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|storemode=property
|title=An In-Depth Case Study of Volkswagen's AI Integration
|pdfUrl=https://ceur-ws.org/Vol-3698/paper7.pdf
|volume=Vol-3698
|authors=Ali Mert Erdoğan,Ourania Areta Hiziroglu,Abdulkadir Hiziroglu
|dblpUrl=https://dblp.org/rec/conf/balt/ErdoganHH24
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==An In-Depth Case Study of Volkswagen's AI Integration==
https://ceur-ws.org/Vol-3698/paper7.pdf
An In-Depth Case Study of Volkswagen's AI Integration⋆
Ali Mert Erdoğan1, † Ourania Areta Hiziroglu1 and Abdulkadir Hiziroglu1
1 Izmir Bakircay University, Department of Management Information Systems, Izmir, 35665, Turkey
† alimert.erdogan@bakircay.edu.tr
Abstract
As Artificial Intelligence (AI) technologies have become increasingly integral to business
operations and many firms aspire to generate business value with that, understanding the
factors that facilitate or hinder successful implementation is crucial for organizations across
industries. Using Volkswagen Group (VW) as a case study, the goal of this study is to
comprehensively examine the AI implementations in a holistic manner, including enablers and
inhibitors, utilization in terms of automation and augmentation, process-level impacts, and
broader firm-level outcomes. This work not only contributes to the understanding of AI
adoption within a major automotive player, but also serves as a resource for organizations by
navigating through the complexities of AI implementation, offering practical insights and
lessons learned from the case.
Keywords
Artificial intelligence, automotive industry, business value
1. Introduction
The rapid advancement of artificial intelligence (AI) has generated new potential value
across various sectors. It is regarded as one of the most promising opportunities for global
trade today, with the potential to boost the world economy by $13 trillion to $15 trillion
by 2030 [1]. However, despite these optimistic estimates, the integration of AI-driven
projects and initiatives into various organizational processes has made it challenging for
all organizations to explore and implement valuable applications [2]. This difficulty in
accessibility and usability poses a significant barrier to the widespread adoption and
successful integration of AI technologies within organizations, ultimately affecting their
ability to fully harness their potential for value creation [3].
One of the industries that has been exploiting AI potential is the automotive industry.
The automotive sector has been undergoing a fundamental transformation primarily
fueled by technological innovations in vehicles, manufacturing, and supply chain, as well
as various other areas where AI is being utilized [4]. Its integration in the vehicle industry
is a good example of how AI technology can take the automotive sector to a new level and
completely change the mobility services [5]. AI-driven automotive innovation process is
Baltic DB&IS Conference Forum and Doctoral Consortium 2024
alimert.erdogan@bakircay.edu.tr (A. M. Erdoğan), ourania.areta@bakircay.edu.tr (O. Areta Hiziroglu)
0009-0006-3443-7253 (A. M. Erdoğan), 0000-0001-8607-6089 (O. Areta Hiziroglu)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
73
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
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not only for increasing efficiency but also is a precedent for other sectors to take
advantage of AI for the similar progress. The heterogeneous use of AI in automotive
contexts can lead to a complex environment in which the effects of AI can be studied,
ranging from design processes to production efficiency [6]. Additionally, the automotive
sector's direction towards the Industry 4.0 principles, including the application of AI to
production processes, creates a strong argument for the study of AI and manufacturing
practices [7]. The automotive industry may be seen as an example of leading the way in
adopting Industry 4.0 technologies to improve productivity and operational efficiency
with the lessons learnt from which, other industries may follow the same path.
Prior research has underlined the need to skillfully manage domain and AI expertise
when introducing AI systems, because of the interdependencies of technology (data,
algorithms etc.) and organization (culture, managerial support, domain knowledge etc.)
infrastructure [8]. Though research on managing AI is still nascent, studying fully exploit
of its value in business will help to advance the productivity of AI in organizations [9].
Such that, the dimensions of the frameworks we examine in this study (Section 2) have
been established through the synthesis of various studies in the literature. Examples
provided for dimensions are either addressed in different domains or in a generic set of
activities, such as marketing, human resource management, and public management.
However, it is important to examine the frameworks through the lens of a single business
and its operations in order to understand AI integration and the value it provides. It's
crucial to note that supporting these frameworks with case studies is imperative for their
validation. Therefore, the aim of this study lies in bridging the gap in the literature by
providing evidence through a case study, thereby enhancing the validity and applicability
of existing frameworks in real-world contexts.
In this study, we utilize the AI business value framework presented by Enholm et al.
[10]. This framework possesses a more comprehensive structure compared to similar
frameworks, allowing us to understand and assess the value that AI can bring to a
business. We use this approach to define its implementation in organizational domain, and
to identify and realize the use cases by involving AI enablers and inhibitors, exploring
whether AI is predominantly employed for automation or augmenting human capabilities,
and extending to the impacts of implementation.
As aforementioned, AI has emerged as a foundational element within the automotive
sector, permeating various processes and revolutionizing the industry's landscape. In this
regard, we identified the sector for the case that will be studied. In our observations, it
was noted that VW possesses contemporary and diverse news sources (VW Media, VW
Newsroom, VW Press, etc.), thereby presenting itself as a viable subject for a case study.
To understand how businesses can gain AI value (i.e., develop the capability to broadly
engage AI systems), we conducted an in-depth case study of VW. Our research was guided
by the research question: How does the integration of AI technologies influence business
value within diverse organizational contexts? Our findings are based on the analysis of
VW’s AI journey. We describe the specific activities that helped VW drive business
processes using AI. We then provide a management perspective of how it can strengthen
the value and cost drivers of a business and conclude with recommended actions for
executives who want to engage their organizations in harnessing the value potential of AI.
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The study is organized into four main sections to explore the adoption of AI within
organizational contexts. Section 2 reviews existing frameworks addressing AI usage in
businesses, providing theoretical background. Section 3 outlines our methodology for
conducting an in-depth case study. Section 4 presents findings from our case study
analysis, highlighting insights into VW's AI journey and its impact on business processes.
Finally, Section 5 offers the conclusion and discussion of our research, synthesizing
implications and recommendations.
2. Frameworks for AI in Business
A multitude of frameworks have emerged to facilitate the integration of AI within various
business sectors, encompassing domains like marketing, human resource management,
and public administration. This comprehensive body of literature provides a nuanced
understanding of AI adoption and implementation, highlighting several overarching
themes and dimensions.
Within the realm of AI adoption frameworks and models, scholars have proposed
diverse conceptual frameworks to delineate the factors influencing successful AI adoption.
For instance, Chen et al. [11] introduced a multifaceted framework that considers
technological, organizational, and environmental factors shaping AI adoption in business-
to-business marketing. Nortje & Grobbelaar [12] offered a comprehensive readiness
model framework, encompassing dimensions such as infrastructure, employee culture,
technology management, organizational governance, and strategy, to assess an
enterprise's preparedness for AI implementation. Additionally, Mikalef et al. [13]
contributed a theoretical framework emphasizing the development of AI capabilities and
their subsequent impact on organizational performance, emphasizing aspects like data
infrastructure, AI culture, and organizational learning.
Exploring the intersection of AI capabilities and business value, scholars have also
delved into the mechanisms through which AI generates value across different
organizational contexts. Enholm et al. [10] conducted a literature review examining the
enablers and inhibitors of AI adoption and delineating its various uses and effects,
including process efficiency, insight generation, and operational performance
improvements. Similarly, Huang & Rust [14] proposed a strategic framework tailored to
the marketing domain, elucidating the roles of AI in decision automation, augmentation,
and support systems. Furthermore, Chowdhury et al. [15] presented an AI capability
framework specifically tailored to human resource management, addressing resources
needed for AI development, contextual factors influencing its use, impacts on the
workforce, and organizational outcomes.
In the realm of AI governance and public management, Wirtz & Müller [16] offered an
integrated AI framework designed to guide public sector organizations in harnessing AI's
potential to enhance service delivery, decision-making processes, and governance
structures. This framework delineates technology infrastructure requirements, functional
layers for data processing and interpretation, and the applications and services layer
encompassing AI-enabled public services and governance mechanisms.
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Table 1.
Comparison of Frameworks for AI in Business
Reference Focus&Sector Dimensions
1. Information Processing Requirements: Digitalization of the external business environment, Task characteristics, task interdependence
[11] Chen et al. 2. Capabilities: IT investment, Information processing capabilities - AI adoption
Marketing
(2021) 3. Learning Capacity: Adaptive learning, Generative learning
4. Outcomes: Financial performance, Relationship quality, Marketing innovation
1. Resources to Develop AI Capability
1.a. Technical Resources: Data resources, Technology infrastructure, AI transparency
1.b. Non-technical Resources: Financial, Time requirements, Technical skills, Business skills, Leadership, Culture, Teams coordinations,
Human Organization change, Knowledge management, AI-employee integration, Governance and regulation
[15] Chowdhury et
Resource 2. Context of Use: Automation, Augmentation
al. (2022)
Management 3. Impacts of AI Capabilities and Use on Human Workforce: Job design, Job characteristics, Autonomy, Trust and confidence, Job
satisfaction and motivation, Career development, Team structure, Creative skills, Innovation mindset
4. Organizational Outcomes: Process efficiency, Data driven decision making, Product/service innovation, Customer satisfaction,
Employee productivity, Sustainable business performance, Brand image
1. Enablers and Inhibitors: Technological, Organizational, Environmental
2. Use: Automation, Augmentation
[10] Enholm et al. Business
3. First-order Effects: Process efficiency, Insight generation, Business process transformation
(2021) (General)
4. Second-order Effects: Operational performance, Financial performance, Market-based performance, Sustainability performance,
Unintended consequences and negative impacts
1. Mechanical AI: Data collection, Segmentation, Standardization.
[14] Huang and
Marketing 2. Thinking AI: Market Analysis, Targeting, Personalization
Rust (2020)
3. Feeling AI: Customer understanding, Positioning, Relationalization
1. AI Capability: Data, AI Culture, Infrastructure, Organizational learning, Technical & Managerial skills
[13] Mikalef et al. Business
2. Use: Automation, Decision support, marketing, innovation
(2019) (General)
3. Outcome: Competitive Performance
1. Infrastructure: Infrastructure Platform, Services, Information Networks, Communication Networks, Technological Sustainability and
Position Map
2. Employee and Culture
3. Technology Management: Technological Categorization and Planning, Technology Requirement Handling, Technology Investment and
Capital Management, Cost Management, Technological Competitors' Analysis, Cloud Resources, Network Connectivity, Technology Risk
[12] Nortje and Business Management, Quality Management, Human Resource Planning
Grobbelaar (2020) (General) 4. Organizational Governance and Leadership: Executive Support, Budget, Business Opportunity, Strategic Leadership, Business Cases
5. Security
6. Strategy: Trial-Ability, Business Clarity, Observable Results, Technology Roadmaps and Scenarios, Technology Forecasting, Technology
Forecasting
7. Knowledge and Information Management: Management Information System and Data Processing, Agent Based Applications, Return
on Investment, ERP in Terms of Databases and Software, Technology Knowledge Management, Technology Identification and Selection
1. Technology Infrastructure Layer: Data Acquisition, Data Processing, Data Embedding
[16] Wirtz and Public
2. Functional Layer: Sense, Comprehend, Act
Müller (2018) Management
3. Applications & Services Layer
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Overall, these articles provide valuable insights into the adoption and implementation
of AI in business contexts. They offer frameworks, models, and strategies for organizations
to leverage AI capabilities effectively, while considering organizational readiness,
complementary assets, and potential business value creation. Additionally, the articles
address the implications of AI in specific domains like marketing, human resources, and
public management.
Upon comparing all frameworks, the following differences and similarities become
apparent. It is observed that [11] and [15] categorize the business impacts and outcomes
of AI specifically within the domains of marketing and human resource management.
Additionally, reference [11] takes a more technical approach to AI classification by
discussing two types of learning capabilities in AI algorithms, which sets it apart from
other studies. In [14], a framework is developed solely based on the capabilities of AI, with
no emphasis on the resources for AI technologies or its impacts and outcomes for
businesses. In [13], the AI use dimension has been examined under four subcategories:
augmentation, decision support, marketing, and innovation. However, rather than being
treated as separate subcategories, decision support and innovation are more accurately
considered under the umbrella of augmentation. Meanwhile, marketing applications can
either be evaluated as automation or augmentation, depending on how they are applied.
On the other hand, reference [12] focuses on the infrastructure, resources, and enablers
that are crucial for an enterprise's AI implementation, but it does not delve into the use of
AI or the outcomes for a business. In [16], a unique dimension is presented that focuses on
the functional layers of an AI system, which outlines the fundamental workflow from
sensing the environment, understanding the data, and taking appropriate actions.
However, in the study, the developed framework only addresses the technology
infrastructure aspect in terms of resources and does not delve into outcomes. In [10], a
comprehensive framework is developed covering enablers and outcomes, encompassing
other studies. The AI use dimension in this study includes subcategories of automation
and augmentation, similar to [15]. This dimension in both studies provides a more generic
and inclusive perspective on the implementation of AI, differing from other works. Upon
reviewing all these studies, it has been observed that the framework presented in [10]
encompasses aspects of other studies and provides a more suitable categorization for the
purpose of this case study.
3. Methodology
We followed a case study approach employing content analysis to investigate VW's
utilization of AI, spanning the last five years. Primary data sources that are used include
news articles from VW’s official media pages, external news channels, organizational
websites interacting with VW, annual reports, and official project documents. The data
collection spans the last five years, ensuring comprehensive coverage of VW's AI
initiatives.
To facilitate data analysis, 12 news shared on news channels related to VW were
examined, along with VW's annual reports from the last five years. The websites of
companies providing AI services or AI infrastructure to Volkswagen were reviewed.
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Additionally, Google searches were conducted with keywords such as "Volkswagen AI,"
"VW AI projects," "VW machine learning initiatives," "Volkswagen autonomous driving
technology," "VW AI applications," "Volkswagen AI research," "VW smart technologies,"
"Volkswagen AI in production," "Volkswagen smart manufacturing," and "Volkswagen AI
investment" to find relevant news from other sources. In total, 18 news found through
searches, the websites of 3 companies providing services to Volkswagen (Amazon,
Argmax, and Actico), and 1 annual report were selected for examination within the scope
of this case. These sources were examined by each author, considering the framework.
Topics related to VW's use of AI were categorized separately within the framework by the
authors. The similarities and differences between the authors observations were
discussed. As a result of the discussion, 30 findings deemed suitable for the framework
from the examined sources were placed on a common schema.
Finally, the findings derived from the content analysis are presented in a tabular format
(Table 2), allowing for a clear and concise representation of key insights. This presentation
facilitates stakeholders’ access to and comprehension of the implications of VW's AI
endeavors, supporting informed decision-making and strategic planning. The insights
provided enable researchers to gain a deeper understanding of the academic and practical
impacts of the initiatives, encouraging further exploration and investigation in the field.
4. Volkswagens’ AI Use Cases
In this comprehensive case study, we delved into VW's strategic integration of AI across
various facets of its operations. The exploration of use cases is meticulously categorized,
encompassing AI enablers, automation and augmentation applications, process-level
impacts, and the far-reaching implications at the firm level. Drawing insights from
research findings, news sources, and organizational reports, this section provides a
nuanced understanding of how VW leverages AI technologies to enhance efficiency,
automation, and sustainability within its diverse range of processes and functions.
4.1. Enablers
At the core of VW's supply chain transformation lies the VW Industrial Cloud, a
sophisticated platform built on Amazon Web Services (AWS). With 124 factory sites
worldwide and a vast network of over 1,500 suppliers, VW sought to consolidate its
operations into a single architecture to enhance efficiency and collaboration [17].
Leveraging AWS technologies in IoT, machine learning, data analytics, and computing
services, VW established a scalable and agile infrastructure tailored to the automotive
industry's unique requirements. Utilizing AWS machine learning services, VW harnesses
data from sensors on the shop floor to drive predictive maintenance and optimize
production processes [18].
Additionally, AWS Outposts extend the capabilities of the Industrial Cloud to factory
sites, ensuring seamless integration and real-time data exchange. By creating an open
platform, VW empowers partners to contribute innovative solutions, fostering a
collaborative ecosystem geared towards mutual benefits and continuous improvement.
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Table 2.
VW AI Use Case Categorization Based on AI Business Value Framework
ENABLERS AI USE PROCESS-LEVEL IMPACTS FIRM-LEVEL IMPACTS
a. Technological Infrastructure a. Automation a. Process efficiency a. Operational performance (New
Amazon Web Services: Machine learning Intelligent Robots (Cobots): Intelligent collaborative Improved productivity: Enhancing products and services)
services — running algorithms using robots (cobots) powered by AI in its manufacturing productivity by integrating collaborative AI-Optimized material structures: Utilizing
information gathered from sensors on the processes, contributing to increased automation and robots that work alongside humans, AI to develop a modular repeating structure
shop floor — and AWS Outposts [17], [18]. efficiency in the automotive domain [20]. leveraging AI to streamline and optimize of tiny pyramids, significantly reducing the
Industrial Cloud: Cloud computing Mobile Charging Robots: Fully autonomous charging manufacturing processes [20]. weight of the steel frame housing electric
solutions tailored for industrial applications of electric vehicles in confined spaces, such as Reduce/eliminate human errors: vehicle batteries, enhancing range, and
and manufacturing processes (IoT underground garages, aiming to contribute to the Accurate application of country-specific providing a durable, lightweight alternative
Integration, Analytics and Insight, SCM, expansion of the electric vehicle charging labels on vehicles, quality testing by [36].
Manufacturing Process Optimization) [19], infrastructure [28]. detecting the finest defects in components b. Financial performance
[20]. Chatbots: The integration of Cerence Chat Pro, using computer vision [34] [30]. Amazon Web Services: Implementation of
VW Automotive Cloud: Expedite the powered by ChatGPT, into VW's voice assistant b. Insight generation the VW Industrial Cloud on Amazon Web
realization of connected vehicle services, enhances in-car interactions, providing advanced Decision quality: Identifying patterns and Services is expected to yield substantial
encompassing intelligent driver-assist capabilities such as controlling infotainment, optimize strategies, emphasizing financial impacts for the VW, including a
systems and personalized communication navigation, and air conditioning through intuitive augmentation rather than replacement of targeted 30 percent increase in productivity,
and navigation solutions [21]. language [29]. human decision-makers, ensuring that a 30 percent decrease in factory costs, and
b. Organizational Enablers NLP: Identifying similarities and patterns in reports algorithms serve to support and empower potential savings of €1 billion in supply
Strategy: Utilizing collaborative approaches and claims. This systematic analysis facilitates the employees rather than supplanting their chain costs [17].
and investing to pioneer software, rapid detection of potential quality issues, allowing for expertise in strategic decision-making [30]. c. Sustainability performance
hardware, mapping, and cloud timely feedback into the early stages of product c. Business process transformation Smart management of energy: To generate
infrastructure for self-driving vehicles [22], development, thereby augmenting overall product Transforming core business operations for sustainable savings, for example in
[23]. quality efforts. [30] digitalization by integrating AI technologies compressed air control systems [26].
Culture: Establishment a proactive embrace b. Augmentation to enhance efficiency and innovation [35]. Inclusive Mobility: Addresses mobility
of AI technology, aimed at driving Prewave: Utilizing AI, specifically an algorithm challenges faced by people with disabilities
innovation in digital products and fostering developed by the Austrian start-up Prewave, to through the development of autonomous
a collaborative culture across global tech proactively identify sustainability risks across their vehicles and related technologies [37].
sectors to swiftly develop and implement supply chain. This AI-driven monitoring system scans d. Unintended consequences and
advanced digital prototypes [24], [25]. publicly available media and social networks in over negative impacts
Managerial & Financial Support: 50 languages and 150 countries, providing early Challenges and financial risks faced
Spearheading initiatives to integrate AI warnings for potential breaches related to highlighting the struggle to develop
technologies across brands and business environmental pollution, human rights abuses, and profitable business models amidst the high
units, manage IT projects, drive cultural corruption [31]. costs and uncertain market demand for
change, spur innovation [26]. Actico: Development of an ML-enabled statistical autonomous mobility services [38].
Funding initiative underscores its forecast model, which evaluates the probability of
commitment to advancing responsible AI fraud in credit applications and thus, enables the
development by providing substantial targeted management of questionable applications
financial support, spanning social and [32].
technical sciences [27]. Blackwood Seven: Employing an AI-driven media
agency, to enhance its media buying decisions in
Germany, leveraging predictive analytics and
transactional data for optimal media investment
strategies [33].
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The adoption of the Industrial Cloud positions VW to achieve global efficiencies in its
manufacturing operations [19]. By facilitating data interchange between systems and
plants VW aims to enhance visibility and decision-making across its supply chain, paving
the way for enhanced agility and responsiveness to market demands.
In a business, enablers for AI aren't solely comprised of the technological infrastructure
itself. The company's managerial approach, strategy, and investment decisions in this area
also contribute as organizational enablers influencing AI value. Under the banner of the
NEW AUTO strategy [26], VW has redefined its vision, aiming to become a software-
oriented provider of sustainable mobility. Central to this strategy is the recognition of
technology and data competence as the key drivers of transformation. VW established the
IT Board position and formulated the NEW IT functional area strategy, focusing on
delivering highly automated enterprise processes, agile development of IT products, and
systematic utilization of data across the organization. This strategy is bolstered by
strategic investments, including a $2.6 billion investment in Argo AI aimed at pioneering
software, hardware, mapping, and cloud infrastructure for self-driving vehicles. After this
investment, despite Argo AI's failure [38], continuing investments in similar areas for
partnerships with Horizon Robotics and CARIAD demonstrate the company's commitment
to this direction of strategy [23]. Additionally, VW has proactively embraced AI
technology, fostering a culture of innovation and collaboration. The establishment of the
AI Lab signifies VW's commitment to leveraging AI for the development of digital products
and services [25]. By acting as a globally networked competence center and incubator, the
AI Lab facilitates collaboration with the tech sector to rapidly develop digital prototypes
and transfer them to VW brands for implementation.
4.2. AI Use
Our findings suggest that, at the heart of VW's automation strategy lies the integration of
intelligent collaborative robots (cobots) powered by AI. These cobots collaborate with
human operators, augmenting their capabilities and driving efficiency in manufacturing
processes [20]. By automating repetitive tasks and streamlining assembly line operations,
VW achieves higher productivity, reduced cycle times, and enhanced product quality.
Another automation use, VW's commitment to electric mobility is exemplified by its
development of fully autonomous charging robots. These robots enable charging of
electric vehicles in confined spaces, such as underground garages, contributing to the
expansion of the electric vehicle charging infrastructure. Through AI-driven automation,
VW ensures convenient and accessible charging solutions for EV owners, driving the
adoption of electric vehicles [28].
In the realm of customer interaction, VW has leveraged AI to enhance in-car
experiences through chatbots and NLP algorithms [29]. By integrating Cerence Chat Pro,
powered by ChatGPT, VW's voice assistant systems enable intuitive language-based
control of various vehicle functionalities. Another usage where NLP is utilized for
automation is analyzing reports and claims systematically, facilitating the rapid detection
of potential quality issues [30]. This automation streamlines customer service processes,
improves user experiences, and ensures timely resolution of quality concerns, ultimately
enhancing customer satisfaction and brand loyalty.
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To bolster its sustainability efforts, VW collaborates with Prewave, an Austrian start-
up, leveraging AI to proactively identify sustainability risks across its supply chain.
Prewave's AI-driven monitoring systems scan publicly available media and social
networks, providing early warnings for potential breaches related to environmental
pollution, human rights abuses, and corruption. By augmenting its supply chain
management with AI-driven insights, VW mitigates risks, upholds ethical standards, and
fosters sustainable business practices [31].
In the realm of financial management, VW harnesses AI-enabled statistical forecast
models developed by Actico to evaluate the probability of fraud in credit applications. By
augmenting traditional risk assessment processes with AI, VW can identify and manage
questionable applications more effectively, minimizing financial risks and optimizing
credit approval processes [32]. This augmentation enhances financial security, ensures
compliance, and strengthens VW's position in the market.
In optimizing its marketing strategies, VW partners with Blackwood Seven, an AI-
driven media agency. By leveraging predictive analytics and transactional data, Blackwood
Seven's AI algorithms enhance VW's media buying decisions, ensuring optimal allocation
of resources and maximizing advertising impact [33]. Through AI-driven augmentation,
VW enhances its marketing effectiveness, reaches target audiences more efficiently, and
drives brand awareness and sales.
4.3. Process-Level Impacts
In terms of process efficiency, Volkswagen has witnessed significant improvements
through the integration of AI technologies. Firstly, the introduction of collaborative robots,
or cobots, alongside human workers has remarkably enhanced productivity within
manufacturing processes [20]. These cobots, powered by AI, streamline operations and
optimize workflows, ultimately leading to reduced cycle times and increased output in
Volkswagen's production facilities. Moreover, Volkswagen has effectively tackled the issue
of human errors through AI implementation. For instance, in the application of country-
specific labels on vehicles, AI algorithms ensure precise placement and adherence to
regulatory standards [30]. Similarly, quality testing procedures leverage computer vision
technology to detect even the minutest defects in components, thereby enhancing overall
product quality and reliability [34].
Moving to insight generation, VW emphasizes augmenting human decision-makers
rather than replacing them entirely with AI. By leveraging AI to identify patterns and
optimize strategies, VW empowers employees to make informed decisions swiftly and
effectively. This approach ensures that AI serves as a supportive tool, enhancing human
expertise in strategic decision-making and ultimately improving decision quality across
the organization [30]. Furthermore, VW is undergoing a profound business process
transformation through the integration of AI technologies. By embracing digitalization,
VW enhances efficiency and innovation across its entire value chain. AI-driven solutions
optimize processes, automate tasks, and unlock new opportunities for growth and
competitiveness [35]. As VW continues to evolve, AI plays a central role in driving
business process transformation, ensuring its position as a leader in the automotive
industry.
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4.4. Firm-Level Impacts
VW's adoption of AI intelligence brings about operational advancements, particularly in
the realm of product innovation. By leveraging AI-optimized material structures, VW
engineers develop a modular repeating structure of tiny pyramids, significantly reducing
the weight of steel frames housing electric vehicle batteries [36]. This innovation not only
enhances the range of electric vehicles but also provides a durable, lightweight alternative,
showcasing VW's commitment to pushing the boundaries of automotive engineering.
Furthermore, the integration of AI in vehicles enables VW to offer smart features that
transform traditional automotive offerings into innovative products. These smart features
enhance safety, connectivity, and overall user experience, setting VW apart in a
competitive market landscape [39]. By embracing AI-driven advancements, VW enhances
its operational performance by delivering cutting-edge products that meet evolving
consumer demands.
The implementation of the VW Industrial Cloud on Amazon Web Services (AWS)
promises significant financial impacts for the VW Group. By leveraging AWS's cloud
infrastructure, VW targets a 30 percent increase in productivity and a corresponding 30
percent decrease in factory costs. Additionally, VW anticipates potential savings of €1
billion in supply chain costs, underscoring the financial benefits of AI integration for
operational efficiency and cost optimization [17].
In pursuit of sustainability, VW employs AI-driven solutions to optimize energy
management, particularly in compressed air control systems. By smartly managing energy
usage, VW not only reduces operational costs but also minimizes environmental impact,
aligning with its commitment to sustainable practices [26]. Furthermore, VW addresses
mobility challenges faced by people with disabilities through the development of
autonomous vehicles and related technologies, promoting inclusive mobility and social
sustainability [37].
However, alongside these positive impacts, VW faces challenges and financial risks
associated with the development of autonomous mobility services [38]. The high costs and
uncertain market demand for autonomous mobility services pose significant hurdles,
highlighting the complexities of developing profitable business models in this emerging
field. VW must navigate these challenges while maintaining its commitment to innovation
and sustainability, ensuring that its AI-driven initiatives deliver long-term value for both
the company and its stakeholders.
5. Conclusion and Discussion
The in-depth case study of the AI integration efforts of VW has thrown up several
intriguing points and implications for the organizations that want to tap into the power of
AI efficiently.
The first thing the case study brings to attention is the key role of having a well-built
technological infrastructure to enable AI integrations. Such as VW's Industrial Cloud and
strategic alliance with major tech providers (AWS), a perfect fit for the kind of scalable and
flexible computing solutions that suit the peculiar nature of an organization's operating
industry or operations. In addition to improving efficiency and collaboration between
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members, VW implements an integrated architecture that allows partners to respond to
changing requirements with new solutions, thus forming a purposeful environment for
mutual benefit and progress in the long term.
In a nutshell, the case study also points out the role of organizational supporters in AI
implementation. The strategic vision of VW, embodied in the words of its NEW AUTO
strategy and the setting up of its AI Lab, clearly shows the critical part leadership, culture,
and cross-functional collaboration plays in the use of AI. With its proactive approach to
embracing AI technology, and by promoting an innovative environment, the carmaker has
become the setting for the quick growth and implementation of top-notch digital
prototypes, ultimately leading the process of turning AI-driven value into reality.
The last part of the study also shows that AI has various implications from automation,
augmentation, and business process transformation among others. The utilization of AI in
the form of intelligent collaborative robots (cobots) by VW manifests the ability of AI to
automate redundancy, as well as improve system efficiencies and productivity in general.
Among all these cases, another notable value that stands out is the adoption of AI in
vehicles. Enhanced in-car assistants utilizing large language models represent a very
current example. On the other hand, taking the concept of autonomous vehicles to a
different perspective through making mobility solutions more accessible and inclusive for
everyone, including individuals with disabilities or special mobility needs is another
noteworthy topic.
Alongside concrete case studies that have tangible outcomes examined within the
framework, there are also potential applications and implications. In addition to the
Automotive Cloud's promises for future automobiles, such as enhancing emergency
assistance and remote vehicle access, it also has the potential to significantly impact
battery optimization and development. By leveraging the driving and battery data
generated by vehicles, the cloud can contribute to the refinement of batteries, leading to
longer ranges and improved performance. This data-driven approach holds immense
potential to drive innovation within the automotive industry, paving the way for more
efficient and sustainable vehicles.
As stated in the introduction, one aim of this study is to test the external validity of the
framework. We are aware that focusing on a single example and a single sector for
validation purposes is a weakness of the study. However, we are able to provide
recommendations for the framework we used and the process of AI integration in
business. Beginning with a suggestion for the framework we use, we can emphasize the
importance of determining whether AI use pertains to products or business processes. In
the cases we examined, we couldn't precisely align business value with either products or
processes within the framework. As a suggestion, similar to how AI use is categorized into
two headings, automation and augmentation, the distinction between whether business
value is related to products/services or the business's processes could be made, thereby
expanding and updating the framework.
During the study, another issue that caught our attention was the geographical bias in
AI integration. The cases we examined cover very diverse destinations. In such a situation,
it's crucial to consider geographical variations and potential biases when applying the
framework across different countries and regions. Local context, cultural norms,
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regulatory environments, and market dynamics all play significant roles in shaping the
adoption and impact of AI technologies on businesses worldwide. Our humble suggestion
is to consider these aspects for future research to develop a more robust framework
design or to ensure successful AI integration efforts.
Beyond the knowledge about the challenges and opportunities of AI implementation
in the automotive giant, we hope that this study also offers benefits for other industries
that are looking for solutions on the same path. Through providing lessons from the AI
travel experience of VW, the study provides a base of knowledge, for strategic planning
and implementation, and for responding to the risks and unintended consequences that
might appear.
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