Website URL:https://www.airccse.org/journal/ijaia/ijaia.html Review of AI Maturity Models in Automotive SME Manufacturing

International Journal of Artificial Intelligence and Applications (IJAIA), Vol.15, No.1, January 2024
DOI:10.5121/ijaia.2024.15104 57
REVIEW OF AI MATURITY MODELS IN
AUTOMOTIVE SME MANUFACTURING
Dharmender Salian
Department of Information Technology, University of the Cumberlands, New York, USA
ABSTRACT
This study reviews studies on Artificial Intelligence (AI) maturity models (MM) in automotive
manufacturing. To stay competitive, SMEs in the automotive industry need to embrace digitalization. SMEs
employ a large segment of the USA's workforce. The benefits of operational efficiency, quality
improvement, cost reduction, and innovative culture have made SMEs more aggressive about
digitalization. Digitalizing operations with Artificial Intelligence are on the rise. In this paper, AI
applications in SMEs are examined through the lens of an AI maturity model.
KEYWORDS
Industry 5.0, Maturity Model, Artificial Intelligence, Strategy & digitalization
1. INTRODUCTION
In the 1940s, the programmable digital computer was invented, which led to the beginnings of
AI. After this, the USA, British, and Japanese governments funded several projects, but it wasn't
until the dawn of the 21st century when machine learning applications started generating AI
interest and investments. AI can deliver sophisticated solutions to advance manufacturing.
Although the changeover will take time, the benefits are likely to be endless. In artificial
intelligence (AI), algorithms or computerized systems resemble human mental processes [1]. In
the last few decades, the field of AI has made rapid progress in developing decision-making
intelligence after the early days were characterized by overpromises and under-delivery [2]. Data
is a crucial component of AI, since it is used to train algorithms to detect patterns based on the
data collected [3]. Data and computing power are required by AI researchers to build more
powerful algorithms [1].
The development of AI technologies was boosted by a summer workshop conducted by
mathematician John McCarthy at Dartmouth College in 1956, which looked at some foundational
problems. AI techniques like machine learning, computer vision, deep learning, automation, and
robotics are now the subject of pioneering research. In 2019, the Artificial Intelligence in
Manufacturing Market was worth USD 1.82 Billion, and by 2027, it is predicted to be worth USD
9.89 Billion [4]. One third of global GDP is produced by the industrial sector, and half of global
energy consumption comes from it [5]. A new study shows that 58% of manufacturers are
positively interested in artificial intelligence, but only 12% are using it [6].
AI will provide the most job security to those who possess creativity and management skills. The
number of publications on artificial intelligence in manufacturing has increased exceptionally
over the last 40 years, attracting much attention within the scientific community [7]. A growing
number of companies are investing in hybrid technology systems to manage inventory, control
quality, and optimize production and costs. Expert systems and robot localization, as well as

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visual surveillance, are less likely to be used by them. Technology can now be valued based on
core use cases [8]. AI is used to simulate human reasoning, learning, planning, and other thinking
activities, thus solving complex problems that were previously only solvable by human experts
[9]. AI is particularly useful for automating learning, acquiring, processing, and using knowledge
to perform tasks, enabling human decision-making processes to be improved through improved
knowledge.
Achieving the goal of AI rivalling human abilities remains a work-in-progress, and it is uncertain
if such a goal can be achieved [10]. Through technological maturity and integration with a variety
of technologies, AI has become more relevant. There has been development in computing and
chip design as well as neural network algorithms, which have developed into deep learning.
There has also been convergence with technology such as augmented reality, robotics, 5G, virtual
reality, and the internet of things [11]. There is a growing recognition among companies that they
do not want to climb an artificial intelligence mountain. To achieve new heights, they just need to
keep taking the right, tiny steps [12]. AI has triggered significant societal concerns, ranging from
technological unemployment to the dominance of algorithms at work and in everyday life [13].
The SME sector employs 61.7 million workers in America, which is 46.4% of the total workforce
[14]. AI is being funded by governments in advanced economies and large technology
companies. Due to previous reviews not adequately addressing AI's use and advancement in
engineering and manufacturing, this review is being conducted. The term "SMEs" is used in this
paper to describe automotive small and medium enterprises.
In automotive manufacturing, Digital Transformation means applying digital tools and methods
to make the manufacturing process more efficient and cheaper. A digital transformation
emphasizes data-driven and AI to derive info on manufacturing processes, optimize production
planning, and improve quality. Having digital transformation has allowed businesses to reduce
the cost of production and time in the manufacturing process as they face cutthroat competition
and innovation is the key. Robotics and automation allow for real-time analysis of data, which
improves productivity. 5G, AI, additive manufacturing, and the Internet of Things are
revolutionizing product innovation and manufacturing efficiency. Automakers are investing more
and more in developing electric cars to meet the growing demand for eco-friendly vehicles, but
these need new suppliers also.
MMs exist in different domains, and the goal is to answer the following questions.
RQ1 What is the role of AI maturity models in automotive manufacturing?
RQ2 How does the literature review describe different stages of maturity model?
RQ3 What are the important characteristics and goals of AI maturity models?
The current AI MMs are systematically reviewed to determine the dimensions for assessment.
Organizations can use the AI Maturity Assessment to evaluate their current AI capabilities,
identify gaps and areas for improvement, and create a guideline to build more successful AI
programs [15]. This study aims to illuminate the research gap and to guide future studies that
need to consider the dimensions noted above. The rest of the paper is divided into the following
sections: Sections 2 and 3 present a SOAR and PESTLE analysis of SMEs. Section 4 provides a
literature review. Digital strategy is discussed in section 5. A description of AI MM for the
aerospace industry can be found in section 6. Future Challenges is discussed in section 7. The
conclusion of this study is in section 8.

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2. SOAR FOR SMES
SME digitalization efforts and progress are discussed in this section. Using a SOAR Analysis is a
good way to plan strategy. The goal is to create a vision that is energizing to work toward by
combining data about an organization's current position with people's ideas and dreams. An
organization's SOAR analysis uses the experiences of employees at all levels and across all
departments, while a SWOT is typically a management-led exercise.
Table 1. SOAR
Strengths Aspirations
 More flexible and
adaptable organizational
structure.
 Ability to innovate,
knowledge, skills,
experience, and
accomplishments.
 Trying to stay fast and flexible
while working sustainably in a
known field.
 Mission, vision, values, and
priorities are clear from the top.
 SME's key stakeholders are
customers, employees, business
partners, and communities
Opportunities Results
 Government assistance.
 Making the organization
more efficient and meeting
customers' needs.
 Expansion can be helped
by innovative funding
methods.
 Technical staff has more time.
to develop cutting-edge skills,
which helps the organization
work better.
 Acquiring new clients.
 Keeping growth positive,
without being weighed down
by big, bureaucratic projects.
 Strengths
An increase in the market share of electric vehicles: EVs have a lot of different components than
conventional vehicles, especially the electric drive units, battery packs, and battery modules.
Non-American SMEs have an entry barrier since they must start from scratch.
 Aspirations
There aren't enough skilled employees: long-term training is needed. Stakeholders play a big role
in the success of any small business. The stakeholder's relationship with the small business
generates goodwill for the company.
 Results
The results serve as a benchmark for progress and help track strategy effectiveness. Achieving
results keeps businesses on track and keeps them accountable.

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 Opportunities
Digitalization efforts are being supported by the government. New manufacturing techniques
lower production costs compared to conventional methods.
Since the early days of automobile development to the current development of electric and
autonomous vehicles, the automotive industry has been constantly evolving. After SOAR
analysis is complete, the organization can move on to other stages like evaluating ideas and
proposals, creating a compelling vision and mission, and setting goals.
3. PESTLE ANALYSIS
Businesses use pestle analysis to track the environment in which new products or projects will
launch (see Table 2).
Table 2 PESTLE
Political Economical
 In the United States, the auto industry is
growing.
 Pandemics hit the automobile industry
Social Technological
 Skilled talent is in short supply in the
auto industry.
 The digital revolution is gaining traction.
Legal Environmental
 There are regulations and restrictions in
the auto industry.
 Impacts of manufacturing on the
environment.
4. LITERATURE REVIEW
Over 30 million SMEs with fewer than 500 employees make up 99.9% of all American
businesses. Digital tools aren't used favourably by 80% of U.S. SMEs [16]. AI maturity models
help organizations evaluate their progress and identify the changes they need to make to be more
productive and efficient. The dimensions above are discussed below. The use of AI to mitigate
adverse environmental impacts is discussed in Sustainability in AI [17]. To increase sustainability
coverage and remit the most profit to stakeholders, organizations need to quantify their
environmental, financial, and social impacts [18]. An AI implementation that is connected
requires the involvement of a multidisciplinary team with expertise in AI, data science,
manufacturing processes, and OT and IT infrastructure. Connecting machines, processes,
workplace health and safety, and managing product lifecycles all requires technical infrastructure
[19].
Resilience is facing situations and recovering from them, and disruption is something we're all
worried about [20]. In today's world, organizations see AI as a strategy to innovate by controlling
and commanding, knowing their objectives, and being opportunistic [21]. Leadership needs to
leverage AI to gain a competitive edge, and there needs to be inspired and proactive leadership to
make AI investments [22]. Supporting customers with AI allows for deeper insights and a better
user experience that indirectly builds mass personalization [23]. It's important to have a healthy
innovation culture to prosper economically and make technology safer [24]. McKinsey found that
70% of manufacturers use or plan to use AI to improve operations in production, which suggests
that many manufacturers are seeing the value of AI and are interested in adopting it [25].

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A comprehensive and systematic search was conducted following the steps of identification,
screening, eligibility, and inclusion. Identifying relevant records required a title and subject
search in various academic databases, including ABI/Inform Global, Springer, ScienceDirect,
IEEE, ACM Digital Library, and ProQuest. A manual search using Google and Google Scholar
were also used to find papers, conference proceedings, books, and technology reports. In this
paper, different search terms were used to identify some keywords based on the research
questions such as "Artificial Intelligence", "Manufacturing", "SME", "Maturity Model", and
"Artificial Intelligence Maturity Model". A good search strategy involves extracting individual
terms from the research question and then using Boolean "ORs" and "ANDs" to perform
advanced searches.
Table 3. Overall comparison of AI dimension
Author Connectivity resilience sustainability Expansive
growth
Strategy Leadership Customers Culture Production
[26] X X X X X X
[27] X X X X X X X
[28] X X
[29] X X X X X X X
[30] X X X X X X X
[31] X X X X
[32] X X X X X
[33] X X X
Figure 1. Dimensions

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As a result of the above comparison, the AI maturity model can be improved by identifying the
shortcomings (see Figure 1). Since SMEs have lagged other domains in digitization efforts, the
research, assessments, and implementation requirements are more intense for them. To save
money and gain a competitive edge, digital transformation combines different technologies.
SMEs will benefit if an AI maturity model fits their requirements and helps assess AI maturity
levels.
5. DIGITAL STRATEGY
Digitization is the process of converting analogue to digital. The digitalization of a business
means changing a business model and providing new revenue and value-producing opportunities,
and it is about moving to a digital world. Today, digital skills are a necessity for success in the
workplace, industry, and region. It's no secret that people's jobs change as companies implement
digital technologies. In digitalization, automation plays a big part, whether it's shifting work roles
or transforming business processes. Digitalization improves data transparency and process
efficiency, and it should boost your bottom line.
Technology is enabling manufacturing companies to become more efficient and customer-centric,
as well as stay competitive and adapt to changing business conditions. Automating production
processes, gathering, and analyzing data, and facilitating speedy decision-making through tools
like the Internet of Things, artificial intelligence, robotics, and cloud computing can help
optimize business operations. Digital technologies like AI, machine learning, and big data
analytics can help manufacturers gain new insights and improve their products. Finally,
businesses and industries need strategic plans to sustain a company's vision, market share growth,
brand equity, employee and product effectiveness, profitability, and stakeholder satisfaction.
These five rules will help get the most out of digital strategy (see Figure 2) [34].
 Assess the Strategic Impact of Digital
The impact and opportunities of digital will vary based on industry and function, so a good digital
strategy starts with a deep understanding of the competitive environment.
 Set Your Digital Ambition High
Most digital strategies fail due to too little ambition instead of too much because first movers and
smart fast followers win.
 Place Big Bets
The best way to manage priority initiatives is as a portfolio and roll out the ones with the most
short-term impact first to free up the resources needed for more strategic, high-impact initiatives
later.
 Build New Strategic Muscles
A properly ambitious digital strategy requires new capabilities and cultural shifts.
 Manage Transformation Actively

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It's important to keep the momentum going, track progress against goals, milestones, and metrics,
and signal when it's time to change directions [34].
Figure 2 The Five Rules of Digital Strategy
Business owners often change their value creation because of using new technologies. These
relate to the impact of digital transformation strategies on firms' value chains, i.e. how far digital
activities deviate from traditional – sometimes still analogue – core businesses.
Figure 3 Relation between digital transformation strategy and other corporate strategies
The digital transformation transforms key business operations, products, and processes, as well as
organizational structures and management concepts. All organizational and functional strategies
should be closely aligned with digital transformation strategies (see Figure 3). The financial side
of the transformation is both a driver and a constraint [35]. Digital transformations can be
financed from within the company as well as externally. Digital transformation is not just about
fixing past mistakes but creating new opportunities for the future. Regardless of industry or firm,
digital transformation strategies share some elements: four essential dimensions which are, the
use of technologies, changes in value creation, structural changes, and financial aspects (see
Figure 4) [35].

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Figure 4 Digital transformation framework: balancing four transformational dimensions.
Regardless of the type of business, differentiation, leadership, and focus are Porter’s three generic
strategies that anyone can use. A digitalization strategy requires the right culture, infrastructure,
and capabilities, which can be achieved with tailored transformation measures and step-by-step
transformation procedures. Digital transformation is changing how companies create value and in
digital transformation, it's the inversion of a company that makes the most money. This becomes
possible when companies move from creating value independently to orchestrating value with
other companies. The most successful companies partner with users, developers, and merchants
at scale. A high market cap is not achieved through automation or shifting labour to capital, but
rather by coordinating external value creation. In standalone companies, network effects don't
happen, but if companies look at their business frame in an ecosystem, it might be easier.
Multiplayer benefits can be realized if companies organize around shared value [36].
Uber and Airbnb are causing severe systemic effects in industries and markets and challenging
the existence of dominant firms. A lot of attention is being paid to such radical digital innovation
- often called digital disruption - and its wider systemic effects. Firms heavily invested in old
conditions are generally perceived as disrupted by digital disruption from their perspective.
Established firms face severe pressure to respond to new digital processes or artifacts, and such
responses can lead to radical changes in operations. The digital revolution is therefore an
aggregated effect that's both triggered and spurred by a lot of digital innovations, some of which
may create digital disruptions [37].
6. AI MM FOR THE AUTOMOTIVE SME MARKET
In an MM, the organization's preparedness is assessed, and faults are identified. These
weaknesses need to be fixed and corrective actions are needed to move forward. AI MM needs to
see how it aligns with Industry 5.0 and forge a more sustainable, human-centric, and resilient
industry. Collaboration between humans and advanced technology is the goal of Industry 5.0.
While Industry 4.0 is technology-driven, Industry 5.0 is a value-driven one [38]. From total
manual production to operating machines and eventually assembly lines, the previous three
industrial revolutions revolutionized our manufacturing industries. AI-related technologies are
assessed with the MM, which extends smart manufacturing MMs by adding specific technical
and nontechnical competencies of these technologies.
It's time to make the organization human-centred, so MM doesn't have to be just about
technology. People are the key to a successful digital strategy. Future SMEs need to be resilient
so they can handle disruptions and assist vital assets. SME's have realized that sustainability
measures can boost their recovery, speed up growth, and make them more profitable [39]. It's

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going to be tough for future SMEs to gain a competitive edge. During a global shortage of
workers and supply chain uncertainty, we're transitioning from predictable internal combustion
engines to variable and software-driven next-generation vehicles, including electric vehicles
(EVs), hybrids, and autonomous vehicles.
Is it time to refocus our current assessment criteria because of the happening EV market? The
digital transformation is the key to success, and EV manufacturers can benefit from a digital
backbone [40]. In contrast to established automakers, new EV entrants are leading disruptive
change. Electric vehicles (EVs) should be included in the MM assessment to help assess progress
to EV and zero-emission vehicles. A vehicle brand is currently known by its engine
manufacturer, but in the next ten years, it'll be known by its software provider (including AI-
powered autonomous driving, advanced infotainment, etc.).
In the world of intelligent manufacturing, AI technology helps develop new models, system
architecture, and technology systems. There are three key technologies that make IoT work: 5G,
Big Data, and AI. Data is provided by IoT frameworks, and AI uses it for specific functions. The
automotive industry is changing thanks to 5G and AI [41]. In comparison to cabled fibre optic
networks, 5G provides a feasible broadband networking option [42]. Industrial IoT needs 5G's
high performance and low latency [43]. Factory automation is an example of a latency critical
IoT use case that involves real-time control of machines and systems and has latency and
reliability requirements [44].
Automotive progress has been helped by electrification and automation, as well as advances in
the communications industry, including 5G and 6G. By improving reliability, lowering latency,
and guiding hyper-automation, 6G will open new growth potential in business. Sixth generation
(6G) mobile communications are also essential for Industry 5.0 to unseat Industry 4.0. High-
quality services, extensive IoT infrastructure, AI capabilities, and other requirements will be
supported by this network.
7. FUTURE CHALLENGES AND OPPORTUNITIES
Enterprises can improve their competitiveness by embracing intelligent transformation, which is
the trend of the future [45]. Production is expected to increase by 40% by 2035 thanks to AI
technologies. Across different industries, economic development will increase by an average of
1.7%. [46]. Although AI can surpass humans on some very particular tasks, humans still
noticeably outperform in all real-world tasks necessitating intelligence. During this era of
intelligent work, artificial intelligence has subverted traditional work methods and is widely used
in medicine, self-driving technology, image recognition, robotic manufacturing, intelligent
assistance, supply chain management, etc., using devices and technologies like cameras, video,
light detection and ranging (LiDAR) and motion tracing [47]. Small and medium-sized
enterprises have difficulty joining the intelligent manufacturing wave, and their implementation
of intelligence is limited [45]. Our pace of invention, however, must keep up. Approximately
seventy-five billion IoT-connected devices are expected in use by 2025, an almost threefold
increase from 2019, and connecting all these devices with intellectual abilities will be a
considerable challenge. [48].
AI's scope is also evolving, as it shifts from being "just a tool" to being a sci-fi creation that
threatens mankind [49]. Although AI is still in its infancy, it has reached milestones that a few
years ago seemed inconceivable. Christof Koch, a neuroscientist at the Allen Brain Institute and
an AI advocate, believes humans will need to enhance their brains to compete with artificial
intelligence [50]. Global competition and the need to develop a green economy have forced

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organizations to implement AI, and IoT in production flows. In the age of intelligent
manufacturing, the manufacturing industry needs to lead innovations to compete globally [51].
CONCLUSIONS
An uncertain and turbulent environment could result in mandatory closures, logistics bottlenecks,
supply issues, and volatility in consumption trends which would have major consequences for the
manufacturing industry. Manufacturers' success and survival are closely linked to their ability to
embrace advanced digital technologies, such as artificial intelligence. Scholars and practitioners
alike have become increasingly interested in AI over the last decade due to the growing amount
of data and information that firms collect and process.
Manufacturing plants can do more than just track operations with machine learning algorithms. It
is possible to contain inefficiencies, assess alternative strategies, and conduct new protocols
simultaneously without affecting the supply chain [52]. AI and digital transformation are
revolutionizing manufacturing. As organizations assess their capabilities, they need a robust AI
maturity model that details incremental improvements. Present maturity models have shown
deficiencies and therefore need to be addressed as per the AI focused SME maturity model
requirements. The lack of technological awareness among SME managers, such as "unclarity
regarding the benefits of AI initiatives" and "insufficient understanding" of AI, is a problem [53].
Manufacturing companies around the world benefit from investing in artificial intelligence (AI)
and optimizing their productivity [54]. Several manufacturers do not have the in-house
capabilities and are risk-averse to upscaling a factory due to the high cost and lack of skills
required for AI adoption [55]. Different analysis tools like SWOT can also detail the
digitalization efforts and progress in SMEs [56]. A maturity model for AI needs to be developed
to measure the current state and developments, and concrete recommendations have been
presented for the model.
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