April 2025
Automation in Management Accounting
and Control: The Role of Robotic Process
Automation (RPA)
April 18, 2025
ABSTRACT
The rapid advancement of digital technologies is reshaping management accounting and control
by automating routine, rules‑based tasks. Robotic Process Automation (RPA) has emerged as a
scalable, low‑code solution that interacts with existing user interfaces to execute high‑volume
back‑office processes, such as invoice processing, reconciliations, financial reporting, and closing
procedures, without extensive system integration. This paper reviews 20 studies to investigate
what is the role of RPA in the automation of processes in the context of Management Accounting
and Control (MAC) functions. Drawing on cases in tax processing, accounts payable,
management reporting, master data management, cost allocations, internal controls, and
budgeting, were identified key benefits such us cost and time savings of 30–60%, near‑zero error
rates, faster payback periods, enhanced compliance, and improved employee satisfaction, as well
as risks related to cybersecurity, governance complexity, and over‑reliance on legacy processes.
While senior‑management commitment, cross‑functional collaboration, robust process selection
frameworks, and continuous improvement mechanisms are critical enablers. The main conclusion
was that while RPA delivers tangible value, firms must align automation initiatives with broader
digital transformation strategies and governance models.
Keywords: Process Automation, Robotic Process Automation, RPA, Management Accounting,
Accounting Automation, Finance Automation, Literature Review
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Table of Contents
ABSTRACT ................................................................................................................................ I
1. INTRODUCTION ................................................................................................................... 1
1.1. Background ..................................................................................................................... 1
1.2. Motivation and Research Objective ................................................................................. 1
1.3. Methodological Note........................................................................................................ 2
1.4. Structure of the Paper ..................................................................................................... 2
2. FUNDAMENTALS OF ROBOTIC PROCESS AUTOMATION ............................................... 3
3. ROBOTIC PROCESS AUTOMATION IN MANAGEMENT ACCOUNTING AND CONTROL 6
3.1. Empirical Evidence from RPA Use Cases in Management Accounting and Control ........ 6
3.2. Benefits, Risks, and Organizational Considerations in RPA Adoption.............................. 8
3.2.1. Benefits and Strategic Value of RPA ........................................................................ 8
3.2.2. Risks, Challenges, and Governance Constraints ...................................................... 9
3.2.3. Organizational Drivers, Enablers, and Human Capital Impacts ................................10
4. CONCLUSION ......................................................................................................................12
REFERENCES .........................................................................................................................13
APPENDIX ...............................................................................................................................17
Appendix A - Summary of research methods, objectives, context of analysis and main
conclusions from select papers. ................................................................................................17
Table of Figures
Figure 1 Main meanings attached to RPA in the literature. ........................................................ 3
Figure 2 Differences between RPA and Intelligent Automation found in the literature................ 4
Figure 3 Characteristics of RPA-suitable process tasks. ........................................................... 5
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1. INTRODUCTION
1.1. Background
The increasing pace of technological transformation is significantly reshaping how organizations
operate, manage information, and deliver value. This evolution is particularly relevant in
accounting and finance functions, where digital technologies are redefining core processes and
altering the roles and responsibilities of professionals (Möller et al., 2020). One of the most
impactful developments within this transformation is Robotic Process Automation (RPA), a
technology that enables the automation of rule-based, repetitive tasks through software-based
bots (Van der Aalst et al., 2018).
In Management Accounting and Control (MAC), where timely, accurate, and cost-effective
information is critical for decision-making, the integration of RPA has emerged as a key enabler
of operational efficiency and agility (Cooper et al., 2020). Traditional accounting functions, such
as invoice processing, reconciliations, financial reporting, and closing procedures, are often
manual, prone to error, and time-consuming. These characteristics make them ideal candidates
for RPA automation (Cooper et al., 2020). Moreover, as businesses continue to generate large
volumes of data, the ability to automate information flows and reduce dependency on human input
becomes increasingly valuable.
1.2. Motivation and Research Objective
Despite the growing attention to RPA, its application in MAC remains underexplored in academic
literature, particularly concerning its strategic role in value creation (Durão & dos Reis, 2024).
While several studies highlight the operational benefits of RPA, such as cost savings and error
reduction, fewer have analyzed how RPA contributes to broader organizational outcomes, or how
it aligns with management control objectives (Syed et al., 2019). Furthermore, challenges such
as organizational readiness, integration with existing systems, and governance risks continue to
limit the scalability and success of RPA initiatives (Asatiani & Penttinen, 2016).
Given these gaps, this review aims to examine the role of RPA in the automation of management
accounting and control processes. This study aims to contribute to a better understanding of how
RPA is reshaping management accounting and control systems and what conditions must be in
place to realize its full potential.
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1.3. Methodological Note
This review is based on peer-reviewed academic articles published in the last decade. The
sources were drawn from leading journals such as Accounting, Organizations and Society,
Journal of Management Control, and the International Journal of Accounting Information Systems,
using Scopus, Web of Science, B-on, and Google Scholar databases. The keywords used in the
literature search included “RPA”, “RPA implementation”, “Process Automation in Accounting”, and
“RPA in Controlling”. The selected studies were analyzed according to their research objectives,
methodologies, contexts, and main findings (please see Appendix A).
1.4. Structure of the Paper
Following this introduction, Section 2 describes the fundamentals of Robotic Process Automation.
Section 3 presents a thematic review of the literature on RPA applications in management
accounting and control, highlighting key findings, challenges, and opportunities. Section 4
concludes with a discussion of the study’s contributions, managerial implications, and suggestions
for future research.
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2. FUNDAMENTALS OF ROBOTIC PROCESS AUTOMATION
Robotic Process Automation (RPA) emerged in the early 2000s as a response to the persistence
of manual tasks, especially those activities requiring user input or involving multiple systems, even
within enterprises already using advanced application systems such as ERP, CRM, and BPM
systems (Scheer, 2017).
RPA enables organizations to automate rule-based, repetitive tasks using software “robots” that
interact with digital systems much like humans do (Moffitt et al., 2018; Van der Aalst et al., 2018).
Typically, RPA operates on existing user graphical interfaces (UI) of applications, requiring no
significant changes to underlying systems, distinguishing it from traditional automation
approaches, which usually require either technical or organizational adaptations. Unlike traditional
automation, RPA does not require complex coding or systems integration, usually automation is
realized on top of them and can work with legacy systems (Asatiani et al., 2022). Therefore, it is
appropriate in situations in which human labor or the use of traditional methods are too expensive
or not justified by business needs (Lu et al., 2018). RPA serves as a transition element between
human work and extensive business process automation (Van der Aalst et al., 2018).
These bots can perform tasks such as data entry, verification, data extraction, report generation,
making RPA especially useful for high-volume, time consuming, back-office functions (Aguirre &
Rodriguez, 2017). For example, a bot can autonomously open Excel, modify a spreadsheet, save
changes, and close the application.
Figure 1 provide a summary of the main meanings associated with RPA in the literature.
Figure 1 Main meanings attached to RPA in the literature.
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There is often confusion between RPA and other emerging technologies like Machine-learning or
Generative AI. While RPA focuses on structured, rules-based processes, generative AI adds
cognitive and creative capabilities (Schmitz et al., 2019). These technologies, rather than
replacing each other, complement one another, representing progressive stages in the evolution
of automation. They can be combined forming more advanced types of process automation,
called intelligent automation, increasing scope to more complex tasks and processes that demand
adaptation and judgment (Siderska et al., 2023).
Figure 2 provides a visualization of the main differences between these two types of automation.
Criterion
RPA
Intelligent Automation
Degree of standardization
High
Low
Data
Structured
Unstructured or both
Decisions
Rule-based
Knowledge/Experience-based
Outcome
Deterministic
Probabilistic
Exceptions
Demand human intervention
Trigger machine-learning
Figure 2 Differences between RPA and Intelligent Automation found in the literature.
Effective RPA deployment depends on identifying suitable process tasks. Typically, those that are
stable, rules-driven, repetitive, and require interaction across multiple systems (Farinha et al.,
2023). However, the decision process is not simple, different questions must be answered before
moving on with automation. Deciding on the correct process may be the difference between
success and failure. Researchers have proposed various methods, including robotic process
mining and surveys, to prioritize which tasks should be automated, that rate process complexity,
volume, and exception rates (Yadav & Panda, 2022). Farinha et al. (2023) proposed a
comprehensive framework for process selection for automation with 32 criteria across five
categories (structure, governance, human resources, environment and data). Figure 3 presents
six generic characteristics that potentially turns a process task highly suitable for RPA.
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Figure 3 Characteristics of RPA-suitable process tasks.
Deployment models vary with on-premises solutions offering control which allows flexibility to
increase or decrease the number of bots available at a given moment and more quickly but are
costly, while cloud-based RPA is more scalable and affordable, and hybrid models are also
common (Asatiani et al., 2023). The selection of the right deployment model depends on an
organization’s infrastructure, compliance needs, and budget.
RPA is already transforming industries such as finance, auditing, education, and customer service
by reducing operational costs and improving accuracy and speed (Rautenstrauch et al., 2024).
Despite its benefits, RPA has limitations, depending on structured data and data quality, and rising
security and privacy issues (Gotthardt et al., 2020). It performs best in environments where
processes are standardized, not subject to frequent change and requires multiple-system access
(Moffitt et al., 2018). Choosing the right approach for the automation of processes requires one
to consider many aspects, including organizational capabilities, available finances, and required
time (Flechsig et al., 2021). For sustained value, organizations must continuously evaluate which
tasks are suitable for automation and should consider how RPA fits into broader digital
transformation strategies (Kokina & Blanchette, 2019; Flechsig et al., 2021).
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3. ROBOTIC PROCESS AUTOMATION IN MANAGEMENT
ACCOUNTING AND CONTROL
Management accounting and control (MAC) involves the processes and systems used to provide
financial and non-financial information for decision-making, planning, and performance evaluation
within organizations. It supports managerial functions by linking strategy with operations and
facilitating accountability. According to Anthony and Govindarajan (2007), it is "the process by
which managers ensure that resources are obtained and used effectively and efficiently in the
accomplishment of the organization’s objectives."
3.1. Empirical Evidence from RPA Use Cases in Management Accounting and
Control
This section synthesizes key empirical findings from multiple international studies and industry
applications.
In the area of tax processing, RPA has proven particularly valuable for automating highly
repetitive, rules-based activities. Zhang et al. (2023) and Bakarich and O’Brien (2021) describe
how bots have been deployed to automate tasks such as submitting documentation to tax
authorities, calculating book-to-tax differences, applying adjustments to trial balances, evaluating
treatment options for specific transactions, and preparing tax return workbooks. These
interventions not only reduce manual workload but also enable tax professionals to concentrate
on strategic planning and analysis.
In financial operations, Szmajser et al. (2022) report substantial benefits from RPA
implementation in accounts payable workflows across Polish business service centers. Their
findings indicate that bots operating through graphical user interfaces effectively eliminate manual
data entry errors, approaching a 100% error reduction, and reinforce internal control procedures,
enhancing compliance with both internal policies and external regulations. The authors also
document considerable efficiency gains, particularly in financial closing activities.
Matthies (2020) explores the automation of management reporting and consolidation processes
in German firms, noting time savings of up to 60% in monthly report generation, successfully
aggregating data across ERP modules and significantly reducing the need for manual
intervention.
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Radke, Dang, and Tan (2020) conducted qualitative analyses in two multinational manufacturing
firms, illustrating how RPA was used to automate item master data workflows. Their study outlines
a model with three stages for deployment - initiation, structured automation, and continuous
improvement - reporting a 50% reduction in processing time and significant decreases in data
entry errors. Similarly, Perdana and Arisandi (2022) evaluated RPA deployment at Truveil, a toy
company, highlighting how detailed process mapping and stakeholder engagement informed the
automation of master data updates, leading to a 45% reduction in data errors and a 55%
improvement in processing speed.
In cost management, Kim (2020) designed an RPA prototype to automate cost center allocations
and variance analysis, demonstrating a 40% reduction in report preparation time and improved
the traceability of cost flows through standardized allocation procedures.
RPA has also been applied effectively in internal control systems, with Harrast (2020)
investigating the integration of RPA into control self-assessment activities, particularly focusing
on control testing tasks such as invoice matching and approval verification. The study found that
automation led to greater consistency in control execution, though it also highlighted the need for
human oversight in nuanced or judgment-intensive scenarios.
Korhonen et al. (2021) present an interventionist case study from a Finnish manufacturing
company where RPA bots were employed to extract historical data, run predictive models, and
populate budgeting templates. This use of automation not only accelerated the budgeting cycle
but also allowed management accountants to redirect their attention to variance analysis and
strategic scenario planning.
Bhardwaj et al. (2024) conducted a comparative study across four invoice processing scenarios,
analyzing performance differentials between RPA bots and human operators. Bots achieved
100% accuracy and completed tasks 85% faster, compared to manual processes with error rates
ranging from 3–5%.
Beyond operational applications, Szmajser et al. (2022) provide a comprehensive breakdown of
RPA cost structures, distinguishing between one-time costs, such as development, testing, and
documentation, and recurring expenses, including licensing, IT maintenance, and infrastructure.
For assessing financial performance is necessary to calculate return on investment (ROI) and
compute total cost of ownership. In this study, while accounts payable automation produced a
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two-month payback period with a 75%-time savings, tax process automation failed to yield
positive returns, probably due to task complexity.
Perdana et al. (2023) evaluated four audit process automation pilots across Big Four and midsized consulting firms. Their findings indicate development cycles of 2–4 weeks per bot with
average payback periods of approximately three months, and user satisfaction ratings exceeding
85%. These metrics suggest that when properly selected and governed, RPA implementations
can deliver fast and tangible business value.
In sum, monetary outcomes are not the only dimensions that matter, both qualitative and
quantitative performance indicators should be considered when assessing RPA projects. These
include improvements in compliance, employee satisfaction, and data integrity. Accordingly,
organizations must define clear, realistic goals at the outset of implementation and approach
automation as a strategic transformation, not merely a cost-cutting initiative.
3.2. Benefits, Risks, and Organizational Considerations in RPA Adoption
This section lists recent empirical findings on the advantages and limitations of RPA, the
conditions that shape its implementation, and the broader organizational impacts.
3.2.1. Benefits of RPA
RPA offers significant benefits at operational, financial levels, and strategic domains, with several
studies trying to quantify these effects.
Cost and Time Savings: RPA reduces labor requirements and overtime costs by replacing
manual data entry, reconciliation, and reporting tasks with automated scripts. For example, bots
used in accounts payable and employee loan processing have cut task times by 30–60%, often
achieving return on investment (ROI) within three to six months (Szmajser et al., 2022).
Error Reduction and Data Accuracy: The risk of human error is significantly reduced through
automated controls executing pre-programmed scripts ensuring consistency and precision,
resulting in higher data integrity and improved reliability in financial reporting and compliance
activities (Harrast, 2020; Bhardwaj et al., 2024).
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Scalability and Flexibility: Unlike human labor, bots can be scaled instantaneously in response
to workload fluctuations and seasonality, such as month-end closes or audit cycles, enhancing
organizational agility without necessitating workforce expansion (Durão & Reis, 2024).
Improved Compliance and Audit Readiness: Bots generate detailed and consistent digital
registers of their activities, providing standardized audit trails and simplifying regulatory reporting,
strengthening internal controls and reducing compliance costs (Radke et al., 2020).
Organizational Efficiency and Value Creation: By reallocating human resources from
transactional tasks to analysis and decision support, RPA fosters innovation and strategic value
generation. Firms can redeploy time and effort toward business steering, scenario planning, and
predictive analytics (Korhonen et al., 2021). Additionally, financial savings can be used to invest
in more automation or in other areas of the company (Durão & Reis, 2024).
Enhanced Employee Satisfaction: The reduction of monotonous and low-value tasks has been
linked to higher employee morale and motivation, with staff being empowered to engage in more
meaningful, analytical work, increasing overall job satisfaction (Durão & Reis, 2024).
Technology Synergies: RPA lays the groundwork for future enhancements through intelligent
automation. Since, once core workflows are automated, organizations can integrate AI and
machine learning for unstructured data processing, exception handling, and predictive forecasting
(Durão & Reis, 2024).
3.2.2. Risks, Challenges, and Governance Constraints
Despite its advantages, RPA is not without risks. In the case of poor implementation, lack of
oversight, or inappropriate process selection can undermine the intended benefits and introduce
new vulnerabilities.
Cybersecurity and Data Privacy: RPA bots often store credentials and process sensitive
financial information, what without robust encryption, access control, and monitoring, these
systems can become exposed to cyber threats (Eulerich et al., 2022).
Process Rigidity and Legacy Issues: When RPA is used to automate outdated or poorly
designed processes, inefficiencies are inadvertently entrenched, with subsequent process
changes may requiring complete bot re-engineering (Gotthardt et al., 2020).
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Governance and Oversight Complexity: Decentralized RPA initiatives often lead to “shadow
bots,” undocumented logic, and uncontrolled proliferation of scripts (Eulerich et al., 2022). A
centralized Center of Excellence (CoE), version control, and formal exception-handling
frameworks are critical to long-term sustainability (Schulze & Nuhn, 2020).
Misalignment of Skills and Resistance to Change: Accountants often lack the technical skills
needed to maintain and configure bots, while IT personnel may not fully understand accounting
workflows. Additionally, fear of job displacement may lead to resistance among staff (Cooper et
al., 2020; Tiron-Tudor et al., 2023).
False Expectations of ROI: While RPA is often marketed as a quick solution with high returns,
some processes may not yield significant savings due to complexity or low volume. In such cases,
the costs of implementation and ongoing maintenance may outweigh the benefits (Szmajser et
al., 2022).
Overreliance and Knowledge Loss: As RPA takes over manual tasks, there is a risk that tacit
knowledge embedded in human judgment and informal practices is lost, making organizations
more vulnerable in the event of system failures or process exceptions (Penttinen et al., 2023;
Eulerich et al., 2022).
3.2.3. Organizational Drivers, Enablers, and Human Capital Impacts
Successful RPA adoption is driven not solely by technical feasibility but also by organizational
readiness, strategic alignment, and cultural factors.
Leadership Commitment and Strategic Vision: Empirical studies show that senior
management’s perception of RPA as a strategic enabler correlates positively with adoption
outcomes (Cooper et al., 2020). RPA should be positioned not as a cost-cutting tool but as part
of a broader digital transformation strategy.
Process Characteristics and Data Quality: The effectiveness of RPA depends heavily on the
nature of the processes being automated, with standardized, rule-based, and high-volume tasks
with clean data inputs are the most suitable candidates (Gotthardt et al., 2020; Bhardwaj et al.,
2024).
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Cross-Functional Collaboration: Collaboration between finance professionals (who understand
the business rules) and IT developers (who build and maintain the bots) is essential to ensure
that automation aligns with operational realities and is technically robust (Schulze & Nuhn, 2020).
Skill Development and Cultural Transformation: The rise of automation necessitates new skill
sets, including digital fluency, analytical thinking, and cross-disciplinary communication. RPA also
reshapes organizational culture by embedding a continuous improvement mindset and fostering
openness to technological change (Durão & Reis, 2024).
Continuous Improvement and Feedback Loops: Rather than treating automation as a onetime project, leading firms approach it as an iterative process, with bots being monitored, refined,
and adapted in response to changing business requirements, and supported by strong feedback
mechanisms (Radke et al., 2020).
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4. CONCLUSION
This review demonstrates that RPA has moved beyond a mere cost saving tool to become a
strategic enabler in MAC, enabling significant efficiencies in transactional processes and freeing
professionals to engage in higher value analytical work. Empirical evidence shows dramatic
improvements, up to 100% error reduction in invoice processing, 60%-time savings in financial
reporting, and payback periods as short as two months, when RPA is applied to high volume,
standardized tasks. However, the full potential of RPA hinges on sound governance, including a
centralized Center of Excellence, clear exception handling protocols, and integration with
emerging AI capabilities for unstructured data handling. Organizational readiness, including
leadership vision, data quality, skills development, and cross disciplinary collaboration, is as
crucial as technology selection for RPA successful deployment.
Contributions and Future Research
Theoretical Contribution: By synthesizing diverse case studies, this review extends the
understanding of RPA’s value dimensions, operational, financial, and strategic, in MAC contexts.
Managerial Implications: Practitioners should adopt structured process selection frameworks,
invest in governance infrastructures, and foster a culture of continuous improvement to maximize
financial and non-financial ROI.
Future Research Directions:
•
Longitudinal Studies to assess RPA’s sustained impact on performance and workforce
dynamics. It's important to understand not just the immediate changes, but how these
shifts develop and settle in the workplace.
•
Exploring how RPA and AI integration forming Intelligent Automation transform and impact
decision support and predictive analytics.
•
Examining how roles are evolving and which upskilling pathways best support a digital
workforce through these changes.
•
How to balance and agility and control for effective Governance Frameworks, particularly
in highly regulated industries like accounting and auditing.
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April 2025
APPENDIX
Appendix A - Summary of research methods, objectives, context of analysis and main conclusions from select papers.
Paper (Citation)
Research Method
Research Objective
To explore the implementation of RPA in accounting firms,
focusing on benefits, challenges, and opportunities in audit
automation
To assess the effectiveness of RPA in structured data
extraction compared to manual processes
To investigate the value creation mechanisms of RPA in
organizations
To identify and analyze cybersecurity risks associated with
RPA implementation
Context of Analysis
Main Findings / Conclusions
RPA enhances efficiency and accuracy in audit processes; challenges
Accounting firms (Big 4 and mid-sized) include integration and change management; provides insights for broader
RPA adoption in accounting
Data extraction from invoices and similar RPA significantly reduces processing time and errors in structured data tasks,
documents
demonstrating high efficiency and reliability
RPA enhances internal operations and customer satisfaction; limited impact on
Various firms implementing RPA
upstream activities; improves efficiency, quality, and employee motivation
RPA implementations across various
Highlights potential cybersecurity vulnerabilities in RPA, emphasizing the need
sectors
for robust security measures during implementation
Public accounting firms
(Perdana, Lee & Kim, 2023)
Case study
(Bhardwaj et al., 2024)
Experimental study
(Durão & Reis, 2024)
Qualitative interviews
(Eulerich, Ocker & Müller, 2022)
Systematic literature review
(Cooper et al., 2019)
Survey and interviews
To examine the adoption and impact of RPA in public
accounting firms
(Kokina & Blanchette, 2019)
Case study
To explore the initial impact of RPA on accounting practices
(Flechsig et al., 2021)
Multiple case studies
To identify potentials, barriers, and best practices in RPA
implementation within purchasing and supply management
(Siderska et al., 2023)
Literature review
To analyze the evolution of RPA towards intelligent
automation, identifying challenges and future trends
RPA developments across industries
(Szmajser et al., 2022)
Quantitative survey (n=162) with
statistical analysis (Spearman's
rho, Kruskal-Wallis test); financial
simulation
To assess the effectiveness of RPA implementation in
accounting and financial processes from practitioners'
perspectives
Business Process Outsourcing (BPO), IT RPA enhances efficiency in financial and accounting services, particularly in
Outsourcing (ITO), Shared Service
Accounts Payable. Implementation leads to reduced workload, minimized
Centers (SSC), consulting/advisory
errors, and increased operational effectiveness. Financial simulations indicate
firms, and their clients in Poland
significant cost savings and quick payback periods in certain processes.
(Cooper et al., 2020)
Survey of Big 4 stakeholders
Assess perceptions and readiness for RPA in accounting
firms
Big 4 professional services
(Gotthardt et al., 2020)
Cross‑sectional survey
Identify process characteristics affecting RPA success
Global pilots in accounting & audit
(Schulze & Nuhn, 2020)
Case study (IGC firms)
Analyze governance requirements for sustainable RPA
International Group of Companies
(Tiron‑Tudor et al., 2023)
Qualitative interviews
Investigate training needs for RPA in accounting curricula
Accounting educators and practitioners
(Matthies, 2020)
Multiple single‑case study
Measure time savings in management reporting
Four German manufacturing firms
(Radke, Dang & Tan, 2020)
Two qualitative case studies
Automate item master data maintenance process
Two global manufacturers
(Kim, 2020)
Prototype development & pilot test
Automate cost center allocations and variance analysis
Mid‑size electronics firm
(Harrast, 2020)
Exploratory case study
Integrate RPA into control self‑assessment
Large retail corporation
(Korhonen et al., 2021)
Interventionist case study
Support budgeting & forecasting via RPA
Finnish manufacturing company
(Parker et al., 2021)
Multiple case studies & thematic
analysis
Explore holistic success factors in RPA roll‑outs
Two global accounting functions
Accounting departments implementing
RPA
19 organizations in public and private
sectors
RPA adoption in public accounting is growing; benefits include increased
efficiency and reduced errors; challenges involve integration and staff training
RPA automates routine tasks, allowing accountants to focus on higher-value
activities; early adoption shows promise in efficiency gains
RPA offers significant efficiency gains; barriers include technical challenges
and organizational resistance; success depends on digital readiness
RPA is evolving towards intelligent automation; challenges include integration
complexity and workforce adaptation; future trends point to increased AI
integration
Leadership sees strategic value; staff worry about job loss and lack
change‑management clarity
Highly standardized, high‑volume processes realize largest efficiency gains;
poor data quality impedes bots
Coherent ownership, maintenance budgets, and control frameworks critical to
avoid “orphaned” bots
Current programs under‑equip students; call for integration of RPA skill
modules
Monthly report turnaround reduced by up to 60% through automated data
aggregation
50% reduction in processing time and major error‑rate decline; advocates
continuous improvement phase
40% faster cost report preparation; improved transparency via standardized
allocation rules
Automated control testing improved consistency, but human oversight remains
essential in complex scenarios
50% reduction in budgeting cycle time; controllers reallocated effort to
variance interpretation and strategy
Workforce impact, IT governance, security, sustainability, and ROI
measurement are critical themes
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