Civil Law Means of Digital Business Transformation

Olga V. Sushkova

Kutafin Moscow State Law University, Moscow, Russia

Keywords: Business Transformation, Digitalization, Business Entities, Artificial Intelligence Technology, Civil Law

Remedies, Legal Phenomena.

Abstract: The author raises the problem of using various civil legal means, specific legal phenomena, such as cloud

technologies, the Internet of Things, artificial intelligence (AI) for transforming business processes in

digitalization. The author analyzes the legal nature and attributes of new technologies that can change the

future of business. First, it is necessary to analyze the tools for digital transformation of business and

applications in various industries to reveal the stated topic. Second, it is necessary to analyze the development

of AI technology and its legal regulation: machine learning (ML) and deep learning (DL). Artificial

intelligence technologies cannot be explored without considering cloud computing and big data: these

technologies together represent a complete mechanism for the digitalization of business processes in business

and professional activities.

1 INTRODUCTION

Entrepreneurial and professional activities are

currently transforming towards digital business,

which is viewed through the prism of four new

technology areas: artificial intelligence, blockchain,

cloud technologies and data analytics. Because of the

dynamic nature of the development of innovations,

the potential of this hybridization, integration,

recombination and convergence of these

technological areas will be analyzed. The author will

use multidisciplinary approaches that show how

digital tools affect all areas of business processes.

Business digital transformation is a strategy that

grabs attention as companies face the challenge of

continually improving their business processes and

capabilities. It stimulates new ways of working and

interacting with customers by stimulating the creation

of new business models.

Digital business transformation can make firms fit

for the future and increase average net revenues, and

use technology to improve a firm's performance. This

digital business transformation has already brought

significant changes to business operations through

improved customer service, payments, business

models and new methods of online interaction. As

noted, Amazon could revolutionize business across a

https://orcid.org/0000-0002-0098-9555

wide range of sectors, including supermarkets,

publishing and logistics. They did this by collecting

and using the information to improve customer

service (Grewal, Hulland, Kopalle, 2020).

Foreign literature has repeatedly noted that digital

business transformation is a way of doing business

and transforming it from traditional to digital (Li, F.,

2015). We believe it to be more than just a transition

from «bricks and mortar» storefront for shoppers to a

«clicks and bricks» environment. Digital

transformation permeates all areas of business

through introducing advanced and often converged

technologies. The goal of digital transformation is to

harness digital opportunities to transform the

traditional enterprise into a leader in the digital

economy. Most digitally advanced firms such as

Google, Netflix, Uber and Airbnb have successfully

developed and leveraged their digitized, open and

collaborative business models embedded in a unified

ecosystem of producers and consumers. Some argue

digital business transformation is an ecosystem of

innovative platforms in which “Uber, the world's

largest taxi company, does not own vehicles and

Facebook, the world's most popular media owner,

does not create any content. Alibaba, the most

expensive retailer, has no inventory, and Airbnb, the

world's largest housing provider, does not own real

Sushkova, O.

Civil Law Means of Digital Business Transformation.

DOI: 10.5220/0010662200003224

In Proceedings of the 1st International Scientiﬁc Forum on Jurisprudence (WFLAW 2021), pages 77-82

ISBN: 978-989-758-598-2

estate ”(Goodwin, T., 2015). In this digital world,

subscription services are preferable to owning assets

or goods with little inventory requirements or

depreciation costs of those assets.

Although digitalization of business seems to be

quite wide, we will pay attention to all four existing

technologies: artificial intelligence, cloud

technologies and data analytics.

Digital transformation is inextricably linked to

digital business strategy (for example, cross-

functional integration, structural changes) and

organizational capabilities (for example, through the

management bodies of a legal entity, considering

their competencies, as well as the capabilities that are

established by the Charter of the organization for the

executive body of the legal entity). The tools for the

digital transformation of business and professional

activities focus not only on implementing of reliable

technologies but also on the formulation of a clear

vision, transformation of the business model, the

development of dynamic opportunities and

understanding of partners and contractors. In foreign

legal order, digital business transformation is

understood in different ways. On the one hand, digital

transformation permeates all operations and

transactions carried out in an organization to control

all business processes (Kumar, Ramachandran,

Kumar, 2020), on the other hand, business

digitalization is focused only on the partners of a

business entity, as well as its digital products and

services (Davenport, Spanyi, 2020), thirdly, digital

transformation of entrepreneurship is a new business

model for creating more powerful mechanisms of

entrepreneurial efficiency in professional activities

(Verhoef, et al., 2019).

2 METHODOLOGY

The methodological basis of the research is the

general scientific dialectical method of cognition and

specific scientific and special legal methods:

comparative legal, formal legal, historical legal,

statistical, methods of economic analysis. About this

study, the formal legal method allows the study of

civil law means, as legal phenomena (AI, cloud

technologies and others) and the impact on the

transformation processes faced by subjects of

entrepreneurial and professional activity. This allows

finding the meaning and significance of the rules of

law governing relations in this area, based on their

content. We associate the comparative legal method

with the specifics of the object and subject of

research. Since a sufficient systemic legal framework

and relevant legal practice at the national and

international level on the issues under consideration

have not been developed, the study of various

doctrinal points of view of foreign and domestic

scientists, legal documents, their comparison and

analysis allows a more detailed study of this area of

research. The historical and legal method allows the

study of legal relations associated with the use of AI,

cloud computing, the Internet of things and others

from the point of view of their evolution in the legal

field, not limited to the analysis of these legal

institutions within any one country. That allows the

definition of their specific legal content through their

historical assessment.

Since the method of economic analysis is aimed

at studying legal norms from the standpoint of their

economic efficiency, impact on public welfare and

optimal allocation of resources, it is used in the study

of relations in which one of the structural elements is

new technologies, as legal phenomena in the system

of legal means.

3 RESULTS OF THE STUDY

3.1 Digital Transformation of Business

in Various Industries

Manufacturers use big data analytics to optimize

equipment utilization, reduce waste of materials and

other resources, develop supply chain networks, and

improve business efficiency. The auto industry, for

example, is trying to compete with leading car

manufacturers such as Tesla and Faraday Future. Any

stable manufacturer understands it is important to

combine digital technology with traditional processes

to stay ahead of the competition. Audi has benefited

enormously from a digital transformation in sales,

marketing and operations to better meet demand

(Dremel, Wulf, Herterich, Waizmann, Brenner 2017).

Smelters are leveraging the power of digital tools to

increase production by visualizing productivity,

optimizing operations, and gaining insight into the

causes of disruption (Hartmann, King, Narayanan,

2020). Pharmaceutical manufacturers now use less

production space and quality control has improved as

it is easier to detect counterfeit drugs and chemicals.

The consumer goods industry has also made

improvements through digital transformation

(Sushkova, 2021) by getting closer to consumers and

forging long-term relationships that are tantamount to

re-doing business and increasing satisfaction (Kumar,

Ramachandran, Kumar, 2020).

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Someone widely used digital transformation tools

in trade, in the banking sector and the field of turnover

of the results of intellectual activity. Retailers deserve

special attention. Retailer «Magnit» has introduced

3D equipment modeling into business processes. The

technology creates three-dimensional images of

structures and various details: the location of the light

source, glare and reflection, the correspondence of

design elements to the design of the store, and many

others.

3D modeling of equipment is a new technology

for domestic retail and is currently not widely used.

With the help of specialized engineering programs for

creating and processing models, the company's

experts can "arrange" and "move" models in the sales

area, select the optimal equipment options by the

company's standards and analysis of customer

preferences.

New technology reduces time, money and labor

costs. For several hours online, employees can

visualize models of commercial equipment. There is

no need to order and pay for many test samples that

do not always meet requests (Sushkova, 2021).

3.2 Afiniti's AI-powered Digital

Transformation Study

Afiniti uses AI to predict interpersonal behavior

patterns for companies seeking success in human

interactions. The goal is to replace the first-in, first-

out (FIFO) caller system, which can make serving

customers more difficult. Afiniti uses AI, big data

analytics, and machine learning (ML) algorithms to

analyze human behavior and leverage the results to

improve customer-agent interactions. Afiniti

customers can tailor their services, delivering better

revenue and improved retention rates for companies

like T-Mobile and Virgin (Afiniti, 2018).

Afiniti collects data from different

communication vectors, call history and CRM

records for customers around the world. It then

combines the results of the interaction layer from

customer data and uses specialized machine learning

algorithms to identify different consumer behavior

patterns and predict outcomes based on their

behavior. Since the number of interactions is quite

low compared to the data available (which includes

demographics, interaction data, and internal

analytics), using machine learning alone can lead to

unreliable results. The system launches the algorithm

in real time upon the customer's call. Afiniti launches

the process in less than 200 ms, allowing the caller to

connect to the right agent. The call results are

recorded for future interaction with the customer to

improve the quality of service.

3.3 Machine learning as an AI

Application

The term machine learning (ML) was coined by

Arthur Lee Samuel in 1995 (Syam, Sharma, 2018).

Machine learning is considered a prerequisite for

developing AI applications - it requires vast amounts

of data. ML can be categorized as supervised learning

where certain data is provided to produce a result, but

this is a unique case for unsupervised learning when

the data is unstructured and unlabeled. Machine

learning without a teacher teaches a machine to detect

hidden patterns and structures without a target

variable (Lim, Tucker, Kumara, 2017). For example,

M6D uses this technology to target potential

consumers by displaying targeted ads for hundreds of

brands (Perlich, Dalessandro, Raeder, Stitelman,

Provost, 2014). With the rise of actual price

exchanges, the advertiser can target specific potential

customers. Machine learning plays an important role

in solving this problem, calculating vast amounts of

data about consumer behavior, deciding and

delivering advertisements in near real time. AI and

machine learning have a positive impact on personal

selling and sales management. While many believe

that AI and machine learning will destroy jobs, others

believe they will create over 2 million new jobs by

2025. Sales management (as we have shown in

Russia, added by the author) can be very effective in

using machine learning due to timely interactive

detailed reports and service data facilitating the

seller's work, allowing companies to translate

discovered patterns and trends into actions.

Deep Instinct is one of the world's first deep

learning companies specializing in cybersecurity.

Deep Instinct leverages DL-based prediction

capabilities to help companies protect themselves

from cyberthreats, complex persistent threats and

zero-day threats, and can run on servers, mobile

devices, and company endpoints. During the

preparation phase, data samples are presented for a

deep learning neural network that contains multiple

flagged files such as malware (viruses), mutations,

etc. During the training phase, raw data is trained

using graphics processing units (GPUs) that run faster

than central processors (CPUs), for example, we can

train data in 3 days. In the deep learning phase, data

is processed using DL algorithms. During the

detection phase, neural networks detect cyber threats

of an ongoing learning process. During the prediction

phase, the deep learning brain can now predict the

Civil Law Means of Digital Business Transformation

level of cyber threat a file may pose. At the stage of

creating an agent, the brain can turn terabytes of

understanding into megabytes of instinct. At the stage

of agent deployment, it is domain independent - it can

be used for endpoints and mobile device servers. At

the stage of protecting and preventing agents, the

latter check every file, macros, script, etc.

One should agree with A.V. Minbaleev, who

notes that «the appearance on the Internet of some

new relations, types of mass communications, active

development and functioning problems necessitate

expanding legal regulation, increasing the number of

areas and aspects of legal regulation about the

Internet». S.S. Alekseev pointed out that the

expansion of the sphere of legal regulation does not

comprise a partial strengthening of coercive measures

of influence, sometimes caused by temporary

difficulties, a difficult situation, but of the expansion

of such areas and aspects of legal regulation, the

functioning of which is characterized by an increase

in moral principles in the life of society, an increase

in the organization of public relations, the order of

responsibility in relationships between people

(Alekseev, 1971). The ideas expressed by the scientist

over forty years ago are still relevant to our days

(Minbaleev, 2014).

4 THE DISCUSSION OF THE

RESULTS

The concept of long-term socio-economic

development of the Russian Federation for the period

until 2020, approved by the order of the Government

of the Russian Federation dated November 17, 2008,

No. 1662-r (Decree of the President of the Russian

Federation dated July 21, 2020 No. 474 «On the

national development goals of the Russian Federation

for the period up to 2030») includes the provisions for

introducing innovations in the socio-economic

sphere, designed for the long term.

I.V. Ershova believes (Ershova, Kashkin, 2020)

that for the introduction and use of the latest

technologies, such organizational forms of innovation

as a business incubator, a techno park, a techno polis,

and a strategic alliance are currently most common in

entrepreneurial and professional activities. The multi-

component nature and the need for a strong

connection and interaction between the components

of the association show the need to introduce platform

technologies and artificial intelligence technologies

into the basis of the functioning of the listed

ecosystems. An effective form of government support

for the establishment and development of a new

business model is a business incubator.

The principles of work in techno parks and techno

polis are currently not fully enshrined in legislation,

which leads to the chaotic nature of the work process

itself and hinders effective scientific development.

The formation of such nuclei of science contributes to

progress on the territory of Russia, regardless of the

remoteness of the subjects from the center. This

contributes to the creation of a single and more

homogeneous economic space in the state and a

single social and legal space, which is necessary for

the effective development of the country at the

present stage.

Such processes in Russia are considered an

ecosystem of the digital economy - a system of

interconnected and interacting elements of the

environment that, based on information technologies,

provide qualitatively new methods and mechanisms

of management with electronic interaction between

participants in civil and trade turnover, a single digital

space, as well as the interpenetration of digital

culture, consciousness and values in today's digital

society (Laptev, 2021). The systemic education

“ecosystem of the digital economy” includes a fairly

large number of elements: electronic interaction (for

example, e-commerce, Internet things, etc.) and

digital space (for example, Big data and cloud

systems, neuronet, etc.). Laptev V.A. notes that with

the transition to the digital economy, the idea of

modern entrepreneurship has changed. Significant

changes have taken place in the organization

(institution) of business and management of the

activities of economic entities; direction of economic

activity; interactions between contractors;

registration of the results of economic activity and the

fulfillment of fiscal obligations; ensuring the rights

and legitimate interests of market participants

(Laptev, Lapteva, 2018).

At the same time, a significant number of

regulatory legal acts have been adopted in Russia

(Federal Law No. 123-FZ of 24.04.2020) regulating

“digital” relations of entrepreneurial activity.

Mikhaylov A.V. determines how existing

technologies (each separately) affect entrepreneurial

activity (from a positive and negative point of view),

which makes it possible to conduct deeper research

on the stated topic.

Considering the possibilities of using AI in

business, the scientist notes that highlighting AI as an

object of civil circulation (Articles 128,1225 of the

Civil Code of the Russian Federation), it is difficult

to argue that this is a separate object. You can recall

the following forecast: the current Internet (Web

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2.20) will be replaced by a new one - Web 4.0

(neuronet) by 2030, where communication will be

carried out on the principles of neuro communication

- the connection between the brain of one person and

the brain of another through a computer (Mikhaylov,

2020).

Introducing of such technologies will require the

adoption of many rules aimed at ensuring the

protection of information and the rights of the subject.

Neurocommunication technologies will influence the

development of AI and the learning process in

schools and universities. Additional mechanisms for

protecting insider and commercial information will

be required in business activities.

Mikhaylov A.V. further defines how Cognitive

Technologies can process information unstructured.

Information processing is considering many factors,

the computer is capable of self-learning. Now, an

ordinary search engine gives out a lot of links when

asked on the Internet, but in the future, the use of

cognitive technologies will allow getting a specific

and accurate answer to the question posed. In this

part, the author sees the convergence of cognitive

technologies and AI. A rather complicated legal issue

arises - responsibility (its nature, forms, types, etc.)

for the provision of inaccurate or incompletely

reliable information got using such technologies.

Mikhaylov A.V. notes that at present, cloud

technologies are widely used - these are systems of

network access to information that is in remote

access. Until now, the legislator does not take any

action on legal regulation of such technologies and

relations that develop in entrepreneurial activity.

Such relationships are usually contractual. The

existing contractual models do not fully reflect the

interests of the subjects of such legal relations. There

is also no law enforcement practice on these issues,

which creates legal uncertainty in using the new

technologies under consideration in entrepreneurial

and professional activities.

5 CONCLUSION

The results show technologies have individual

benefits, and we can derive more business value from

leveraging their interconnectedness to speed up

business growth and productivity. Technology is

driving the development of transformative business

models with new platforms that automate processes,

align supply and demand, dynamically set prices, and

decide in real time. This section discusses some

challenges and limitations of these technologies from

the perspective of various stakeholders.

This study explores not only the positive business-

oriented use cases of AI, but also the negative ones.

There are widespread concerns, such as ethics,

privacy, and algorithmic bias. The danger of AI lies

in responsibility in regulation and its safe use.

Because of these problems, business and professional

actors trust AI less because they think AI cannot

mitigate their business risks in entrepreneurship.

Research shows that people do not trust AI solutions

such as medical diagnosis, financial planning, or

business risk mitigation in the business and

professional fields. While varieties of AI such as

machine learning and deep learning, are just being

introduced into entrepreneurial practice, the opinions

expressed in the scientific legal literature show that

autonomous systems should not replace humans.

Foreign scientists have expressed a position

according to which (Davenport, Ronanki, 2018), the

full disclosure and transparency of an intelligent

agent or hybrid systems (both human and automated

devices) should clarify the roles of man and machine,

since most subjects of business communities have a

negative attitude to bots and virtual assistants,

although research has shown that certain

demographic groups would prefer to use the proposed

technologies.

Although AI has a high accuracy based on a high-

quality and varied dataset, it can also make mistakes

that are known in the scientific legal literature as false

negatives and false positives. People who make

mistakes because of poorly defined algorithms can

experience asymmetric effects. Taking longer to train

different datasets will reduce errors in size and

frequency, making the systems more robust. AI

systems can be used in law enforcement agencies and

courts to determine both the right to arrest, the timing

of sentencing, and when analyzing judicial practice to

make a more informed decision, despite the

specificity of each specific civil or administrative

case (Sushkova, Verdiyan, 2020). Since unstructured

data as visual analytics become available for analysis

using complex AI, algorithms (for example, people

search) can be regulated at the level of a constituent

entity of Russia, considering national legislation.

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