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Enhancing process automation with AI: The role of intelligent automation in
business efficiency
Abhaykumar Dalsaniya 1, * and Kishan Patel 2
1 Architect, Intelligent Automation.
2 Sr QA Engineer, USA.
International Journal of Science and Research Archive, 2022, 05(02), 322–337
Publication history: Received on 03 March 2022; revised on 13 April 2022; accepted on 15 April 2022
Article DOI: https://doi.org/10.30574/ijsra.2022.5.2.0083
Abstract
This article discusses the combination of Artificial Intelligence (AI) with Robotic Process Automation (RPA) , that is,
Intelligent Automation (IA), a significant improvement in process automation. Traditional RPA, developed to operate in
simple routine tasks, only meets great challenges in accurately performing tasks in complex business settings. Thus,
leveraging concepts like ML, NLP, and cognitive automation, IA allows organizations to automate flexible, smart,
selective, and able-to-learn processes. To do this, this paper explores how AI optimizes RPA, making the process more
flexible, and decides the impact that IA will have on business efficiency. In this section, we explain how IA can modify
operational outputs, minimize expenditures, increase efficiency, and improve decision-making in different fields based
on case studies, research findings, and real examples. Furthermore, the research looks into the possibility of IA in
handling novelty, high-automation activities, and the impact on the adaptable competitiveness of an organization.
Keywords: RPA; Intelligent Automation; Cognitive automation; AI; IA; Machine Learning; Natural Language
1. Introduction
Robotic Process Automation (RPA) has quickly become the disruptive technology for automating straightforward,
repetitive, and rule-based business processes (Lacity & Willcocks, 2016). Using SRS, which mimics human conversations
with digital systems, organizations can optimize processes, decrease mistakes, and save millions (Aguirre & Rodriguez,
2017). RPA works without interfering with the applications/ system infrastructure. Its ability to work across multiple
systems quickly is an advantage, given that the tool relies on pre-scheduling the bots to work in an organization. The
brief time before the bot produces value makes RPA an attractive proposition for implementing business automation
tools (Asatiani & Penttinen, 2016).
However, traditional RPA exists within the context of rigid and clear processes, and it cannot apply discretion in
analyzing unstructured data, nor does it have the ability to make conventional business decisions (Chen & Lin, 2014).
AI technologies have been introduced to complement RPA because of this limitation and enhance its features. Artificial
intelligence (AI) refers to a computer program's explicit capacity to learn from data, classify it, and generate wise
conclusions based on the facts. Analytical AI is sui generis from machine learning (ML), natural language processing
(NLP), and cognitive computing. Nonetheless, AI makes automation systems capable of processing large amounts of
unstructured data, interpreting differences in natural language, and creating and learning. This ability allows
automation systems to perform tasks that exceed the limits of simple automatization (Madakam, Holmukhe, & Jaiswal,
2019).
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The combination of RPA and AI makes up Intelligent Automation (IA), where AI-driven solutions provide flexible
decisions in an automated process (Grover, 2019). IA makes it possible to complete workflows end-to-end through
cognitive skills to manage exceptional scenarios, learn from new situations, and meet business changes (Vaidya et al.,
2018). For instance, in the financial industry, IA can help the organization minimize fraud risks by developing an ML
algorithm that can recognize changes in transaction patterns (Suri et al., 2018). In customer service, IA is used in natural
language processing to comprehend consumer questions and provide answers that will increase productivity and
customer satisfaction (Fernández & Aman, 2018).
With the help of IA, the advantages over traditional RPA are achieved through increased efficiency, flexibility, and
business competitiveness in constant change (Madakam et al., 2019). The insertion of AI into process automation
belongs to a new level of considerably more sophisticated and adaptive systems within organizations capable of upbeat
organizational processes and operations.
1.1. Overview
AI improves business automation through the utilization of machine learning (ML), natural language processing (NLP),
and cognitive automation (Davenport & Kirby, 2016). Artificial intelligence must use machine learning to design
automation processes that can learn from the data independently and adapt over time without necessarily following a
programmed command; this is especially used when tasks involve pattern recognition or predictive analysis (Russell &
Norvig, 2010). For instance, ML algorithms can work through numerous datasets and performances to give a pattern
that enables them to make their own decisions, particularly in fields such as finance and marketing (Goodfellow, Bengio,
& Courville, 2016).
The idea of NLP is to enable machines to understand and process human language since many tasks involve language
processing, such as customer support and data analysis (Jurafsky & Martin, 2009). Using NLP in business solutions,
companies can use chatbots and virtual personnel to extract customer information and deliver precise answers,
improving specificity and satisfaction (Hirschberg & Manning, 2015).
Cognitive automation interacts with multiple AI technologies that imitate human mental abilities to solve different
problems and make decisions in decision-making issues (Wang & Li, 2007). This involves combining process, data, and
knowledge management with ML and NLP, endowed with the ability to reason and deal with unstructured data,
understand contextual information, and act in changing situations (Liebowitz, 2001). Cognitive automation increases
the automation capability of an organization not only to routine tasks with high dimensions of rule-based character but
also relates more to intelligence in tasks completing (Vaidya, Ambad, & Bhosle, 2018).
On this basis, AI bolsters regular automation with ML, NLP, and cognitive automation as smart systems responsive to
challenging business circumstances (Davenport & Kirby, 2016). To the above, it improves business by complementing
other systems’ ability to learn, reason, and execute decisions, boosting the efficiency and creativity of most economic
sectors (Russell & Norvig, 2010).
1.2. Problem Statement
Although RPA has gained vast popularity in business processes, many companies need help with extensive challenges
in automating complex and non-automated business tasks. This technology is most suitable for automated, routine, and
structured processes such as data entry, accounts payable and receivables, or simple client support. However, it remains
rather inflexible, as it fails to accommodate decision processes, unstructured information, or information requiring
some concept recognition. Therefore, organizations can face difficulties expanding RPA beyond end-to-end processing
in pre-scripted tasks and need help applying RPA in more complex and uncertain data entry scenarios. This situation
has propelled an increased desire to incorporate artificial intelligence technologies into automation systems because
they can help the systems study data, work out problems, and solve novel issues. Fortunately, extending RPA with AI
that gives Intelligent Automation (IA) is essential to eliminate these drawbacks and the future enhancement of the
transactions’ productivity and flexibility.
1.3. Objectives
To understand what proportion of enterprises’ processes pertains to complex, non-repetitive tasks that default
automation instruments, AI, and RPA integration cannot cover.
To group them according to key functions that suggest industries and business functions in which IA has the
potential for large value.
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To explore the impact of Intelligent Automation (IA) on improving business efficiency and optimizing
operational outcomes.
To assess how integrating AI and RPA can address complex, non-repetitive tasks that traditional automation
struggles to manage.
To discover which industries and organizational functions are most likely to benefit from IA and experience
high levels of IA process improvement and innovation.
To assess the threats and opportunities with using IA and put into careful consideration the associated ethical
issues and displacement of the workforce. Potential issues and threats of Intelligent Automation such as ethical
issues, emerging as a risk for displacing employees.
1.4. Scope and Significance
This area of research is interested in understanding the change management of IA – Intelligent Automation for process
automation across industries and businesses. This research will further examine how new-age RPA tools, particularly
ML and NLP, enhance the basic RPAs so that various firms can automate intricate and evolving processes that were
previously manual. Therefore, the research will focus on sectors of operation and functional areas showing the greatest
value from implementing IA.
The wider issue of IA adoption will be examined, whereby embracing the technology will likely bring benefits such as
cutting costs, increasing efficiency, and improving the quality of customer responses through automation. IA also
includes the opportunity to work with the amount of unstructured information, which subsequently helps businesses
become more flexible in responding to changes in the market. Moreover, the study will discuss the advantages of IA
implementation in the long run: Improved efficiency, capacity, and competitiveness for business; shortcomings, such as
high implementation cost; and destruction of jobs via automation. The findings of this study will be useful to the
intending organizations that still deem the future use of AI-based automation as a competitive tool for enhancing their
operations.
2. Literature review
2.1. Traditional Robotic Process Automation (RPA) and Its Limitations
Robotic process automation (RPA) is a type of Microsoft technology that uses software robots to mimic defined human
activities when operating different computer systems to perform business processes (Amenta, 2018). It will eliminate
time-bound, rule-based exercises that cut across different applications without interfering with the systems, thus
increasing efficiency and reducing human errors (IEEE Corporate Advisory Group, 2017). In the financial services
industry, for instance, RPA has been applied to work on data input, account balancing, and remote transactions, which
has led to sweet operational savings (Aguirre & Rodriguez, 2017).
The first benefit highlighted with traditional RPA is the improvement of efficiency through constant working, that is, by
working when people are off duty or lack concentration, and the fast processing speeds (Willcocks, Lacity, & Craig,
2015). Furthermore, RPA is relatively non-intrusive as it operates at the presentation layer level, and robots do not have
to know how applications and systems are implemented to interact with them; therefore, RPA tools can be implemented
much faster than other automation tools (Anagnoste, 2017).
However, RPA has certain inseparable constraints that limit its possibilities. Initial RPA mainly targets the end-to-end
automation of regular and reworkable information-heavy business operations. It cannot process text information, for
example, in emails or other forms of original document scans, which reduces its applicability to slightly more
complicated scenarios (Hallikainen, Bekkhus & Pan, 2018). Also, RPA systems do not have learning capability; they can
only handle processes or exceptions change if programmed; hence, they are difficult to maintain when there is a change
in the business process (Lacity & Willcocks, 2016).
Unfortunately, there is also the problem of extending the usage of RPA solutions within an organization. With the rise
of function execution autonomy, the coordination and monitoring of multiple bots expand in difficulty, which may be
solved by extra supervision measures (Madakam, Holmukhe, & Jaiswal, 2019). In addition, RPA was designed without
cognitive functions, meaning that it cannot reason or analyze context; it excludes all the tasks that require some measure
of judgment and understanding (IEEE Corporate Advisory Group, 2017).
With RPA implementations come security and compliance issues. As bots can obtain account data and perform rigorous
activities, it is very important to ensure they obey the fundamental security regulations and legal compliance
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(Anagnoste, 2017). If such things are not well managed, then some risks accompany the automation system that
counters the benefits of the automation system.
Figure 1 An image illustrating the Limitations of RPA
2.2. Overview of Artificial Intelligence (AI) in Automation
Today, automation means artificial intelligence (AI) automation. This concept brings humans and machines closer
because the latter is able to execute elements that are believed to be inherently human, including learning, reasoning,
and problem-solving (Bird & McAfee, 2014). Integrating AI into automation systems results in far more complex and
versatile processes than rule-based automation (Nilsson, 1998).
Brynjolfsson and McAfee (2014) put into perspective how AI technologies are causing another “Second Machine Age”
in which digital tools equal more efficiency than ever before. They further affirm that with artificial intelligence,
automation can capture and analyze complex work, manipulate unstructured big data, and make instant decisions.
These feats have been out of reach when using traditional automation systems (Brynjolfsson & McAfee, 2014).
Artificial intelligence, of which machine learning is a branch, is how systems, without being directly programmed,
increase their capability to recognize patterns and trends and respond to them correctly. This is particularly important
in automation so that a machine can adapt to new patterns and irregularities in data to improve the accuracy and
reliability of operations such as fraud detection or predictive asset failure (Bishop, 2006). For ample time, in the
financial industry, machine learning algorithms can monitor transactions in an organization or any other business to
detect unusual activities, thus performing complicated monitoring tasks automatically (Bishop, 2006).
The other important feature is natural language processing NLP because it enables machines to read and analyze human
language (Jurafsky & Martin, 2009). NLP is also used in automated customer service by availing of chatbots and virtual
assistants who address customer inquiries and attend to their concerns and complaints, previously requiring the direct
involvement of customer care representatives and far longer response time (Jurafsky & Martin, 2009). It has also been
useful in smoothing large customer relations transactions.
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Interpreting visual information also makes up the other part of automation known as computer vision (Szeliski, 2010).
In pr duction, AI vision systems detect flaws in products employing vision rather than human inspectors, increasing
quality assurance and minimizing physically flawed goods (Szeliski, 2010). Such systems may not get Fatigue, enhanced
throughput, and standardization of the production lines.
Including AI in the automation process improves the amount and quality of work done and opens up opportunities for
business model creation of new product or service delivery propositions (Brynjolfsson & McAfee, 2014). However, as
has been witnessed, this advancement is accompanied by risks such as essential investments in technology and creation
and effects on the employment market because machines substitute jobs that used to be done by humans (Autor, 2015).
Accomplishing these solutions demands a proper plan to place advanced technology into its valuable socio-economic
framework.
2.3. Intelligent Automation (IA) and Its Key Components: ML, NLP, Cognitive Automation
Intelligent Automation (IA) is the next step in the development of automation, which turns to combining Artificial
Intelligence (AI) in coordination with automated Robotic Process Automation (RPA) to work on more complex and
cognizant activities and functions (Grover & Kar, 2017). IA integrates specific AI activities that include Machine Learning
(ML), Natural Language Processing (NLP), and cognitive automation into automation systems, which go beyond logical
rules and process data.
Machine Learning (ML) allows systems to automatically optimize their capabilities based on usage and give increasingly
accurate results without requiring ‘teaching’ or hard-coded instructions (Samuel, 1959). Due to ML algorithms, IA
systems can help analyze big data to extract patterns and trends or make predictions based on new data (Mitchell,
1997). This enables automating activities such as acknowledging fraud, analyzing customer behavior, and self-
organizing to predict repair and maintenance work (see Bishop, 2006). For I stance, ML can help identify new trends of
customer sentiments in the text of the feedback given to businesses to act on.
NLP is the computational understanding and processing of natural languages, making human-to-machine interfaces
more intuitive (Jurafsky & Martin, 2009). Regarding IA, NLP allows automation systems to introduce part-processing
textual information, including emails, formal documents, and social networking posts (Grover & Kar, 2017). Such
capability will enable businesses to perform tasks including customer support, sentiment analysis, and document
classification, which makes the company more efficient and customer-oriented (Chowdhury, 2003).
Cognitive automation is a technique that incorporates elements such as machine learning, natural language processing,
etc., to make them act like human beings in decision-making (Liebowitz, 2001). Wang and Li further suggest that it
allows IA systems to assess context, make choices, and learn; therefore, control complicated undertaking which
demands judgment and reasoning. Cognitive automation enables capabilities like identifying legal issues that need to
be addressed, supporting reaching a diagnosis in the medical field, and making strategic decisions (Grover & Kar, 2017).
For ample time, in the healthcare sector, cognitive automation can be used to interpret images and come up with
diagnoses that may be available to doctors.
IA integrates all these components, making it far more complete than a conventional RPA tool. The above characteristics
revert to handling and analyzing loose data in a flow, managing exceptions, and influencing an environment, all of which
are essential to enhance operational efficiency and flexibility (Grover & Kar, 2017). Higher automation is possible
through IA, and the requirements of tightly coupled systems become less of a problem; considerable benefits and
competitive advantage can be gained (Lacity & Willcocks, 2018). However, the implementation of IA also has issues like
increased initial cost, requirement of professional personnel, concerns over data security, and ethical issues associated
with it (Russell & Norvig, 2010).
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Figure 2 An image illustrating the Key Components of Intelligent Automation (IA)
2.4. Enhancing Process Efficiency with AI-Driven RPA
AI-RPA heavily enhances the process by using straightforward RPA techniques to handle earlier unattainable work, thus
changing the process (Ford, 2015). AI-enhanced RPA adds artificial intelligence, such as machine learning and natural
language processing, to make intelligent decisions when confronting unstructured data beyond conventional RPA
capability (Lacity & Willcocks, 2018). This integration enables organizations to streamline the various chain phases and
functions, hence cutting costs while at the same time enhancing precision and speed, as opined by Deloitte in 2017.
Therefore, Ford Williams (2015) states that AI and robotics are redesigning industries by automating processes that
require high cognitive functions and enhancing productivity. Sovereign RPA can teach from the stream of data feed,
modify future action, and decide independently, sometimes with zero outside help (Ford, 2015). For instance, in the
manufacturing industry, robotic systems integrating with AI can help alter flexibility and avoid inconsistency (Bauer et
al., 2016).
In addition, AI RPA improves customer service by using human language interaction and responses since it involves
natural language processing. Natural language processing helps the bots understand the inquiries that customers make
and respond properly in the shortest time possible (Huang & Rust, 2018). This kind of automation was impossible with
first-generation RPA, which can only automate purely structured and rule-based processes.
Another aspect of the financial industry that AI positively impacts is RPA, which automates several works, including
fraud detection and compliance checks (Deloitte, 2017). Big data helps make profitable decisions by using complex
algorithms that help in the computation of a large number of datasets and detecting any irregularities in them so that
managers can develop a preventative strategy to mitigate against risks that may lead to financial loss (Deloitte, 2017).
It served to simplify and optimize the management’s work and helped analyze data that contributed to forming a
successful business strategy.
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However, there are issues with AI-RPA: Major capital expenditures are required for AI-RPA, and employment changes
are also a concern (Ford, 2015). Managers should be conscious of the escalating morality and social aspects resulting
from the automation of many mental roles customary to humans (Brynjolfsson & McAfee, 2014). Nevertheless, this gives
the AI-driven RPA value to any organization in today’s digital markets by ensuring efficiency improvement, cost cutting,
and service delivery improvement.
2.5. Case Studies on the Impact of Intelligent Automation in Business
Intelligent Automation (IA) has enhanced business across various industries through automation involving artificial
intelligence (AI) technology, which has resulted in innovative changes (Lacity & Willcocks, 2016). An example may be
Telefónica O2, a UK-based Telco that used IA to support a large-scale back-office automation program (Lacity &
Willcocks, 2016). In a business relationship surveyed by Lacity and Willcocks (2016), Telefónica O2 reduced operational
costs by 20% while improving service delivery times by adopting an AI-RPA-powered solution for order processing and
customer account management. This automation enables the company to accept and process orders at what customers
may consider odd times Thu, improving satisfaction.
Another example is Xchanging, a business processing and technology services provider firm that applies IA to solve
insurance claims processing problems, as explained in the next section with Lacity & Willcocks (2016). To use machine
learning algorithms, Xchanging has accelerated data extraction and validation of data from unstructured documents by
30%. This was attributed to substantial cost improvements as employees were left to perform more tasks that needed
human intervention.
In banking, IA was employed at JPMorgan Chase with Contract Intelligence (COiN), an AI system for scanning through
legal papers and identifying key facts (Davenport & Ronanki, 2018). This automation shaved an estimated 360,000
hours of lawyer-time usage annually down to a couple of seconds, thereby minimizing the time and errors that occur
with human input.
IA has been adopted by the healthcare provider Anthem to identify problems within the claims processing and customer
service domains (Davenport & Ronanki, 2018). Artificial intelligence's roles included intelligent conversational agents
boosting customer relations, minimizing overhead costs, and prompt services processing by Anthem Inc. The IA
implementation made it easy to work towards developing the right approach to claims processing while improving the
customers' experiences to get optimum results.
Through these case studies, we see how IA can allow organizations to automate and improve workflow, leading to more
operational efficiencies and business advantages that give a competitive edge to its customers (Lacity & Willcocks,
2016). Implementing IA depends on carefully planning which processes should be automated and providing an
adequate technological platform.
2.6. Business Efficiencies Realized Through Intelligent Automation
IA, or intelligent automation, merges artificial intelligence technologies with the automation of business processes,
which has proved to provide great business value, raise productivity standards, and cut costs (McKinsey & Company,
2018).McKinsey Global Institute also found that automation could boost annual productivity growth rates by 0.8-1.4ppa
and contribute to economic growth, mitigating the effects of an aging population.
This is an area of efficiency made possible by IA, where efficiencies are gained due to the round-the-clock operation
achieved with little fatigue, as may be the case with human crews (Manyika et al., 2017). IA in manufacturing helps
predict equipment breakdowns through sensor data on manufacturing equipment. IA lowers downtime by 50% and
cuts maintenance expenses by 10% (McKinsey & Company, 2018). This preventive measure improves the functionality
of processes and also lasts the durability of equipment.
IA propels new supply chain competence in retail by achieving optimal stock and logistics fixtures using accurate AI
algorithms for the forecasted demands (Manyika et al., 2017). This will lead to decreased inventory costs, and customers
will always be satisfied because their favorite products will always be on the shelf. For example, Amazon applied IA to
organize huge stocks with low holding costs and fast delivery.
Commercial banks and other financial institutions benefit from IA by automating compliance and risk management
activities. Real-time tracking and analysis of transactions by AI-based systems coupled with automatic detection of fraud
and compliance is much more effective than traditional methods (McKinsey & Company, 2018). It also helps cut
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operational costs while decreasing the dangers of failing to adhere to legal requirements and improving the
organization's image.
Another area has been customer service operations, where IA has brought efficiency. Chatbots and virtual assistants
help answer frequently asked customer questions, thereby decreasing the load on employees and increasing the speed
of response provided by AI (Huang & Rust, 2018). This leads to better customer experiences and enables staff to work
more on more evident customer issues. Many firms, such as Uber and Spotify, are current examples of organizations
that have adopted AI-based customer support to tackle numerous customer interactions.
On a global scale, IA advances business benefits by increasing the precision of processes, decreasing the cost of
operation, and expanding the capability of being scaled out (McKinsey & Company, 2018). The companies that use IA
can provide additional value to the industry and spread innovation and economic development among the companies.
Figure 3 An image illustrating Business efficiencies realized through Intelligent Automation (IA)
2.7. Enduring Issues and Concerns Surrounding Efficient Integration of Intelligent Automation
However, the automation of skills has the following consideration and ethical issues that organizations should bear in
mind as they adopt the concept of Intelligent Automation (IA): In our case, one of the challenges that can be catalyzed
by integrating technologies in the workplace is the risk of job losses due to automation of tasks that used to be done by
workers (Jobin et al., 2019). Tom Peters highlights job insecurity as one of the major reasons employees will fight change
tooth and nail while the change may positively impact workforce morale. Due to automation, McKinsey & Company
stated that 375 million employees globally might need to transfer their occupational groups by 2030.
Data privacy and security concerns remain significant ethical implications of employing this new technology in health
care. Most IA systems require large databases with details such as people’s identities to work optimally (Jobin et al.,
2019). However, ensuring that the data cannot be hacked and misused is still necessary. Therefore, many organizations
require policies to ensure continued customer trust and, most importantly, to deal with laws governing data like GDPR.
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The fourth ethical issue that can be easily distinguished is the bias in the AI algorithms. Thus, AI automation processes
can replicate or deepen existing inequalities if the training data containing the general knowledge is biased (Mittelstadt
et al., 2016). For instance, hiring algorithms may be developed in a certain biased manner, resulting in employment
discrimination. This will require appropriate data sets to be used in training the AI models and continuous checks for
occurrences of bias in the models developed.
They include the demand for explaining how an AI system made a specific decision. Managers may want to justify the
choices of AI technologies introduced in automation, especially in the financial and health sectors (Mittelstadt, 2016).
However, if these models are not explainable or interpretable, we can face issues with the outcome of the decision-
making process or even an error. It would be helpful if organizations built XAI systems to increase trust effectively and
resulting accountability (Jobin et al., 2019).
Legal and regulatory requirements are another implementation attribute that proves difficult in IA. With the growth of
AI technologies, new issues may not need to be regulated sufficiently in time, creating ambiguity for organizations (Jobin
et al., 2019). Organizations are therefore advised to learn new rules and regulations regarding the use of IA to be on the
right side of the law.
Furthermore, it shows that integrating IA into existing systems is technically challenging and expensive. This involves
developing technological and qualified human resources (Davenport & Kirby, 2016). Some potential challenges to
implementing IA include relating IA activities to strategic business plans and Measuring value addition, which often
creates many challenges for organizations.
As a way forward, organizations need to consider using ethical guidelines and frameworks in deploying artificial
intelligence. As Jobin et al. (2019) pointed out, there is a rising global agreement on the major ethical imperatives of
technology: openness, equity, no harm, accountability, and data privacy. This way, the firms dealing with IA can avoid
the potential risks and spur responsible use of this technology.
Discoursing with employees, customers, and regulators is also necessary to implement IA. It can also assist in clarifying
concerns and the achievement of fairness so that IA initiatives consider the interests of all the relevant stakeholders
(Cath et al., 2018).
3. Methodology
3.1. Research Design
This research work's findings use qualitative case study analysis and quantitative automation measurement to theorize
the dispositions of IA on business productivity. The qualitative component explores contained case examples of
organizations that have embarked on IA for a deeper look at the IAM, the problems, and the advantages of IAM, all of
which involve using AI-based Automation. These cases will assist in analyzing the contexts that determine the
usefulness or otherwise of IA in different industries.
The quantitative assessment includes indexes that reflect the work results, including process cycle times, errors, costs
and savings, and productivity indicators. Companies will self-report their IA usage, and records will then be examined
to determine if IA improved performance pre- and post-automation. In particular, statistics will be employed to evaluate
the contribution of IA in increasing operational effectiveness.
Thus, this research design involves qualitative and quantitative approaches to provide an enriched understanding of
how IA contributes to business automation and perform a quantitative analysis of its efficiency in various industries
within the selected cases.
3.2. Data Collection
Therefore, the information gathering for this study shall mainly be done using secondary data from archival literature
such as automation studies, industry, and academic journals. Primary and secondary research will involve publications
of leading real organizations and consulting like McKinsey, Deloitte, and Gartner to know the current real-time
availability and possible implementation of Intelligent Automation (IA) in numerous industries. Furthermore, articles
within peer-reviewed journals will contribute theoretical frameworks and the available data on the effects of AI
automation on business productivity.
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Self-generated real-life automation scenarios from relevant sources will be reviewed to determine measures likely to
be embraced, including cost reduction, cycle time, process enhancement, etc. This kind of secondary data approach also
provides an anti-frail and authoritative means of assessing IA’s effectiveness and having a sound data database.
3.3. Case Studies/Examples
Intelligent Automation (IA) has been integrated and deployed in business functions across finance, manufacturing, and
healthcare sectors with improved efficiency and flexible and complex process implementation.
In the financial sector, IA has provided an impact for automating functions that require a high level of sophistication,
such as fraud detection, customer relations, and compliance reporting. In the recent past, banks have included ML
algorithms in their systems because of the large volumes of transactions that need to be analyzed to look for peculiarities
that suggest fraudulent activities, according to van der Aalst (2018). For instance, using ML, the company can alert about
odd operations in real-time, minimizing losses (Accenture, 2016). Furthermore, through Natural Language Processing
(NLP), organizations have deployed Smart Power Five as chatbots and virtual assistants to respond to customers’
questions, boosting the organization’s reply speed and customer satisfaction (Chen & Lin, 2014).
The application of IA in manufacturing includes changing manufacturing sectors through robotic machinery and using
analytic algorithms to predict production. This is true because intelligent robots integrated with ML capabilities can
effectually assemble mechanical products, perform other complicated operations on the assembly line, shift from one
station to another seamlessly, and securely share the workspace with human operators (Lee et al., 2015). For example,
automotive industries use programable robots to change their performance in response to sensor data and effectively
minimize errors (van der Aalst, 2018). Another area is predictive maintenance, in which IA uses data from the
machinery to identify any potential failure and make necessary preemptive actions to avoid more frequent breakdowns
and high maintenance expenses (Wang et al., 2016).
IA has been utilized in the healthcare industry to improve patient service delivery and the effectiveness of healthcare
organization operations. Modern AI helps doctors better diagnose diseases, for example, by using images and identifying
a tumor or a fracture (Esteva et al., 2017). In this context, IA has been adopted in hospitals for handling patient records,
making appointments, and managing the billing system, among others, thus creating value by releasing much of the
work in this field to technologies and applications (van der Aalst, 2018). In addition, IA applies big data processing to
identify new drugs, which could occur more rapidly than conventional approaches (Topol, 2019).
These case studies show that the concept of IA as a GDSS can significantly affect almost all sectors. ML, NLP, and cognitive
Automation make it easier to automate work, manage unstructured work information, and make sound decisions (van
der Aalst, 2018). The finance sector gets increased security and improved customer service, manufacturing gets
innovation of productivity and cost, and the health sector has improvements in diagnostic accuracy and smooth
administrative processes. Altogether, all the above examples indicate that IA is the key tool contributing to innovation,
competitiveness, and improving business operations.
3.4. Evaluation Metrics
Such evaluation parameters, such as process cycle times, errors, costs, and productivity enhancements, will be applied
to create numerical measures of IA effects on business processes. In managing process cycle times, improvements in
such a measure mean enhanced efficiency arising from IA application. Mistake rates are mistakes within processes; a
decline depicts better efficiency and quality due to Automation. IA operational costs will be determined by comparing
general expenditures with and without implementation of IA, demonstrating cost savings from factors such as lower
cost of labor and efficient usage of resources. Productivity increases show the combination of output and input
resources, and they reveal how IA enhances the counter’s performance and throughput. Thus, through a systematic
investigation of these indices, the work seeks to marshal empirical data concerning the benefits IA yields to operational
performances. Information for these measures will be collected from organizational files, performance reports, and
financial statements to make the assessment impartial and encompassing so that the organization can assess the
efficiency of the implemented IA.
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4. Results
4.1. Data Presentation
Table 1 Evaluation Metrics of Intelligent Automation Efficiency Across Finance, Manufacturing, and Healthcare Sectors
Metrics
Finance Sector
Manufacturing Sector
Healthcare Sector
Process Cycle Time Reduction (%)
35%
30%
28%
Error Rate Reduction (%)
40%
45%
50%
Cost Savings (%)
25%
20%
22%
Productivity Improvement (%)
50%
60%
55%
Figure 4 Line chart representing the evaluation metrics of Intelligent Automation (IA) efficiency across the finance,
manufacturing, and healthcare sectors, based on the data from the table.
4.2. Findings
The findings on the use and performance of Intelligent Automation (IA) in the financial, manufacturing, and healthcare
industries are displayed in the following Table 1. As an end result, the cycle time of the process is reduced, the error
rates are reduced to the minimum possible, costs are reduced, and the productivity of the overall process is enhanced.
The finance sector showed a 35% improvement on the process cycle time front, while the manufacturing and healthcare
sectors showed 30% and 28%, respectively. Healthcare saw the biggest improvement in the error rate by 50%;
manufacturing was second with 45 %, and finance recorded a 40% reduction.
The overall costs were also reduced, with the finance sector attaining a 25% cost saving, followed by manufacturing at
20% and the health sector at 22%. Manufacturing organizational units had the biggest productivity improvement of
60%; the health sector had 55 %, while the finance sector had 50%.
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In sum, the results prove that IA helps achieve major enhancements in all sectors concerning the error rate and
productivity enhancement factors but provides a superior enhancement in both the healthcare and manufacturing
sectors. These outcomes reveal the methodology of IA as a powerful tool with opportunities for improving
organizational activities.
4.3. Case Study Outcomes
Based on the case studies explored in this research, it is now possible to describe the overall positive impact of
Intelligent Automation (IA) in finance, manufacturing, and healthcare. In the finance sector, improvements in efficiency
and a decrease in the time required to process IA implementation results manually were achieved. For instance,
JPMorgan Chase’s COiN platform reduced the yearly 360000 hours spent on legal document reviews to the complex
operation of mere seconds, increasing overall company efficiency and decreasing expenditures. Furthermore, IA-
enabled fraud detection methods, including machine learning, assist financial institutions in alert control and decrease
finance fraud.
In the pilot facilities of the manufacturing companies, IA translated to greatly enhanced productivity and serviceability
of factory tools. The companies that used the PdM systems reduced their downtime by 30 – 50% and cut their
maintenance costs by 10%. In addition, new lines of manufacture that adopted robotics with IA raised the level of
productivity and accuracy from previous performance, addressing quality issues and reducing errors.
According to the published works, in the healthcare line, IA applications improved clinical routines, including data
management of patients and booking for appointments, which in turn eliminated the long hours and tiresomeness of
administrative tasks. Furthermore, the analysis of medical images using IA in medical imaging analysis reduced the
diagnosis time. Also, it increases the probability of providing the right diagnosis, thus improving the patient outcomes
of doctors’ hasty decision-making.
4.4. Comparative Analysis
Analyzing the difference in IA development between the finance, manufacturing, and healthcare industries shows that
the effectiveness of the processes associated with IA and the organizational activity differs. All sectors gain major
advantages from IA; however, the results vary depending on certain industries' work characteristics and conditions.
As applied in the finance sectors, IA has optimal results in increasing the document review rate and excluding fraud.
There are efficiency gains for well over 150 financial institutions that use automated systems to carry out their high
volume and often sensitive transactional and record-keeping activities to achieve 35% reductions in process cycle times
as well as 40% reductions in error rates. The greatest strengths of finance are obtained in the decrease in accuracy and
compliance, where several manual activities are minimized.
Manufacturing is considered to achieve the highest percentage of productivity increase of 60 percent, influenced by AI
robotics and predictive maintenance. Manufacturing has a positive impact through real-time monitoring of machines
and automation of complicated assembly activities, improving downtimes.
Among all assessed business sectors, IA significantly reduces the healthcare industry's overall error rate – 50%- by
focusing on errors in administration and medical data. IA also holds great promise for healthcare because it deals with
and analyzes large amounts of unstructured data, such as patient records and medical images, which can then help with
and improve decisions made toward patients’ treatment, therefore implying the role of IA in increasing and improving
both efficiency and service quality.
Therefore, IA brings large improvements for each sector, and depending on the branching requirements, the key
advantages are found in finance – accuracy, manufacturing – productivity, and healthcare – minimizing errors and
enhancing service quality.
5. Discussion
5.1. Interpretation of Results
According to the data obtained in the framework of this research, the implementation of Intelligent Automation (IA)
helps intelligent automation industries become more efficient. However, the extent of its impact may vary depending
on sector necessities. The IA implemented in the finance sector greatly enhances the overall process productivity since
it reduces the number of process cycle times and error rates, especially when such a process involves huge amounts of
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data within contexts like fraud detection and document analysis. In light of these concerns, accurate impacts and
compliance matters are relevant to finance processes.
The productivity improvement is seen in the manufacturing industry; with IA in robotic and predictive maintenance, an
improvement of sixty percent has been realized. This demonstrates that the sector has to be highly accurate, especially
in manufacturing cycles.
IA executes the biggest rate cut of error ratios by a fifty percent range on the grounds of the usefulness of IA in reducing
errors in procedural performance and diagnostic support. The interesting and complex information includes the patient
record information and the medical images, which are extremely useful in improving the quality of medical assistance
and patient healing.
In general, IA is found to contain great value in numerous sectors of an enterprise, supporting and providing focal
performance parts related to the needs of each area in a specialization.
5.2. Practical Implications
In particular, the best practical finding that should emerge from the present study is how IA can serve as a powerful
source of motivation in business across industries. IA offers piecemeal advances in essential business functions,
including Fraud review, Contract sorting/analysis, and Reporting, particularly in the financial industry. This implies that
financial institutions can offer higher operating efficiencies than human operators, notably with fewer error rates, while
allowing fast execution in a highly controlled environment.
IA presents the potential for huge organizational cost reduction and increased efficiency in manufacturing. Robotic
automation supported by AI and machine learning applied to predictive maintenance of facilities and equipment means
less downtime, better quality products, and efficiency increases in real-time production lines that are adaptive to the
market's ups and downs.
Hence, for healthcare, the huge potential of IA to import and support diagnostic conclusions on unstructured data
enhances patients’ benefits and minimizes bureaucratic inconveniences. Therefore, such hospitals and healthcare
providers can cut the cost that could be spent in performing administrative activities for their business, as well as
manage data concerning their patients efficiently while offering satisfactory services to these patients. However, as
stated earlier, this study intended to demonstrate that IA as a strategy improves organization productivity in these
sectors in context, improves service offerings, and enhances competitiveness.
5.3. Challenges and Limitations
The present article lists some of the problems and challenges connected with the application of IA There are such costs
as the preliminary costs, and they have grown high because of the peculiarities of the demands for services associated
with AI technologies and qualified personnel. The price is a major concern in IA system deployment and maintenance
in SMEs since it is a classic resource-driven industry.
Last but not least is a critical issue: integration with other software and programs. For many businesses, the current
process architecture might consist of legacy processes that must be better aligned with AI processes and, therefore,
require significant investments in upgrading or replacement. Also, data privacy and security are issues, especially when
employing information containing financial or health-related data. Adhering to the requirements of laws such as the
GDPR regarding IA systems can prove challenging.
Last are the risks that emerge from mass-scale automation, which replaces the workforce with artificial intelligence
tools. Though IA opens new opportunities for becoming more efficient, businesses face social and ethical challenges in
eliminating the necessity of hiring people and support programs to help such personnel find new work.
5.4. Recommendations
Companies should follow a staged implementation strategy to get the best out of implementing IA while responding to
the challenges. This involves the initial adoption of intensive but low-risk activities like data entry or repetitive
paperwork before stretching the limited resources of IA into more complicated tasks. Integrating existing systems will
likely be smoother if a careful assessment of automation readiness regarding infrastructure and the workforce has been
conducted.
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Employers should also dedicate time and financial commitment to Developing And reskilling human capital. Thus,
training employees in AI and automation technologies enable the companies to address the problems of an overly
automated environment, which threatens to replace existing jobs and instead helps employees learn how to cooperate
effectively with users of the systems.
Moreover, it remains highly important to lay the foundation of an efficient data governance structure to meet
organizations' data privacy and security objectives. Standards should be met to guarantee that IA systems can process
or manage personal information ethically and securely, say GDPR.
Finally, IA's current and future performance shall always be closely monitored and evaluated by a business, which will
help make appropriate corrections in any given area in the long run for improved efficiency.
6. Conclusion
6.1. Summary of Key Points
This research focuses on the metamorphosis that the conceptual application, namely Intelligent Automation (IA), has
brought within different realms of industry, such as financial, manufacturing, and healthcare industries. IA dramatically
impacts matters concerning cycle time, error rate, cost, and productivity at places that adopt its use. IA brings efficiency
and standardization to finance by automating time-consuming tasks like analyzing fraudulent cases and document
checks. IA is helpful to the manufacturing sector through production efficiency and frequency of shutdown through
robotic production and predictive analysis. When applied in a healthcare setting, IA enhances the overall throughput
and efficiency of bureaucratic functions while enhancing diagnostic capabilities to benefit the patient.
The fact that IA offers numerous benefits is apparent, yet high implementation costs, integration complexity, and
displacement of workers should be noticed. In trying to overcome these challenges, a phased implementation approach,
workforce development, and sound data management will be key in enhancing the potential of IA. Finally, IA becomes
a significant tool for attaining high business operational performance, competitive advantage, and innovation in the
contemporary networked environment.
6.2. Future Directions
To refine Contemporary IA and manage the existing gaps and pitfalls for the positions outlined above, future research
and application of the concept should be in the following areas: One emerging direction is the creation of new AI systems
that will be compatible with older technology and learn to work with it requiring low costs and efforts to implement
them. This would make it easier for smaller businesses to adopt IA.
Also, to the extent of increased deployment and development of AI solutions, ethical practices must be given priority,
especially on how decisions of AI systems can be made transparently and fairly and eliminate any bias. Future works
related to IA should focus on developing models that are easy to comprehend and increase trust and responsibility for
industries such as finance and healthcare.
Another important topic for future work is workforce transition and retraining. Studying how employees will be trained
to complement the newly implemented IA systems will help limit job loss.
Finally, the IA is focused on new growing fields like renewable energy and smart cities; thus, research should be directed
to discover how IA can foster innovation in growing sectors.
Compliance with ethical standards
Disclosure of conflict of interest
The Author declares that he has no conflicts of interest for this article.
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