The Integration of Artificial Intelligence in Business Communication

Channels: Opportunities and Challenges

STAVROS KALOGIANNIDIS1, CHRISTINA PATITSA2, MICHAIL CHALARIS 3

1Department of Business Administration,

University of Western Macedonia,

GREECE

2Department of Tourism Management,

University of West Attica,

GREECE

3School of Chemistry,

Democritus University of Thrace,

GREECE

Abstract: - The development of artificial intelligence is the most intriguing technological advancement of the

twenty-first century. Artificial intelligence has become a ubiquitous tool in modern times, and the business industry

is no exception. Even though AI is still emerging, it has already had a significant impact on the business sector. It

has enabled business managers to devise creative methods to package and even convey the final product to the

consumer. The purpose of this study is to examine the different opportunities and challenges associated with the

integration of artificial intelligence in business communication channels. Data was collected from 384 business and

technology experts in Greece using a well-designed questionnaire. The business sector is going through a

significant change in how it interacts with consumers and other companies. AI has been effectively used in several

business areas, including biometrics, chatbots, robots, integrated buying and inventory, recommendation and

suggestion engines, and kiosks. In addition to keeping up with the rapid advancements in artificial intelligence, it is

also assisting in the transformation of consumer behavior and the business sector. Undoubtedly, the industry has

benefited much from the deployment of artificial intelligence, but many individuals are still ignorant of its

potential. The findings highlight key issues that are unique to businesses driven by AI. The results provide light on

the particular complexity and difficulties that businesses may run into when using AI in business procedures by

identifying these difficulties.

Key-Words: - Chatbots, Artificial Intelligence, Speech Recognition, Natural Language Processing (NLP), Business

Communication Channels, Email Filtering.

Received: March 2, 2024. Revised: July 29, 2024. Accepted: August 23, 2024. Published: September 30, 2024.

1 Introduction

The rise of AI has been an important factor of change

being seen in the current fast-changing corporate

environment, [1], [2]. AI has revolutionized the

business landscape through its power to perform data

analysis, automate processes, and make intelligent

decisions, [3]. The core drivers of this change as well

as the substantial impacts of Companies are starting

to apply AI to solve complicated issues and their

respective significance. Applications of AI can be

seen in machine learning, data analysis, process

automation, and natural language processing, [4], [5],

[6], [7]. Artificial intelligence technologies are being

applied to improve consumer satisfaction and

efficiency, as well as promote innovation in multiple

sectors including, [8], [9], [10]. [11], pointed out that

AI integration into business workflows was involved

and affected variety of areas ranging from industries

to businesses. Support and customer services have

always been the important parts of all companies in

any area, [12]. These duties are crucial for

responding to any questions from the customers,

fixing any problems, and eventually ensuring the

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happiness, [12], [13]. Traditional customer service

methods are experiencing some shortcomings. So

there is a shift towards more effective, advanced, and

improved approaches, [14]. In previous cases, human

employees were applied generally for face-to-face,

phone, or email communications, [15]. No doubt

human interaction is vital for developing and solving

problems, but unfortunately, the scale, availability,

and consistency are limited. This is a trend that

becomes more evident in the digital age where the

client always wants instantaneous service and 24-

hour accessibility, [15], [16]. Some of these problems

have already been addressed by the creation of

automation and AI technology. Chatbots and virtual

assistants, powered by AI and ML technologies, are

commonly seen as the next generation of customer

service and support tools. These technologies are

language processing (NLP) and can comprehend

customer inquiries instantly and reply on the go, [17].

Particularly adept at managing mundane and

repetitive jobs like processing orders, responding to

commonly requested queries, and offering basic

troubleshooting advice are chatbots, [18].

Conversely, virtual assistants have more

sophisticated features that allow for more intricate

interactions and customized replies, [19]. The

promise of cost savings, higher efficiency, and better

client experiences is what is driving the transition

toward AI-driven customer service, [20], [21].

AI-driven chatbots are being employed in

commerce these days for a variety of purposes, most

of which are aimed at improving customer service

applications, [22]. Customers may submit questions

at any time, particularly when human agents are not

accessible, and get individualized advice, assistance,

and support, among other benefits, [23]. Businesses

that use AI chatbots may also benefit from lower

staffing expenses for human support staff, handling

several consumers at once, and more customer

engagement, which is directly linked to higher

revenue, [24]. Notwithstanding these benefits, using

artificial intelligence chatbots to raise customer

happiness has several drawbacks and restrictions,

[25]. This might include a variety of problems, such

as the limited capacity to decipher the purpose and

content of user communications and the challenges

associated with producing natural language answers

that are meant to resemble human behavior, [26].

Furthermore, human beings naturally can

comprehend, identify, and react to the feelings and

experiences of others, even in the face of barriers

preventing chatbots from recognizing and expressing

empathy in conversation, [24]. As [3] noted, the

integration of more sophisticated chatbots with NLP

skills is restricted and not often used in e-commerce.

However, it is important to look at integrating NLP

capabilities into E-commerce chatbots given the

possible advantages and untapped prospects, [27],

[28].

1.1 Problem Statement

The fast development of artificial intelligence (AI)

technologies has led to improvements in

communication channels within organizations, where

AI plays a big role in this area. Although AI proves

to be a remarkable tool in reconstructing business

communication with applications such as chatbots,

email filtering, speech recognition, and NLP among

others, the road to integration is not without

constraints and risks. The benefits of AI in the area of

customer service and internal communications where

a lot has been said are not sufficient to cover the

entire implications that the technology has in

business communication.

Conversely, there is a double-sided aspect to AI

in terms of the quality and kind of customer

interactions it has. On the other hand, AI-enabled

solutions such as chatbots which have unmatched

ability to enhance customer experience and level of

engagement are readily available contributing to the

satisfaction of customers, [3], [4]. Nevertheless, there

is a risk of human element loss in customer service

and AI's incapability to comprehend complex human

emotions and subtleties raising issues, [5], [6].

Moreover, although advanced AI technologies have

shown the potential for increased efficiency and data

management through email filtering and analytics,

such capabilities are still under tremendous

uncertainty of accuracy in terms of the prioritization

and protection of sensitive data.

In addition, the use of speech recognition and

NLP brings forward a new horizon with its array of

possibilities and obstacles. The ability of these

technologies to create more natural and user-friendly

modes of interaction is obvious for everyone, [9],

[10]. Nevertheless, the adequacy and efficiency of

these tools in multicultural and language contexts, as

well as the lasting effect on the workers, especially

on writing and analytical thinking, are issues that

need deeper scrutiny, [11], [12]. The existing

research gap requires designing a holistic framework

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to optimize the AI integration into the

communication channels used by businesses that

maximize efficiency and personalization while

preserving the most critical human features.

Additionally, the role of AI in business

communication calls for the exploration of strategies

for mitigating the limitations and challenges arising

from the use of AI, ensuring that they complement

the human aspect rather than displacing it. Therefore

this study focuses on analyzing the complex

influence of AI on business communication channels

and providing guidelines on how to maximize the

benefits of AI while making sure that people and

human connections are not compromised when AI is

being used in the business context.

1.2 Purpose of the Study

The purpose of this study was to examine the

opportunities and challenges associated with the

integration of Artificial Intelligence in Business

Communication Channels. The study was also based

on the following objectives:

1. To examine the use of chatbots and Virtual

Assistants in enhancing communication in

businesses

2. To assess the benefits of email Filtering

towards effective Business Communication

3. To examine the influence of Speech

Recognition in Effective Business

Communication

4. To examine the use of Natural Language

Processing (NLP) in enhancing

communication in businesses

5. To establish the challenges associated with

the integration of artificial intelligence in

business communication channels

1.3 Research Hypotheses

Hypothesis One (H1): Chatbots and Virtual

Assistants have a positive and significant use in

enhancing communication in businesses.

Hypothesis Two (H2): Email filtering has positive

benefits that help in enhancing the effectiveness of

business Communication

Hypothesis Three (H3): Speech recognition has a

positive and significant influence on business

Communication

Hypothesis Four (H4): Natural Language Processing

(NLP) has a significant effect on enhancing

communication in businesses

2 Literature Review

2.1 Uses of Chatbots and Virtual Assistants

Chatbots are becoming more widely acknowledged

in the literature as a significant technology

advancement that enhances customer service. Few

studies have looked at the usage of chatbots. For

instance, [29] found that consumers' acceptance of

chatbots may be decided by several aspects, such as

the authenticity of conversation, perceived utility,

and perceived pleasure. This is based on the

technological acceptance model and satisfaction

theory. On the contrary, although several earlier

studies have looked at the adoption of chatbots in

different sectors, such as textiles and telecoms, very

few have looked at the actual usage of chatbots, [3],

[20].

Factors like perceived customization and website

aesthetics may have an impact on chatbot adoption,

[30]. According to a recent study [20], people who

get along well with chatbots may communicate with

them for prolonged periods. Both studies examined

chatbot use from the perspective of the customer and

within the framework of mobile services, [15], [26].

A cutting-edge method of communicating with

clients is via chatbots. According to [9], chatbots'

anthropomorphic traits help to improve users'

experiences. Getting better information and services

may make customers happier, [17], [20], [24]. [31],

claim that chatbots may help companies provide their

customers with high-quality services and gain a

variety of benefits, such as happy customers and

word-of-mouth referrals. More recently, [15] has

shown that anthropomorphism (identification, short

conversation, empathy) and satisfying basic demands

increase the likelihood of consumer compliance. To

sum up, companies have started using chatbots to

help customers in an attempt to improve customer

satisfaction and customer service, [8].

The use of chatbots and virtual assistants in

wireless service customer support is a crucial aspect

of their integration, [26]. To effectively use AI-

driven technologies, service providers need to have

strong natural language processing (NLP)

capabilities, choose the right platforms, and

seamlessly integrate these technologies into their

present customer support systems, [32]. This entails

using machine learning algorithms to constantly

improve chatbot answers by teaching them to learn

from encounters, [20]. Facilitating multichannel

communication via integration with websites and

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messaging applications improves user accessibility.

According to [33], the deployment process

necessitates a detailed comprehension of customer

requirements, the improvement of the chatbots'

knowledge bases, and the calibration of their replies

to conform to the tone and identity of the brand.

Sufficient testing is necessary to guarantee a precise

understanding of user inquiries, effective transfer of

complicated problems to human agents, and constant

performance monitoring, [34]. Wireless service

providers may maximize operational efficiency while

streamlining customer questions, offering quick

answers, and improving user engagement via the

smart usage of chatbots and virtual assistants, [9].

Chatbot classification is one of the taxonomies; it

includes notification, process, and conversational

(Table 1). In particular, conversational chatbots are

becoming more and more common. The same queries

are often posed by customers to customers, [12].

Answering a broad variety of inquiries at various

times becomes nearly usual to maintain high quality

of service and customer satisfaction. Remember that

a satisfied client has a big impact on the success of

the business from the brand's perspective, [5], [15],

[24].

Table 1. Classification of chatbots and sample tasks

Type of chatbot

Communication

method

Examples

Notifying

One-way user

communication that

functions like a

"newsletter" and

delivers notifications

in line with a

predetermined

timetable [9].

Notifications about

shipments, local and

international news,

and weather forecasts

Process

The procedure lets

the user follow a

preset, linear process

that necessitates

making several

judgments from a

limited set of

options.

Purchasing movie

tickets, doing online

shopping over

Messenger, choosing

a vacation package

from a travel agency,

and completing an

application to create

a bank account

Conversational

Enables users to

have informal

conversations and

respond to inquiries

in their native

tongue by following

instructions, [35].

The FAQ office's

implementation

2.2 Benefits of Email Filtering

Attackers often exploit email communication as a

means of entry into the targeted organization since it

is an essential component of daily business

operations. Hacking is a major problem, and both

distributed denial of service assaults and attacks on

cloud server management flaws are steadily evolving

[8]. Attackers possess the ability to transmit

potentially harmful material, such as links to risky

websites or malware files, to the receiver via email

messages. One of the several detrimental

repercussions that these assaults usually have on the

organization is the loss or leaking of important data.

Phishing begins with a phony email or other kind of

communication that is meant to lure a target. The

communication is crafted to seem as if it was sent

from a trustworthy source. If the target becomes a

victim, they may be persuaded to provide personal

information, usually on a fraudulent website, [30],

[36], [37].

[38], noted that many Internet service providers

(ISPs) use spam filters at every network tier, such as

in front of email servers or at mail relay locations

where firewalls are present. A network security

system called a firewall keeps an eye on and controls

incoming and outgoing network traffic by pre-

established security standards. At the network border,

the email server functions as an integrated anti-virus

and anti-spam solution, offering comprehensive

safety protection for email, [15], [39]. To act as a

bridge between certain endpoint devices, filters may

be installed in clients, where they can be installed as

add-ons in PCs [27]. Filters prevent unwanted or

suspect emails from entering the computer system

and pose a danger to network security. Additionally,

the user may have a personalized spam filter at the

email level, which will prevent spam emails based on

predetermined criteria, [36], [40].

According to [41], over 20% of emails based on

permission often end up in the wrong person's

mailbox. To reduce the risks that email users face

from ransomware, malware sent via emails and

phishing, email providers have developed a variety of

technologies for use in email anti-spam filters, [39].

Each incoming email's risk level is determined by the

procedures. Sufficient spam restrictions, sender

policy frameworks, whitelists and blacklists, and

receiver verification tools are a few examples of

these systems, [20]. More spam may get past the

spam filter and into users' inboxes when the

acceptable spam threshold is set too low.

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Fig. 1: Spam filtering architecture for email servers.

Source: [39]

In the meanwhile, if the administrator doesn't

reroute them, having a very high threshold may result

in some critical emails getting isolated, [42], [43],

[44], [45].

There are two main parts to an email message:

the header and the content. The section of the email

with the most general information about its contents

is the header. The sender, recipient, and topic are all

included, [39]. Web pages, audio, video, analog data,

files, graphics, and HTML syntax are a few

examples. The email header contains information

about the sender and destination, as well as a

timestamp that shows when the message was

transmitted from intermediate servers to the Message

Transport Agents (MTAs), which act as an office for

mail organization. Typically, the header line begins

with "From" and undergoes modifications each time

it traverses an intermediary server to transit from one

server to another. The user may see the email's path

and the time it takes for each server to process it by

viewing the headers. Before the classifier can utilize

the provided data for filtering, it must first undergo

certain processing, [24], [36], [39]. The mail server

architecture and spam filtering process are shown in

Figure 1.

2.3 Influence of Speech Recognition

Scientists and engineers have been fascinated for

decades by the idea of creating a machine that can

communicate with humans, especially one that can

understand spoken language, [46]. Speech

Recognition System (SRS) also referred to as

Automatic audio Recognition (ASR) or computer

speech recognition, SRS is a computer software that

uses an algorithm to translate an audio signal into a

string of words, [5], [25]. The potential for it to be a

significant means of communication between people

and computers is there. Speech technology-enabled

apps are now offered for sale for a small but

intriguing range of jobs. These technologically

advanced computers provide very helpful and

valuable services by accurately and consistently

reacting to human voices, [47]. Though many

significant scientific and technological advancements

have been made, we are still far from having a

machine that replicates human behavior. These

advancements are meant to get us closer to the "Holy

Grail" of robots that recognize and interpret spoken

speech, [48]. Table 2 lists the speech recognition

system (SRS) applications along with their industry,

region, and disciplines:

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Table 2. Speech recognition system (SRS) applications along with their industry, region, and disciplines:

Application

Education sector

Speech-to-text processing, accurate pronunciation of foreign language words.

Students with disabilities may type text vocally using a keyboard.

Medical Sector

Automatic wheelchairs, precise surgery, and medical transcription (digital voice-to-

text)

Military Sector

Automatic aircraft control, helicopter, training air traffic controller, Automatic

ammunition control

Communication Sector

Ringing on the phone and looking for numbers without an operator's help.

General

Use of dictation systems for security needs in very secure locations. To convert

data between languages, play video games, and enter data into an ATM [48].

Fig. 2: Speech Recognition System (SRS) general stages

Several speech-processing approaches convert an

unknown audio input into a series of feature vectors

for use in speech recognition systems. Through the

use of algorithms, it transforms feature vectors to

phoneme lattice, [47]. A recognition module uses a

lexicon to convert the phoneme lattice into a word

lattice, and then it applies grammar to the word

lattice to identify particular words or text.

The information for the general stages in the

speech recognition system (SRS) is shown in Figure

2. There are several processes involved in the voice

recognition process. Step 1 is to get signal properties

such as total energy and zero crossing strength across

different frequency ranges, etc., the spoken signal is

separated into evenly spaced blocks in this step, [47].

Each block and phoneme are combined using these

attributes' feature vectors to create a string of

phonemes. The next step involves a bank of

frequency filters, the fast Fourier transform (FFT),

and the linear predictive coding approach used to

apply spectrum analysis to each block in this phase,

[5]. With stage 3, every block undergoes a decision-

making process in this stage. The field is narrowed

by the distinctive characteristics of each phoneme,

[47]. Step 4 involves using various algorithms, this

step improves the performance of the decision-

making process to achieve a high degree of success.

An algorithm is created for every vocabulary word,

and a phoneme string is then compared to each

algorithm, [25], [36].

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2.4 Use of Natural Language Processing

(NLP)

The notion of automating language processing was

originally explored by linguists and computer

scientists in the 1950s which is when natural

language processing (NLP) got its start. Earlier

attempts were rule-based, analyzing and producing

text using manually created language rules, [17].

NLP research was further impacted by Chomsky's

invention of transformational grammar theory in the

1960s. But in the 1990s, with the introduction of

statistical and machine learning techniques,

significant advances were made, [49]. To address

language-related challenges, researchers began using

methods like probabilistic models and Hidden

Markov Models (HMMs). Large annotated corpora

like the Penn Treebank made it possible for data-

driven techniques to emerge, which completely

changed natural language processing, [17].

Natural language processing (NLP) is a

technique used by AI chatbots that simulates human

interaction by understanding and reacting in natural

language, [19]. Natural language processing (NLP) is

a language model that is used in chatbot designs to

mimic real human speech and enable communication

between humans and machines, [17]. Instead of

depending only on in-person interactions for

communication, modern organizations use natural

language processing (NLP) software to develop

chatbots that can respond to customer requests, [17].

The combination of NLP and AI has had a

tremendous impact on how customers interact and

communicate with chatbots on e-commerce

platforms, [19]. Fundamentally, NLP serves as a

conduit to let users engage and communicate with

computers. By evaluating text and adhering to the

structure of human language in which words create

phrases, phrases form sentences, and sentences

express ideas natural language processing (NLP)

enables computers to comprehend human language,

[24]. NLP has limitations as well and may not be able

to do some tasks as well as a human employee. For

example, comprehending the complexities and layers

of human language, makes it challenging to capture

subtleties and essential facts, [33], [50].

Figure 3 explains the most popular apps used by

businesses to further their goals and how they are

customized for their line of goods. Google Translate

is mostly used for smart contracts, virtual help,

frequently asked questions, and intelligent

communication technologies including audio and

video conferencing. These choices facilitate how

clients including those with special needs or

disabilities interact with the goods of the company,

[49].

Fig. 3: The most popular apps used by businesses

Source: [49]

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The most popular business apps these days are

shown in Figure 3. Modern corporate operations are

searching for improved choices to delight clients as a

result of technological advancements, [26]. The

relevance of natural language processing (NLP) rests

in its potential to revolutionize human-machine

interaction by allowing computers to produce and

understand human language at a level previously

thought to be science fiction, [44]. NLP has emerged

as a key component of many applications in a wide

range of sectors, transforming the efficacy and

efficiency of a variety of jobs. Natural language

processing (NLP) has shown its worth in automating

procedures, deriving insights from text data, and

opening up new channels of communication, [19].

Examples of its applications range from improving

customer service with chatbots to supporting medical

diagnosis via language-based analysis, [15], [29].

Natural language is full of contextual ambiguities

and complexities that make it challenging for robots

to comprehend correctly, [33]. Contextualized word

embeddings, attention processes, and transformer

models like BERT are noteworthy developments in

addressing this issue, [51]. However, there's still

room for improvement in terms of handling complex

linguistic ideas like sarcasm, irony, and metaphors.

Advances are being explored via enhanced

contextual representation and contextual reasoning

systems. The capacity of NLP to handle a range of

communication formats helps to determine its future,

[24]. Multimodal natural language processing (NLP)

aims to create machines that can understand and

generate content that seamlessly combines many

modalities. This includes multimodal pretraining,

cross-modal attention processes, and joint embedding

spaces for several modalities. Applications include

interactive chatbots that have a deeper

comprehension of user input, picture captioning, and

video summarization, [35].

2.5 Challenges and Business Specificities in

Implementing AI

Businesses looking to improve customer interactions,

reduce processes, and get a competitive advantage

may find that integrating AI technologies into

business systems is a potential option, [11]. But

despite the seeming high failure rate of AI initiatives

in many sectors, this undertaking is by no means

easy. Prior research has mostly examined the

difficulties businesses encounter when deploying AI

generally, [4]. To fully realize AI's potential, some

prerequisites must be met, including the need for

access to large, high-quality datasets and the

necessary technology infrastructure for data

processing [48].In contrast to stand-alone AI

solutions, AI for business requires a seamless

connection with databases and platforms already in

place, [11]. It also often entails complicated data

environments and little disturbance. Furthermore,

business necessitates close attention to requirements

for scalability and customization, [36].

Determining explicit goals for AI algorithms

becomes more important as AI systems become self-

sufficient, [52]. However, a major obstacle is that

the field of CRM often contains implicit and

difficult-to-quantify objectives, [14]. The lack of

domain expert supervision, the difficulty in

comprehending AI algorithms, and the intrinsic

complexity of AI decision-making all contribute to

this challenging environment, [53]. Furthermore, in

the CRM domain, tight coordination between

marketing and sales teams is necessary artificial

intelligence (AI) needs to act as a catalyst for

bringing these two departments together by offering

insights and suggestions that efficiently connect their

activities, [51]. AI systems must be able to detect and

react to emotional indicators during customer

interactions as CRM puts a high value on

comprehending the feelings and emotions of its

customers. This emotional component gives AI

models an additional level of complexity which

distinguishes CRM from more basic applications,

[24].

A prevalent issue that many businesses have

when using AI is a reluctance to change, [54].

Businesses often require tight cooperation between

AI and human agents (sales, customer service),

therefore striking a balance between their respective

roles and duties in these exchanges may call for a

special strategy. These unique obstacles must be

acknowledged and overcome to successfully

integrate AI into business systems, [46] The context

of AI-powered CRM poses unique problems that may

vary dramatically from those experienced in more

general AI adoption or marketing AI adoption

situations, making this absence more important, [24].

Business has special problems compared to other

settings because of its distinctive features, which

include a concentrated focus on customer

interactions, a complex data environment, planning

needs, emotional concerns, and ethical duties, [36].

These unique problems must be acknowledged and

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addressed to successfully integrate AI into business

systems, [6].

Europe

Greece—study area

Fig. 4: Maps of Europe and Greece

3 Methodology

3.1 Research Design

The study was quantitative and it made use of the

cross-section research methodology. The study

design made it easier to gather and analyze

quantitative data to describe particular phenomena

using the most recent patterns, incidences, and

connections between various variables. The cross-

sectional survey research technique, which offered

data on the study's topic, allowed the researcher to

effectively generalize the numerous study results to a

larger community of business and technology

specialists in Greece.

3.2 Target Population, Sample Size and

Sampling Technique

The research focused on various business and

technology experts in Greece as its target

demographic, since it is thought that they have

superior expertise in the integration of artificial

intelligence in commercial communication channels

(Figure 4). The population served as the foundation

for selecting the study's ideal sample. As a result,

from a research population of 10,000 distinct

government officials across Greece, a sample size of

384 different business and technology experts was

chosen. Equation 1 is [55] formula, which was used

to determine the sample size.

  

  

(1)

Calculation of the minimum sample of respondents

where:

n =is the sample size,

N = the population,

e =the level of significance, and 1 is the constant.

Using a 5% (0.05) level of significance

  

  󰇛󰇜    

  

Probability sampling techniques namely stratified

and basic random sample procedures, were used in

this investigation. In this instance, stratified sampling

was used to generate the goal sample, then stratified

random sampling was used to extract the final sample

from the strata. By using stratification, the researcher

separated the participants into groups known as strata

according to common attributes. Following division,

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DOI: 10.37394/23207.2024.21.157

Stavros Kalogiannidis, Christina Patitsa, Michail Chalaris

E-ISSN: 2224-2899

1930

Volume 21, 2024

a different probability sampling technique, simple

random sampling in this case is used to randomly

sample each subgroup. Because the leadership

population was varied, stratified sampling was

crucial to the study's success. Without it, a random

sample would not have produced particularly

accurate. The benefit of using simple random

sampling is that it produces samples that are fairly

representative of the community.

3.3 Data Collection

The researcher used an online survey to gather

information from Greece's business and technology

specialists. One of the easiest and most often used

ways to get data is via a survey. This is because it

quickly covers a huge number of respondents, is less

costly, and enables respondents to freely answer

challenging questions without worrying about being

accepted or rejected by the researcher. To get the

most relevant data for assessing the difficulties and

prospects of integrating artificial intelligence in

business communication channels, an online survey

questionnaire was used. To measure each

independent variable for this study, well-crafted

statements based on the various indicators of each

independent variable as derived from the literature

were used, and respondents were asked to indicate

whether they strongly agreed, disagreed, or were not

sure. Conversely, a nominal scale was used to

generate and assess very detailed statements about

the benefits and problems of integrating artificial

intelligence into business communication channels to

quantify the dependent variable. Among the options

for Artificial Intelligence in Business

Communication that were provided, respondents in

this instance could simply choose the best option. In

general, the research focused on four main

independent variable aspects: the use of virtual

assistants and chatbots; the advantages of email

filtering; the impact of speech recognition; and the

use of natural language processing (NLP). Every

independent variable was evaluated using tightly

worded sentences, and the answers were recorded on

a 5-point Likert scale. First, there was Strongly

Disagree (SD), followed by Disagree (D), Not Sure

(#3), Agree (A) (#4), and Strongly Disagree (SD) (5).

A statement measurement on a likert scale of 1 to 5

was selected for each variable, and it was cross-

tabulated with the various SDG characteristics. This

would subsequently assist in illuminating the

relationship between a certain independent variable

and the dependent variable.

3.4 Data Analysis

In addition, SPSS was used for analysis once the

quantitative data gathered from the chosen research

participants was coded. Frequencies and percentages

were used to analyze the data, which were shown in

tables. To find out how much artificial intelligence

contributes to the efficacy of business

communication, regression analysis was also used.

Equation 2 of a multiple regression model was used

in this instance to determine the different anticipated

values.

      

(2)

where:

Y represents effective business communication,

 is the constant coefficient of intercept,

 Represents the uses of chatbots and Virtual

Assistants,

 Represents the benefits of Email Filtering,

 Represents the influence of Speech

Recognition,

 Represents the uses of Natural Language

Processing (NLP), and

 Represents the error term in the multiple

regression model.

The hypothesis of the study was tested and the

mode of accepting or rejecting the stated hypothesis

was performed at a 0.05 level of significance.

In terms of ethics, the researcher obtained

informed permission from the respondents to make

sure they were willing to participate in the study.

Furthermore, the data of the responders was handled

with confidentiality, privacy, and personal integrity.

It was simpler to get thorough answers to certain

issues since respondents were free to interpret the

various opinion questions to answer them.

4 Results

This section presents the results obtained after

analyzing data collected from the selected

respondents.

4.1 Demographic Characteristics

The results in Table 3 show that the largest portion of

respondents (71.6%) was male, while females

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Volume 21, 2024

accounted for 28.4% (109 out of 384). This disparity

highlights a gender imbalance within the technology

expert group, reflecting broader trends in the

technology sector where men often outnumber

women.

Table 3. Showing participants’ demographic information (technology experts)

Characteristic

Frequency

Percentage (%)

Gender

Male

275

71.6

Female

109

28.4

Age bracket

Below 30 years

17.7

30-40 years

218

56.8

41-50 years

19.5

50 years and above

6.0

Years spent in the technology sector

Below 10 years

9.1

10-20years

281

73.2

20 years and above

11.7

Total

384

100

Source: Primary data (2024)

Regarding age, the majority of participants fall

within the 30-40 years age bracket, making up 56.8%

(218 out of 384) of the sample. This is followed by

those in the 41-50 years age bracket, representing

19.5% (75 out of 384), and those below 30 years at

17.7% (68 out of 384). Participants aged 50 years and

above constitute the smallest group, at 6.0% (23 out

of 384). The experience level in the technology

sector is heavily weighted towards those with 10-20

years of experience, who account for 73.2% (281 out

of 384) of participants. This is a significant majority,

indicating that most participants have a substantial

depth of experience in the field. Those with less than

10 years of experience make up a small fraction at

9.1% (35 out of 384), and those with more than 20

years of experience represent 11.7% (68 out of 384).

The predominance of individuals with 10-20 years of

experience underscores the involvement of seasoned

professionals in the study, which could influence the

insights and perspectives gathered, particularly

regarding the integration of AI in business

communication.

4.2 Descriptive Results

Table 4 presents results concerning the use of

chatbots and virtual assistants in enhancing

communication within businesses.

The majority (58.5%) of respondents agree that

chatbots and virtual assistants significantly reduce

response times for customer inquiries. This indicates

a widespread acknowledgment of the efficiency gains

associated with automation in handling customer

queries, likely driven by the ability of these AI

systems to handle multiple inquiries simultaneously.

An overwhelming majority (79.2%) to agree that the

use of chatbots leads to a more personalized

experience for customers. A significant portion

(55.8%) agrees that virtual assistants are capable of

handling complex customer service tasks as

effectively as human agents. However, a substantial

percentage (23.4%) still disagrees, indicating some

skepticism regarding the ability of AI to handle

nuanced or highly specialized customer inquiries.

The majority of respondents (72.7%) agree that

implementing chatbots improves the efficiency of

business communication internally. A considerable

majority (74.0%) agree that chatbots and virtual

assistants often misunderstand or misinterpret

customer queries. While a majority (61.0%) agree

that the presence of virtual assistants makes

customers feel more engaged with the brand, a

notable percentage (16.9%) remains neutral. A

significant majority (62.3%) agree that relying on

chatbots can lead to a decrease in human

employment in customer service roles. This reflects

concerns about the potential displacement of human

workers by automation, highlighting the need for

organizations to consider the ethical and social

implications of AI integration in the workforce.

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Table 4. Use of Chatbots and Virtual Assistants in Enhancing Communication in Businesses

Statement

Strongly

Disagree

Neutral

Agree

Strongly Agree

Chatbots and virtual assistants significantly reduce response

times for customer inquiries.

0.0

6.5

23.4

58.5

11.7

The use of chatbots leads to a more personalized experience

for customers

2.6

6.5

11.7

79.2

0.0

Virtual assistants are capable of handling complex customer

service tasks as effectively as human agents.

0.0

23.4

5.2

15.6

55.8

Implementing chatbots improves the efficiency of business

communication internally

1.3

2.6

11.7

72.7

11.7

Chatbots and virtual assistants often misunderstand or

misinterpret customer queries

0.0

2.6

11.7

74.0

11.7

The presence of virtual assistants makes customers feel more

engaged with the brand

3.9

6.5

11.7

61.0

16.9

Relying on chatbots can lead to a decrease in human

employment in customer service roles.

2.6

1.3

13.0

62.3

20.8

Source: Primary data (2024)

Table 5. Benefits of email filtering towards effective business communication

Strongly

Disagree (1)

Disagree

(2)

Neutral

(3)

Agree

(4)

Strongly

Agree (5)

email filtering dramatically lowers the amount of spam

and irrelevant emails received











email filtering powered by AI enhances the

prioritization of crucial messages











Email filtering technologies might occasionally

misclassify crucial communications











AI-based email filtering saves time for employees,

allowing them to focus on more critical tasks











The integration of AI in email systems enhances data

security and privacy











AI email filtering systems are easy to implement and

integrate with existing email platforms











Dependence on email filtering can reduce the ability of

employees to manually manage and organize their

inboxes











Source: Primary data (2024)

The study also identified the benefits of email

filtering integration business communication

channels and results are presented in Table 5.

The majority of the respondents (62.3%) agreed

that email filtering dramatically lowers the amount of

spam and irrelevant emails received, demonstrating

the usefulness of AI in helping organizations deal

with the issue of email overload. This is in line with

the hypothesis that AI algorithms would be able to

quickly and precisely recognize and separate

undesirable messages, optimizing communication

routes and raising productivity. There is strong

agreement (64.9%) that email filtering powered by

AI enhances the prioritization of crucial messages. A

sizable majority (70.8%) concur that these

technologies might occasionally misclassify crucial

communications thereby creating concerns about

false positives and the possibility that crucial material

will be missed. This emphasizes how crucial human

monitoring and ongoing improvement are to reducing

algorithmic mistakes and guaranteeing the

dependability of email filtering systems.

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Table 6. Influence of Speech Recognition in Effective Business Communication

Strongly

Disagree (1)

Disagree

(2)

Neutral

(3)

Agree

(4)

Strongly

Agree (5)

Speech recognition technology significantly enhances the

accessibility of business communications.











The accuracy of speech recognition software is sufficient

for professional business communication.











Speech recognition technology can effectively transcribe

meetings and conferences in real time.











The use of speech recognition supports multilingual

business communication.











Speech recognition technologies sometimes misunderstand

accents or dialects, leading to communication errors.











Implementing speech recognition technology is cost-

effective for businesses in the long run.











Reliance on speech recognition may discourage the

development of typing and writing skills.











Source: Primary data (2024)

Furthermore, although there is recognition of the

time-saving benefits (31.7%) and improved data

security (44.2%) of AI-driven filtering, there are still

obstacles identified.

Regarding the effects on employees' ability to

properly manage their inboxes manually (25%) and

the simplicity of implementation (22.1%) for

example a significant portion of respondents express

indifference or disagreement.

The Table 6 presents result on the influence of

speech recognition technology on various aspects of

business communication.

The majority of respondents agree that speech

recognition significantly enhances the accessibility of

business communications (76.9%). A sizable portion

(38.7%) strongly agree that the accuracy is sufficient.

This suggests a level of doubt or even past

encounters with errors that have impacted opinions

about the accuracy of the technology in work-related

settings. Furthermore, there is broad consensus

regarding the efficacy of speech recognition

technology in real-time conference and meeting

transcription (51.7% strongly agreeing and 27.9%

agreeing) highlighting the technology's potential to

improve and expedite collaborative communication

processes in the business world. The fact that a

sizable percentage of respondents (36.4%) disagree

with the assertion suggests that speech recognition

systems as they exist now have difficulties when it

comes to correctly comprehending a wide range of

linguistic nuances. There is broad agreement among

respondents (71.7% agreeing and 9.7% strongly

agreeing) that voice recognition technology

implementation is a cost-effective investment for

enterprises over the long term. This implies an

understanding of the possible cost and efficiency

savings linked to implementing such technology.

Finally, there is a worry expressed over the possible

harm that depending too much on speech recognition

could have to the growth of one's typing and writing

abilities. Although this statement is agreed with by

most respondents (67.8%), it's important to note that

a significant number (9.1%) strongly disagree. This

demonstrates differences in beliefs about the wider

effects of technology on the improvement of

communication skills.

The study further examined the different uses of

NLP in business communication channels and the

results are presented in Table 7.

The majority of respondents (69.6%) either

agreed or strongly agreed that NLP significantly

improves the understanding of customer feedback

and inquiries. The overwhelming majority of the

respondents (70%) said that the employment of NLP

in business communication results in the right and

timely responses. The result was that more than 80%

(86.6%) of the parties agreed or strongly agreed that

NLP technology is capable of analyzing the

sentiment in customer communications. A significant

part (77.9%) of respondents (77.9%) saw that the

implementation of NLP decreases the need for

human interaction in routine messages. This shows

that NLP is increasingly used to perform repetitive

tasks and release the human resources for complex

and strategic positions. It's noteworthy that while a

significant percentage (94.8%) agreed or strongly

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agreed that NLP technologies are susceptible to

context and nuance errors, a fair percentage (5.1%)

disagreed or strongly disagreed. The greatest part of

the participants who were 69.8% rated integrating

NLP with the existing communication platforms as

being simple and easy. This demonstrates that NLP

could be implemented into current business

structures with minimal inking. It is also worth

mentioning that a minority (9%) disagreed or

strongly disagreed while the majority of the

participants (94.8%) agreed or strongly agreed that

overdependence on NLP could result in losing

personal contact in customer service roles. This

implies that the authors have different views relating

to individualized customer service interactions, and

the efficiency gains through automation. The results

concerning opportunities and challenges associated

with integrating artificial intelligence (AI) into

business communication channels are presented in

Table 8.

Table 7. Use of Natural Language Processing (NLP) in Enhancing Communication in Businesses

Strongly

Disagree (1)

Disagree

(2)

Neutral

(3)

Agree

(4)

Strongly

Agree (5)

NLP significantly improves the understanding of

customer feedback and inquiries.

0.0

7.8

22.1

58.4

11.7

The use of NLP in business communication leads to

more accurate and timely responses.

2.6

15.6

10.4

48.1

23.4

NLP technology can effectively analyze sentiment in

customer communications.

5.8

5.2

24.7

2.6

61.7

The implementation of NLP reduces the need for

human intervention in routine communications.

1.3

7.8

13.0

66.2

11.7

NLP technologies are prone to errors in

understanding context and nuance.

0.0

1.3

3.9

51.9

42.9

Integrating NLP with existing communication

systems is straightforward and seamless.

0.0

6.5

23.7

50.6

19.2

Overreliance on NLP could diminish the personal

touch in customer service communications.

9.0

0.0

5.2

52.9

41.9

Source: Primary data (2024)

Table 8. Results on challenges associated with the integration of artificial intelligence in business communication

channels

Statement

Strongly

Disagree (1)

Disagree

(2)

Neutral

(3)

Agree

(4)

Strongly

Agree (5)

The cost of implementing AI in communication channels is

prohibitively high for many businesses.

0.0

2.9

78.4

18.7

AI technologies in communication can lead to a loss of

personal touch in customer interactions.

0.0

5.6

9.4

59.7

25.4

There are significant privacy and security concerns with the

use of AI in business communications.

5.8

45.2

4.7

32.6

11.7

AI tools in communication often require extensive training

data, which can be difficult to obtain.

0.0

7.8

3.0

76.2

15.7

The integration of AI can lead to a significant reduction in

employment opportunities in the communication sector

0.0

1.3

3.9

51.9

42.9

AI technologies are not yet advanced enough to handle all

nuances of human communication.

0.0

46.5

23.7

10.6

19.2

The maintenance and updating of AI communication tools

can be challenging and resource-intensive.

0.0

5.2

52.9

50.9

Source: Primary data (2024)

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Volume 21, 2024

As shown in Table 8, almost three-quarters

(78.4%) of the participants think that the costs of

applying AI in communication channels are so high

that most businesses cannot afford it. An

overwhelming majority (59.7%) of people believe

that customer interactions involving AI technologies

will result in a lack of personal touch. On the other

hand, privacy and security concerns are linked to

AI’s usage in business communications, with 32.6%

of the respondents stating they have such concerns.

Respondents show concerns about the

requirement of abundant training data for AI models

with 76.2% agreeing that AI tools in communication

mostly demand extensive training data, which might

be hard to find.

It is also worth mentioning concerns of possible

job cuts, where 51.9% of respondents agreed that AI

technological integration can result in significant

unemployment in the communication sector.

Moreover, even though AI technologies are

expanding at an accelerating pace there is still a

doubt that they can comprehend all human subtleties.

communication as indicated by 46.5% of respondents

who disagreed that AI technologies are advanced

enough. The maintenance and updating of AI

communication tools are perceived as challenging

and resource-intensive, with 52.9% agreeing that the

maintenance and updating of AI communication

tools can be challenging and resource-intensive.

The results regarding the different outcomes

relating to effective business communication are

presented in Figure 5.

The majority of respondents (25.8%) highlighted

the enhancement of internal communication as the

most significant outcome resulting from the

integration of artificial intelligence (AI) in business

communication channels. 21.6% of respondents

indicated that AI integration led to improved

efficiency in business communication. Enhanced

customer service emerged as another significant

outcome, with 17.4% of respondents acknowledging

its importance. Improved data-driven insights, cited

by 13.6% of respondents signify the valuable role AI

plays in extracting actionable intelligence from

communication data. Better automated responses,

noted by 7.5% of respondents, highlight the

efficiency gains achieved through AI-driven

automation. By implementing AI-powered response

systems, businesses can handle a higher volume of

inquiries and requests with minimal human

intervention, ensuring faster response times and

improved customer satisfaction. Improved real-time

assistance, identified by 9.5% of respondents,

underscores the importance of AI-driven solutions in

providing timely support and guidance to customers

and employees alike. The last portion of respondents

(4.6%) noted other additional benefits, including

reduced errors, cost savings, and sentiment analysis.

Fig. 5: Results on the outcomes relating to effective business communication

Source: Primary data (2024)

Percent ; Enhanced

customer service;

17,4%

Percent ; Better

automated responses;

7,5%

Percent ; Improved

efficiency in business

communication ; 21,6%

Percent ; Improved

data-driven insights;

13,6%

Percent ; Enhanced

internal

communication; 25,8%

Percent ; Improved

real-time assistance;

9,5%

Percent ; Others

specify…; 4,6%

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4.3 Regression Analysis

The efficacy of the integration of artificial

intelligence in business communication channels was

established using regression analysis as presented in

the subsequent tables.

The dependent variable is effective business

communication. As shown in Table 9, regressing the

independent and dependent variables results in an R2

value of 0.686. This demonstrates that 68.6% of the

variation in the dependent variable (effective

business communication) can be attributed to the

independent variables. Furthermore, the results of the

regression analysis demonstrated that 31.4% of the

changes were unaffected by any of the study's

independent variables.

Table 10 shows that there is a significant linear

relationship between the dependent variable

(effective business communication) and the

independent variables (use of chatbots and virtual

assistants, benefits of email filtering, influence of

speech recognition, and use of natural language

processing (NLP), as indicated by the F-statistic of

71.421 at prob. (Sig) = 0.014 conducted at 5% level

of significance.

As shown in Table 11, the regression coefficients

helped to determine the impact of various artificial

intelligence (AI) technologies on effective business

communication. The unstandardized coefficient (B)

is 0.238, indicating that, holding all other variables

constant, the use of chatbots and virtual assistants is

associated with a 0.238 unit increase in effective

business communication. The high standardized

coefficient (Beta = 0.371) and significant t-value

(1.124) with a p-value of 0.000 strongly suggest that

chatbots and virtual assistants have a positive and

significant impact on enhancing business

communication. This result supports Hypothesis One

(H1), suggesting that chatbots and virtual assistants

are valuable in improving communication in business

contexts. With an unstandardized coefficient of 0.124

and a standardized coefficient of 0.062, email

filtering is associated with a slight increase in the

effectiveness of business communication. The t-value

of 0.507 and a significance level of 0.001 indicate a

positive impact, albeit smaller than that of chatbots

and virtual assistants. This finding supports

Hypothesis Two (H2), affirming the positive role of

email filtering in enhancing business communication.

The coefficient for speech recognition is 0.106 with a

standardized beta of 0.051, suggesting a moderate

positive influence on effective business

communication. The t-value of 0.817 and a

significance level of 0.012 indicate a statistically

significant effect, which supports Hypothesis Three

(H3). This result underscores the importance of

speech recognition technology in improving business

communication. Uses of Natural Language

Processing shows a B coefficient of 0.166 and a Beta

of 0.082, with a t-value of 0.411 and a significance

level of 0.002. These figures suggest that NLP has a

significant positive effect on business

communication, supporting Hypothesis Four (H4).

NLP technologies contribute to enhancing

communication effectiveness within business

environments.

Table 9. Model Summary.

Model

R Square

Adjusted R Square

Std. Error of the Estimate

0.698

0.686

0.654

0.10214

Predictors: (Constant): Use of chatbots and Virtual Assistants, Benefits of Email Filtering, Influence of Speech Recognition,

Use of Natural Language Processing (NLP)

Table 10. ANOVA analysis

Model

Sum of Squares

Df.

Mean Square

Sig.

Regression

76.204

28.031

73.261

0.014

Residual

71.051

380

0.413

Total

147.255

382

Dependent variable: Effective Business communication, Predictors: (Constant): Use of chatbots and Virtual Assistants,

Benefits of Email Filtering, Influence of Speech Recognition, Use of Natural Language Processing (NLP).

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Table 11. Regression coefficients

Model

Unstandardized Coefficients

Standardized Coefficients

Sig.

Std. Error

Beta

(Constant)

0.588

0.126

1.941

0.007

Use of chatbots and Virtual Assistants

0.238

0.054

0.371

1.124

0.000

Benefits of Email Filtering

0.124

0.062

0.507

0.001

Influence of Speech Recognition

0.106

0.081

0.051

0.817

0.012

Uses of Natural Language Processing (NLP)

0.166

0.046

0.082

0.411

0.002

Dependent Variable: Effective Business Communication.

5 Discussion

This study examined the different opportunities and

challenges associated with the integration of artificial

intelligence in business communication channels.

The findings showed that AI tools such as chatbots

have a significant effect on the effectiveness of

business communication. A chatbot is a computer

program designed to conduct textual or sound-based

conversations. A chatbot is often employed in

customer care or data security. Chatbots help

discover that placing an online order at a business is

much quicker than picking a few buttons, putting the

item in the basket, and then finishing the transaction,

[17]. While some systems look for keywords from

the inputs and then react from the database with the

appropriate keywords, other chatbots utilize the

rendering system for natural language. The results

show that businesses that have adopted chatbots have

a great advantage since they will be crucial for all

organizations in the future. Bots are economical and

capable of responding and interacting with several

people at once, [4], [9]. Machine learning bots are

much more intelligent and they help in enhancing the

effectiveness of communication between customers

and business owners, [3], [6], [19]. It is important to

note that business executives may design or choose

effective chatbot solutions based on the connections

this research validates, [20]. According to [8],

companies have the opportunity to enhance their

customer service performance, therefore increasing

their sustainability and yielding several long-term

advantages. For instance, businesses might choose

chatbots that generate internal agility by

automatically responding to consumer inquiries,

addressing customer issues, and recognizing

customer preferences to enhance customer

experience and satisfaction, [17]. Companies might

use chatbots that generate external agility with data

analysis to spot shifts in consumer demand and the

market to better service customers and stay

competitive, [26], [56]. Chatbots and virtual

assistants embody a major progress in business

communication. These intelligence-driven tools

allow users to interact with customers in a real-time

and efficient manner. Such AI-based tools can handle

multiple queries at a time without compromising the

quality of service. This feature of the AI technology

is in line with the outcome of study [1], which

emphasized efficiency improvement due to

automation in customer service. The personalized

customer interaction capability endowed on chatbots

makes them even more vital in building satisfaction

and loyalty, a very critical competitive edge in

today's market. In addition, the use of machine

learning algorithms for chatbots enables their ability

to learn from their interactions leading to more

accurate and relevant responses, [3]. This dynamic

adaptability emphasizes the transformative nature of

chatbots in reshaping the connections between

business and communication.

The findings show that NLP is essential to AI-

powered business communication because it makes it

possible for robots to comprehend and produce

human language, [6], [11], [15]. In customer support,

voice-based interactions are becoming more and

more common, [57]. The emergence of voice-based

customer support interactions is a significant turning

point. Voice recognition technology makes it

possible for chatbots and virtual assistants to

comprehend spoken language and reply accordingly,

resulting in a more intuitive and natural user

experience, [3], [5], [19]. AI's useful uses are

constantly growing, proving how flexible and

adaptable it is in a variety of settings, [9]. Since the

regulatory environment around AI changes all the

time companies have to keep aware of what they

need to do to comply, [8]. The future seems

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Stavros Kalogiannidis, Christina Patitsa, Michail Chalaris

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Volume 21, 2024

promising for the dynamic and revolutionary

integration of AI into corporate operations.

Businesses will need to find a balance between

realizing the promise of artificial intelligence (AI)

and guaranteeing its ethical and responsible use [3].

Natural Language Processing (NLP) forms the

backbone of AI's effect on business communication,

allowing computers to comprehend and generate

human language comprehensively and concerning the

context. This technology not only automates routine

communications and improves the efficiency of

customer service but also analyses the sentiment and

understands the subtleties of customer feedback, [4].

The development of NLP technologies, especially in

sentiment analysis, helps organizations understand

the preferences and sentiments of their customers,

thus permitting them to establish communication

strategies that line up perfectly with the intended

audiences, [5] of despite the complexities of

understanding context and the nuances in NLP, it is

projected that AI will develop to the point where it

will be delivering even higher and more sophisticated

levels of business communication.

Examining the possible opportunities and

difficulties of the digital transition and the

incorporation of AI systems into future work

practices is becoming more and more necessary, [5],

[14]. The use of AI in the workplace has the potential

to have of several effects on human behavior,

including the establishment of new morals, ethical

standards, and values as well as the learning and

prediction of human behavior, [16], [49], [52]. A

research study examines the potential issues with AI

in the workplace while also offering insights into the

changing nature of work in light of the newly

identified COVID-19 difficulties, [2], [20]. Past

studies contend that key components of a policy

agenda and implementation plan for future AI

practices in the workplace are explainability,

accountability, and digital literacy among all

stakeholders and users (decision-makers, employers,

and workers), [15], [34], [58]. The management of

employees in an organization, for instance, is one use

of AI that may have ethical, technological, and

societal ramifications in the workplace, [2], [13],

[25]. AI systems allow organizations to monitor,

coordinate, and make decisions regarding their

workforce, including hiring, promoting, assigning

tasks, and other matters. To maximize work

performance, AI prediction models have been

proposed to evaluate the degree of staff competency,

[16], [59]. Using a suggested artificial intelligence

model, job knowledge, self-motivation, self-concern,

and role perception are utilized to predict work

performance as a function of job competence, [60].

The implementation of AI in business

interactions can raise ethical and social issues, [61].

AI technologies are increasingly integrated into

business models leading to issues of employment,

privacy, security, and the loss of that human factor in

communication and customer support, [20]. Ethics of

AI Management necessarily involves a balanced

approach that includes both the pros of efficiency and

innovation as well as the possible negative impacts

on society and the workforce. To address these

difficulties effectively, future AI practices in the

workplace should focus on transparency,

accountability, and the development of digital

literacy among all interested parties.

6 Conclusions

This study focused on establishing the different

opportunities and challenges associated with the

integration of artificial intelligence in business

communication channels. The results demonstrate

that creative and professional usage of AI tools has a

greater impact on both internal and outward

communication in businesses. As per this study,

business communication may become effective by

integrating sensors, chatbot, email filtering, speech

recognition, and NLP into various business

processes. As a result, this research provides valuable

information regarding ways in which organizations

can effectively implement chatbots to gain business

advantages. The next stage of the research may

involve exploring the nature of other organizational

factors that break the correlation between the use of

chatbots and their shortcomings. The progress toward

natural language processing will permit bots and

virtual assistants to have a more creative

understanding of complex questions and context.

These findings give insight into specific intricacies

and difficulties that companies might come across

when they use AI in business procedures through the

identification of these difficulties. The difficulties in

integrating AI might vary based on the particular

circumstances of any company. Future developments

that are intriguing are shown by the trajectory that

virtual assistants and chatbots have taken in customer

care for cellular services. It is envisaged that these

technologies can be integrated with the Internet of

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Stavros Kalogiannidis, Christina Patitsa, Michail Chalaris

E-ISSN: 2224-2899

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Volume 21, 2024

Things (IoT), enabling smooth communication

between gadgets and AI-powered support systems.

Another development is improving multilingual

capabilities, which makes content more accessible to

a wider range of users. This research has significant

applications as well. This research demonstrates a

relationship between customer service success and

chatbot agility, both internal and external. This

research gives practitioners a better knowledge of the

critical role chatbot-enabled agility plays in

enhancing customer service. Companies are

substantially investing in the development of AI

technologies and entering this new environment as

customers spend more time in digital spaces enabled

by AI. Therefore, by incorporating chatbots into

corporate operations and increasing the effectiveness

of business communication, our research may help

business executives improve business

communication.

6.1 Recommendations

This research has shown that businesses have unique

obstacles and difficulties when it comes to using AI.

The success of these interventions should be

aggressively promoted, notwithstanding the many

EU, national, public, and commercial programs that

support digitalization and artificial intelligence. This

is because businesses are critical to the adoption of

AI in the European industry.

It will be crucial to make sure that investment

and funding assistance for businesses, in particular,

are suitably targeted and successful, as the primary

financial impediments to business adoption of AI.

Additionally, keeps the Commission must keep

assisting businesses in their digital transformation of

using its COVID-19 recovery plan. Additionally, the

monitoring procedure may be combined with self-

assessment AI maturity tools at the start and finish of

these programs, allowing businesses to take part in

the accelerator initiatives to reap legitimate

advantages.

that support the Greek government needs to

support initiatives that use AI and other new

technologies to strengthen the resilience of Greek

supply chains within the framework of global value

chains (GVCs). By doing this, supply shocks or the

economy may not cause future supply bottlenecks for

European industry. AI-powered big data analysis

may aid in the early detection of issues. Risks may be

decreased by diversifying suppliers and thinking

about moving some manufacturing to Europe, made

possible by AI and other cutting-edge technology. If

big businesses and multinationals invested in near-

shore outsourcing to more locally focused

manufacturers, this may assist in strengthening

businesses across Greece and Europe.

6.2 Areas for Future Research

Our findings might be expanded in several ways by

future research. Initially, research in the future might

evaluate our approach using several AI tools to see

which kind of tool performs better in other settings,

such as human resource management. For example,

chatbots designed to help consumers and workers, for

instance, are likely to be used in various contexts.

Subsequent research endeavors may include

conducting field trials to investigate the most

effective amalgamation of chatbot usage scenarios

and their impact on chatbot agility. Future research

can also focus variables that affect the desire to

continue using the emerging AI tools on modern

businesses.

Declaration of Generative AI and AI-assisted

Technologies in the Writing Process

During the preparation of this work, the authors used

ChatGPT in order to improve the readability of the

manuscript. After using this tool/service, the authors

reviewed and edited the content as needed and take

full responsibility for the content of the publication.

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DOI: 10.37394/23207.2024.21.157

Stavros Kalogiannidis, Christina Patitsa, Michail Chalaris

E-ISSN: 2224-2899

1944

Volume 21, 2024