Management information system
1
CIS 9300-23SYT05 Computers and Internet Applications Management
Ahmed Hazem Abozied
Apollos University
Session 8 Research and Application Forum
DBA
Dr. Willis Lambrecht
May 2023
Management information system
2
Title: Artificial Intelligence in Innovation: How to Spot Emerging Trends and
Technologies
Introduction:
The article "Artificial Intelligence in Innovation: How to Spot Emerging Trends and
Technologies," published in IEEE Transactions on Engineering Management, presents a
data mining model based on artificial intelligence (AI) to aid firms in detecting emerging
topics and trends. The authors, Christian Mühlroth and Michael Grottke emphasize the
need for strategic foresight in technology and innovation management to identify
discontinuous changes early, assess their consequences, and develop future strategies.
This review will provide a synopsis of the article, highlighting its central themes, relevance
to evolving IT trends, and implications for industry leaders.
Synopsis:
The article’s primary objective is to introduce an AI-driven data mining model that
enhances the efficiency of strategic foresight processes in technology and innovation
management. The model offers a modular structure comprising components such as
query generation, data collection, preprocessing, topic modeling, analysis, and
visualization. By minimizing manual efforts during its setup and incorporating selfadaptive capabilities, the model enables the automated detection of emerging
technologies.
The authors conducted retrospective analyses of three case studies to verify the model's
effectiveness as an early warning system. The results demonstrated that the model
successfully identified emerging technologies before their first publication in the Gartner
Hype Cycle for Emerging Technologies. The authors derive theoretical and practical
implications for technology and innovation management based on their findings. They
also suggest future research opportunities to advance the field further.
The article directly relates to evolving IT trends and technologies by addressing
organizations’ challenges in keeping up with rapid technological advancements. With the
exponential growth of data, the authors highlight the importance of leveraging AI and
data mining techniques to collect, analyze, and interpret vast amounts of information. By
detecting emerging trends early, organizations can make informed decisions, allocate
resources effectively, and gain a competitive edge.
Management information system
The IT trends and technologies that will most impact my industry/organization include
artificial intelligence and machine learning, the Internet of Things (IoT), and cybersecurity
and data privacy. The AI-based data mining model discussed in the article aligns well with
these trends. By implementing such a model, organizations can harness the power of AI
and machine learning to automate the identification of emerging technologies, facilitating
proactive decision-making and staying ahead of the curve.
Leaders can apply the information presented in the article in several ways. Firstly, they can
explore adopting AI-based data mining techniques within their organizations to automate
the detection of emerging trends and technologies. This would enable them to allocate
resources strategically and seize opportunities presented by new IT trends. Secondly,
leaders should foster a culture of innovation within their teams, encouraging
experimentation and continuous learning. Organizations can stay agile and adapt to
technological advancements by embracing emerging technologies and encouraging their
adoption.
Furthermore, leaders should actively engage in industry forums, conferences, and
collaborations to stay informed about emerging IT trends and technologies. Networking
with experts and thought leaders can provide valuable insights and partnership
opportunities. By staying informed and contributing to the industry's advancement,
leaders can position their organizations as frontrunners in leveraging emerging
technologies.
I find the AI-driven data mining model presented in the article intriguing. As a leader in
my organization, I plan to explore implementing AI-based data mining tools and
techniques to enhance our technology and innovation management. By leveraging
advanced analytics and machine learning algorithms, we can extract valuable insights from
our data and make data-driven decisions. Additionally, I will strive to foster a culture of
innovation within our teams, encouraging them to explore emerging trends and
technologies. Actively participating in industry events and collaborations will also be a
priority, as it will enable me to stay updated and contribute to the advancement of our
industry.
Management information system
Conclusion:
"Artificial Intelligence in Innovation: How to Spot Emerging Trends and Technologies"
offers valuable insights into applying AI and data mining techniques for strategic foresight
in technology and innovation management. The article's modular AI-driven model
provides an efficient and automated approach to detecting emerging topics and trends.
By implementing such a model and embracing emerging IT trends, organizations can
proactively adapt to the changing technological landscape and gain a competitive
advantage. Leaders can apply the information presented by exploring AI-based data
mining tools, fostering innovation, and actively participating in industry collaborations.
Overall, the article serves as a valuable resource for understanding and leveraging
emerging IT trends and technologies to shape the future of organizations.
Reference:
Mühlroth, C., & Grottke, M. (2020). Artificial Intelligence in Innovation: How to Spot Emerging
Trends and Technologies. IEEE Transactions on Engineering Management, 69(2), 493-510. DOI:
10.1109/TEM.2020.2989214
Management information system
Title: "Emerging desalination technologies: Current status, challenges, and future
trends"
The article "Emerging desalination technologies: Current status, challenges, and future
trends" provides a comprehensive overview of the advancements in desalination
technologies, focusing on emerging technologies that can revolutionize the desalination
industry. The authors aim to highlight the recent developments in forward osmosis,
membrane distillation, and electrochemical processes and discuss the challenges and
future trends associated with these technologies.
The article’s main theme is to explore the possibilities of diversifying the desalination
industry beyond the traditional thermal desalination and reverse osmosis processes. The
authors emphasize the importance of emerging technologies in addressing global water
scarcity and meeting the increasing demand for fresh water. They provide an in-depth
analysis of forward osmosis, membrane distillation, and electrochemical processes,
discussing their potential for commercialization and their applications in various sectors.
The article effectively relates to evolving IT trends and technologies by highlighting the
role of nanomaterials in advancing emerging desalination technologies. The authors
discuss the integration of intelligent process monitoring and control through innovative in
situ methods, which aligns with the growing trend of digitalization and automation in the
IT industry. Additionally, the article explores the use of renewable energy sources and
hybrid systems in enhancing the energy efficiency of desalination processes, which aligns
with the increasing focus on sustainability and green technologies.
The IT trends and technologies that will most impact the desalination industry include
advancements in materials science, data analytics, and automation. Developing novel
membrane materials with improved selectivity and permeability will significantly enhance
the performance and efficiency of desalination processes. Furthermore, integrating data
analytics and machine learning algorithms can optimize process control and decisionmaking, leading to more efficient and cost-effective operations. Automation technologies,
such as robotics and AI-powered systems, can improve desalination plants' reliability and
scalability, enabling large-scale freshwater production.
Leaders in the desalination industry can apply the information presented in the article by
investing in research and developing emerging desalination technologies. They can
collaborate with academic institutions and technology companies to explore new
materials, process optimization strategies, and innovative monitoring and control systems.
By adopting and implementing these emerging technologies, leaders can enhance their
desalination operations' energy efficiency, reliability, and sustainability.
Management information system
As a leader in the IT industry, I can apply the information from the article by promoting
research and development in desalination technologies within my organization. I can
encourage interdisciplinary collaboration between materials scientists, data analysts, and
engineers to explore innovative solutions for improving desalination processes.
Additionally, I can advocate for adopting renewable energy sources and integrating
intelligent monitoring and control systems in our desalination projects. I can contribute to
developing sustainable and efficient solutions for addressing water scarcity challenges by
staying updated with the latest trends and advancements in desalination technologies.
In conclusion, the article provides valuable insights into the emerging desalination
technologies and their potential impact on the future. The authors effectively discuss the
article’s main themes, relate the content to evolving IT trends and technologies, and
provide suggestions for applying the information in the industry. The review of the article
highlights the importance of investing in research and development of emerging
desalination technologies to address water scarcity and achieve sustainable development.
Reference:
Ahmed, F. E., Khalil, A., & Hilal, N. (2021). Emerging desalination technologies: Current status,
challenges, and future trends. Desalination, 517, 115183.
https://doi.org/10.1016/j.desal.2021.115183