Artificial Intelligence – The Big Picture

Abele, David; D’Onofrio, Sara

doi:10.1007/978-3-658-27941-7_2

Cited by 12 publications

(5 citation statements)

References 75 publications

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“…assessing the single process at the process level, while only one study focused on the system level (Abele & D'Onofrio, 2020). For example, AI management (Lichtenthaler, 2020) assists an organization in achieving an integrated intelligence architecture.…”

Section: Ai Analysis Level Authormentioning

confidence: 99%

Artificial intelligence maturity model: a systematic literature review

Sadiq

Safie

Rahman

et al. 2021

PeerJ Computer Science

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Organizations in various industries have widely developed the artificial intelligence (AI) maturity model as a systematic approach. This study aims to review state-of-the-art studies related to AI maturity models systematically. It allows a deeper understanding of the methodological issues relevant to maturity models, especially in terms of the objectives, methods employed to develop and validate the models, and the scope and characteristics of maturity model development. Our analysis reveals that most works concentrate on developing maturity models with or without their empirical validation. It shows that the most significant proportion of models were designed for specific domains and purposes. Maturity model development typically uses a bottom-up design approach, and most of the models have a descriptive characteristic. Besides that, maturity grid and continuous representation with five levels are currently trending in maturity model development. Six out of 13 studies (46%) on AI maturity pertain to assess the technology aspect, even in specific domains. It confirms that organizations still require an improvement in their AI capability and in strengthening AI maturity. This review provides an essential contribution to the evolution of organizations using AI to explain the concepts, approaches, and elements of maturity models.

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Section: Ai Analysis Level Authormentioning

confidence: 99%

Artificial intelligence maturity model: a systematic literature review

Sadiq

Safie

Rahman

et al. 2021

PeerJ Computer Science

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“…By integrating ML into Big Data Analytics, organizations can uncover hidden patterns within the vast datasets they accumulate. Artificial Intelligence (AI) goes beyond ML, introducing cognitive capabilities that enable machines to understand, reason, and make autonomous decisions (Abele, andD'Onofrio,, 2020, Adebukola et al, 2022). Natural Language Processing (NLP) and computer vision are AI components integrated into Big Data Analytics, allowing organizations to derive insights from unstructured data sources, such as text or images.…”

Section: Principle Of Big Data Analyticsmentioning

confidence: 99%

Big Data Analytics: A Review of Its Transformative Role in Modern Business Intelligence

Chioma Ann Udeh,

Omamode Henry Orieno,

Obinna Donald Daraojimba

et al. 2024

Comput. sci. IT res. j.

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In the dynamic landscape of modern business intelligence, Big Data Analytics has emerged as a transformative force, reshaping the way organizations derive insights from vast and diverse datasets. This paper provides a concise overview of the key themes explored in the comprehensive review of Big Data Analytics and its impact on modern business intelligence. Big Data Analytics represents a paradigm shift in decision-making processes, offering organizations the capability to harness the full potential of their data assets. The review delves into the multifaceted role of Big Data Analytics, emphasizing its significance in strategic planning, risk management, operational optimization, and customer-centric initiatives. Strategic planning takes a quantum leap forward as organizations leverage predictive analytics to anticipate market trends. The integration of analytics-derived insights aligns decision-making with overarching organizational objectives, driving a more informed and forward-thinking approach to strategic initiatives. Risk management becomes more proactive with the integration of Big Data Analytics, particularly in fraud detection and prevention. The ability to process large volumes of data in real-time enhances vigilance, mitigating financial risks associated with fraudulent activities cenario modeling further empowers organizations to assess and address potential risks before they materialize. Operational optimization becomes a focal point as analytics uncovers inefficiencies in manufacturing processes, supply chains, and retail operations. Real-time decision-making in retail, enabled by data analytics, ensures agility and responsiveness to changing market dynamics and customer preferences. Customer-centric initiatives are elevated through personalized marketing campaigns and predictive analytics in customer support. The review explores how Big Data Analytics enables organizations to craft personalized experiences, enhancing customer satisfaction and loyalty. The study encapsulates the transformative journey of Big Data Analytics in modern business intelligence, emphasizing its role in navigating strategic complexities, mitigating risks, optimizing operations, and placing the customer at the center of decision-making processes. The comprehensive review provides insights for organizations seeking to harness the transformative potential of Big Data Analytics in the data-driven era. Keywords: Big Data, Business Intelligence, Data Analytics, Modern Business, Review.

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“…There are many definitions of what is considered intelligent behavior. But most of them agree on having previous data as verified facts and some kind of reasoning engine as the starting points [8]. Metamodeling or surrogate modeling techniques such as response surface, inductive learning, kriging, etc are considered soft computational techniques [9,10].…”

Section: Related Workmentioning

confidence: 99%

Data-Driven and Real-Time Prediction Models for Highly Iterative Product Development Processes

Rad

Raudberget

Stolt

2022

Advances in Transdisciplinary Engineering

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Some high-level technical products are associated with transdisciplinary simulation-driven design processes. Therefore, their design process involves many stakeholders and is prone to frequent changes, leading to a highly iterative process with a long lead time. Despite the decades of statistical approximations and metamodeling techniques on prediction models, companies are still striving to achieve fully automated real-time predictions in early design phases. The literature study shows a gap in existing methods such as not being fully real-time or suffering from high dimensionality. This paper presents a generic model for the development process of such described products and motivation for such modeling through a series of semi-structured interviews with an automotive sub-supplier company. The proposed process model points to the digital verification in every design loop as the bottleneck which is then confirmed by interviewees. As alternative solutions to overcome the problems, a method for data-driven and real-time prediction models is presented to enable the designer to foresee the consequence of their decision in the design phase. To evaluate the method, two examples of such real-time meta-modeling techniques, developed in an ongoing research project are discussed. The proposed examples confirm that the framework can reduce lead time spent on digital verification and therefore accelerate the design process in such products.

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Artificial Intelligence – The Big Picture

Cited by 12 publications

References 75 publications

Artificial intelligence maturity model: a systematic literature review

Artificial intelligence maturity model: a systematic literature review

Big Data Analytics: A Review of Its Transformative Role in Modern Business Intelligence

Data-Driven and Real-Time Prediction Models for Highly Iterative Product Development Processes

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