A Review of Literature on Enhancing Organizational Efficiency

through the Synergy of AI and Robotic Process Automation (RPA)

R. Yasotha and B. Ranjani

Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam - 611 002, Tamil

Nadu, India

Keywords: Artificial Intelligence (AI), Robotic Process Automation (RPA), Industry 4.0, Digital Transformation,

Artificial Neural Networks (ANN), Text Mining, NLP.

Abstract: The next generation of digital technology features innovations such as artificial intelligence (AI) and robotic

process automation (RPA), transforming organizational operations, workplaces and daily life, making digital

transformation an essential strategy for businesses and their leaders. Innovation is key to success and

competitive advantage in today's evolving business landscape. A service technology company that has

developed an intelligent IT operations ecosystem, integrating various technologies to streamline processes

and enhance efficiency, requiring companies to be adaptable to internal and external factors. This highlights

the growing importance of employee creativity within organizations. The authors of this literature study

examined a service technology solutions company that has developed an intelligent IT operations ecosystem.

They shared leadership perspectives and thought processes regarding the company’s next steps. The rapid

technological advancements of the rise of sophisticated information systems have shifted service delivery

predominantly to digital platforms, driven by Industry 4.0, which emphasizes increased connectivity and

automation. RPA offers significant advantages in automating business processes. This paper explores RPA

tools integrated with AI to improve in the evolving landscape of Industry 4.0, organizations can transform

their operations by strategically implementing advanced technologies. Embracing AI-driven approaches such

as ANN, Text Mining, and NLP is essential for enhancing efficiency and effectiveness, enhancing RPA

capabilities. These advancements play a crucial role in optimizing operations, extracting and interpreting vital

information from unstructured data sources, such as customer feedback and social media, and streamlining

classification and forecasting processes for improved accuracy and productivity.

1 INTRODUCTION

The rise of digital services in businesses highlights

the integration of information systems. Organizations

utilize these systems to enhance efficiency and

improve customer experiences and the rapid

technological advancements across various sectors.

Communication among citizens, businesses, and

institutions has largely transitioned to digital

information exchange. However, the sheer volume of

digital data and documentation makes it nearly

impossible for humans to process all this information

efficiently and manage internal workflows.

The roles of AI and RPA as key digital

technologies shaping the modern workplace and daily

activities, with digital transformation becoming a

core strategy for many businesses. Leadership is vital

in creating an environment where individuals can

collaborate toward a shared vision. By prioritizing

innovation and continuous learning over mere

financial gains, leaders can foster a culture that drives

technological progress and organizational success

Infopédia (2020) and Xie et al., (2018). Robotic

Process Automation (RPA) tools consist of various

techniques aimed at improving work efficiency by

automating repetitive tasks and their effectiveness

increases, introducing machine learning and data

analysis that improve speed and accuracy. This

synergy minimizes errors and boosts productivity,

enabling informed decision-making and greater

competitiveness in a digital world.

The fourth industrial revolution, merges advanced

technologies and sensors to boost AI-driven

automation within organizational processes, resulting

in improved performance and the creation of new

opportunities.

Yasotha, R. and Ranjani, B.

A Review of Literature on Enhancing Organizational Efﬁciency through the Synergy of AI and Robotic Process Automation (RPA).

DOI: 10.5220/0013911400004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 4, pages

255-263

ISBN: 978-989-758-777-1

255

2 ROBOTIC PROCESS

AUTOMATION

With recent technological advancements and the

widespread adoption of digital systems, many

services businesses provide today are digital. Industry

4.0 is transforming industries through automation and

emerging technologies, with RPA playing a pivotal

role in increasing operational efficiency. As a key

enabler of digital transformation, RPA combines

software, this paper reviews the role of AI (AI) and

RPA in Industry 4.0, analyzing and comparing

various proprietary and open-source tools by their

functionalities and enhancing productivity Aguirre et

al., (2017) and Kudlak, L (2019). Business leaders

must predict disruptive technologies to ensure their

firm's survival and competitiveness in the digital era

Krotov, V., (2019). It discusses the benefits of RPA

in automating business processes and how AI

improves RPA’s accuracy.

The document begins by analyzing RPA and AI

together with their connection to Industry 4.0 after

which it explores proprietary along with open-source

tools. Then it proceeds to the discussion segment.

This investigation ends with a list of references that

support the conducted study. The combination of

software robots with machine learning enables RPA

to automate repetitive manual work which brings

about increased efficiency Aguirre et al., (2017).

Screen recordings and variable settings provided by

developers serve as instructions for defining tasks

which enable data entry alongside email management

and form completion Kudlak, L (2019). RPA

distinguishes itself from conventional automation

since it operates through user interface interactions.

The automation tool identifies elements instead of

using screen coordinates or XPath selections thus

becoming more intelligent Kudlak, L (2019).

RPA tool demand increased significantly since

2016 and now operates across multiple sectors

including forensics, industry and auditing Krotov, V.,

(2019). RPA advances through Industry 4.0 by using

smart device data to automate business rules. AI

techniques integrated with human resources and

accounting functions help improve classification and

data recognition as well as automation systems Fluss,

D., (2018) and Leno et al., (2020).

Different fields like robotics, computer vision and

natural language processing Nilsson, N. J. (2014) are

included in AI’s evolution which has progressed over

time. It has been incorporated into RPA and has

greatly increased worker productivity and customer

experience in strategic domains Watson et al., (2020).

Real-time sensor data is used through AI-driven

manufacturing to increase efficiency, quality, and

accountability to the manufacturing Ustundag, A. and

Cevikcan, E. (2018). Software robots deployed

through RPA automation result in reduced costs by

30-50% during the execution of accurate processes

Aguirre, S. and Rodriguez, A., (2017) and Van Der

Aalst et al., (2018).

3 ARTIFICIAL INTELLIGENCE

AND INDUSTRY REVOLUTION

The key fields of AI concentrate their research and

development efforts on NLP which enables

computers to interpret human language. Automatic

Programming enables software creation with minimal

human input. Robotics exists to develop machines

which perform work autonomously. Computer

systems with this technology can understand visual

information. Automatic Theorem Proving can solve

complex mathematical problems. Intelligent Data

Retrieval: Enhances information retrieval efficiency

Nilsson, N. J. (2014). AI has a role in making

interactions with the world more interesting. NLP,

robotics and computer vision have become

subdomains of fields developed to a great extent and

have been the source of innovation and efficiency in

different industries. Smart factories and Industry 4.0

are heavily reworking manufacturers in the use of AI.

The application of AI-powered machines and systems

in this approach improves product quality, reduces

costs and helps in performing complex tasks. AI

optimizes the production process and also predicts

when maintenance will be needed to improve

efficiency and productivity by reducing or less human

action in repetitive or hazardous tasks Bahrin et al.,

(2016).

Industry 4.0 achieves its main focus through

cyber-physical systems that unite digital and physical

elements. These systems provide manufacturers with

capabilities to access data obtained from connected

devices and sensors. Organizations use data for

operational efficiency improvements, production

optimization and productivity enhancement. The

ability to monitor operations in real-time allows

organizations to resolve problems more rapidly which

shortens production downtime and allows better

resource control to create a faster-reacting

manufacturing system. Processing a large amount of

industrial equipment data through Artificial

Intelligence (AI) helps manufacturers adapt their

operations to prevailing challenges. AI has proved

itself as an effective solution to meet requirements for

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personalized products allied with rapid market

delivery. AI-driven production systems establish

adaptable production lines that handle fast customer

demand variations since they operate without

traditional human involvement Zheng et al., (2018).

AI technology improves industrial robots by

allowing them to acquire new capabilities and adjust

to operations that exceed standard robotic systems.

The combination of AI with flexible robots leads to

efficient manufacturing of various products without

needing frequent reprogramming thus optimizing

workflow operations and cutting down production

downtime. Five technological paradigms used to

create 4.0 industries utilize AI as a data processing

tool that collects information from industrial sensors

to generate manufacturing insights for better

decision-making. The system enhances operational

efficiency and enables predictive maintenance

because of which equipment breakdowns are

minimized together with operational interruptions

Ustundag, A. and Cevikcan, E. (2018).

4 RPA TOOLS WITH AI

SUPPORT

RPA technology delivers a wide array of advantages

through its implementation of Artificial Intelligence

methods. RPA receives an AI-powered boost that

enhances administrative scientific and industrial

operations and increases efficiency through

automated repetitive work elimination. The

combination of AI and Machine Learning (ML)

enables RPA to handle challenging undefined tasks

by observing scenarios for adaptation similar to

human cognitive processes Aguirre, S. and

Rodriguez, A., (2017). RPA tools become more

powerful through their integration of AI operations

which lets them handle unanticipated scenarios

alongside human operator support according to van

der Aalst, Bichler, and Heinzl. The impact of AI

algorithms and machine learning Mitchell, T. M.

(1997). has been substantial on different businesses

through better data classification and optimization

processes during the previous years. The combination

of AI and RPA technologies enables organisations to

apply this automation solution across Enterprise

Resource Planning, Accounting and Human

Resources departments for better decision-making

capabilities. Studies show that RPA aids in automatic

discovery, and audit processes Leno et al., (2020),

and boosts productivity FLUSS, D. (2018). Reports,

including one from Deloitte Delloite (2019), highlight

AI's benefits in accuracy, fraud prevention, and

compliance. While automation has challenges, AI

significantly improves business processes and

decision-making. The following sections will discuss

key RPA tools that leverage AI.

RPA vendors like UiPath and process mining

companies such as Celonis collaborate to identify and

automate high-potential processes by analyzing

workflows and implementing RPA solutions. While

early digital transformation focused on customer

service improvements, there's been a recent shift

toward automating business processes across

industries. UiPath, a leading RPA tool GitHub

(2020a), offers three core modules: UiPath Studio for

workflow design, UiPath Robot for execution, and

UiPath Orchestrator for coordination GitHub

(2020a). It integrates with Microsoft’s services and

open-source tools for enhanced data visualization and

includes an AI-driven UI Automation module for

tasks like image recognition and data optimization.

Kofax develops process automation software with

features like RPA, BPO, OCR-based data

recognition, and advanced data analysis. It extracts

data from various sources, optimizing ERP tasks. The

software uses AI modules for content recognition,

classification, and information extraction from

emails, web portals, and documents D. Schmidt

(2018). It incorporates machine learning for

document classification and validation, and natural

language processing for content analysis. Kofax

offers AI capabilities through its IA platform and

CDA module. Automation Anywhere is an AI

integration that makes this tool an advanced RPA

solution to boost automation capabilities. The

software finds applications across different business

domains including human resources, CRM and SCM

functions specifically in ERP systems. The system

achieves its strength by integrating with SAP and

Oracle alongside other major ERP platforms enabling

full automation across different software

environments. The core value of Automation

Anywhere rests upon its "Digital Workers" according

to the company. These digital robots excel at

performing sophisticated automated procedures that

require minimal human supervision. By integrating

cognitive automation, the Digital Workers improve

traditional RPA technology to make decisions

through real-time data sources. Automation

Anywhere provides advanced tools and extensive

resources for users to understand RPA system

maintenance along with data analytics tools to

optimize workflow automation. The Bot tool from

Automation Anywhere operates by using artificial

intelligence to extract details from structured and

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unstructured documents through techniques that

integrate fuzzy logic with ANN and NLP. The

platform enhances validation efficiency through its

capabilities which minimize processing time and

eliminate human errors. The IQ Bot enhances data

extraction and document classification accuracy,

benefiting business operations and decision-making

E. Global (2017). Overall, the integration of AI-

driven cognitive automation positions Automation

Anywhere as a valuable asset for organizations

pursuing digital transformation and seamless process

automation.

5 RECENT RPA TOOLS

Win Automation, developed by Soft motive, is an

RPA tool that automates tasks like email

management, file handling (PDF and Excel), OCR,

and other workplace activities. It features process

design, web automation, macro recording, and user

interface design. The Process Robot module, created

with Capture Fast, adds AI-driven functionalities for

data extraction and document classification, although

its AI capabilities are somewhat limited. Assist Edge,

from Edge Verve (an Infosys subsidiary), offers both

proprietary and open-source RPA versions. It uses

OCR and AI algorithms, including Neural Networks,

for data capture and process monitoring. Automagical

is another RPA tool available in both open-source

versions and proprietary on GitHub. Built in Python,

it guides OCR and PDF text mining, allowing AI

integration via Google TensorFlow for text

identification and image recognition.

• Blue Prism: A leading enterprise RPA tool

offering scalable and secure automation. Best

suited for large-scale automation with minimal

human intervention. Uses a drag-and-drop

visual design for process automation A.

Mukherjee (2021).

• Microsoft Power Automate: An automation in

the cloud-based platform by Microsoft. Enables

integration with Microsoft 365, SharePoint,

Teams, and third-party applications. Provides

AI Builder for intelligent automation X. Wang

(2023). Pega Robotic Automation: AI-driven

automation solution integrated with Pega BPM

(Business Process Management). Offers low-

code capabilities for faster implementation G.

Pandy et al., (2024). Work Fusion: AI-powered

RPA platform specializing in cognitive

automation. Uses machine learning for

intelligent decision-making and automation.

Best for document processing and customer

support automation. Automation Edge: Cloud-

native automation tool that supports IT Process

Automation (ITPA) and RPA. Integrates with

ITSM tools like ServiceNow, BMC Remedy,

and Jira. Offers AI-powered chatbots and

predictive analytics V. Mafeni and Y. Kim

(2024). Kryon RPA: Known for Process

Discovery, which automatically identifies

automation opportunities. Supports both

attended and unattended automation. SAP

Intelligent RPA is Specifically designed for

SAP-based automation. Enables end-to-end

business process automation in SAP

applications W. Zhang and L. Chen (2024).

Redwood RPA Enterprise-grade automation

tool offering cloud-native and on-premise

deployment. Focused on finance, HR, and IT

process automation R. Malhotra (2022).

Electroneek RPA is Designed for small

businesses and startups with cost-effective

pricing. Provides no-code automation and

integrates with Google, Microsoft, and CRMs

M. A. Kossukhina et al., (2021). Jacada RPA:

Specializes in customer service automation and

call centre process automation. Uses AI-driven

bots for conversational automation S. Ray et al.,

(2021). Help Systems Automate: Offers scalable

and flexible automation for IT and business

workflows O. A. Duah. OpenBots: Open-source

RPA platform with no bot licensing costs.

Provides enterprise-grade automation features

with AI/ML integration K. Ersen (2017).

• Robocorp: Python-based open-source RPA

platform. Ideal for developers and data

automation workflows. Provides cloud-native

orchestration and scalable automation J.

Siderska (2024). Apache Nifi: Open-source

dataflow automation tool. Designed for real-

time data integration and processing. Useful in

IoT, big data pipelines, and enterprise

automation A. Cakir et al., (2022). Tangentia

RPA: AI-powered end-to-end automation tool.

Works with BFSI, healthcare, and e-commerce

industries J. Calvo (2020). AirSlate: Specializes

in document workflow automation. Provides a

drag-and-drop no-code automation builder.

Integrated with e-signatures and contract

management K. Devaki et al., (2023). AISeon

AI-driven hyper automation platform; Focused

on intelligent document processing and AI-

driven bots. Xceptor: Best for data

transformation and financial process

automation. Uses AI and NLP for complex data

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extraction C. Zhang et al., (2022). Rocketbot:

Low-code RPA tool with Windows, Linux, and

macOS support. Compatible with cloud services

like AWS and Google Cloud. Leapwork: No-

code test automation and RPA platform. Uses a

flowchart-based design for automation

workflows T. Kavitha et al., (2024). Jiffy.ai: AI-

powered cognitive automation platform.

Specializes in business process transformation.

ElectroNeek Studio Pro: No-cost bot runner,

making it a cost-effective choice. Suitable for

SMEs and IT service providers. Softomotive

(now part of Microsoft Power Automate):

Windows-based RPA tool, formerly

WinAutomation. Now integrated into Microsoft

Power Automate. Automai: Specializes in end-

to-end testing automation and RPA. Good for

citizen developers with no coding experience.

AntWorks: AI-powered data-driven RPA tool.

Specializes in intelligent document processing.

Facets RPA: Industry-specific RPA tool

designed for healthcare and insurance. Helps

automate claims processing and patient data

handling. BP Logix Process Director: Combines

workflow automation with AI-powered insights.

• Best suited for enterprise digital transformation.

NICE Advanced Process Automation: AI-

powered contact centre automation tool.

Specialized in customer service and telecom

industries. Signavio Process Intelligence: RPA

and process mining solution. Helps discover

automation opportunities in business processes.

6 RESULT AND DISCUSSION

RPA in Industry 4.0: RPA is a pivotal technology in

Industry 4.0, enhancing efficiency by automating

cyclic tasks and reducing transactional costs by 30%

to 50% (Aguirre & Rodriguez, 2017; Van Der Aalst

et al., 2018; Williams & Allen, 2017). Its capability

to interact with user interfaces makes it effective for

tasks such as data entry and email management. The

addition of AI allows RPA to classify data and

optimize processes.

Artificial Intelligence's Impact on Industry 4.0: AI

significantly boosts industrial automation through

predictive maintenance, real-time data analysis, and

intelligent manufacturing, enhancing production

efficiency. AI-powered robots scan sensor

information to develop more efficient workflows that

lower downtime. The ability of AI systems to process

extensive data from cyber-physical systems enhances

adaptation performance along with quality control

and reduces costs while supporting rapid

customization of personalised products

6.1 RPA Tools with AI Support

UiPath: UiPath is known for its AI-driven automation

tools and provides workflow design, process

execution, central control, improved image

recognition and data processing. Kofax: This tool is

the best in AI-based document recognition, data

extraction and document classification for industries

with unstructured data like finance and healthcare.

Automation Anywhere: With its ability to provide

cognitive automation features, it integrates AI for

intelligent process automation and decision-making

in real-time. AI-Driven RPA Advancements: By

using AI together with RPA, businesses realize

improved accuracy to automate their complex tasks,

reduce costs, and enhance compliance. Nevertheless,

challenges such as data security and implementation

costs still prevent ethical AI use and data privacy and

require a strong governance framework for such use

and privacy. The table provides a comparison of

leading RPA tools with AI capabilities, highlighting

their key features, use cases, and pricing models. It

helps in selecting the right automation solution based

on AI-driven functionalities like OCR, NLP, and

cognitive automation. Table 1 shows the AI-enhanced

RPA tools.

Table 1: Ai-Enhanced RPA Tools and Their Applications in Industry 4.0.

Section Content Summary

Robotic Process

Automation (RPA)

Industry 4.0 integrates RPA to enhance operational efficiency. RPA automates

repetitive tasks, interacts with UI elements, and leverages AI for intelligent

automation. Businesses use RPA for data entry, email management, and workflow

automation. AI integration improves classification, data recognition, and process

automation.

Artificial Intelligence

(AI) and Industry 4.0

AI includes NLP, robotics, computer vision, and data retrieval. It enhances

manufacturing efficiency, product quality, and predictive maintenance. Cyber-

physical systems collect and analyze data for real-time decision-making. AI-driven

robots adjust to tasks without frequent reprogramming.

A Review of Literature on Enhancing Organizational Efﬁciency through the Synergy of AI and Robotic Process Automation (RPA)

259

RPA Tools with AI

Support

RPA combined with AI and ML adapts to dynamic scenarios. RPA tools like

UiPath, Kofax, and Automation Anywhere integrate AI for enhanced automation.

Digital Workers in Automation Anywhere use cognitive automation for data-driven

decision-making. Deloitte highlights AI’s role in improving accuracy, fraud

detection, and compliance.

Key RPA Tools Various RPA tools leverage AI to enhance automation capabilities.

WinAutomation Automates email management, file handling, and OCR tasks. Limited AI features.

AssistEdge Uses OCR and AI, including Neural Networks, for process monitoring.

Automagical Python-based tool supporting OCR and AI integration with Google TensorFlow.

Blue Prism Enterprise RPA tool using a visual drag-and-drop interface.

Microsoft Power

Automate

Cloud-based automation integrating with Microsoft 365. Provides AI Builder.

Pega Robotic

Automation

AI-driven with low-code capabilities, integrated with BPM.

WorkFusion Uses ML for intelligent automation, specializing in document processing.

Automation Edge Supports IT Process Automation and RPA with AI-powered chatbots.

Kryon RPA Uses Process Discovery for automation opportunities.

SAP Intelligent RPA Designed for SAP-based business automation.

Redwood RPA Focuses on finance, HR, and IT process automation.

Electroneek RPA Cost-effective automation for small businesses.

Jacada RPA Specializes in customer service and call center automation.

Help Systems

Automate

Scalable automation for IT and business workflows.

OpenBots Open-source RPA with enterprise features and AI/ML integration.

Robocorp Python-based open-source RPA for data automation.

Apache Nifi Open-source dataflow automation tool for real-time processing.

Tangentia RPA AI-powered automation for BFSI, healthcare, and e-commerce.

AirSlate No-code document workflow automation with e-signature support.

AISeon Hyper automation platform for intelligent document processing.

Xceptor AI and NLP-powered financial process automation.

Rocketbot Low-code RPA supporting multiple OS and cloud services.

Leapwork No-code RPA platform with a flowchart-based interface.

Jiffy.ai Cognitive automation for business process transformation.

ElectroNeek Studio

Pro

Free bot runner for SMEs and IT service providers.

Softomotive Formerly WinAutomation, now part of Microsoft Power Automate.

Automai Focuses on testing automation and RPA for citizen developers.

AntWorks AI-driven RPA tool specializing in document processing.

Facets RPA Industry-specific RPA tool with AI-powered automation.

7 CONCLUSIONS

Research findings demonstrate that AI-driven RPA

enhance productivity level along with precision

performance while improving decision quality.

Businesses can stay competitive during the digital era

through process automation optimizers which include

tools such as UiPath, Kofax and Automation

Anywhere with their AI capabilities. The successful

use of AI-supported RPA solutions depends on

overcoming current scalability issues and resolving

ethical AI and integration challenges. The focus of the

research should be on developing more adaptive AI

models, automating real-time more efficiently and

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incorporating more security protocols to mitigate the

risks arising from AI-powered automation.

REFERENCES

A. Tripathi, *Learning Robotic Process Automation: Create

Software Robots and Automate Business Processes

with the Leading RPA Tool, UiPath*. Packt Publishing,

2018.

A. Mukherjee, “Robotic process automation with Blue

Prism to optimize inventory management,” Ph.D.

dissertation, Technische Hochschule Ingolstadt, 2021.

A. Cakir, Ö. Akın, H. F. Deniz, and A. Yılmaz, "Enabling

real-time big data solutions for manufacturing at scale,"

*J. Big Data*, vol. 9, no. 1, p. 118, 2022.

Aguirre, Santiago & Rodriguez, Alejandro. (2017).

Automation of a Business Process Using Robotic

Process Automation (RPA): A Case Study. 65-71. DOI:

10.1007/978-3-319-66963-2_7.

Aguirre, S. and Rodriguez, A., 2017. Automation of a

Business Process Using Robotic Process Automation

(RPA): A Case Study. In: J.C. Figueroa-García, E.R.

López-Santana, J.L. Villa-Ramírez and R. Ferro-

Escobar, eds. Applied Computer Sciences in

Engineering. Cham: Springer International Publishing.

pp.65–71. https://doi.org/10.1007/978-3-319-66963-2

_7.

AssistEdge, “AssistEdge RPA OpenSource Community,”

2020. [Online] Available: https://www.edgeverve.co

m/assistedge/community/

Automagica, “Automagica Documentation,” 2020.

[Online]. Available: https://automagica.readthedocs.io

/index.html

Automagica, “Automagica GitHub Repository,” 2020.

[Online]. Available: https://github.com/automagica/au

tomagica

Automation Anywhere, “Bot Execution Orchestrator API,”

2020. [Online]. Available: https://docs.automationany

where.com/bundle/enterprise- v11.3/page/enterprise/to

pics/control-room/control-room-api/api-deploy-and-

monitor-bot-progress.html

Automation Anywhere, “Automate any ERP process with

RPA,” 2020. [Online]. Available: https://www.autom

ationanywhere.com/lp/automate-any-erp-process-with-

rpa

Automation Anywhere, “Actions in the Workbench,” 2020.

[Online]. Available: https://docs.automationanywhere

.com/bundle/enterprise- v11.3/page/enterprise/topics/a

ae-client/metabots/getting-started/selecting-actions-in-

the-logic-editor.html

Automation Anywhere, “Robotic process automation to

ERP,” 2020. [Online]. Available: https://www.automa

tionanywhere.com/solutions/robotic-process-automati

on-to-erp

Automation Anywhere, “IQBot – Intelligent Document

Processing,” 2020. [Online]. Available: https://www.a

utomationanywhere.com/products/iq-bot

Automation Anywhere, “Automation Management API,”

2020. [Online]. Available: https://docs.automationany

where.com/bundle/enterprise- v11.3/page/enterprise/to

pics/control-room/control-room-api/api-bot-

deployment.html

Bahrin, M. A. K., Othman, M. F., Azli, N. N., & Talib, M.

F. (2016). Industry 4.0: A review on industrial

automation and robotic. Jurnal Teknologi, 78(6-13),

pp:137-143.

C. Zhang, B. Li, E. Edirisinghe, C. Smith, and R. Lowe,

"Extract data points from invoices with multi-layer

graph attention network and named entity recognition,"

in *Proc. IEEE Int. Conf. Artif. Intell. Comput. Appl.

(ICAICA)*, 2022, pp. 1-6.

D. Schmidt, “RPA and AI,” 2018. [Online]. Available:

https://www.kofax.com/Blog/2018/september/rpa-and-

ai-the-new-intelligent-digital-workforce

Delloite (2019). Automation with intelligence Reimagining

the organisation in the ‘Age of with’. Available from:

https://www2.deloitte.com/content/dam/Deloitte/tw/D

ocuments/strategy/tw-Automation-with-intelligence.p

E. Global, “Automating Content-Centric Processes with

Artificial Intelligence,” 2017. [Online]. Available:

https://www.automationanywhere.com/images/lp/pdf/

everest-group-automating-content-centric-processes-

with-ai.pdf

FLUSS, D. (2018). Smarter Bots Mean Greater Innovation,

Productivity, and Value: Robotic process automation is

allowing companies to re-imagine and re-invest in all

aspects of their businesses. CRM Magazine, 22(10),

38–39.

Fluss, D., 2018. Smarter bots mean greater innovation,

productivity, and value: robotic process automation is

allowing companies to re-imagine and re-invest in all

aspects of their businesses. CRM Magazine, 22(10),

pp.38-39.

G. Pandy et al., “Enhancing Pega Robotics Process

Automation with Machine Learning: A Novel

Integration for Optimized Performance,” in *2024

IEEE 17th Int. Symp. Embedded Multicore/Many-core

Syst.-on-Chip (MCSoC)*, 2024, pp. 210–214.

GitHub (2020a). Open Source, Distributed, RESTful

Search Engine. [Online]. Available from:

https://github.com/elastic/elasticsearch

GitHub, “Your window into the Elastic Stack,” 2020.

[Online]. Available: https://github.com/elastic/kibana

Haenlein, Michael & Kaplan, Andreas. (2019). A Brief

History of Artificial Intelligence: On the Past, Present,

and Future of Artificial Intelligence. California

Management Review

Infopédia (2020). Dicionário Infopédia da Língua

Portuguesa, 2020. [Online]. Available from:

https://www.infopedia.pt.

J. Calvo, *Journey of the Future Enterprise: How to

Compete in the Age of Moonshot Leadership and

Exponential Organizations*. Libros de Cabecera, 2020.

J. Siderska, S. N. B. M. Aini, and D. Kedziora,

"Complementing robotic process automation with

generative artificial intelligence (ChatGPT), case of

A Review of Literature on Enhancing Organizational Efﬁciency through the Synergy of AI and Robotic Process Automation (RPA)

261

Robocorp," in *Future of Information and

Communication Conference*, Cham, Switzerland:

Springer Nature, 2024, pp. 37-53.

K. Ersen, M. Erhan Oztop, and S. Sariel, "Cognition-

enabled robot manipulation in human environments:

requirements, recent work, and open problems," *IEEE

Robot. Autom. Mag.*, vol. 24, no. 3, pp. 108-122,

2017.

K. Devaki, V. M. Bhaskaran, and S. Anjana, "The existing

IT functions and robotic process automation," in

*Confluence of Artificial Intelligence and Robotic

Process Automation*, Singapore: Springer Nature,

2023, pp. 313-336.

Kofax, *Kofax Capture (Version 10.0) *, 2011. [Online].

Available: https://issues.alfresco.com/jira/secure/attac

hment/56073/KofaxCaptureDevelopersGuide_10.pdf

Kofax, “Product summary Kofax RPA,” 2019. [Online].

Available: https://www.kofax.com/-/media/Files/Dat

asheets/EN/ps_kofax-rpa_en.pdf

Kofax, “Cognitive Document Automation,” 2020. [Online].

Available: https://www.kofax.com/Blog/Categories/C

ognitive-Document-Automation

Kofax, “Maximize Your ERP with Integrated Accounts

Payable Automation,” 2020. [Online]. Available:

https://www.kofax.com/Solutions/Cross- Industry/Fina

ncial-Process-Automation/AP-and-Invoice-

Automation/ERP-Integration

Kofax, “Power your process,” 2020. [Online]. Available:

https://www.kofax.com/-/media/Files/E-books/E N/e b

_how-rpa-capture-empowers-customer-journey_en.pdf

Kofax, “Kofax intelligent automation platform,” 2020.

[Online]. Available: https://www.kofax.com/Products

/intelligent-automation-platform

Kofax, *Developer's Guide Version 11.0.0*, 2020.

[Online]. Available: https://docshield.kofax.com/RPA

/en_US/11.0.0_qrvv5i5e1a/print/KofaxRPADeveloper

sGuide_EN.pdf

Krotov, V., 2019. Predicting the future of disruptive

technologies: The method of alternative histories.

Business Horizons, 62(6), pp.695-705.

Kudlak, L., 2019. Don’t underestimate the power of robotic

process automation. Will the Age of Ultron come to our

world? Technology4Planet. [online] Available at:

<https://medium.com/tech4planet/dont-underestimate-

the-power-of-robotic-process-automation-8ffb8262d6

2f> [Accessed 2 April 2023].

L. Vilhelmsson and P. Sjöberg, "Implementation and

evaluation of a data pipeline for Industrial IoT using

Apache NiFi," unpublished, 2020.

Leno, V., Dumas, M., La Rosa, M., Maggi, F. M., &

Polyvyanyy, A. (2020). Automated Discovery of Data

Transformations for Robotic Process Automation.

https://arxiv.org/abs/2001.01007

Leno, V., Dumas, M., La Rosa, M., Maggi, F.M. and

Polyvyanyy, A., 2020. Automated Discovery of Data

Transformations for Robotic Process Automation.

[online] https://doi.org/10.48550/ARXIV.2001.01007.

M. A. Kossukhina et al., “Features of robotic automation of

auxiliary processes of enterprises in the electrical and

electronic industry during the pandemic,” in *2021

IEEE Conf. Russian Young Res. Electr. Electron. Eng.

(ElConRus)*, 2021, pp. 1901–1905.

Mitchell, T. M. (1997). Machine Learning. New York:

McGraw-Hill. ISBN: 978-0-07-042807-2.

Nilsson, N. J. (2014). Principles of artificial intelligence.

Morgan Kaufmann Editors.

O. A. Duah, “The assessment of technology and company

readiness for robotic process automation (RPA)

implementation in retail,” Ph.D. dissertation,

Technische Hochschule Ingolstadt.

R. Malhotra, “Robotic process automation (RPA):

integration of robotic process automation portfolio in

accessing business processes with automation maturity

of small and medium sized companies to avoid

failures,” Ph.D. dissertation, Technische Hochschule

Ingolstadt, 2022.

R. K. Burila, *Data Pioneers: Unlocking Big Data

Engineering Potential*. Libertatem Media Private

Limited, 2024.

S. Ray et al., *Magic Quadrant for Robotic Process

Automation*, 2021.

S. Mirampalli, R. Wankar, and S. N. Srirama, "Evaluating

NiFi and MQTT based serverless data pipelines in fog

computing environments," *Future Gener. Comput.

Syst.*, vol. 150, pp. 341-353, 2024.

T. Kavitha, S. Saraswathi, and G. Senbagavalli, "Journey to

hyperautomation: the pathway of today's e-industries to

next-generation industries," *Hyperautomation Next-

Gener. Ind.*, pp. 1-34, 2024.

UiPath, “About the UI automation activities pack,” 2020.

[Online]. Available: https://docs.uipath.com/activities

/docs/about-the-ui-automation-activities-pack

UiPath, “Prerequisites for Installation,” 2020. [Online].

Available: https://docs.uipath.com/orchestrator/docs/p

rerequisites-for-installation

UiPath, “Artificial Intelligence RPA Capabilities,” 2020.

[Online]. Available: https://www.uipath.com/product/

ai-rpa-capabilities

UiPath, “UiPath Studio: Introduction.” [Online]. Available:

https://docs.uipath.com/studio/docs/introduction

Ustundag, A. and Cevikcan, E., 2018. Industry 4.0:

Managing the Digital Transformation. Springer Series

in Advanced Manufacturing. Cham: Springer

International Publishing. https://doi.org/10.1007/978-

3-319-57870-5.

V. Mafeni and Y. Kim, “An Automated Edge Computing

Approach for IoT Device Registration and Application

Deployment,” *IEEE Syst. J.*, 2024.

Van Der Aalst, W.M.P., Bichler, M. and Heinzl, A., 2018.

Robotic Process Automation. Business & Information

Systems Engineering, 60(4), pp.269–272. https://doi.

Org/10.1007/s12599-018-0542-4.

W. Zhang and L. Chen, “Artificial Intelligence and RPA-

Enabled SAP Variant Configuration: Transforming

Modern Supply Chain Management,” *Baltic

Multidiscip. Res. Lett. J.*, vol. 1, no. 1, pp. 42–48,

2024.

Watson, J., Hatfield, S., Wright, D., Howard, M.,

Witherick, D., Coe, L. and Horton, R., 2020.

Automation with intelligence: reimagining the

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

262

organisation in the’age of with’. [online] Available at:

https://www2.deloitte.com/content/dam/Deloitte/tw/D

ocuments/strategy/tw-Automation-with-intelligencep.d

f> [Accessed 12 February 2023].

WinAutomation, “Desktop automation,” 2020. [Online].

Available: https://www.winautomation.com/product/a

ll-features/desktop-automation

Work Fusion, “Work Fusion,” 2022. [Online]. Available:

https://www.workfusion.com/

X. Wang, “Enhancing Business Processes through

Dynamics Solutions with Microsoft Power Platform,”

2023.

Xie, Y., Xue, W., Li, L., Wang, A., Chen, Y., Zheng, Q.,

Wang, Y., Li, X., 2018. Leadership style and innovation

atmosphere in enterprises. Technological Forecasting

& Social Change, 135, pp 257-265

Zheng, P., Sang, Z., Zhong, R. Y., Liu, Y., Liu, C.,

Mubarok, K., ... & Xu, X. (2018). Smart manufacturing

systems for Industry 4.0: Conceptual framework,

scenarios, and future perspectives. Frontiers of

Mechanical Engineering, 13(2), pp:137-150.

A Review of Literature on Enhancing Organizational Efﬁciency through the Synergy of AI and Robotic Process Automation (RPA)

263