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AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact

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Digital transformation systems generate a substantial volume of data, creating opportunities for potential innovation, particularly those driven by artificial intelligence. This study focuses on the intricate relationship between artificial intelligence and innovation as foundational elements in the digital transformation framework for sustained growth and operational excellence. This study provides a holistic perspective on the cultivation and pillars of AI-powered innovation, highlighting their pivotal role in revolutionizing industries, including healthcare, education, finance, manufacturing, transportation, and agriculture. The work emphasizes the key pillars essential for fostering AI-powered innovation, including monitoring performance measurement to use the power of the present, continuous learning and innovation, data analytics and insights, predictive analytics, and innovative product development. This study investigates how these pillars serve as the foundation for groundbreaking advancements, driving efficiency, enhancing decision-making processes, and fostering creativity within organizations. This study explores the significance of continuous learning, interdisciplinary collaboration, and industry partnerships in nurturing a thriving AI-powered innovation ecosystem. By understanding and harnessing these fundamental elements, businesses can navigate the complexities of the digital age, fostering innovation that not only optimizes processes but also enhances the overall human experience, ushering in a new era of technological excellence and societal progress.
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Citation: Aldoseri, A.; Al-Khalifa,
K.N.; Hamouda, A.M. AI-Powered
Innovation in Digital Transformation:
Key Pillars and Industry Impact.
Sustainability 2024,16, 1790.
https://doi.org/10.3390/su16051790
Academic Editors: Young-Chan Lee
and Runhui Lin
Received: 22 January 2024
Revised: 17 February 2024
Accepted: 17 February 2024
Published: 22 February 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
AI-Powered Innovation in Digital Transformation: Key Pillars
and Industry Impact
Abdulaziz Aldoseri , Khalifa N. Al-Khalifa and Abdel Magid Hamouda *
Engineering Management Program, College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar;
aa2009989@qu.edu.qa (A.A.); alkhalifa@qu.edu.qa (K.N.A.-K.)
*Correspondence: hamouda@qu.edu.qa
Abstract: Digital transformation systems generate a substantial volume of data, creating opportuni-
ties for potential innovation, particularly those driven by artificial intelligence. This study focuses on
the intricate relationship between artificial intelligence and innovation as foundational elements in
the digital transformation framework for sustained growth and operational excellence. This study
provides a holistic perspective on the cultivation and pillars of AI-powered innovation, highlighting
their pivotal role in revolutionizing industries, including healthcare, education, finance, manufac-
turing, transportation, and agriculture. The work emphasizes the key pillars essential for fostering
AI-powered innovation, including monitoring performance measurement to use the power of the
present, continuous learning and innovation, data analytics and insights, predictive analytics, and
innovative product development. This study investigates how these pillars serve as the foundation
for groundbreaking advancements, driving efficiency, enhancing decision-making processes, and
fostering creativity within organizations. This study explores the significance of continuous learning,
interdisciplinary collaboration, and industry partnerships in nurturing a thriving AI-powered inno-
vation ecosystem. By understanding and harnessing these fundamental elements, businesses can
navigate the complexities of the digital age, fostering innovation that not only optimizes processes
but also enhances the overall human experience, ushering in a new era of technological excellence
and societal progress.
Keywords: technology disruptions; digital transformation; DT; artificial intelligence; innovation
ecosystem; digital age; Industry 5.0
1. Introduction
The rapid and continuous development of digital network infrastructures and personal
smart devices has led to widespread applications based on digital transformation. This has
created significant growth in big data generated by various smart digital devices. Artificial
intelligence (AI)-driven big data processing technologies that utilize pattern recognition,
machine learning, and deep learning have emerged as solutions for handling large-scale
heterogeneous data [
1
,
2
]. AI-driven big data processing opens doors for AI-powered
innovations based on created data. This has found applications in different industry sectors,
such as those discussed in [
3
], education [
4
], medical applications [
5
], and E-government
services [6].
Digital transformation systems generate a substantial volume of data, giving rise
to a rich landscape of opportunities for potential innovation, particularly those driven
by AI. The sheer magnitude of data produced by these systems provides organizations
with an unprecedented wealth of information that can be harnessed and leveraged by
AI algorithms. By processing and analyzing this data, AI-powered applications have
the capability to unlock profound insights, uncover hidden patterns, and predict future
trends. This ability to extract meaning from vast amounts of data enables organizations
to make data-driven decisions, optimize processes, and drive transformative change. AI
Sustainability 2024,16, 1790. https://doi.org/10.3390/su16051790 https://www.mdpi.com/journal/sustainability
Sustainability 2024,16, 1790 2 of 25
empowers organizations to automate mundane tasks, enhance productivity, and reimagine
traditional business models. Moreover, AI-driven innovation can deliver personalized
and seamless experiences for customers, thus increasing engagement and loyalty. By
embracing the potential of AI in their digital transformation journey, organizations open
up a world of unlimited possibilities for growth, competitiveness, and long-term success
in the ever-evolving digital landscape. This has led to AI-powered innovation in digital
transformation [7].
AI-powered digital transformation is not just a buzzword; it is a powerful force
that fuels innovation, creativity, efficiency, and competitiveness across sectors [
8
]. This
paradigm shift marks the dawn of a new era in which human ingenuity collaborates
with artificial intelligence, transforming the world in unprecedented ways. Embracing
this transformative power, businesses and societies are poised to thrive in the digital age,
shaping a future that is intelligent, interconnected, and full of endless possibilities. AI-
powered digital transformation serves as a fertile ground for innovation and creativity.
Individuals can channel their creative energies toward solving complex problems and
developing revolutionary concepts by automating routine tasks.
As we transition into a world increasingly dominated by AI, the essence of innova-
tion is undergoing a transformation. Historically, innovation was perceived largely as
a product—a novel idea, a groundbreaking tool, or a disruptive model. However, the
infusion of AI into business processes and models has shifted this perspective. In the
context of AI-driven enterprises, innovation is not solely about inventing the next big thing
or introducing novel algorithms. It is an iterative journey, a continuum, that starts with
ideation but extends far beyond [9]:
Evolution over time: Unlike the static technologies of the past, AI systems learn and
adapt over time. This evolutionary nature necessitates constant refinement of these
systems to ensure that they remain aligned with their intended objectives and the
ever-changing external environment.
Perpetual beta: The phrase “always in beta” aptly describes the state of AI tools and
solutions [
10
]. Given their dynamic nature, they are perpetually undergoing testing,
learning from new data, and evolving. Innovation, in this space, means embracing this
continual state of flux and being prepared to adjust strategies and systems accordingly.
The convergence of the human intellect with artificial intelligence [
11
] is reshaping our
world in profound ways, heralding an era of unprecedented progress and boundless
opportunities. The convergence of the human intellect with artificial intelligence
represents a monumental leap forward for humanity, ushering in an era characterized
by unparalleled advancement and limitless opportunities. This synergy between hu-
man creativity and the computational power of AI systems is revolutionizing various
aspects of our lives, transforming industries, and shaping the future in profound ways.
This study presents the key pillars of AI-powered innovation in the digital transfor-
mation process framework. These pillars encompass performance monitoring, continuous
learning, data analytics, predictive analytics, and innovative product development. To-
gether, these pillars serve as the cornerstones upon which groundbreaking advancements
are constructed, driving efficiency enhancements, facilitating informed decision-making
processes, and nurturing creativity within organizations.
The literature reveals a significant gap in the exploration of how these pillars collec-
tively form a robust foundation and a framework for AI-powered innovation processes in
the digital transformation process. Despite the widespread acknowledgment of AI’s trans-
formative power in digital transformation, there remains a significant gap in understanding
how AI-powered innovation can be systematically harnessed to drive sustainable growth
and operational excellence across industries. The integration of performance monitoring,
continuous learning and innovation, data analytics, predictive analytics, and innovative
product development into organizational strategies constructs a resilient framework for
AI-powered innovation. By integrating these pillars, organizations elevate their operational
efficiency and decision-making processes and diffuse a culture of creativity and innova-
Sustainability 2024,16, 1790 3 of 25
tion. This comprehensive approach not only propels transformative advancements but
also ensures sustainable growth, enabling businesses to remain agile and relevant in the
ever-changing business landscape.
This study aims to explore the intricate relationship between artificial intelligence and
innovation within the digital transformation framework, highlighting the key pillars of AI-
powered innovation essential for fostering sustainable growth and operational excellence.
To this end, we pose the following research questions:
RQ1: How do AI’s innovation pillars contribute to the success and sustainability of digital
transformation efforts?
RQ2: What are the implications of AI-powered innovation for industry-specific transforma-
tion and overall societal progress?
By answering these questions, this study intends to illuminate the paths through
which AI-powered innovation can be harnessed to navigate the complexities of the digital
age, thus contributing to the academic and practical discourse on digital transformation.
2. Materials and Methods
The research methodology employed to investigate the intricate relationship between
artificial intelligence and innovation within the context of the digital transformation frame-
work for sustained growth and operational excellence is designed to be comprehensive and
nuanced. This study utilizes a distinguished “experience-driven” orientation, harmonized
with a meticulous literature review, to form a sophisticated hybrid strategy that synthesizes
pragmatic, field-based insights with a rigorous examination of academic discourse. The
investigation integrates experiential knowledge from active participation in industrial
system engineering, enriching and contextualizing practical expertise through compre-
hensive scrutiny of pertinent scholarly works. This hybrid strategy facilitates a holistic
understanding of the subject matter.
1.
Literature review: The start of this research endeavor involves an exhaustive literature
review, in which a methodical examination is conducted on academic articles, re-
search papers, and theoretical frameworks of artificial intelligence, innovation, digital
transformation, sustained growth, and operational excellence. This thorough review
serves as the cornerstone for comprehending the current state of scholarly discourse
in these domains, laying the groundwork for the development of a framework for
AI-powered innovation aimed at revolutionizing industries [
12
]. The review follows
the following steps:
Scope definition: The literature review begins with a precise definition of the
scope, elucidating the key themes and parameters relevant to AI, innovation,
digital transformation, sustained growth, and operational excellence. This step
ensures a focused and purposeful exploration of the existing body of knowl-
edge [13].
Systematic review methodology: Employing a systematic review methodology,
academic databases, research repositories, and relevant journals are systemat-
ically searched and scrutinized. This rigorous approach ensures the inclusion
of comprehensive and relevant literature while maintaining a structured and
organized process [14].
Thematic categorization: The identified literature is categorized thematically,
allowing for the systematic organization of information. This categorization aids
in discerning common themes, trends, and patterns across diverse sources, thus
contributing to a comprehensive understanding of the subject matter [15].
Identification of key concepts: Key concepts related to AI, innovation, digital
transformation, sustained growth, and operational excellence are distilled from
literature. This identification facilitates the development of a conceptual founda-
tion for the framework for AI-powered innovation.
Sustainability 2024,16, 1790 4 of 25
Critical appraisal: Each source undergoes critical appraisal to evaluate its method-
ological rigor, reliability, and relevance to the research objectives. This discerning
analysis ensures the inclusion of high-quality literature, which contributes to the
robustness of the subsequent framework development [16].
Synthesis of literature: The synthesized information from the literature review
serves as the intellectual basis for conceptualizing the framework for AI-powered
innovation. Insights, theories, and empirical findings from the literature are
combined to inform the subsequent stages of the research [17].
Conceptual framework development: A conceptual framework is developed
based on the literature review. This framework delineates the theoretical un-
derpinnings and defines key concepts, relationships, and variables essential to
understanding how AI can revolutionize industries through innovation, digital
transformation, sustained growth, and operational excellence.
Title integration: The conceptual framework developed through the literature
review lays the foundation for the subsequent exploration of an AI-powered
innovation framework that revolutionizes industries. The synthesized insights
guide the framing of innovative solutions within this conceptual framework.
By rigorously pursuing these steps in the literature review, we aim to not only establish
a comprehensive understanding of the existing academic discourse but also discern oppor-
tunities for contributing novel insights to the intricate relationship between AI, innovation,
digital transformation, sustained growth, and operational excellence.
2.
Experience-driven approach: This study integrates an “experience-driven” orientation,
drawing on practical knowledge derived from active involvement in the field of
industrial system engineering. This involves first-hand experiences, observations, and
engagements with AI and innovation in real-world contexts. These experiences are
documented and analyzed to extract valuable insights that complement and enrich the
theoretical perspectives. The incorporation of an “experience-driven” approach in this
study signifies a deliberate integration of practical knowledge acquired through active
participation in the field of industrial system engineering to complement and enrich
theoretical perspectives related to AI and innovation. This methodological orientation
emphasizes first-hand experiences, direct observations, and engagements with AI and
innovation within real-world contexts [18]. This follows the following steps:
Field immersion: Researchers actively immerse themselves in the operational
milieu of industrial system engineering by engaging in AI applications and
innovative practices. This immersion allows for a first-hand understanding of
the practical challenges, opportunities, and dynamics inherent in the integration
of AI and innovation within industrial settings [18].
Qualitative data collection techniques: The experiential approach is augmented
through the judicious incorporation of qualitative data collection methodologies,
specifically interviews and surveys. These rigorous methods are strategically
applied to solicit insights from eminent industry professionals, practitioners, and
stakeholders operating within the domains of industrial system engineering,
AI, and innovation. The conducted interviews serve as a conduit for acquiring
nuanced perspectives, substantiating findings with anecdotal evidence, and
imparting an invaluable real-world context. Qualitative data not only validate
experiential knowledge but also contribute to a holistic understanding of the
interplay between theory and practice [19].
Observational analysis: Through keen observation, researchers systematically
analyze the implementation of AI and innovation in real-world scenarios. This
involves documenting how these technologies are applied, identifying patterns
of usage, and discerning the nuances of their impact on industrial processes and
outcomes [20].
Sustainability 2024,16, 1790 5 of 25
Hands-on involvement: The research team actively participates in hands-on
activities related to AI and innovation in industrial system engineering. This
could include collaborative problem-solving, experimental projects, or direct
involvement in the development and implementation of technological solutions.
This hands-on approach facilitates a deeper understanding of the practical impli-
cations of these technologies.
Documentation of experiences: Experiences, insights, and observations are metic-
ulously documented in a systematic manner. This documentation includes
detailed records of specific scenarios, challenges encountered, solutions devised,
and lessons learned. This comprehensive record serves as a valuable dataset for
analysis [21].
Analysis for insights: The documented experiences are subjected to rigorous
analysis, with a focus on extracting insights that complement and augment
the theoretical perspectives derived from the literature review. This analytical
process involves identifying patterns, successes, failures, and emerging trends in
the practical application of AI and innovation within industrial contexts.
Contextualization of findings: The insights derived from the experience-driven
approach are then contextualized within the broader theoretical framework
established earlier. This process ensures that the practical knowledge gained
is aligned with and contributes to the conceptual understanding derived from
academic literature, creating a cohesive and comprehensive narrative.
Validation of theoretical assumptions: Through an experience-driven approach,
this study seeks to validate or challenge theoretical assumptions and hypotheses.
The practical insights obtained offer a real-world perspective that enhances the
credibility and applicability of the research findings.
Continuous iteration: The experience-driven approach is not static but rather iter-
ative. As the study progresses, ongoing experiences and observations may lead
to refinements or expansions of the theoretical framework, creating a dynamic
interplay between practical insights and theoretical foundations.
The experience-driven methodology used in this study possesses inherent limitations,
primarily due to the subjective nature of being “experience-driven”, such as subjectivity
and bias, limited objectivity, context dependence, overemphasis on self, difficulty in valida-
tion, inadequate training, and cultural variations. Participant availability and willingness
to share insights may also introduce scope constraints. To address these limitations and up-
hold research rigor, a structured protocol was established for data collection, emphasizing
standardized procedures. Inter-rater reliability assessments were integral to the systematic
review process, ensuring consistency and mitigating biases through independent reviews
by multiple researchers. However, there are also some strengths that derive from the
ultimate unity and relationship between the researcher and the subject and are valuable
when used judiciously and in conjunction with other research methods such as literature
review or empirical methods. The strengths of this hybrid approach include rich qualitative
insights, self-reflection, contextual understanding, enhanced empathy, a holistic approach,
theory development, enhanced reflexivity, qualitative data triangulation, personal engage-
ment, and inspirational sources. Its unique strengths make it a valuable qualitative research
method, particularly in fields where subjective experiences and self-awareness are integral
to research inquiry. Researchers can leverage these strengths to enrich their qualitative
investigations and contribute meaningfully to their respective disciplines.
3.
Synthesis and conclusion: The final phase involves synthesizing the findings from the
literature review, the experience-driven approach, and the data analysis. This study
aims to draw meaningful conclusions regarding the relationship between AI and inno-
vation within the digital transformation framework, providing insights into how these
elements contribute to sustained growth and operational excellence. In the synthesis
and conclusion phase, the study brings together diverse strands of information gath-
ered from the literature review, the experience-driven approach, and the data analysis.
Sustainability 2024,16, 1790 6 of 25
This integration aims to derive comprehensive insights into the intricate relationship
between AI and innovation within the digital transformation framework, shedding
light on their collective impact on sustained growth and operational excellence.
4.
AI systems a group: In navigating the intricate landscape of AI, the ability to discern
and comprehend the distinct features of various AI systems becomes paramount.
Understanding these nuances is crucial for unlocking insights into their diverse
applications and the transformative impact they wield in digital evolution. However,
it is noteworthy that the focus of this particular work extends beyond a dedicated
examination of any singular AI system. Instead, it takes on the broader perspective
of encompassing all AI tools and systems as an integrated collective. By adopting
this holistic approach, the study aims to capture the synergies, interconnections, and
overarching trends that characterize the collaborative dynamics within the broader
spectrum of AI technologies. As digital transformation continues to redefine industries
and reshape the technological landscape, this comprehensive exploration of AI as a
unified group seeks to offer insights into the collective intelligence driving innovation,
automation, and the ever-expanding boundaries of artificial intelligence.
3. Results
3.1. Cultivating an Innovative Mindset for AI-Powered Digital Transformation
In AI-powered digital transformation, an innovative mindset is the cornerstone of suc-
cess. It empowers businesses to harness the full potential of AI, driving creative solutions,
user-centric designs, and ethical practices. By cultivating curiosity, encouraging creative
problem-solving, fostering adaptability, and embracing a long-term vision, businesses can
not only navigate the complexities of digital transformation but also lead the way, shaping
a future where innovation and AI-driven advancements go hand in hand, creating a more
intelligent, efficient, and equitable world for all. Figure 1summarizes the principle of
cultivating an innovative mindset in the realm of AI-powered digital transformation:
Sustainability 2024, 16, x FOR PEER REVIEW 7 of 26
Figure 1. Principles of cultivating an innovative mindset.
(a) Embracing a Culture of Curiosity:
Embracing a culture of curiosity is not just a cultural shift; it is a strategic imperative
in the world of AI-powered digital transformation [22]. An innovative mindset encourages
the questioning of existing norms and processes. This culture of curiosity is the corner-
stone upon which innovative solutions and groundbreaking advancements are built. By
challenging the status quo, businesses can identify ineciencies and explore innovative
AI solutions that drive digital transformation. This culture does not just lead to techno-
logical advancements; it fosters a mindset that transforms challenges into opportunities,
stagnation into evolution, and curiosity into a driving force that propels businesses into a
future where the possibilities of AI are not just imagined but also realized, creating a
world where innovation knows no bounds [23]. Embracing a culture of curiosity involves
continuous learning. In the rapidly evolving eld of AI, staying updated with the latest
technologies, algorithms, and industry trends is essential. Continuous education fosters a
mindset open to new possibilities and innovations. With an innovative mindset and con-
tinuous learning, businesses not only survive in the AI-powered DT age but also thrive,
shaping a future that is dened by creativity, ingenuity, and a never-ending quest for pro-
gress.
(b) Encouraging Creative Problem-Solving:
An innovative mindset promotes divergent thinking and encourages teams to ex-
plore various solutions to a problem. In the context of AI, this means exploring various
algorithms, data sources, and application scenarios to nd the most eective and creative
solutions [24]. Bringing together professionals from diverse elds fosters creative prob-
lem-solving. AI-powered digital transformation often benets from collaboration among
data scientists, engineers, designers, and domain experts, each contributing unique per-
spectives to innovative solutions [25]. This diversity enhances problem-solving by incor-
porating varied insights, leading to innovative AI solutions. Collaborative teams leverage
collective expertise to tackle problems from multiple perspectives. Engineers might focus
on the technical feasibility, designers on user experience, and domain experts on real-
world applications. This holistic approach results in AI solutions that are technically ro-
bust, user-friendly, and applicable in practical scenarios.
Figure 1. Principles of cultivating an innovative mindset.
(a) Embracing a Culture of Curiosity:
Embracing a culture of curiosity is not just a cultural shift; it is a strategic imperative
in the world of AI-powered digital transformation [
22
]. An innovative mindset encourages
the questioning of existing norms and processes. This culture of curiosity is the corner-
stone upon which innovative solutions and groundbreaking advancements are built. By
challenging the status quo, businesses can identify inefficiencies and explore innovative AI
Sustainability 2024,16, 1790 7 of 25
solutions that drive digital transformation. This culture does not just lead to technological
advancements; it fosters a mindset that transforms challenges into opportunities, stagna-
tion into evolution, and curiosity into a driving force that propels businesses into a future
where the possibilities of AI are not just imagined but also realized, creating a world where
innovation knows no bounds [
23
]. Embracing a culture of curiosity involves continuous
learning. In the rapidly evolving field of AI, staying updated with the latest technologies,
algorithms, and industry trends is essential. Continuous education fosters a mindset open
to new possibilities and innovations. With an innovative mindset and continuous learning,
businesses not only survive in the AI-powered DT age but also thrive, shaping a future
that is defined by creativity, ingenuity, and a never-ending quest for progress.
(b) Encouraging Creative Problem-Solving:
An innovative mindset promotes divergent thinking and encourages teams to ex-
plore various solutions to a problem. In the context of AI, this means exploring various
algorithms, data sources, and application scenarios to find the most effective and creative
solutions [
24
]. Bringing together professionals from diverse fields fosters creative problem-
solving. AI-powered digital transformation often benefits from collaboration among data
scientists, engineers, designers, and domain experts, each contributing unique perspectives
to innovative solutions [
25
]. This diversity enhances problem-solving by incorporating
varied insights, leading to innovative AI solutions. Collaborative teams leverage collec-
tive expertise to tackle problems from multiple perspectives. Engineers might focus on
the technical feasibility, designers on user experience, and domain experts on real-world
applications. This holistic approach results in AI solutions that are technically robust,
user-friendly, and applicable in practical scenarios.
(c) Emphasizing User-Centric Design:
An innovative mindset involves conducting thorough research to understand the
challenges, preferences, and expectations of end users. This analysis goes beyond surface-
level understanding, diving deep into the context in which users will interact with the
AI-powered solution. Empathizing with users allows businesses to design AI-powered
solutions that are not only technically proficient but also intuitive and user-friendly, enhanc-
ing the overall user experience [
26
,
27
]. Creating detailed user personas helps visualize the
target audience. By understanding the diverse needs of different user segments, businesses
can tailor AI applications to cater to specific requirements, ensuring a more personalized
and satisfying user experience. Rapid prototyping and iterative development are key
components of user-centric design. Businesses can ensure that AI solutions align with user
expectations and preferences by quickly creating prototypes, gathering user feedback, and
iterating on designs.
(d) Nurturing a Growth Mindset:
An innovative mindset perceives failure as a steppingstone to success. Failures are
viewed as valuable learning opportunities that provide insights for improvement [
28
].
Embracing failure as a natural part of the innovation process is crucial in AI-powered
digital transformation. Businesses can experiment with AI solutions, learn from failures,
and iterate to achieve innovative breakthroughs. When an AI solution fails to meet ex-
pectations, it provides insights into the weaknesses or gaps in the approach. Analyzing
these failures helps in pinpointing areas that need improvement, leading to a more refined
strategy in subsequent iterations. Failure prompts a thorough analysis of the root causes.
Understanding why a particular approach failed is invaluable. This could be an issue with
data quality, algorithm selection, or user interaction. Identifying the root causes guides the
direction of future innovations. Innovations in AI rarely happen overnight. They are the
result of continuous refinement and iteration. Each failure provides an opportunity to refine
the approach, tweak algorithms, or reassess the problem statement. Iterative improvement
ensures that failures are steppingstones toward eventual success. Agile methodologies,
which emphasize quick iterations and adaptive responses, align well with the innovative
Sustainability 2024,16, 1790 8 of 25
mindset [
29
]. When failures occur, agile development allows businesses to pivot swiftly,
making necessary changes and optimizing their AI solutions based on real-time feedback.
Viewing failure as a part of the process builds resilience within teams. Resilient teams
bounce back from setbacks, using failures as motivation to explore new avenues and cre-
ative solutions. This resilience is vital in overcoming challenges and persistently pursuing
innovation. Failure teaches adaptability. Businesses that learn from failures are better
equipped to adapt to changing market demands, technological advancements, and user
preferences. This adaptability ensures that AI solutions remain relevant and effective in
dynamic environments.
(e) Embracing a Long-term Vision:
An innovative mindset involves strategic planning for the long term and foreseeing
industry trends and disruptions [
30
]. Businesses should envision how AI technologies
can transform their industry in the coming years and align their innovation efforts with
this vision. Businesses can anticipate how AI advancements might reshape their sector by
studying market dynamics and emerging technologies. This foresight enables proactive
innovation rather than reactive adaptation. Long-term planning ensures that innovation
efforts align with broader business goals. By integrating AI strategies into the overall
business strategy, companies can effectively channel their resources, ensuring that AI
initiatives contribute meaningfully to the organization’s mission and vision [
31
]. By being at
the forefront of AI advancements, companies can gain a significant competitive advantage.
Early adoption of transformative AI technologies can position their businesses as industry
leaders. Investing in research and development initiatives allows businesses to explore
cutting-edge AI technologies. By allocating resources to R&D, businesses can stay ahead of
the curve, pioneering innovations that have the potential to reshape industries and markets.
3.2. Development of the Pillars of AI-Powered Innovation
The drive for innovation in AI-based digital transformation is not just about the
creation of new ideas or tools. It is also about meticulous evaluation, understanding
their impacts, and refining them for maximum efficacy. Here lies the significance of
monitoring, measurements, and metrics, which act as feedback mechanisms that ensure
the right direction and pace of innovation. In the ever-evolving landscape of AI-powered
digital transformation, monitoring, measurements, and metrics are the linchpins that drive
progress, efficiency, and innovation. Businesses and industries are increasingly relying on
these crucial elements to gauge the impact of AI technologies, foster continuous learning,
and drive sustainable practices. Figure 2depicts the following pillars:
Sustainability 2024, 16, x FOR PEER REVIEW 9 of 26
ahead of the curve, pioneering innovations that have the potential to reshape industries
and markets.
3.2. Development of the Pillars of AI-Powered Innovation
The drive for innovation in AI-based digital transformation is not just about the cre-
ation of new ideas or tools. It is also about meticulous evaluation, understanding their
impacts, and rening them for maximum ecacy. Here lies the signicance of monitoring,
measurements, and metrics, which act as feedback mechanisms that ensure the right di-
rection and pace of innovation. In the ever-evolving landscape of AI-powered digital
transformation, monitoring, measurements, and metrics are the linchpins that drive pro-
gress, eciency, and innovation. Businesses and industries are increasingly relying on
these crucial elements to gauge the impact of AI technologies, foster continuous learning,
and drive sustainable practices. Figure 2 depicts the following pillars:
Figure 2. Pillars of AI-powered innovation.
(a) Monitoring Strategic Insights:
Monitoring strategic insights (i.e., the power of now) is becoming the cornerstone of
the future AI-powered digital transformation. This crucial aspect, nestled within the heart
of innovation, ensures that businesses adapt to change and proactively shape their future.
It is a strategic approach that yields profound insights and fosters continuous innovation
and improvement. By monitoring various facets of AI applications, businesses can glean
valuable data, enabling them to make informed decisions, enhance user experiences, and
stay ahead in the competitive landscape. Continuous monitoring and measurement of AI
applications provide valuable data on their performance [32]. Metrics such as accuracy
rates, response times, and error rates help businesses assess the eectiveness of their AI
solutions. Continuous monitoring oers real-time data, empowering businesses to make
proactive, data-driven decisions [33]. Instead of reacting to issues after they occur, busi-
nesses can anticipate challenges and opportunities, enabling strategic planning and swift
action. Proactive decision-making is a pivotal advantage of continuous monitoring in AI-
powered digital transformation [34]. By anticipating challenges, identifying opportuni-
ties, and taking swift, data-driven actions, businesses can position themselves as industry
leaders, driving innovation and ensuring long-term success. Here is a closer look at why
it is so crucial:
Figure 2. Pillars of AI-powered innovation.
Sustainability 2024,16, 1790 9 of 25
(a) Monitoring Strategic Insights:
Monitoring strategic insights (i.e., the power of now) is becoming the cornerstone of
the future AI-powered digital transformation. This crucial aspect, nestled within the heart
of innovation, ensures that businesses adapt to change and proactively shape their future.
It is a strategic approach that yields profound insights and fosters continuous innovation
and improvement. By monitoring various facets of AI applications, businesses can glean
valuable data, enabling them to make informed decisions, enhance user experiences, and
stay ahead in the competitive landscape. Continuous monitoring and measurement of AI
applications provide valuable data on their performance [
32
]. Metrics such as accuracy rates,
response times, and error rates help businesses assess the effectiveness of their AI solutions.
Continuous monitoring offers real-time data, empowering businesses to make proactive,
data-driven decisions [
33
]. Instead of reacting to issues after they occur, businesses can
anticipate challenges and opportunities, enabling strategic planning and swift action.
Proactive decision-making is a pivotal advantage of continuous monitoring in AI-powered
digital transformation [
34
]. By anticipating challenges, identifying opportunities, and
taking swift, data-driven actions, businesses can position themselves as industry leaders,
driving innovation and ensuring long-term success. Here is a closer look at why it is
so crucial:
Anticipating challenges: With real-time data, businesses can foresee potential chal-
lenges before they escalate. For instance, if an AI application shows a sudden increase in
error rates, it could indicate an issue that, if left unaddressed, might affect user experience
or business operations. Early detection allows businesses to quickly rectify problems.
Identifying opportunities: Real-time monitoring not only highlights issues but also un-
covers opportunities. Businesses can identify trends or patterns in user behavior, which
helps them understand what customers want or need. By promptly capitalizing on these
opportunities, businesses can gain a competitive advantage and increase their market share.
Strategic planning: Continuous monitoring provides a wealth of data that can inform
long-term strategic planning. Businesses can track the performance of AI applications
over time, allowing for the identification of trends and patterns. This data can be invalu-
able for making strategic decisions, such as allocating resources to specific AI projects or
entering new markets where AI solutions are in high demand. Swift action: Real-time
data empowers businesses to take immediate action. If a monitored metric falls below a
predefined threshold, automated alerts can notify relevant stakeholders, enabling them
to respond swiftly. This agility is invaluable in preventing minor issues from escalating
into significant problems. Customer satisfaction: Proactive monitoring ensures that AI
applications consistently meet customer expectations. By monitoring user feedback and
behavior in real-time, businesses can promptly address user concerns, leading to improved
customer satisfaction and loyalty. Satisfied customers are more likely to become repeat
buyers and brand advocates. Risk mitigation: Continuous monitoring helps businesses
identify and mitigate risks promptly. Whether it is a security breach, data integrity issue, or
performance bottleneck, real-time monitoring allows for immediate action, reducing the
impact of risks on the business and its stakeholders. Innovation: By proactively analyzing
real-time data, businesses can foster a culture of innovation. Identifying patterns and
trends can inspire new ideas for AI applications or enhancements to existing solutions.
This constant cycle of innovation keeps the business ahead of the curve and ensures the
relevance of its offerings in the market.
By understanding current performance through real-time monitoring, businesses can
swiftly adapt to market changes and technological advancements. Moreover, this insight
allows businesses to shape their future strategies, ensuring that they are always at the
forefront of innovation. Real-time monitoring enables businesses to remain agile in response
to market fluctuations [
35
]. Whether it is changes in customer preferences, economic shifts,
or industry trends, businesses can swiftly adapt their strategies and offerings. This agility
ensures that businesses can align their AI applications with current market demands
while maintaining relevance and meeting customer expectations [
36
]. Technology has
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evolved rapidly, especially in the field of artificial intelligence. Real-time monitoring allows
businesses to stay abreast of the latest advancements. By understanding the performance
of current AI solutions, businesses can make informed decisions about integrating new
technologies. This integration might include adopting more sophisticated algorithms,
incorporating machine learning enhancements, or leveraging novel AI applications that
align with business objectives. Real-time monitoring not only informs internal strategies
but also facilitates external collaboration. By sharing relevant data with strategic partners or
industry collaborators, businesses can collectively shape the future of AI applications [
37
].
Collaborations can lead to the development of groundbreaking technologies or the creation
of industry standards, ensuring that businesses remain at the forefront of innovation. Real-
time monitoring provides data points for long-term planning. By analyzing historical
performance data alongside real-time insights, businesses can create a roadmap for the
future. This roadmap outlines the evolution of AI applications, ensuring that businesses
have a clear vision of how their technology will develop over time. A well-defined roadmap
is essential for sustained innovation and strategic growth.
(b) Continuous Learning and Innovation:
The key to sustained success in AI-powered digital transformation is continuous
learning and innovation [
37
]. Continuous learning and innovation form the bedrock of
a thriving ecosystem in AI-powered digital transformation. By fostering a culture where
learning is constant, organizations create an environment where creativity, adaptability,
and forward-thinking are not just encouraged but are essential. This dynamic approach
ensures that businesses do not merely keep up with technological advancements; they lead
the charge, shaping the future of AI-powered digital innovation and driving transformative
change across industries. As organizations harness the power of data-based innovation,
the ability to adapt, evolve, and innovate becomes paramount. Here is how continuous
learning and innovation serve as catalysts that drive the transformative journey in the
realm of AI-powered digital transformation:
Embracing technological evolution: Technology, especially AI, is in a constant state of
flux. Continuous learning ensures that individuals and organizations stay updated with
the latest tools, algorithms, and methodologies. This ongoing education allows businesses
to harness the full potential of AI technologies, optimizing their applications for efficiency
and effectiveness. Keeping pace with innovation: The field of AI is marked by continuous
innovation. New algorithms, tools, and methodologies are developed regularly, each
offering unique capabilities and efficiencies. Continuous learning ensures that individuals
and organizations are aware of these advancements, allowing them to incorporate the
latest technologies into their solutions [
38
]. Optimizing existing applications: Continuous
learning enables professionals to revisit existing AI applications with fresh knowledge.
By staying updated, individuals can identify areas where new algorithms or techniques
could enhance the efficiency of their current solutions. This optimization leads to improved
performance, reduced costs, and enhanced user experiences. Adopting best practices:
Continuous learning involves understanding new technologies and adopting best practices
in AI development and implementation. Learning from the successes and failures of
others in the field helps businesses avoid common pitfalls and optimize their AI strategies
effectively. Enhancing problem-solving capabilities: New technologies often introduce
novel ways of solving problems. Continuous learning exposes individuals to diverse
problem-solving approaches, thus expanding their capabilities. This broadened skill set
allows professionals to tackle complex challenges in AI development with creativity and
innovation. Innovative problem-solving requires cross-disciplinary learning driven by
collaboration among data scientists, engineers, domain experts, and creative thinkers. This
diverse collaboration leads to innovative problem-solving and fresh perspectives. This
requires the workshop engagement of teams in design thinking sessions to foster creativity
and empathy. Such workshops often lead to innovative AI-powered solutions that address
real-world problems. Staying ahead of the curve: Innovation is driven by market awareness.
Hence, continuous learning keeps organizations informed about market trends, customer
Sustainability 2024,16, 1790 11 of 25
behaviors, and competitor strategies. This awareness is crucial for strategic decision-making
and innovation to stay updated with the latest advancements, ensuring that businesses
leverage the most cutting-edge AI tools and data analytics platforms. Feedback loops and
iterative development: Continuous learning involves gathering feedback from end users.
This iterative feedback loop ensures that AI-powered solutions are user-centric and align
with evolving user preferences. This enables rapid prototyping and iterative development,
allowing organizations to refine AI applications based on real-world user experiences,
thereby enhancing usability and functionality. This leads to continuous enhancement,
which can be explained as follows. Sentiment analysis: Beyond merely collecting feedback,
AI-driven tools can gauge the sentiment behind user comments across platforms, such as
social media, review sites, and customer support channels. This provides businesses with
nuanced insights into user satisfaction and areas for enhancement [
39
]. Adaptive products:
By integrating AI into products, they can inherently adapt based on user feedback. Such
real-time adaptability not only enhances user experience but also fosters trust and loyalty,
as users feel that their feedback directly shapes the products they use [
40
]. Proactive issue
identification: Before issues escalate to critical levels or widespread user dissatisfaction, AI
can pinpoint emerging problems by analyzing patterns in user feedback. Such proactive
issue detection is invaluable for maintaining product reputation and ensuring continued
user satisfaction [
41
]. Experimentation and risk-taking: Establishing innovation labs or
dedicated spaces for experimentation encourages teams to explore unconventional AI
applications. This environment fosters creativity and risk-taking. Failure as learning
embraces failure as a stepping stone to innovation. Organizations learn valuable lessons
from failed experiments, leading to more refined and innovative solutions. Upskilling the
workforce: Continuous learning is not just about technology. As AI evolves, professionals
need to upskill, ensuring that they can leverage new tools, understand novel algorithms,
and apply best practices in AI implementation [42].
(c) Data Analytics and Insights
Data analytics and insights are the engines that drive innovation. Data analytics
and insights are the cornerstone of driving innovation and shaping strategic decisions
in AI-powered digital transformation. By harnessing the power of data analytics and
transforming raw data into actionable insights, businesses can unlock invaluable insights,
enabling data-based innovation that fuels the journey toward digital excellence [
43
]. This
iterative process of analyzing data, gaining insights, and innovating based on those insights
propels organizations toward digital excellence, ensuring that they keep up with the times
and lead the way in the transformative journey of AI-powered digital innovation [
44
].
Data analysis can stimulate innovation by unearthing new opportunities and identifying
areas ripe for improvement. Companies can leverage data-driven insights to create new
products or services, penetrate new markets, and enhance customer experiences. For
example, analysis of customer behavior and feedback can help a company recognize the
market demand for a new product feature, thus steering product development [
45
]. Here
is how data analytics and insights catalyze innovation in the realm of AI-powered digital
transformation:
Understanding the business landscape: Data analytics provides a comprehensive
view of market trends, customer behaviors, and competitor strategies. These insights
guide businesses in identifying opportunities and developing competitive strategies in
alignment with market demands. Analyzing customer data enables businesses to create
detailed customer profiles. These profiles are instrumental in tailoring products, services,
and marketing campaigns to specific customer segments, thereby enhancing customer
satisfaction and loyalty. Enhancing operational efficiency: Innovation in process opti-
mization is based on data analytics. Organizations identify bottlenecks and inefficiencies
within their operations. Insights derived from operational data help streamline workflows,
optimize resource allocation, and improve overall efficiency. Predictive maintenance in
industries such as manufacturing uses data to anticipate equipment failures. By performing
maintenance before issues arise, businesses ensure continuous operation, thereby reducing
Sustainability 2024,16, 1790 12 of 25
downtime and associated losses. In the HR realm, analytics can reveal patterns affecting
employee turnover, enabling the formation of proactive retention strategies [
46
]. Personal-
izing customer experiences: Data analysis of customer interactions and behavior data helps
businesses gain insights into customer preferences. This knowledge allows for the creation
of personalized experiences that enhance customer engagement and loyalty. Real-time data
analytics enable innovative businesses to personalize customer interactions on the fly [
47
].
Dynamic content and offers based on real-time customer behavior led to higher conversion
rates and customer satisfaction. Informed decision-making: Data analytics provide reliable,
data-driven insights rather than relying solely on intuition or observation. Leaders can
make strategic decisions backed by concrete evidence, thereby reducing the risk associated
with intuition-based decision-making. Data analytics allows businesses to simulate various
scenarios. Organizations can make informed decisions and choose the most promising
path forward by analyzing the potential outcomes of different strategies. For instance, a
company in the e-commerce sector can harness data analysis to adjust product pricing
optimally, influenced by parameters such as demand, competitor pricing, and customer
behavior trends [
48
]. Continuous feedback and improvement: Data analytics process user
feedback from various channels. Businesses gain valuable insights into customer sentiment,
enabling iterative improvements to products and services. Businesses can continuously
iterate their offerings by analyzing user feedback and usage data. This agile approach
ensures that products remain relevant, competitive, and aligned with user needs.
Data analytics and insights are vital in driving innovation and shaping strategic deci-
sions in AI-powered digital transformation. While the ability to transform raw data into
actionable insights can fuel data-based innovation, it is essential to address the inherent
challenges and limitations, such as data collection, data quality, and data privacy concerns.
Challenges in data collection: Effective data analytics begins with high-quality data collec-
tion. However, businesses often face challenges accessing relevant data due to fragmented
data sources, inconsistent data formats, and the sheer volume of data generated. Orga-
nizations must develop robust data collection strategies that ensure comprehensive and
representative data capture while navigating issues such as data silos and integration com-
plexities. Importance of data quality: The reliability of data analytics is directly tied to the
quality of the data used. Poor data quality, characterized by inaccuracies, incompleteness,
and inconsistencies, can lead to erroneous insights and flawed decision-making. Businesses
need to invest in effective data management practices, including regular data cleaning,
validation, and updating processes, to ensure data integrity and reliability. Data privacy
considerations: With increasing concerns about data privacy and the rise of stringent
data protection regulations such as GDPR, businesses must navigate the complex land-
scape of data privacy. This includes ensuring that data collection and analytics practices
comply with legal standards, protecting sensitive customer information, and maintaining
transparency with customers regarding data usage. Personalizing customer experiences:
While personalization can significantly enhance customer engagement, it must be balanced
with privacy concerns. Businesses should employ data analytics to understand customer
preferences and behaviors, but they must also respect customer privacy and preferences
regarding data usage. Informed decision-making and data governance: As businesses rely
more on data-driven insights for decision-making, establishing robust data governance
frameworks becomes crucial. This includes defining clear policies for data access, usage,
and sharing within the organization and ensuring accountability for data quality and
security. Continuous feedback and improvement: The dynamic nature of data analytics
requires a continuous loop of feedback and improvement. Businesses should monitor the
effectiveness of their data analytics practices and remain adaptable to changes in data
environments, market dynamics, and regulatory requirements. Ethical considerations in
data analytics: Beyond privacy and compliance, ethical considerations should be at the
forefront of data analytics practices. This involves being transparent about data collection
and usage, avoiding biases in data analysis, and ensuring that data analytics practices do
not harm or disadvantage any group.
Sustainability 2024,16, 1790 13 of 25
By addressing these challenges and limitations, businesses can leverage data analytics
more effectively, ensuring that their data-driven innovation is technically sound, ethically
responsible, and aligned with broader societal values.
(d) Predictive Analytics
Predictive analytics is the linchpin of data-based innovation in AI-powered digital
transformation [
49
]. Predictive analytics has emerged as a game-changing force that propels
businesses toward innovative solutions and strategic decision-making. By forecasting
future trends and behaviors, businesses gain a competitive advantage, allowing them to
innovate proactively rather than reactively. Through predictive insights, organizations
meet current market demands and anticipate future needs, positioning themselves as
leaders in the rapidly evolving landscape of digital innovation. Predictive analytics does
not just offer insights; it offers foresight, enabling businesses to foresee trends, anticipate
customer needs, and optimize operations, ushering in a new era of data-based innovation
and shaping the future of their industries with innovation and strategic agility [
50
]. Here
is how predictive analytics serves as the engine of innovation in the realm of AI-powered
digital transformation:
Anticipating customer behavior: Predictive analytics categorize customers into seg-
ments based on their behavior and preferences. By understanding each segment’s
needs, businesses can tailor products and services, enhancing customer satisfaction
and loyalty. Analyzing historical customer data enables organizations to predict churn.
By identifying customers at risk, businesses can implement retention strategies to
reduce customer attrition rates.
Optimizing marketing strategies: Predictive analytics evaluate past marketing cam-
paigns to determine what worked and what did not. The insights derived help
optimize future campaigns and ensure a higher return on investment. By analyzing
customer interactions and demographics, predictive analytics assign scores to leads
based on their likelihood of conversion. This aids sales teams in focusing their efforts
on high-potential leads, thus improving conversion rates.
Demand forecasting and inventory management: Predictive analytics analyze histori-
cal sales data and market trends. Businesses can accurately forecast the demand for
products and services by optimizing inventory levels and reducing excess stock. By
predicting demand patterns, organizations streamline their supply chains. Predictive
insights ensure that supplies are aligned with demand, thereby reducing storage costs
and minimizing waste.
Streamlining operations: In the manufacturing and service industries, predictive
analysis forecasts equipment failures and maintenance needs. Proactive maintenance
reduces downtime, extends equipment life, and enhances overall operational efficiency.
Predictive analytics analyze historical supplier performance and demand patterns.
Businesses can optimize their supply chains by ensuring timely deliveries, minimizing
costs, and maintaining efficient inventory levels.
Risk mitigation: Predictive analytics can be used for risk mitigation by identifying
potential threats or issues before they materialize. By identifying patterns, trends, and
anomalies in historical and real-time data, organizations can make proactive decisions
to mitigate potential risks. For instance, in cybersecurity, predictive analytics can
help identify potential threats or attacks. In healthcare, it can help predict disease
outbreaks [51].
Challenges and considerations: While predictive analytics offers numerous benefits, it
is important to consider its limitations and challenges. For instance, the accuracy of
predictions heavily depends on the quality and completeness of the data. Therefore,
data cleaning, pre-processing, and quality assurance are crucial steps. Moreover,
predictive models might not fully account for abrupt changes or black swan events;
hence, regular model review and refinement are needed [52].
Sustainability 2024,16, 1790 14 of 25
Potential biases in predictive analytics: AI algorithms, which are dependent on histori-
cal data, can inadvertently perpetuate existing biases. For instance, if the data reflect
past discriminatory practices or societal biases, the predictive models may produce bi-
ased outcomes. This is particularly concerning in areas such as hiring, lending, and law
enforcement, where biased predictions could lead to unfair or prejudicial outcomes.
Ethical implications: The use of predictive analytics raises significant ethical questions,
particularly regarding privacy, consent, and transparency. There is a risk of misuse of
predictive analytics in ways that infringe on individual privacy or autonomy, such as
through intrusive surveillance or predictive policing.
Strategies to mitigate biases: To address these biases, it is crucial to implement strate-
gies such as:
Diversifying data sources: Ensuring that the data used to train predictive
models is representative of diverse populations and scenarios.
Regular audits: Conduct regular audits of AI algorithms to check for and
correct biases.
Transparency: Maintain transparency about how predictive models are built
and the data on which they are trained, allowing for accountability.
Promoting responsible AI practices: Responsible AI practices should be at the
core of predictive analytics. This includes ethical data collection, ensuring
informed consent where personal data is used, and implementing data privacy
safeguards. Organizations should also establish ethical guidelines for the use
of predictive analytics, ensuring that the technology is used in a manner that
respects individual rights and promotes fairness.
Addressing data quality and completeness: The accuracy of predictions heavily
depends on the quality and completeness of the data. Therefore, data cleaning,
pre-processing, and quality assurance are crucial steps. Predictive models
might not fully account for abrupt changes or ‘black swan’ events, necessitating
regular model review and refinement.
Continuous ethical and bias training: Organizations should invest in contin-
uous ethical training and bias awareness programs for their teams, ensuring
that those developing and deploying predictive models are aware of and can
mitigate potential ethical issues and biases.
In summary, predictive analytics, while offering transformative capabilities for orga-
nizations, comes with significant responsibilities. Addressing potential biases and ethical
implications is critical for harnessing the full power of predictive analytics responsibly and
effectively. By incorporating these strategies, organizations can mitigate risks and foster
trust and credibility in their AI initiatives.
(e) Innovative Product Development
AI-powered innovative product development represents a transformative paradigm
shift in the business world. In the era of AI-powered digital transformation, innovation
and product development are at the heart of driving change and revolutionizing industries.
By combining human creativity with the analytical prowess of AI, organizations can
conceptualize and bring to market products that are not only technologically advanced but
also deeply attuned to user needs [
53
]. These intelligent products are not just commodities;
they are solutions that enhance lives, streamline processes, and open the door to a future
where innovation knows no bounds. In the age of AI-powered product development,
the potential for creativity and impact is limitless, marking the dawn of a new era in
business and technology. Here is how innovation and product development are propelling
AI-powered digital transformation to new heights, as presented in Figure 3:
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Sustainability 2024, 16, x FOR PEER REVIEW 16 of 26
Figure 3. Innovative product development.
(a) Idea generation and market insights: AI analyzes vast datasets to identify emerging
trends and customer preferences. This insight fuels creative brainstorming sessions,
generating ideas for innovative products and services that align with market de-
mands. AI-powered tools can assess competitors’ products, customer feedback, and
market positioning [54]. By understanding the competitive landscape, businesses can
identify gaps and opportunities for product dierentiation under personalization at
scale. This may be elaborated as follows:
Real-time data analysis: AI algorithms can process vast amounts of user data in real
time. These data may include user behaviors, preferences, and feedback that inform prod-
uct modications or feature enhancements that resonate with distinct user segments [55].
Predictive customization: AI can forecast future user needs or preferences based on past
behaviors. This predictive power ensures that products evolve in alignment with user ex-
pectations, even pre-empting them [56]. Maintaining eciency: While personalization of-
ten sounds resource-intensive, AI systems ensure that tailoring products to individual
preferences does not compromise the eciency or scalability of the production process
[57].
(b) Customer-centric product design: AI analyzes vast datasets, oering insights into
customer preferences, behaviors, and pain points. This data-driven approach informs
product design, ensuring that oerings are tailored to meet customer needs. AI ena-
bles the creation of highly personalized products and services. From customized rec-
ommendations to individualized user experiences, personalization enhances cus-
tomer satisfaction and loyalty. The digital era has given consumers unprecedented
power. With myriad options available at their ngertips, their expectations are higher
than ever. Immediate gratication: The digital consumer expects instantaneous re-
sponses, whether in e-commerce deliveries, app performance, or customer service
[58]. Participation in development: Crowdsourcing, beta testing, and community-
Figure 3. Innovative product development.
(a)
Idea generation and market insights: AI analyzes vast datasets to identify emerging
trends and customer preferences. This insight fuels creative brainstorming sessions,
generating ideas for innovative products and services that align with market demands.
AI-powered tools can assess competitors’ products, customer feedback, and market
positioning [
54
]. By understanding the competitive landscape, businesses can identify
gaps and opportunities for product differentiation under personalization at scale. This
may be elaborated as follows:
Real-time data analysis: AI algorithms can process vast amounts of user data in real
time. These data may include user behaviors, preferences, and feedback that inform product
modifications or feature enhancements that resonate with distinct user segments [
55
].
Predictive customization: AI can forecast future user needs or preferences based on past
behaviors. This predictive power ensures that products evolve in alignment with user
expectations, even pre-empting them [
56
]. Maintaining efficiency: While personalization
often sounds resource-intensive, AI systems ensure that tailoring products to individual
preferences does not compromise the efficiency or scalability of the production process [
57
].
(b)
Customer-centric product design: AI analyzes vast datasets, offering insights into
customer preferences, behaviors, and pain points. This data-driven approach informs
product design, ensuring that offerings are tailored to meet customer needs. AI enables
the creation of highly personalized products and services. From customized recom-
mendations to individualized user experiences, personalization enhances customer
satisfaction and loyalty. The digital era has given consumers unprecedented power.
With myriad options available at their fingertips, their expectations are higher than
ever. Immediate gratification: The digital consumer expects instantaneous responses,
whether in e-commerce deliveries, app performance, or customer service [
58
]. Partici-
pation in development: Crowdsourcing, beta testing, and community-driven product
development have become more prevalent, blurring the lines between consumers and
creators [
59
]. Demand for digital integration: With the proliferation of smart devices
Sustainability 2024,16, 1790 16 of 25
and interconnected systems, there is a growing demand for products that seamlessly
integrate into the broader digital ecosystem [60].
(c)
Agile development and iteration: Agile methodologies combined with AI-powered
tools facilitate rapid prototyping, experimentation, and iterative development. Busi-
nesses can quickly create and test product prototypes, gather user feedback, and
iterate on designs. This agile approach accelerates time-to-market and ensures that
products align with user expectations. AI-powered analytics provide real-time insights
into product performance. Organizations can monitor user behavior and feedback,
making continuous improvements to enhance usability, functionality, and overall user
experience. As digital tools and AI become ubiquitous, the products of the future are
not just static tools but adaptive solutions. Products can be learned from user behavior.
Products equipped with AI can analyze user behavior in real time, adapt function-
alities to individual preferences, and ensure a personalized user experience [
61
]. In
addition, the products can self-evolve. Modern software solutions frequently update
themselves to fix bugs, enhance security, or introduce new features, ensuring constant
alignment with user needs and technological advancements [62].
(d)
AI-infused product enhancements: AI integration enhances products with intelligent
features such as predictive analytics, natural language processing, and computer
vision. These features add value, making products more versatile, efficient, and user-
friendly. AI automates repetitive tasks within products, increasing efficiency and
allowing users to focus on higher-value activities. Automation not only saves time but
also enhances user productivity.
4. Discussion
The findings of this study elucidate the intricate relationship between AI and inno-
vation as foundational elements in the digital transformation framework. Our research
has highlighted the pivotal role of key pillars of AI-powered innovation—performance
monitoring, continuous learning, data analytics, predictive analytics, and innovative prod-
uct development—in revolutionizing industries such as healthcare, education, finance,
manufacturing, transportation, and agriculture. Our study’s focus on the synergistic impact
of AI’s key pillars aligns with the growing recognition in the literature of AI’s multifaceted
role in driving digital transformation. Previous studies have often examined these elements
in isolation, such as the role of data analytics in decision-making or predictive analytics in
operational efficiency. However, our research contributes to a more holistic understanding
by demonstrating how these pillars work collectively to foster an environment conducive
to innovation and sustained organizational growth. This study was guided by the research
questions that an integrative approach to AI-powered innovation is more effective than
isolated applications of AI technologies. The findings support this, showing that the in-
tegration of various AI aspects into a cohesive framework leads to more profound and
sustainable organizational transformation. This has significant implications for businesses
aiming to thrive in the digital age, emphasizing the need for a comprehensive strategy that
goes beyond implementing AI technologies to embed them into the fabric of organizational
culture and processes.
4.1. AI-Innovations: Transforming Diverse Industries
Rapid advancements in AI technology have sparked a wave of innovation, revolution-
ized numerous industries, and reshaped our lifestyles. AI’s influence is boundless, from
enhancing patient outcomes in healthcare to optimizing financial decision-making pro-
cesses and tailoring personalized learning experiences in education to crafting immersive
entertainment. Its transformative power continues to redefine the way we live and work,
showcasing its potential to drive progress and innovation across the globe. Here is how AI
is making a significant impact across various sectors:
Sustainability 2024,16, 1790 17 of 25
(a) Healthcare:
AI’s ability to analyze vast amounts of patient data enables more accurate and timely
diagnoses. By examining medical records, imaging results such as X-rays and MRIs, and
even genetic information, AI algorithms can recognize patterns and detect anomalies that
human doctors might miss. This significantly enhances diagnostic accuracy, leading to
better patient outcomes and timely interventions. This can be processed as follows:
Processing vast data sets: AI systems can process and analyze massive volumes
of patient data, including medical records, diagnostic images, laboratory results, and
genetic information [
63
]. This computational power allows AI to handle large and complex
datasets efficiently. Pattern recognition: AI algorithms excel at identifying patterns within
these datasets. In medical imaging, for instance, AI can recognize subtle patterns or
anomalies in X-rays, magnetic resonance imaging (MRI), or computed tomography (CT)
scans that might not be immediately clear to human eyes [
64
]. This ability to discern
intricate details enhances the accuracy of diagnostic assessments. Anomaly detection: AI’s
anomaly detection capabilities enable the identification of irregularities or deviations from
the norm within patient data [
65
]. For instance, AI algorithms can flag abnormal levels in
blood tests or deviations in genetic sequences, indicating potential health risks or specific
conditions. Comparative analysis: AI can compare patient data with vast databases of
medical information. This comparative analysis helps identify similarities with known
cases, thereby aiding the diagnostic process. By drawing parallels with existing cases, AI
can assist healthcare professionals in diagnosing rare or complex conditions. Real-time
monitoring: AI-powered systems can continuously monitor patients in real time. For
instance, wearable devices equipped with AI algorithms can track vital signs and alert
healthcare providers to deviations from normal values [
66
]. This proactive monitoring
ensures timely interventions, especially for patients with chronic conditions. Predictive
analytics: AI’s predictive analytic capabilities involve forecasting potential outcomes based
on historical and current patient data. By analyzing trends and patterns, AI can predict
disease progression, recommend personalized treatment plans, and assess the likelihood of
specific health events occurring in the future [
67
]. Clinical decision support: AI is a valuable
tool for clinicians by providing decision support. It offers evidence-based recommendations
and insights derived from vast datasets, helping doctors make informed decisions about
diagnostics, treatments, and prognoses [
68
]. Rapid diagnostics and triage: AI algorithms
can automate the triage process by quickly analyzing symptoms and patient data. This
rapid assessment ensures that urgent cases are prioritized, allowing healthcare providers
to focus on critical situations promptly.
(b) Personalized Learning:
AI-driven systems analyze vast amounts of student data, including learning prefer-
ences, strengths, and areas that need improvement. By processing this data, AI customizes
educational content and methods to suit individual learning styles and progress. Personal-
ized learning pathways are designed to ensure that students receive tailored instruction
and resources. This individualized approach enhances engagement, understanding, and
learning outcomes by catering to diverse student needs and abilities. This can be processed
as follows:
Data analysis: AI-driven systems process extensive student data, encompassing learn-
ing preferences, strengths, weaknesses, and historical performance. By analyzing this data,
AI gains insights into individual learning patterns and needs [
69
]. Customized educational
content: Based on data analysis, AI customizes educational content and teaching methods.
It tailors learning materials, exercises, and activities to match individual learning styles,
ensuring that the content is engaging and relevant to each student [
4
]. These pathways
are unique and adapt to the specific needs and progress of learners. By providing a cus-
tomized curriculum, students receive targeted instruction and resources that align with
their abilities and requirements. Enhanced engagement and understanding: Personalized
learning enhances student engagement by presenting content that resonates with students’
Sustainability 2024,16, 1790 18 of 25
interests and preferences. As a result, students are more motivated to participate actively
and understand the material in-depth. The tailored approach bridges gaps in understand-
ing, ensuring that students grasp concepts thoroughly. Improved Learning outcomes: The
individualized approach to personalized learning directly translates into improved learning
outcomes. Students receive the support they need precisely when they need it, leading to
better academic performance, confidence, and a positive attitude toward learning. Catering
to diverse needs: Personalized learning acknowledges and accommodates the diverse
needs and abilities of students. It provides additional support for struggling learners
and challenges for advanced students, ensuring that every student receives an education
tailored to their level.
(c) Finance:
AI-driven algorithms revolutionize financial trading by providing institutions with
unparalleled speed, accuracy, and efficiency. Through real-time data analysis, pattern
recognition, and optimal trade execution, AI empowers financial institutions to navigate
complex markets, make strategic decisions, and maximize profits, ultimately reshaping the
landscape of the financial industry. AI-driven algorithms analyze vast amounts of market
data in real time. By identifying patterns and trends, these algorithms execute trades at
optimal times, maximizing profits for financial institutions. AI’s ability to process data at a
speed and scale far beyond human capability gives institutions a competitive advantage in
the trading landscape. It enables high-frequency trading, where trades are executed within
milliseconds to exploit even the slightest market inefficiencies [
70
]. This can be further
elaborated as follows:
(1)
AI in financial trading. Real-time data analysis: AI-driven algorithms process
vast amounts of real-time market data. This includes historical data, current
market prices, trading volumes, and various other indicators. Analyzing this
data swiftly is crucial for making informed trading decisions [
35
]. Pattern and
trend recognition: AI algorithms excel at identifying complex patterns and trends
within market data. By recognizing these patterns, algorithms can anticipate
market movements, enabling more accurate predictions about the future perfor-
mance of stocks, currencies, or commodities [
35
,
71
]. Optimal trade execution:
AI algorithms execute trades at optimal times based on the identified patterns
and trends. They assess market conditions and execute trades swiftly and effi-
ciently, ensuring that transactions are made at the most advantageous prices. This
strategic execution maximizes profits for financial institutions [
72
]. Speed and
scale advantage: AI processing capabilities far exceed human capacity. It can
analyze data, identify patterns, and execute trades at speeds measured in millisec-
onds. This rapid processing gives financial institutions a significant advantage
in promptly reacting to market changes, especially in high-frequency trading
environments. Competitive edge: The ability to process vast amounts of data and
execute trades swiftly provides financial institutions with a competitive edge. By
leveraging AI technologies, institutions can stay ahead in dynamic and fast-paced
financial markets, making split-second decisions that can result in substantial
profits. High-frequency trading: AI enables high-frequency trading, a strategy
in which trades are executed within milliseconds to exploit even the slightest
market inefficiencies. This approach allows institutions to capitalize on small
price differentials across multiple trades, leading to significant profits over time.
(2)
Fraud detection with AI. Pattern and anomaly identification: AI-powered systems
analyze vast amounts of transactional data in real time. By comparing ongoing
transactions with historical data, these systems identify patterns consistent with
legitimate transactions and anomalies that deviate from the norm. Unusual activ-
ities, such as atypical spending patterns or multiple transactions from different
locations in a short time, can raise red flags [
73
]. Real-time analysis: AI algorithms
perform this analysis swiftly and in real time. As transactions occur, AI continu-
Sustainability 2024,16, 1790 19 of 25
ously evaluates them, ensuring the immediate detection of suspicious behavior.
Real-time analysis is essential for preventing fraudulent transactions before they
are completed, providing a proactive approach to fraud prevention [
74
]. Fraud
pattern recognition: AI systems are trained to recognize known fraud patterns
and can evolve to identify new, emerging patterns. Machine learning algorithms
learn from historical fraud data, enabling them to adapt and recognize novel
fraud schemes as they develop. This adaptability ensures that fraud detection
mechanisms remain effective against evolving and sophisticated fraud tactics [
75
].
Prompt detection and prevention: By promptly identifying suspicious activities,
financial institutions can take immediate action to prevent fraudulent transac-
tions. This might involve temporarily blocking an account, flagging a transaction
for manual review, or notifying the customer to confirm the legitimacy of the
transaction. Timely intervention helps protect both customers’ and institutions’
assets. Multi-factor analysis: AI systems employ multi-factor analysis, consider-
ing various parameters simultaneously. These factors include transaction amount,
location, time, device used, and spending behavior. By assessing multiple factors,
AI algorithms enhance the accuracy of fraud detection, thereby reducing false
positives and negatives [
76
]. Continuous learning and improvement: AI-driven
fraud detection systems continuously learn from new data. As they process more
transactions and encounter new fraud attempts, they refine their algorithms,
improving their accuracy over time. This iterative learning process ensures that
the system becomes increasingly proficient in identifying fraudulent activities.
(d) Predictive Maintenance:
AI analyzes sensor data from machinery, such as temperature, vibration, and usage
patterns, to predict when equipment is likely to fail. By foreseeing maintenance needs,
manufacturers can schedule repairs or replacements proactively, thereby reducing unex-
pected downtimes. This predictive approach saves costs associated with emergency repairs,
extends equipment lifespan, and ensures continuous production [
77
]. This can be processed
as follows:
Sensor data analysis: AI algorithms analyze sensor data from machinery, including
parameters such as temperature, vibration, and usage patterns. These sensors continuously
monitor the equipment, generating vast amounts of data that AI processes in real time [
78
].
Failure prediction: By analyzing sensor data, AI can identify patterns and trends that
indicate potential issues or signs of wear and tear. Machine learning algorithms recognize
abnormal patterns that precede equipment failures. By detecting these early indicators, AI
predicts when a machine is likely to fail [
79
]. Proactive repairs and replacements: Predic-
tive maintenance enables manufacturers to schedule repairs or replacements before the
equipment fails proactively. This proactive approach minimizes the risk of unexpected
downtimes because maintenance tasks are performed on the basis of actual needs rather
than fixed schedules [
80
]. Cost savings: By foreseeing maintenance needs, manufacturers
can avoid costly emergency repairs that often arise when equipment breaks down unex-
pectedly. Proactively replacing worn-out components or conducting timely repairs reduces
overall maintenance costs. Extended equipment lifespan: Regular and timely maintenance
ensures that the machinery operates at its optimal level. Predictive maintenance extends
the lifespan of equipment by preventing wear and tear from escalating into severe issues.
This not only saves replacement costs but also maximizes the return on investment for
manufacturing equipment. Continuous production: Perhaps most crucially, predictive
maintenance ensures continuous production. By minimizing unplanned downtimes, man-
ufacturers can maintain their production schedules without interruptions. This reliability
in operations is vital for meeting customer demands and fulfilling orders on time.
(e) Transportation Route Optimization:
AI algorithms analyze real-time traffic patterns and historical data to propose optimal
routes for vehicles. By considering factors such as traffic congestion, road conditions, and
Sustainability 2024,16, 1790 20 of 25
weather, AI-powered route optimization systems minimize travel time and reduce fuel con-
sumption [
81
]. This not only enhances efficiency for individual drivers but also positively
impacts the environment by reducing emissions. This can be processed as follows:
Real-time traffic analysis: AI algorithms process real-time traffic data, including
congestion levels, accidents, and road closures. These systems can dynamically adjust
routes by continuously analyzing this information to avoid traffic bottlenecks and reduce
delays [
82
]. Historical data utilization: AI integrates historical data, including traffic
patterns at different times of the day and week. By understanding regular traffic flow,
algorithms can predict potential congestion and propose alternative routes, optimizing
travel plans for various times and scenarios [
83
]. Consideration of multiple factors: AI-
powered route optimization considers various factors such as traffic congestion, road
conditions, weather, and even events affecting traffic. By comprehensively analyzing these
variables, the system proposes the most efficient routes tailored to the current conditions.
Minimized travel time and fuel consumption: AI-powered systems significantly reduce
vehicle travel time by avoiding congested routes and selecting paths with optimal traffic
conditions. This saves drivers time and reduces fuel consumption, leading to cost savings
and, importantly, decreased emissions, contributing to environmental sustainability [
84
].
Environmental impact: One of the key benefits of AI-driven route optimization is its
positive impact on the environment. By minimizing travel time and fuel consumption,
these systems help decrease greenhouse gas emissions, contributing to cleaner air and
a reduced carbon footprint. Enhanced efficiency and user experience: AI’s ability to
optimize routes enhances overall transportation efficiency. It ensures that vehicles reach
their destinations more quickly and efficiently, thus improving the overall user experience
for both individual drivers and commercial transportation services.
(f) Precision Farming:
Precision farming powered by AI optimizes agricultural processes, leading to increased
productivity, reduced environmental impact, and a more sustainable future for agriculture.
AI processes data from various sources, such as sensors, satellites, and drones, to enable
precision farming. AI algorithms optimize farming practices by analyzing this data, in-
cluding irrigation, fertilization, and pest control [
85
]. This approach benefits farmers by
enhancing yields and efficiency and contributes to global food security and environmental
conservation efforts. This can be processed as follows:
Data integration: Precision farming uses data from various sources, including sensors
installed on the field, satellite imagery, and drones equipped with specialized sensors. These
sources provide diverse data points, offering a comprehensive view of the agricultural
landscape [
86
]. Data analysis: AI algorithms process data collected from sensors, satellites,
and drones. Using advanced analytics, these algorithms identify patterns and trends in data
related to soil quality, moisture levels, crop health, and pest presence. Optimizing farming
practices: Based on the insights gained from data analysis, AI algorithms optimize various
farming practices. This includes precise adjustments in irrigation schedules, targeted
fertilization techniques, and data-driven pest control strategies. By tailoring these practices
to specific areas of the field, resources such as water, fertilizers, and pesticides are used
with maximum efficiency [
87
]. Improved crop yields: The optimized use of resources and
farming techniques ensures that crops receive the ideal conditions for growth. This precision
leads to improved crop yields as plants receive the right amount of water, nutrients, and
protection from pests. Enhanced crop yields directly translate to increased productivity
for farmers [
88
]. Reduced environmental impact: Precision farming benefits farmers
and positively impacts the environment. Using resources more efficiently reduces the
risk of overuse of water, fertilizers, and pesticides. This sustainable approach minimizes
environmental pollution and helps conserve natural resources. Data-driven efficiency:
Precision farming is a data-driven approach to agriculture. By harnessing the power of AI
and data analytics, farmers can make informed decisions. This technology-driven efficiency
ensures the long-term sustainability of farming practices.
Sustainability 2024,16, 1790 21 of 25
4.2. Future Research
Future research in the field of AI-enabled innovation and digital transformation shows
many promising avenues. A key area is exploring the synergistic integration of artificial
intelligence with emerging technologies such as blockchain, the Internet of Things, and
edge computing. Incorporating multi-criteria decision-making techniques, particularly
the analytical hierarchy process (AHP), is critical to assess the relative importance of
these integrations. Using these methods, experts can determine which combinations can
significantly improve the robustness and safety of AI control systems, opening up new
areas of innovation.
Furthermore, it is becoming increasingly important to study the contribution of arti-
ficial intelligence to sustainable business practices. This includes its potential for energy
management, resource optimization, and alignment with the United Nation’s Sustainable
Development Goals. The use of the AHP will enable a detailed assessment of the impact of
artificial intelligence on environmental protection and social responsibility, highlighting its
key role in promoting sustainable development.
Another important area of research is the impact of organizational culture, structure,
and leadership on the introduction and effectiveness of AI technologies. Future research
should include examining barriers to AI adoption and fostering an AI-ready culture within
organizations. Here, the AHP can help identify key factors affecting AI adoption and effec-
tiveness and facilitate the development of targeted strategies to overcome these barriers.
As AI capabilities continue to expand, its ethical implications and the need for a
comprehensive governance framework will become clearer. Future research should aim
to develop ethical guidelines, address privacy concerns, and establish robust governance
protocols that consider multi-criteria decision-making. This will ensure responsible and fair
use of AI and focus on developing governance structures that reflect the complex priorities
and values in AI-driven innovation.
Moreover, this discussion highlighted the transformative potential of AI in driving
innovation across different industries and highlighted the need for a multifaceted approach
to integrating different AI capabilities through structured decision-making processes such
as the Analytical Hierarchy Process. The implications of this research are far-reaching,
showing that businesses and society can use artificial intelligence to improve operational ef-
ficiency and make significant contributions to social progress and sustainable development.
Proposed future research directions are enriched by incorporating MCDM techniques and
are expected to deepen our understanding of the role of AI in shaping the future landscape
of digital transformation and innovation.
5. Conclusions
This study has meticulously examined the multifaceted integration of performance
monitoring, continuous learning, data analytics, predictive analytics, and innovative prod-
uct development within AI-powered innovation frameworks. These components synergis-
tically forge a formidable foundation for organizational efficacy, propelling functionality
optimization, cultivating a culture steeped in continuous advancement, and enhancing
informed decision-making processes. Our findings reveal that such a holistic approach
is instrumental in securing sustainable growth and nurturing a milieu of creativity and
innovation, thereby bolstering organizational agility.
The rapid advancement of AI technology has been a cornerstone in revolutionizing
myriad industries and lifestyles, leaving a significant imprint on sectors including but
not limited to healthcare, finance, education, predictive maintenance, transportation, and
agriculture. The transformative prowess of AI is a testament to its role as a catalyst for
global progress and innovation, as delineated in our analysis. The ramifications of our
research are profound, underscoring AI’s pivotal contribution to digital transformation and
advocating for an integrated approach to AI adoption that transcends mere technological
upgrades to include a paradigm shift towards embracing a culture of continuous learning
and innovation.
Sustainability 2024,16, 1790 22 of 25
Moving forward, this study illuminates AI’s crucial influence on innovation and digital
transformation and carves out pathways for exhaustive future research. Delving deeper
into these prospects will augment our comprehension of AI’s potentialities and serve as a
beacon for its responsible and efficacious deployment across diverse domains. In doing
so, we advocate for a forward-looking stance on AI, envisioning a future where AI drives
technological and economic milestones and addresses ethical, social, and environmental
concerns. This calls for a collaborative effort among scholars, industry practitioners, and
policymakers to forge an AI-empowered future that is equitable, sustainable, and inclusive.
In essence, the journey of AI and digital transformation is on the cusp of a new era
that promises unparalleled innovation but also demands conscientious stewardship of AI
technologies. As we navigate this evolving landscape, we must harness AI’s potential
responsibly, ensuring that the digital transformation it engenders is beneficial for all sectors
of society.
Author Contributions: Conceptualization, A.A. and A.M.H.; writing—original draft preparation,
A.A., A.M.H. and K.N.A.-K.; writing—review and editing, A.M.H. and A.A.; supervision, K.N.A.-K.
and A.M.H. All authors have read and agreed to the published version of the manuscript.
Funding: This work was partially supported by NPRP14C-0920-210017 provided by the Qatar
National Research Fund (a member of Qatar Foundation). Open Access funding is provided by the
Qatar National Library.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: No new data were created in this study. Any data or information used
during the study are available from the corresponding author by request.
Conflicts of Interest: The authors declare no conflicts of interest.
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