Special Issue: AI-Powered Paradigms in RD&E Management: Opportunities and Challenges

Guest Editors

Francesco Paolo Appio (f.appio@psbedu.paris), Paris School of Business (FR)

Davide La Torre (davide.latorre@skema.edu), SKEMA Business School (FR)

Hatem Masri (hatem.masri@asu.edu.bh), Applied Science University in Bahrain (BH)

Raja Jayaraman (raja.jayaraman@ku.ac.ae), Khalifa University, (UAE)

Theme

In an era where rapid technological advancement dominates the industrial landscape, effective management of Research, Development, and Engineering (RD&E) is more critical than ever. With the recent surge in Artificial Intelligence (AI) capabilities and its widespread applications, there arises an opportunity to harness AI for revolutionizing RD&E management. This special issue of the IEEE Transactions on Engineering Management invites authors to contribute papers shedding light on the transformative role AI can play in optimizing RD&E functions across various sectors. AI offers promising avenues for predictive analytics, optimization, real-time monitoring, and resource allocation, which can significantly enhance the efficiency and effectiveness of RD&E processes. For instance, AI-driven models can forecast technology trends, enabling organizations to steer their research endeavors proactively. Moreover, AI can aid in automating routine tasks, freeing up human capital for more nuanced and creative aspects of RD&E. Simultaneously, there are potential pitfalls and challenges in integrating AI into RD&E, including ethical concerns, data privacy issues, and the need for a skilled workforce to oversee and maintain AI systems. We seek submissions that explore both the opportunities AI presents and the challenges it introduces in the context of RD&E management.

Special Issue’s objective

The special issue primarily seeks to achieve two overarching objectives. Firstly, it aims to illuminate the transformative role that Artificial Intelligence plays in optimizing RD&E functions, revealing how AI-driven strategies can redefine efficiency, innovation, and strategic foresight in various sectors. Secondly, while recognizing the myriad opportunities AI presents, the issue also endeavors to deeply explore the challenges intrinsic to its integration ensuring a balanced and comprehensive dialogue on AI’s multifaceted role in RD&E management. In line with the journal’s emphasis, we particularly value contributions that provide insights into intra-organizational studies and their implications for decision-making in RD&E. By fostering a comprehensive dialogue on AI’s role in RD&E management, this issue aims to equip professionals and researchers with the knowledge to navigate the evolving technological landscape strategically.

Special Issue’s scope, including potential themes to be addressed in the Special Issue

The transformative trajectory of RD&E in the contemporary era is being largely shaped by the pervasive influence of AI. As professionals and scholars gravitate towards harnessing the computational prowess of AI to drive innovation, delineate insights, and optimize methodologies, it becomes imperative to systematically explore the multifaceted integration of AI within the RD&E continuum, making sense of the challenges and opportunities that this may pose to organizations (e.g., Hagendorff & Wezel, 2020; Appio et al., 2023). This exploration encompasses not only the technical and functional dimensions of AI-driven research paradigms but also delves into the broader socio-technological implications, ethical quandaries, and pedagogical considerations emergent in this evolving landscape. The ensuing discourse seeks to offer an overview of the main facets of AI in RD&E, encapsulating its transformative potential, human-AI synergies, infrastructural imperatives, ethical conundrums, and the educational paradigms necessitated by this paradigm shift.

In recent years, AI implementation’s profound influence on RD&E has attracted significant academic attention. At the intersection of decision-making frameworks and AI, Kelleher et al. (2015) highlighted the transformative potential of predictive analytics in driving informed decisions across research domains, corroborating the rise of deep learning in RD&E decision processes (LeCun, Bengio, & Hinton, 2015). Furthermore, Turban, E., Pollard, C., & Wood, G. (2021) elucidated the capabilities of AI in real-time decision support systems, suggesting an enhanced efficiency in instantaneous decision-making. Transitioning to RD&E process optimization, Davenport and Ronanki (2018) underscored AI’s promise in process automation, amplifying efficiency and streamlining operations. Concurrently, Jordan & Mitchell (2015) propounded the role of machine learning in fostering research analysis and hypothesis generation, while Botega & da Silva (2018) accentuated AI’s role in improving prototype testing and evaluation. In the domain of resource management, Bertsimas & Kallus (2020) presented insights into AI-driven resource allocation, with Makridakis, Spiliotis, & Assimakopoulos (2018) underscoring machine learning’s prowess in forecasting resource needs. The novel paradigm of predictive maintenance, especially in RD&E settings, has been expounded upon by Adnan, Alsaeed, Al-Baity, & Rehman (2021), emphasizing AI’s potential in preemptive measures. Lastly, in the realm of collaborative and multi-disciplinary RD&E, Seeber et al. (2020) championed the integration of AI in collaboration tools, promoting enhanced teamwork dynamics. The fusion of AI with cross-disciplinary research has yielded profound insights (McFarland, Lewis, & Goldberg, 2016), and Doss et al. (2023) have postulated the expansive role of AI in automating knowledge dissemination. This burgeoning synergy between AI and RD&E underscores an evolution in research methodologies and paves the path for future interdisciplinary endeavors.

The integration and optimization facilitated by AI in RD&E, as explored previously, sets the stage for a melding of human intuition with machine precision. Human-AI interaction and collaboration refers to the multidisciplinary study and practice of designing, implementing, and evaluating systems where humans engage with artificial intelligence agents in cooperative and coordinated activities. It encompasses both the exploration of how humans perceive, understand, and work alongside AI systems, as well as how these systems can be designed to be more intuitive, transparent, and effective in supporting human objectives. This symbiotic relationship aims not merely for humans to use AI as a tool, but to create integrated human-AI teams where each entity amplifies the capabilities of the other, leading to outcomes that neither could achieve alone. In the quest to understand the nuances of the human-machine partnership in RD&E, Dubova, Galesic, and Goldstone (2022) delve into the dynamics of teamwork between humans and AI, suggesting a balance of complementary skills. Hancock et al. (2011) offer an evaluative lens, probing both the strengths and pitfalls of AI as a collaborative partner, emphasizing the indispensability of human oversight. Transitioning to cognitive augmentation, Macpherson et al. (2021) highlight AI-driven cognitive tools as indispensable adjuncts that amplify human reasoning and problem-solving capabilities. In a similar vein, Heer & Agrawala (2006) underscore the transformative role of AI in data visualization, aiding researchers in intricate data analysis and interpretation tasks. Ensuring a harmonious marriage of AI and human cognition in RD&E, Gunning & Aha (2019) propound techniques that foster seamless integration, thus enhancing research efficacy. Lastly, with AI assuming the role of research assistants and collaborators, Rajkomar, Dean, and Kohane (2019) laud the potential of AI in preliminary data harvesting and analysis, relegating mundane tasks to machines. Furthermore, Bostrom (2014) emphasizes the automation of repetitive research tasks, while Clark (2015) sheds light on the novel paradigm of enhancing creative processes in RD&E through the insights AI offers. The confluence of human prowess and AI in RD&E marks a revolution in research modalities, positing a promising trajectory for future collaborative ventures.

Furthermore, the integration of AI in RD&E has also heightened the need to examine infrastructure and scalability aspects. A salient aspect underpinning AI in RD&E is the infrastructure required to facilitate AI-powered research. Key considerations include hardware, with an emphasis on Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and distributed systems, which have been observed to be instrumental in enabling efficient AI computations (Goodfellow, Bengio, & Courville, 2016). Moreover, as AI-driven research routinely deals with vast datasets, specialized data storage solutions have become pivotal. De Filippo et al. (2022) outline techniques optimized for storing and retrieving these large-scale datasets. Complementing hardware and storage is the criticality of network infrastructure, where attributes like speed, latency, and reliability gain prominence (Gul, Shams, & Al-Khalili, 2023). Cloud versus on-premises solutions for AI deployment in RD&E remains a topic of discussion. The agility and scalability offered by cloud solutions come with concerns about data security and ongoing costs (Gupta et al., 2022). On the contrary, on-premises solutions, while presenting an upfront investment, provide more granular control over data and resources (Resende et al., 2021). A comparative cost analysis by Netto et al. (2018) discerned the long-term financial implications of both approaches, underscoring the need for institutions to align their choice with their strategic objectives. Lastly, as RD&E ventures into heavier AI computations, architectures that are inherently scalable gain precedence. Distributed computing models, which divide computations across multiple machines, have proven effective in managing resource-intensive AI tasks (Tuli et al., 2023). Additionally, techniques like parallel processing optimization are being deployed to expedite AI model training (Memeti et al., 2019). To further the scalability and flexibility, the realm of RD&E is gradually adopting microservices and containerization, which offer modular and isolated environments for AI research, as highlighted by Al-Doghman et al. (2023).

However, the AI adoption in various sectors has brought forth numerous challenges. Central to this discourse is the ethical considerations of AI-driven RD&E. Recent work by O’Neil (2017) highlighted the inherent biases in AI algorithms, emphasizing the potential pitfalls if these biases are not addressed. Her seminal book discusses how big data can amplify socioeconomic inequalities. In tandem, Barocas and Selbst (2016) in their paper titled “Big Data’s Disparate Impact” published in the California Law Review shed light on the biases in decision-making processes driven by machine learning. To ensure diverse and representative data for AI training, efforts have been channeled towards creating techniques that can identify and rectify dataset biases. Buolamwini and Gebru’s (2018) groundbreaking paper in Proceedings of Machine Learning Research titled “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification” emphasizes the importance of interpretable AI in not just validating research outcomes, but also in fostering transparency. The rapid adoption of AI has also led to an increased dependence, resulting in an over-reliance and potential trust issues with AI outputs. In light of this, Ribeiro, Singh, and Guestrin (2016) suggested ‘human-in-the-loop’ solutions for better decision-making, proposing methods for making AI models more interpretable to researchers. To counteract the over-dependence on AI, Doshi-Velez and Kim (2017) emphasized the role of traditional research methodologies in validating AI results. Furthermore, there is a growing consensus on the need to educate researchers about AI’s limits and capabilities, ensuring a balanced and informed application in RD&E (e.g., Robertson, Fossaceca, & Bennett, 2022; Hansen & Bøgh, 2021).

Finally, the influx of AI into the realm of RD&E poses another relevant managerial challenge in that it necessitates an in-depth understanding and the requisite skills among professionals. Central to this paradigm shift is the curricula development, designed to acquaint researchers with AI principles germane to RD&E. Bosman et al. (2023) delve into creating coursework which does not merely introduce AI but marries it seamlessly with RD&E. Their emphasis on incorporating hands-on AI projects echoes the sentiments of Javed et al. (2022), who advocate for research curricula that underscore the ethical dimensions of AI, particularly biases and societal impacts. Transitioning from theory to practice, training modules crafted for AI integration in RD&E emerge as pivotal. A model posited by Goodfellow, Bengio, & Courville (2016) underscores tailored training, emphasizing the nuances of specific domains. The increasing prominence of workshops and bootcamps focusing on niche AI tools and methodologies, as outlined by Leoste et al. (2021) supports the idea of bespoke training. This is further emphasized by the recognition of continuous learning, essential to remain abreast of the ever-evolving landscape of AI technologies. Finally, as AI integration matures, the importance of assessing and refining skills has gained traction. Platforms harnessing AI for skill assessment in RD&E are examined by Lamberti et al. (2019). Their work highlights the emergence of personalized learning paths, curated using AI, to bridge existing skill gaps. Complementing the technical skills, Caputo et al. (2019) underscores the primacy of soft skills such as critical thinking and adaptability, particularly in an environment increasingly shaped by AI.

Overall, potential topics and research questions for the special issue may include but are not limited to:

AI Implementation in RD&E:

• AI-driven decision-making frameworks:
  • Predictive analytics for informed decision-making.
  • Role of deep learning in RD&E decision processes.
  • Real-time decision support systems using AI.
• AI-enhanced RD&E process optimization:
  • Process automation and efficiency improvements.
  • AI-driven research analysis and hypothesis generation.
  • Prototype testing and evaluation using AI.
• AI in RD&E Resource Management:
  • AI-driven resource allocation and optimization.
  • Forecasting resource needs using machine learning.
  • Predictive maintenance in RD&E environments.
• AI in Collaborative and Multi-disciplinary RD&E:
  • Integration of AI in team collaboration tools.
  • AI-driven insights for cross-disciplinary research.
  • Automated knowledge sharing and dissemination.

Human-AI Interaction and Collaboration in RD&E:

• Exploring the human-machine partnership in research:
  • Understanding the dynamics of teamwork between humans and AI.
  • Assessing the benefits and limitations of AI as a collaborative partner.
  • Case studies showcasing successful human-AI partnerships in RD&E.
• Cognitive augmentation in RD&E tasks:
  • AI-driven cognitive tools to enhance human thinking and problem-solving.
  • Role of AI in data visualization, analysis, and interpretation.
  • Techniques for seamless human-AI cognitive integration in research tasks.
• AI as research assistants and collaborators:
  • Utilizing AI for preliminary data collection and analysis.
  • AI-driven automation of repetitive research tasks.
  • Enhancing creative processes in RD&E through AI insights and suggestions.

Infrastructure and Technical Aspects of AI in RD&E:

• Scalability and Infrastructure for AI in RD&E:
  • Infrastructure requirements for AI-powered research:
    • Hardware considerations: GPUs, TPUs, and distributed systems.
    • Data storage solutions optimized for massive datasets.
    • Network infrastructure: Speed, latency, and reliability.
• Cloud vs. on-premises solutions for RD&E AI:
  • Benefits and drawbacks of cloud-based research solutions.
  • Data security considerations in cloud vs. on-premises.
  • Cost analysis: Ongoing cloud costs vs. upfront on-premises investment.
• Scalable architectures for heavy AI computations:
  • Distributed computing models for AI.
  • Optimizing AI models for parallel processing.
  • Microservices and containerization in AI research.

Challenges and Barriers in AI Adoption:

• Challenges in AI integration:
  • Ethical considerations in AI-driven RD&E:
    • Bias and fairness in AI algorithms.
    • Ethical dilemmas of AI’s role in research conclusions.
    • Data privacy and user consent in AI research.
• Addressing AI biases in research outcomes:
  • Techniques to identify and rectify dataset biases.
  • Ensuring diverse and representative data for training.
  • The role of interpretable AI in validating research outcomes.
• Over-reliance and trust issues with AI outputs:
  • Human-in-the-loop solutions for decision-making.
  • Validating AI results with traditional research methodologies.
  • Educating researchers about the limits and capabilities of AI.

Training and Skill Development for AI in RD&E:

• Curricula development for AI in research:
  • Designing coursework to introduce AI principles relevant to RD&E.
  • Incorporating hands-on AI projects and experiments in research curricula.
  • Emphasizing ethics, bias, and societal impacts of AI in RD&E education.
• Training modules for integrating AI in RD&E processes:
  • Tailored training for researchers based on their domain and expertise.
  • Workshops and bootcamps focused on specific AI tools and methodologies.
  • Continuous learning modules to keep up with evolving AI technologies.
• Skill assessment and enhancement with AI tools:
  • AI-driven platforms for skills assessment in RD&E contexts.
  • Personalized learning paths using AI to bridge skill gaps.
  • Emphasizing soft skills like critical thinking and adaptability in an AI-augmented research environment.

Notes for Prospective Authors

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Conference papers may only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper. Manuscripts should be submitted through the publisher’s online system. Submissions will be reviewed according to the journal’s rigorous standards and procedures through double-blind peer review by at least two qualified reviewers.

Submission Process

Please, prepare the manuscript according to IEEE-TEM’s guidelines (http://ieee-tmc.org/tem-guidelines) and submit to the journal’s Manuscript Central site (https://mc.manuscriptcentral.com/tem-ieee). Please, clearly state in the cover letter that the submission is for this special issue.

Schedule

Papers submitted by July 31 2024.

Guest Editor Bios

Francesco Paolo Appio, Ph.D., HDR, is a Full Professor of Innovation at Paris School of Business (Paris, France). He earned both the French national qualification as Professeur des Universités in Management Science (CNRS 06) and the Italian national qualification as Professore Ordinario in Management (13/B2) and Business and Management Engineering (09/B3). He visited important international academic institutions such as Bocconi University (Italy), MIT Sloan School of Management (USA), and K.U. Leuven (Belgium). His research is interdisciplinary and mainly revolves around the impact of digital transformation on innovation at multiple levels (ecosystems, city, organization, teams), taking into account different perspectives such as sustainability, socio-technical systems, technological change, and innovation capabilities. He has recently been appointed as a member of the Editorial Board for the journals like Journal of Product Innovation Management, Technological Forecasting and Social Change, and IEEE Transactions on Engineering Management. His research has been published in Journal of Product Innovation Management, Long Range Planning, Technological Forecasting and Social Change, International Journal of Production Research, Industrial Marketing Management, among others. He is guest editor of multiple special issues in journals like Journal of Product Innovation Management, Organization Studies, Technological Forecasting and Social Change, IEEE Transactions on Engineering Management, and Journal of Urban Technology.

Davide La Torre, Ph.D., HDR, is a Mathematician and Full Professor of Applied Mathematics and Computer Science at SKEMA Business School, Sophia Antipolis Campus, France. He is qualified in the French national university system as Professeur des Universités in Applied Mathematics (CNRS 26), Computer Science (CNRS 27), and Economics (CNRS 5) as well as in the Italian national university system as Professore Ordinario in Mathematical Methods for Economics (13 D4), Political Economy (13 A2), and Public Economics (13 A3). His research and teaching interests include Applied Mathematics, Artificial Intelligence, Business Analytics, Mathematical Modeling, and Operations Research. He got his HDR (Habilitation à Diriger des Recherches) in Applied Mathematics from the Université Côte d’Azur, Nice, France (2021) and a Doctorate in Computational Mathematics and Operations Research from the University of Milan, Milan, Italy (2002) as well as professional certificates in Analytics from the Massachusetts Institute of Technology, USA. In the past, he held permanent and visiting university professor positions in Europe, Canada, Middle East, Central Asia, and Australia. He also served as Associate Dean, Departmental Chair, and Program Head at several universities. He has more than 190 publications in Scopus, most of them published in high IF journals ranging from Engineering to Business.

Hatem Masri is a Professor of Business Analytics and Vice President for Academic Affairs and Development at the Applied Science University, Kingdom of Bahrain. He received a Ph.D. in Management Science in 2004 and Master’s in Operations Research in 1999 from the University of Tunis, Tunisia. His research interests include business analytics, supply chain management, financial engineering, and Islamic finance. He published more than 30 articles and six books among them a textbook on Islamic business administration. Hatem is founder and chair of the INFORMS Bahrain International Group, past president of the African Federation of Operational Research Societies, general secretary of the Tunisian Decision Aid Society, member of INFORMS and IEEE, and volunteer/mentor with the AACSB.

Raja Jayaraman academic journey commenced with the attainment of his Bachelor’s and Master’s degrees in Mathematics in India, earned in 1996 and 1999, respectively. Subsequently, he pursued further academic excellence by obtaining a Master’s degree in Industrial Engineering from New Mexico State University in 2005 and culminating with a Ph.D. in Industrial Engineering from Texas Tech University in 2008. Following the completion of his doctoral studies, Raja embarked on a post-doctoral research fellowship at the Center for Innovation in Healthcare Logistics, University of Arkansas, where he devoted his expertise to the exploration of technology adoption and innovative practices aimed at enhancing healthcare supply chain logistics and service delivery. His contributions during this period included spearheading several successful research projects and pilot implementations focused on the adoption of supply chain data standards within the U.S. healthcare system. Raja’s academic pursuits and research interests center on the practical application of Industry 4.0, systems engineering, operational excellence and process optimization methodologies to analyze and model intricate systems. His primary focus areas encompass supply chains, maintenance planning, and healthcare delivery. In August 2011, Raja assumed the role of Assistant Professor within the Department of Industrial and Systems Engineering at Khalifa University in Abu Dhabi, United Arab Emirates. His dedication and scholarly achievements led to his promotion to the position of Associate Professor in November 2017. Within the academic sphere, Raja imparts his knowledge through the instruction of graduate and undergraduate courses, covering subjects such as supply chain and logistics, optimization, stochastic models, systems engineering, and quality management. Raja’s prolific research output is underscored by his impressive portfolio of over 120 publications featured in prestigious journals across the domains of Engineering, Technology, and Business. His work has found its place in esteemed outlets, including but not limited to the Annals of Operations Research, IISE Transactions, Computers & Industrial Engineering, IEEE Transactions on Engineering Management, IEEE Network, Energy Policy, Applied Energy, Knowledge-Based Systems, Journal of Cleaner Production, Technology Forecasting and Social Change, and Engineering Management Journal, among others.

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