Difficulties Faced by SMEs and Traditional Industries in Digital

Intelligent Transformation Under the Background of Digital

Intelligent Transformation and Suggestions

Tong Chen

Program - Science Bach, Science Mcmaster University Hamilton, Canada

Keywords: SMEs, Digital Intelligent, Transformation.

Abstract: Enterprise digital intelligence is far deeper than just a catchphrase; it is the actual application of information

technology to completely reshape business development and management modes as well as the decision that

businesses have to make to move from the industrial to the digital economies. This paper starts by discussing

the challenges of digital intelligent transformation that small and medium-sized businesses and traditional

industries must overcome. It next examines methods and strategies for optimizing the transformation while

managing costs and talent. Lastly, it provides an overview of a workable enterprise digital intelligent

transformation optimization plan in the context of digital intelligent transformation. This project's research

examines the need for and benefits of digitally intelligent transformation, examines the cost conundrum that

arises when small and medium-sized businesses and traditional industries undergo digital transformation,

offers these businesses a post-transformation plan, weighs the pros and cons, and resolves current issues. In

the evolving digital age, businesses overcome the current challenge and present useful optimization concepts

and helpful recommendations.

1 INTRODUCTION

Over the last decade, digital transformation has

emerged as a vital step for businesses to compete in

today’s digital environment and offering myriads of

benefits including improved efficiency, reduced costs,

and happy customers (Adams, 2004). The digital era

has transformed aggressively the way businesses are

operated, with a great contribution from the digital

intelligence which dictates new and dynamic

management models (Berman, 2022). The conversion

is not merely an empty terminology but an influx of

IT which completely redesigns the firm structure and

opens up a new horizon for the enterprise

development patterns. Small and medium-sized

enterprises (SMEs) and traditional industries often

encounter their chances in the process of digital

intelligent transformation (Koumas, 2021). The main

challenges are related to cost and talent constraints,

which could lead to an ineffective adoption of digital

intelligence strategies. Along with the hurdles

brought by digital transformation are the benefits,

https://orcid.org/0009-0004-0071-1141

which are unquestionable as well, and the time to

uphold the business digital economy era has

determined to be now.

The era of digital intelligence has transformed

lives as the internet, customer support, smart

production and big data become easier and more

effective to use (Moreira , 2018). Digital intelligence

approaches have also led to massive economic, time,

and human savings, streamlined security and stability

and strengthened management of the countries.

Traditional business sectors are accelerating digital

intelligent transformation process by transferring

their primal stage of information to more advanced

digital intelligent stage (Moreira , 2018; Ghosh , 2021;

Berman, 2022). This transition allows businesses the

opportunities to better engage their customers,

increase output and be competitive in the modern

technology. Digital intelligent transformation is an

inevitable need, and firms must be aware of the

challenges and opportunities that are associated to

this transformation. By dealing with the challenges of

cost and talent shortage and applying solutions such

736

Chen, T.

Difﬁculties Faced by SMEs and Traditional Industries in Digital Intelligent Transformation under the Background of Digital Intelligent Transformation and Suggestions.

DOI: 10.5220/0012970600004508

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

In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2024), pages 736-741

ISBN: 978-989-758-713-9

as data-led decision making, iterative system

updating and talent development, SMEs and

traditional industries will be able to successfully

implement a digital transformation in their operations

(Li, 2020). This research aims to conduct an analysis

and study the significance and benefits of digital

intelligent transformation, touch on the cost

conundrum of digital transformation in SMEs and

traditional industries, provide institutions with a plan

after transformation, evaluate merits and demerits,

and solve the existing issues. Through the evolution

of digital era, enterprises can escape from the past

impasse and provide concrete plans and efficient

solutions.

2 THE BENEFITS OF DIGITAL

INTELLIGENCE

2.1 Digital Intelligence and Supply

Chain Efficiency

The role of digital intelligence is steadily growing in

modern business, enabling companies to take

advantage of different opportunities (Adams, 2004).

There are several digital advantages in the supply

chain. For example, the customers can be served

faster and much better and there are cost savings

deriving from automating some tasks and efficient

use of hot stock inventory with the data provided by

the real time collection and interpretation of the

system of business. The intelligence of digital

appraisals boosts supply chain effectiveness with the

features of real-time data, process automation and

basing on informed decisions (Mohsen, 2023). These

result in declining economic and time costs, enhanced

security, and growing profits. Digital intelligence in

supply chain management empowers one to

automatically perform tasks, analyze information and

make data-driven choices. AI systems equipped with

the ability to analyze massive data sets in real-time

deliver information that allows the businesses to base

their decisions on objective data and auto-optimize

their supply chain processes (Mohsen, 2023). For

example, Artificial intelligence (AI) can be employed

to fine-tune delivery routes enabling the goods to be

carried by the shortest and quickest routes. The use of

the right transportation modes, delivery routes, and

fuel consumption tracking ensure resources to fuel

costs, energy, and emission reduction; hence,

increasing the business efficiency.

Automated vehicles are rightly among the key

technological developments that result in better

supply chain efficiency (Reed, 2021). In particular,

autonomous long haul trucks have the potential to

revolutionize the efficiency of supply chains,

especially through the widespread labor shortage of

today. For instance, the autonomous semi-truck

company TuSimple ran a groundbreaking test with a

driverless tractor-trailer that finished its 80-miles

route with zero human intervention. Autonomous

vehicles are predicted to have such a beneficial effect

on emissions as the AI in vehicles can accurately

calculate auto fuel consumption and, thus, minimize

it (Reed, 2021). Moreover, one of the latest

innovations in delivery is drone deliveries that can

effortlessly do what human delivery people otherwise

do (Boysen, 2018). Drones with automation are able

to fly from local distribution centers to chosen

destination points, taking the air route without roads

and traffic interfering in the process (Boysen, 2018).

This can change the effectiveness of supply chain in

the very last mile dramatically if it is realized,

shortening the delivery times and thus making

customers more satisfied.

2.2 Digital Intelligence Strategy

Digital Intelligence Strategy is a holistic method that

incorporates modern technologies such as AI,

machine learning and big data analytics to enhance

performance by optimizing business processes and

improving decision-making. Digital intelligence

strategy reduces economic cost, time cost and human

cost at the same time; it makes security, stability and

efficiency and strengthens management (Ashwell,

2017). AI is one of the technologies that are

transforming digital innovation by making it possible

for companies to gather and analyze big data,

automate processes, and come up with smart

decisions. In particular, AI is recently driving the new

type of digital innovation into Human Resource

Management (HRM) through opening the new fronts

for collection and analysis of data while being

compliant with the General Data Protection

Regulation (GDPR), lowering biases, and bringing

forth the accurate recommendations (Trocin, et al.,

2021). AI technology has three main features that are

essential to digital innovation: storage, analytics, and

recommendation. These elements harmonize and lead

to a tailored regulation which is designed for spurring

in innovations of the digital sphere. Besides crucial

players like top management, AI developers, and HR

employees, a smooth utilization and implementation

of AI technology is guaranteed. The top management

actors leads the implementation and recognition of AI

based technology, by performing strategic planning

Difﬁculties Faced by SMEs and Traditional Industries in Digital Intelligent Transformation under the Background of Digital Intelligent

Transformation and Suggestions

737

and domination of specific directions to reach the

decisions made (Marnewick and Marnewick, 2021，

Ghosh et al. 2021) suggest that traditional industries

are gradually implementing digital intelligent

transformation techniques, progressing from basic

digitization to more sophisticated digital intelligence.

Businesses have the ability to better satisfy consumer

needs, increase efficiency, and maintain their

competitiveness in the digital age due to this

transformation. For example, digital innovation and

AI-supported digital advancement processes have

implications for managing massive amounts of data

(Trocin et al., 2021). This implies that information

gathering along with data analysis are the two

primary stages that businesses pursue. Data gathering

is the process of potentially obtaining data from

various sources in a variety of formats while adhering

to security and privacy laws. Data analysis is the

process of creating objective methods for data that is

supported by evidence. Digital intelligence strategy is

a critical component of digital innovation, enabling

organizations to collect and analyze vast amounts of

data, automate processes, and make informed

decisions (Ashwell, 2017). For instance, AI y is a key

enabler of digital innovation, providing organizations

with the necessary conditions and opportunities to act,

while actors decide the type of information to collect,

when to process it, and based on which criteria.

3 PROBLEMS IN THE

TRANSFORMATION PROCESS

3.1 Cost Challenges

Digital transformation provides a plethora of

advantages but it as well poses some challenges and

organizations need to address them in order to ensure

seamless implementation (Deloitte, 2015). Digital

transformation is a priority for companies aiming at

productivity improvement, operational efficiencies

and the provision of customers’ experiences.

However, the cost of digital transformation can range

considerably on the businesses that invest depending

on their size, industry, and objectives. The main

reason for the cost problems in digital transformation

is that it can be a serious obstacle for an organization

(Zhong, 2019). Technology expenditures, including

investments in network equipment, servers, software

licenses, and network infrastructural upgrades, will

take a heavy toll on budgets. It is usually not

affordable to them because it often involves complete

setting up of digital solutions, which these companies

are unable to implement given their limited resources.

The ten key developments driving the cost of digital

change fall into three categories namely; poor

adoption practices, lack of buy-in from stakeholders

and lack of engagement (Ovington, 2024). These

elements may materially affect the outcome of

digitalization which is the primary reason for an

organization to take into account them when

preparing their transformations or any change

initiatives.

Particularly for SMEs, which typically experience

a structural shortage of financial resources, it is

imperative to comprehend the straight-forward

financial consequences of Open Innovation policies

(Costa, 2023). Such costs include developing internal

skills for monitoring the outside world, locating

reliable outside data sources, putting internal asset

protection plans into action, and the possible

expenses associated with potential losses of

competitiveness brought on by the spread of

important proprietary information (Costa, 2023). In

addition, there is also the problem of deciding if the

transmutation will be really bringing along all those

benefits as promised which are enough to justify the

invested money both the initial and the ongoing.

Enterprises have to measure the profitability of the

digital transformation projects in comparison to the

anticipated results as well as keep the financial

expenditure in line with the expected outcomes. The

assessment or evaluation process is not a simple

matter and goes beyond the direct costs involved, also

incorporating indirect factors like the productivity

improvement, customer satisfaction, and competitive

advantages.

3.2 Talent Challenges

Talent scarcity and an innovative search for new

models make it a difficult task for companies in their

digital transformation process. The majority of SME

entrepreneurs conduct marketing of their business

online through digitalization (Nawawi 2024). The

primary goal is still the traditional transactions, so the

internet transactions are the alternative to expand

their options and get additional income. The speedy

rate of technological evolution necessitates

institutions to have access to proficient qualified

individuals that can navigate the complex digital

landscapes and lead to innovations. Nevertheless,

scarcity of such talents indeed presents the

challenges, especially in the areas of machine

learning, data analytics and cybersecurity.

In addition, the scarcity and its corresponding cost

is a factor that makes it harder for the organizations,

EMITI 2024 - International Conference on Engineering Management, Information Technology and Intelligence

738

especially SMEs due to their lack of resources and the

fact that they have a backlog in digitalisation of their

corporate processes. To acquiring and retaining the

professionals with specialized expertise in new

emerging technologies which is often referred to as

the digital talent gap by various stakeholders

(Sommer, 2023). The competition among companies

for high-quality job seekers get fiercer, which leads to

talent shortage and hike in recruitment costs.

Furthermore, the need for new business approaches

and digital channels requires a skill set with technical

abilities and creativity, strategic thinking, and

adaptability.

4 SOLUTIONS TO ADDRESS

CHALLENGES

4.1 Use of Data for Strategic Insights

Data can be utilized by organizations for detecting

areas of improvement, reduction of costs and

operations efficiency due to better informed decision-

making (Vassakis, 2018). Data analytics increases the

ability of SMEs to detect relationships, consider

tendencies, and to determine some ratios that cannot

be immediately observed by the traditional methods.

Subsequently, the conventional business will become

endowed with a capacity to make more meticulous

decisions, spend money wisely, and implement

strategies whose core purpose is to lead to the growth

and innovation of their industries. In a digital

economy transformation, data analysis can be a

valuable for organizations when there are decisions

such as if the technology investments will give out

maximum return (Vassakis et al., 2018). Capacity to

implement historical data charts provides the same

case predictive analytics the power to predict future

trends, enabling organizations be proactive in

meeting client needs, stock level and supply chain

efficiency. Other than that, data analytics used to

identify and then to fix the inefficiencies, to save

resources, to finally better the performance of the

organization (Vassakis, 2018). The companies use the

data analytics for competitive advantage necessitates

having a proper budget to buy software tools,

technologies, and a good workforce. These include

creating a data-oriented culture that values data

driven decision making, allocating funds for data

analytics platforms and tools, and equipping the

workforce with the skills and ability to dissect and

interpret data.

4.2 Iterative System Updates

Developing iterative system updates is paramount to

keep abreast with the agility and flexibility emerging

in digital ecosystem (Li, 2011; Altın, 2017).

Constantly changing systems increase ability of

employees to adapt faster, improve their skills, and

boost overall productivity. Implementing iterative

updates allows companies to get ahead of emerging

technologies, to have a solution to a wide range of

challenges and to optimize and provide

responsiveness for their digital infrastructure based

on the dynamically changing organizational

conditions. System iterative updates include regularly

updating software, hardware, as well as digital

infrastructure components to integrate functions, bug

fixes, and performance improvement (Li, 2011).

SMEs and traditional businesses can minimize the

disruption of their system updates and lessen the risk

of system failures functionality using an iterative

approach so that their digital infrastructure remains

up-to-date and secure. Similarly, iterative upgrades

allow organizations to address user requirements and

collect feedback to further improve the system.

Through regular solicitation of feedback from users,

SMEs are able to identify areas of their digital

infrastructure to improve, focus on feature

development, and make sure that they meet their

users' expectations. One of the ways of ensuring an

iterative system among organizations is to create

processes and procedures that will streamline the

updates. It also entails formulation of a change

management framework, which covers the allocation

of roles and duties, and the setting up of

communication channels to inform users about

inevitable adjustments and updates. This implies that

SMEs implement the mechanisms that ensure their

transactional systems work correctly are secure and

can be tweaked based on their individual needs.

4.3 Addressing Information Silos

There are two ingredients which make all the

difference: effectively getting rid of data islands and

making sure the data is shared among all partners.

Successful dismantlement of the walls, with the

development of a team work culture that is

characterized by effectiveness in terms of

communication, decisions and operations, should be

the result of the following recommendations (Fox,

2021, Bouwer, 2021). Through cross-department and

function data sharing, these organizations can

eliminate duplications, enhance the data accuracy and

generate synergies producing cost cuts and enhanced

Difﬁculties Faced by SMEs and Traditional Industries in Digital Intelligent Transformation under the Background of Digital Intelligent

Transformation and Suggestions

739

performance. The drawback of information silo is a

set of intercorrelated behavior performed by

individuals who experience a kind of border between

the units and, therefore, do not share information

(Bouwer, 2021). This can be achieved by introducing

platforms for information sharing, setting multi-

professional teams, and by fostering a culture of

collaboration and transparency. SMEs and traditional

businesses can leverage data silos by anchoring in

their decision-making processes, avoiding

redundancy in assignments, improving efficiencies in

operational processes. One of the weakest points of

SMEs is the data on their customers that they do not

have the opportunity to use for quality strategies and

thus the consumers' satisfaction is getting lower.

Likewise, sharing customer information cross-

departmentally can help SMEs understand more their

clients' needs and their likes and so the departments

can present the best solution. SMEs can reduce

information silos by developing well-defined data

sharing policies and procedures to identify and assign

roles and responsibilities as well as avenues for

collaborations and information exchanges between

employees. Indeed, they will make certain that the

process of digital infrastructure is focused on the joint

circulation of information and collaboration and

ensure their achievement of the digital transformation

objectives.

4.4 Resource Optimization Through

Digitalization

The consumption of resources such as energy,

manpower and time is reduced under the

digitalization. Digital transformation involves

elimination of resource wastage such as energy,

manpower and time by application of optimal

utilization techniques (Brüggemann , 2020). Through

digitalization, computerization, repetitive tasks

automation, and technology utilization, organizations

can decrease resource consumption, improve the

productivity, and make their operations more

streamlined. Digitalization enables more efficient

allocation of resources that reduces waste and enables

organization to function leaner and more sustainably

(Brüggemann, 2020). The utilization of digitalization

in resource optimization includes the implementation

of technology that eliminates the manual processes

from the system, reduces paper-based workflows, and

streamlines operations (Topić, 2020). Thus, SMEs

and traditional be able to cut down on resource

consumption including energy, manpower and time,

therefore, improving efficiency and sustainability of

operations. Digitalization also empowers companies

utilizing data and analytics to achieve optimal

resource use. For instance, SMEs can use the energy

consumption data to identify energy efficiency tips

that can significantly reduce cost with minimal

negative environmental impacts.

5 CONCLUSION

In summary, digital intelligent transformation is a

fundamental aspect of the current corporate operation

models. Enterprise digital intelligence is not only an

empty slogan, but the real use of information

technology to fundamentally transform enterprise

management mode, is the innovation and

transformation of the enterprise development. This is

the inevitable choice for the enterprise to evolve from

the industrial economy era to the digital economy era.

Building from the case of digital transformation

dilemma which affects SMEs and traditional

industries seeded in the wake of digitalization, the

paper explores the methods and strategies of

optimization of transformation as well as cost cutting

and shortage of workforce and finally presents the

realistic digital transformation optimization scheme

for enterprises under the backdrop of digitalization.

The cost and talent issues can be targeted through

data-driven planning, system iterations and talent

development. As a result, SME and traditional

companies can achieve seamless transition into

digital age. Research exploring the long-term effects

is needed as well as providing expertise for small and

medium enterprises and traditional sectors. Hence,

SMEs and traditional businesses can better assure that

their digital transition activities would be successful

in creating opportunities of growing and come up

with innovation in the virtual world.

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Transformation and Suggestions

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