artificial intelligence with the cognitive power of natural language understanding, enabling the creation of systems that can not only process unstructured information but also make contextualised judgements. This was evidenced by a reduction in operational workload and completion times, as well as a reduction in human error. This allows the employees of the organisation to focus on higher-value activities while repetitive tasks are efficiently automated.

The personalisation of interactions through voice agents and the management of large volumes of data are clear examples of how these tools are revolutionising organisational processes. From a data engineering perspective, these solutions have enabled automated processing pipelines that can handle multiple input formats, perform complex transformations, and distribute processed information to different target systems, all while maintaining data integrity and consistency throughout the flow.

In conclusion, these RPA tools represent an advance in business digitalisation, not only focusing on replicating human processes but also enhancing them.

ACKNOWLEDGEMENT

We would like to thank the National University of Trujillo for its institutional support in carrying out this research. We would also like to express our gratitude to the academic community and the authors of the studies analysed in this systematic review, whose contributions have been fundamental in advancing our understanding of the impact of RPA tools in modern organisations.

FUNDING

This research did not receive any financial support from any commercial or non-profit company or agency. The work was carried out independently.

AUTHOR CONTRIBUTION

Fabian Ruiz Santos: Conceptualisation, methodology, data analysis, literature search, content analysis, article writing, proofreading and final review of the manuscript.

Fernando Olivert Pantoja Payano: Literature search, data collection, content analysis and writing of specific sections of the document.

Alberto Carlos Mendoza de los Santos: Overall supervision of the project, methodological validation, critical review of the intellectual content, and approval of the final version.

CONFLICTS OF INTEREST

We declare that there are no conflicts of interest, financial or otherwise, that could have influenced the results or interpretation of this research. We have no personal or institutional relationships with organisations or companies that could be considered a potential conflict of interest in relation to the subject addressed in this study.

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