AI Integration

INTRODUCTION The digital era transformation has created a level of connectivity never seen before, allowing businesses, governments, and individuals to run in much networked settings. However, such interconnectedness has dramatically increased the potential attack target area of cyber adversaries and made organizations susceptible to threats like ransom ware, advanced persistent threats (APTs), insider attacks, and highly sophisticated phishing (Haider & Bhutto, 2022). Perimeter-based security models that rely on trust within organizational boundaries are becoming outdated, particularly with the increasing adoption of cloud services, remote work, and the Internet of Things (IoT), which erode traditional network perimeters (Mareedu, 2023). To overcome these challenges, the modern security paradigm, Zero Trust Architecture (ZTA), has become a viable solution. ZTA revolves around the maxim of never-trust-always-verify and therefore requires constant verification of identities, devices, and applications, no matter the location in the network (Ghasemshirazi et al., 2023). With the use of micro-segmentation, least-privilege access, and robust identity and access management, ZTA can reduce risks of lateral movement and unauthorized access (Cate, 2023). Although promising, traditional ZTA implementations have limited applicability in practice, particularly in accommodating real-time threat landscapes, achieving scalable distribution, and minimizing operational overhead (Inaganti et al., 2020). Artificial intelligence (AI) comes in with the power to change paradigms here. Artificial intelligence (AI) and machine learning (ML) have been demonstrated to be very promising in cybersecurity, in particular,

This paper presents QaaMS (Quantum-as-a-Microservice), a proof-of-concept framework demonstrating how quantum algorithms can be exposed as REST/-gRPC endpoints in .NET 9 environments using microservice patterns. The framework provides an abstraction layer enabling quantum algorithms (Grover's search, QAOA optimization, and quantum random number generation) to integrate into existing business workflows through Microsoft Quantum Simulator via C# wrapper classes that simulate quantum behavior. Experimental validation demonstrates 569ms average response time across 252 requests with 8.61 requests/second throughput while maintaining 100% service availability in development environment testing. The system architecture demonstrates microservice patterns through containerization with Docker and Kubernetes orchestration, telemetry integration, and security features. All results are reproducible with Docker containerization and Locust load testing. The framework addresses the gap between quantum algorithm development and experimental deployment, enabling organizations to explore quantum computing capabilities through familiar microservice patterns in development environments.

This study examines the impact of EFL teachers' professional development activities on the integration of AI tools in teaching and learning. A mixed-methods approach, combining a questionnaire survey and focus group discussions, was used in the current study, which aimed to address the following research questions: (a) To what extent have teachers incorporated AI tools into their teaching practices after attending professional development sessions? (b) How significantly have EFL teachers' professional development training on AI integration contributed to enhancing student engagement in classroom participation across various language skills? The results indicate that the professional development activities on AI integration were instrumental in helping teachers focus on utilising AI tools to optimise student engagement in English language learning tasks. However, it was also found that students' motivation levels sometimes declined as AI overshadowed the task of achieving language production. The study has implications for stakeholders and policymakers to recognise the urgent need for an AI-driven pedagogical approach. It also proposes several necessary recommendations to enhance teachers' professional development activities, aiming to boost AI literacy and innovative technology in English Language Teaching and to bring about positive changes in the planning and implementation of future Teacher Professional Development (TPD) projects.

Automation, optimization, and enhanced decision-making are just a few ways artificial intelligence (AI) changes the game across several sectors. Its applications extend across diverse fields, including healthcare, transportation, finance, education, and software engineering. This study explores the integration of AI in software engineering, highlighting its transformative role in streamlining development workflows, improving software quality, and fostering collaboration between technical and non-technical stakeholders. The rise of no-code and low-code platforms has democratized access to AI, allowing users with limited technical expertise to implement AI-powered solutions like NLP and predictive analytics. Key benefits of AI in software development include automation of repetitive tasks, early bug detection, efficient project management, and personalized user experiences. The study also discusses the current trends in AI integration, including ML, NLP, robotics, and explainable AI, while addressing the challenges. Furthermore, AI tools for software development demonstrate their impact on education and skill development. Finally, the paper explores prospects in AI-driven software development. By analyzing the current and future trends, this study provides insights into how AI can shape the next generation of software development.

This document represents the culmination of our research, in which the Law of Maxim Kolesnikov, also known as Lex Universalis Praesentiae Coherentis, was developed and successfully verified. We have proven that a human is not merely the sum of biological components, but a single, indivisible singularity-operator (H) that mathematically and topologically encodes the entire essence of human existence.

Artificial Intelligence (AI) is rapidly being integrated into educational systems globally, making AI literacy a critical educational priority. This paper comprehensively reviews AI literacy, focusing on its definition, essential competencies, integration opportunities, and associated challenges within educational contexts. AI literacy refers to the knowledge, skills, and attitudes necessary to understand AI principles, effectively utilize and create AI tools, and critically evaluate AI technologies' ethical and societal impacts. The review synthesizes existing literature, international policy initiatives, and educational frameworks to conceptualize AI literacy and identify core competencies needed across different educational stages, categorized into knowledge-based, skillbased, and ethical-attitudinal dimensions. Opportunities explored include fostering interdisciplinary learning, preparing students for AI-driven workplaces, leveraging AI educational tools, and driving curriculum and policy innovation. However, significant implementation challenges, such as insufficient teacher training, infrastructure disparities, curriculum integration uncertainties, and ethical concerns related to bias, privacy, equity, and societal impacts, are highlighted. Addressing these challenges requires coordinated efforts among educators, policymakers, researchers, and stakeholders. Future research, policy development, and educational practice should focus on developing comprehensive competency frameworks, equitable access strategies, targeted professional development for educators, and responsible approaches to embedding AI literacy into education.

As enterprises expand their digital footprint across cloud platforms, remote environments, and mobile endpoints, traditional perimeterbased security models have become increasingly ineffective against sophisticated cyber threats. The proliferation of insider threats, credential misuse, and lateral movement within trusted networks calls for a paradigm shift in how organizations conceptualize security. This study explores the design and implementation of Zero Trust Architectures (ZTA) to secure distributed enterprise networks from both insider and external threats. Zero Trust operates on the principle of "never trust, always verify," assuming no implicit trust in users, devices, or applications, whether inside or outside the network perimeter. This research outlines a layered Zero Trust framework integrating key components such as continuous authentication, micro-segmentation, device posture assessments, and policy-based access controls. The architecture leverages identity and access management (IAM), software-defined perimeters (SDP), and real-time behavioral analytics to dynamically evaluate trust and restrict access accordingly. The study also evaluates Zero Trust deployment in hybrid and multi-cloud enterprise environments, highlighting integration challenges with legacy systems and solutions for secure policy orchestration across disparate platforms. Use cases from finance, healthcare, and manufacturing sectors are presented to demonstrate how ZTA mitigates risks related to insider privilege abuse, supply chain compromise, and credential-based attacks. Performance metrics such as mean time to detect (MTTD), mean time to respond (MTTR), attack surface reduction, and policy enforcement effectiveness are analyzed to validate the security benefits of ZTA. This work contributes a practical roadmap for CISOs and IT leaders to transition from implicit trust models to adaptive, risk-based Zero Trust frameworks.

Drones, often called unmanned aerial systems (UAS), are changing the way the military works, as well as how we handle surveillance, logistics, and civil applications. The rise of autonomous capabilities, AI integration, and swarming technologies brings both strategic benefits and new risks. NATO needs to accept new drone technology and come up with a single plan to stop their use by enemies, defend infrastructure, and keep air dominance. This paper talks about what will happen in the future, the biggest threats, and what NATO and its member states should do about them.

This study explores the profound impact of digital transformation on the traditional insurance model in India, with a particular focus on the integration of advanced technologies such as Artificial Intelligence (AI). Employing a descriptive research design, the study uses non-probability convenience sampling to gather insights from 153 employees of insurance companies across Gujarat, including Ahmedabad, Baroda, Surat, and Rajkot. Data were collected through site visits and analyzed using Jamovi software, applying Structural Equation Modeling (SEM) to investigate the relationships among key variables. These variables include the impact of digital transformation, technology adoption by insurers, challenges posed by digital changes, the future of the traditional insurance model, and customer satisfaction with digital insurance services. The findings indicate that while digital transformation, including AI, offers substantial opportunities for innovation and enhanced operational efficiency, it also introduces challenges related to data privacy, regulatory compliance, and skill development. The study concludes that the future success of the insurance industry in India will hinge on effectively addressing these challenges.

This article examines the impact of artificial intelligence (AI) implementation on company efficiency, focusing on human resource management, employee interaction, and operational optimization. It presents a critical analysis of the benefits and risks associated with AI integration, highlighting both potential productivity gains and organizational challenges. Particular attention is paid to generational differences in AI perception, data security concerns, and the psychological effects of digital oversight on employees. The study emphasizes the importance of a balanced and human-centric approach to digital transformation, advocating for tailored implementation strategies based on the specific needs, readiness, and structure of each company. Practical recommendations are proposed for ensuring sustainable AI integration, such as hybrid workflows, intergenerational task-sharing, and independent staff readiness assessments.

The rapid progression of industrial automation and control systems is driven by the integration of intelligent and interconnected technologies. These advancements enable real-time monitoring, predictive maintenance, optimized resource allocation, and minimal human intervention. This paper explores how these integrated technologies enhance operational efficiency and reliability in industrial systems. Using a combination of literature review, real-world applications, and data analysis, the study highlights the importance of converging AI, IoT, and advanced control frameworks in fostering sustainable, reliable, and efficient automation infrastructures

As enterprises explore integrating AI-driven functionality into existing infrastructure, the dominance of Python in the AI ecosystem presents integration challenges for systems based on statically typed platforms such as .NET Core 8. This paper outlines a system architecture in which a Pythonbased sidecar microservice is used to perform local inference and semantic enrichment alongside .NET services, without modifying existing application logic or introducing external cloud dependencies. The design introduces a language-agnostic communication protocol, the Model Context Protocol (MCP), to standardize the exchange of context and inference directives between .NET Core 8 services and the Python sidecar. The sidecar handles inference tasks such as intent classification, metadata enrichment, or content transformation using locally deployed LLMs or language processors. The architecture is designed for internal network or on-premise environments, where network security, data residency, or infrastructure policy prohibits reliance on external AI APIs. Although the system described here is scoped to .NET Core 8 and Python, the integration strategy can generalize to other enterprise platforms with similar constraints.

The focus of this research is towards integrating AI and DevOps practices in cybersecurity in Security frameworks for enhancing utility Infrastructure resilience. This can be put on continuous deployment and automate cybersecurity measures by bringing the space to continuous deployment and combining the capabilities of AI’s predictive powers and DevOps’ continuous deployment model. It studies the way these technologies assist threat detection, lower response time, and expedite the update. However, the infrastructure security can be strong and seamlessly integrated with real-time monitoring and continuous upgrades, with these findings. These integrated solutions provide more adaptive and proactive solutions for utility systems that are becoming increasingly exposed to cyber threats. It formulates proposals for implementing these technologies most effectively.

Maintaining financial stability and strategic planning in companies depends on cash flow projections. Often relying on static spreadsheets and historical data, traditional approaches might not precisely reflect real-time financial situations. This study presents an AI-driven cash flow forecasting method included into Enterprise Resource Planning (ERP) systems. Combining real-time transaction data with outside economic indicators-such as inflation rates, interest rates, commodity prices, and geopolitical sentiment-helps companies to create more accurate and responsive cash flow models. This paper offers a unique AI-driven forecasting system that combines real-time ERP transaction data with external economic indicators-including inflation rates, interest trends, commodity prices, prices and geopolitical sentiment-to generate responsive, context-aware cash flow projections. Using a hybrid modeling strategy that includes LSTM-based deep learning, ARIMA time-series models, and ensemble machine learning algorithms (e.g., XGBoost), the suggested system adjusts constantly to internal and external financial dynamics. Our hybrid forecasting system combines time-series models, ensemble learning, and economic feature engineering inside cloud-native ERP platforms (e.g., Oracle Fusion Cloud). We show how artificial intelligence can dynamically predict cash inflows and outflows, early risk identification, and treasury operations support in maximizing liquidity situations. Simulations and case studies reveal notable decreases in working capital inefficiencies and up to 30% increase in forecast accuracy.

Through improving diagnosis, therapy personalizing, and operational efficiency, artificial intelligence (AI) and machine learning (ML) are transforming healthcare. Faster and more accurate diagnosis, predictive analytics, and enhanced automation spanning clinical and administrative operations are made possible by these technologies. Although its advantages are clear-cut, integration of artificial intelligence/machine learning with ERP (Enterprise Resource Planning) systems-especially on platforms like Oracle Cloud-remains limited and understudied in healthcare environments. This work highlights present use cases, explores the transforming possibilities and major hurdles of AI-ERP integration in healthcare. We provide a paradigm for seamless AI-ERP integration to improve decision-making, resource allocation, and patient outcomes by means of a mix of literature study, case analysis, and model design.

This paper investigates the integration of Robotic Process Automation (RPA) with advanced authentication technologies to address the complexities of last-mile delivery. As e-commerce and online food delivery services expand, optimizing last-mile logistics becomes crucial due to high costs and operational challenges. The study proposes an AI-driven robotic delivery system that incorporates sophisticated authentication methods-PIN codes, biometric verification, and AI models-to enhance security and efficiency. Two key modules are introduced: the Cooperative User Authentication Module, which combines PIN codes with biometric verification for secure recipient identification, and the Non-Cooperative User Identification Module, which utilizes AI-powered facial recognition for passive recipient verification. Implemented on the Turtlebot3 robot, the system demonstrates improved delivery accuracy and security. This approach addresses last-mile delivery challenges by reducing labor costs, enhancing delivery speed, and ensuring secure parcel delivery, providing a robust solution for autonomous delivery systems.

Digital transformation in large organizations is possible through a complete study of customer experience, identifying needs and designing new banking services along with creating and developing a business ecosystem. In this research, an attempt has been made by using a mixed method through 200 questionnaires and in-depth interviews with 15 experts using the snowball sampling method of actors to provide a model of digital transformation based on artificial intelligence in municipal services and improve customer satisfaction through the development of business strategies. and electronic work (the study of Tehran municipality). In this research, data and information after determining the structural model by extracting in three stages by thematic analysis method (open coding, axial coding and selective coding) with Max Kyuda software (2018) It has been analyzed and finally, according to the interviews, the components of digital transformation of banking services and improvement of customer experience and satisfaction have been extracted. In this study. Eight main themes of the qualitative section: municipal technological management, municipal technological marketing, system and electronic quality of services, improving the quality of municipal technological services, technological software and hardware infrastructure, customer experience management, organizational culture and risk management were identified.

In recent years, generative AI and large language models (LLMs) have rapidly transformed the way we interact with technology. Their influence is spreading across countless domains, and their full impact is only beginning to unfold. The integration of LLMs into real-world applications-especially through AI agents, tool use, and Retrieval-Augmented Generation (RAG)-has become a major focus in modern software development. With a background in the military, I have been particularly interested in how generative AI might enhance Command and Control (C2) systems. In this article, I share my experience building a proof-of-concept C2 chatbot designed to extract land units and their associated locations from natural language input. Once a unit is identified, the system derives its 30-character Symbol Identification Code (SIDC), extracts the unit's unique designator, and generates a brief summary of its activities. If a location is mentioned, it is geocoded to obtain its latitude and longitude. All

It can be noted that digital platforms and search engines have revolutionized the way people and organizations look for information. However, query analytics does introduce a new avenue through which AI can improve the understanding and projection of trends involved in financial searches for significant advantages that businesses, researchers, and policy thinkers can take advantage of. Still, it can work millions of user search queries, discern trends in new currents, and offer a precious understanding of markets' probable behavior or where widespread interest could sooner or later shift. These enable financial institutions to improve the decision-making process about providing products needed by customers and the movement of the market. Studies have shown that these simplified search patterns are accurately identified and analyzed by natural language processing and machine learning algorithms, which propels innovation in financial services (Smith & Jones, 2021). Even more, integrating AI analytics ensures that real-time flexibility is deemed crucial in the unforgiving financial market where timing and accuracy are everything, as Brown et al. (2020) pointed out. However, there remain many issues associated with the use of analytic AI tools, medical imaging being a good example. Three main issues raised with using diffusion on a large scale are data privacy, concerns of bias within the algorithm, and combining multiple data sources. They also reveal that since the quality and variability of the input data affect the precision of the predictions, strict data management measures are required. This has also been backed by the fact that domain-specific training is one of the essentials to achieving the best performance, particularly in high-nuanced domains such as the financial domains (Johnson et al., 2019). In this respect, the article describes some primary methodologies, key challenges, and practical implications of applying AI-driven query analytics for prediction in financial search trends. To this end, this work will consider case scenarios and explore empirical literature to assess how AI is changing the face of financial data analysis and make valuable recommendations for managing its implications (Peterson, 2022). Keywords: Artificial intelligence technologies, Dynamics in searching financial information, MID in analyzing the search terms, how to pave AI in finance, natural language processing, predictive modeling of markets, data mining of financial info, analyzing the trends in the search engines, algorithmic prediction, data-driven approaches to decision making, financial innovations, real-time analysis, consumption trends in finance, data management in finance.

It can be noted that digital platforms and search engines have revolutionized the way people and organizations look for information. However, query analytics does introduce a new avenue through which AI can improve the understanding and projection of trends involved in financial searches for significant advantages that businesses, researchers, and policy thinkers can take advantage of. Still, it can work millions of user search queries, discern trends in new currents, and offer a precious understanding of markets' probable behavior or where widespread interest could sooner or later shift. These enable financial institutions to improve the decision-making process about providing products needed by customers and the movement of the market. Studies have shown that these simplified search patterns are accurately identified and analyzed by natural language processing and machine learning algorithms, which propels innovation in financial services (Smith & Jones, 2021). Even more, integrating AI analytics ensures that real-time flexibility is deemed crucial in the unforgiving financial market where timing and accuracy are everything, as Brown et al. (2020) pointed out. However, there remain many issues associated with the use of analytic AI tools, medical imaging being a good example. Three main issues raised with using diffusion on a large scale are data privacy, concerns of bias within the algorithm, and combining multiple data sources. They also reveal that since the quality and variability of the input data affect the precision of the predictions, strict data management measures are required. This has also been backed by the fact that domain-specific training is one of the essentials to achieving the best performance, particularly in high-nuanced domains such as the financial domains (Johnson et al., 2019). In this respect, the article describes some primary methodologies, key challenges, and practical implications of applying AI-driven query analytics for prediction in financial search trends. To this end, this work will consider case scenarios and explore empirical literature to assess how AI is changing the face of financial data analysis and make valuable recommendations for managing its implications (Peterson, 2022). Keywords: Artificial intelligence technologies, Dynamics in searching financial information, MID in analyzing the search terms, how to pave AI in finance, natural language processing, predictive modeling of markets, data mining of financial info, analyzing the trends in the search engines, algorithmic prediction, data-driven approaches to decision making, financial innovations, real-time analysis, consumption trends in finance, data management in finance.

Background: This research examines the changing influence of artificial intelligence (AI) technology on marketing with specific attention paid to the influence it has on influencer identification, content curation, and campaign analysis and evaluation. Algorithms under natural language processing (NLP) and machine learning (ML) are rapidly transforming the business world by enabling brands to easily identify suitable influencers through the automation of content customization and ROI computation. Methods: The study takes a qualitative approach by analyzing secondary data such as recent peer-reviewed articles, industry reports, white papers, and others. In addition, several case studies were conducted to assess the application of AI technology in marketing through influencers. Results: The results collected from the interviews highlight that AI tools in general perform far better than traditional systems in almost all areas of influencer marketing. The determined relevancy of target influencers concerning to their audiences increased campaign performance. AI utilized content creation led to better response rates owing to responsiveness to prevailing trends and higher levels of personalization. AI's real-time learning abilities enables it to alter strategy implementation to achieve the most favorable results within digital ecosystems. Conclusion: AI technology is increasingly becoming an essential element of modern marketing and influencer strategy. Decision-making processes, customized content creation, and effortless influencer profiling can all be automated, enabling AI to forge more meaningful connections between brands and consumers. However, the application of AI does present some difficulties concerning the use and safeguarding of sensitive data, algorithmic bias, and the authenticity of synthetic influencers.

The integration of artificial intelligence and emerging technologies has fundamentally transformed digital commerce landscapes across customer experience, supply chain operations, and security frameworks. From conversational interfaces enhancing customer interactions to sophisticated demand forecasting systems revolutionizing supply chain management, the evolution encompasses multiple dimensions of modern commerce. The incorporation of blockchain technology and advanced authentication mechanisms has strengthened security while improving operational efficiency. Through ElasticPath technology integration, these systems demonstrate remarkable scalability and performance improvements, marking a new era in digital commerce operations.

The use of artificial intelligence (AI) in financial reporting has transformed decision-making and data management. This journal discusses how Fusion Analytics Warehouse facilitates automatic financial reporting by utilizing machine learning algorithms and predictive analytics. AI-based automation increases accuracy, automates workflows, and facilitates compliance with changing financial regulation. The research delves into configuration approaches to implementing AI in finance workflows, maximizing reporting efficiency, and minimizing manual handling. Other than that, it also analyzes how AI analytics enhance forecasting, risk analysis, and fraud detection capabilities. Businesses can enhance business intelligence, make informed decisions, and gain better operational efficiency using AI-based financial reporting software. The study has real-world implementations and case studies and shows the revolutionary role of AI in finance. Future trends in the incorporation of AI into financial reporting as well as implementation issues are also discussed, paving the way to continued innovation in financial analytics.

This article explores the transformative role of declarative rules in modern business process systems, highlighting their fundamental departure from traditional procedural approaches. Declarative rules focus on defining desired outcomes rather than explicit execution paths, creating inherently adaptable systems that respond effectively to

This study investigates the perceptions and concerns of teachers, administrators, and students regarding the integration of Artificial Intelligence (AI) in education. The aim was to examine the potential of AI, its impact on educational practices, and the challenges faced by stakeholders during adoption. A qualitative approach was employed, using semi-structured interviews with IT teachers, school administrators, and students to gather comprehensive insights into their experiences and expectations. The findings revealed general enthusiasm for AI’s potential to enhance personalized learning and increase administrative efficiency. However, teachers raised concerns about the reduction in human interaction and the potential for job displacement, while administrators highlighted the need for clear policies, training, and resources. Students, while more receptive to AI, raised concerns about data privacy and over-dependence on technology. The results highlight the importance of developing clear guidelines and ethical frameworks for AI integration in education. It is concluded that, while AI presents significant opportunities, its integration requires careful planning, stakeholder collaboration, and ongoing support. Future research should focus on evaluating the long-term impacts of AI on educational outcomes and developing comprehensive teacher training programs to ensure responsible AI use in the classroom.

This article explores the transformative role of declarative rules in modern business process systems, highlighting their fundamental departure from traditional procedural approaches. Declarative rules focus on defining desired outcomes rather than explicit execution paths, creating inherently adaptable systems that respond effectively to  changing business requirements. The article explores how this paradigm shift enables organizations to separate business logic from technical implementation, resulting in enhanced transparency, reduced technical debt, and empowered business stakeholders. Through implementations across financial services, healthcare, and supply chain management, declarative approaches demonstrate significant advantages in regulatory compliance, decision consistency, and operational agility. The article further discusses critical implementation considerations including rule management infrastructure, performance optimization techniques, and integration strategies, while exploring emerging trends such as AI integration, natural language processing for rule definition, and applications in blockchain environments.

process systems, highlighting their fundamental departure from traditional procedural approaches. Declarative rules focus on defining desired outcomes rather than explicit execution paths, creating inherently adaptable systems that respond effectively to

The cybersecurity domain sees radical changes through the combination of Zero Trust Architecture (ZTA) with Artificial Intelligence (AI). All-access requests, including internal and external ones, fall under ZTA's fundamental "never trust, always verify" policy for thorough verification procedures. The advanced abilities of AI allow it to detect anomalies, forecast threats, and execute automated decisions to respond to cyber threats in real-time. Modern security frameworks receive enhanced protection through these technologies, which provide dynamic threat adaptation capabilities and improved response time and accuracy. The integrated approach between these systems produces enhanced cybersecurity performances that enhance vulnerability detection and produce predictive defense capabilities. Enhanced security infrastructure depends increasingly on ZTA and AI combined solutions to combat advanced evolving cyber threats.

Across all industries, especially in the recent century, the 4Ms framework-Man, Machine, Material, and Method-is a fundamental component of Continuous Improvement (CI), providing an articulated means of improving operational effectiveness. Although the "Machine" and "Method" components have been transformed by Industry 4.0 technology, this study argues that the "Man" component is not just crucial but pivotal, even if it is often prone to underestimation. Through an analysis of essential human-centered elements such as organizational culture, skill development, leadership, and adaptability, this study emphasizes the workforce's indispensable contribution to maintaining CI initiatives in the face of automation. This article demonstrates how people-centric strategies are crucial to developing CI initiatives by providing a thorough examination of human-technology synergy. This allows organizations to scale new heights in creativity, resilience, and sustainable success in a rapidly changing digital ecosystem. The review demonstrates why organizations should prioritize human capital and human capital development, even as they invest in technology, positioning people at the center of and as active drivers of CI in Industry 4.0 and beyond.

Effective communication is the cornerstone of societal cohesion and professional success, yet digital platforms disrupt nuance, trust, and intent. This paper merges insights from cultural psychology, neuroscience, digital media analytics, and AI research, exploring how cultural disparities, accountability gaps, and emerging AI technologies shape contemporary discourse. In a survey of 1,000 participants, 72% attributed miscommunication to cultural and inclusivegender dynamics, while 68% reported trust erosion in virtual interactions. Case studies spanning corporate missteps and diplomatic tensions illustrate the cost of neglected cultural nuance. AI's role in communication is analyzed through speculative scenarios-one envisioning enhanced emotional decoding, another foreseeing amplified biases, and ethical pitfalls. Integrating robust theoretical frameworks, comparative data visuals, and actionable recommendations, this paper offers a blueprint for fostering authentic connection in an increasingly digital world.

Implementations of the developed ML models are related to important questions of scale, resource, and management. This work investigates the use of serverless cloud models to address these issues and enhance and optimize the deployment, scalability, and maintenance of ML models. Reviewing main serverless platforms and their compatibility with different ML development and usage stages, the research compares the effectiveness, cost, and adaptability to the common application deployment practices. The study in this paper employs case and performance analysis to show and explain how serverless solutions can cut infrastructure costs and reduce the need for scaling and maintenance, among others. This paper outlines guidelines for implementing serverless technologies in ML applications and areas of concern that organizations might expect. Consequently, this research adds to the existing literature on deploying ML-based applications in the cloud while providing useful findings for developers and organizations interested in efficient, costeffective solutions.

This study explores the perceptions and concerns of teachers, administrators, and students regarding the
integration of Artificial Intelligence (AI) in educational settings. The objective is to examine how these key
stakeholders view AI’s potential, its impact on education, and the challenges associated with its adoption. A
qualitative methodology was employed, utilizing semi-structured interviews with IT teachers, school administrators,
and students to gather in-depth insights into their experiences and expectations of AI. The results indicate a general
enthusiasm for AI’s ability to enhance personalized learning and improve administrative efficiency. However,
teachers expressed concerns about the potential reduction of human interaction and job displacement, while
administrators highlighted the need for clear policies, training, and resources to ensure effective integration. Students
were more receptive to AI but raised issues regarding data privacy and over-reliance on technology. The findings
underscore the need for educational institutions to develop comprehensive plans and ethical guidelines for AI
adoption. It is concluded that while AI holds significant potential, its successful integration into education requires
careful planning and stakeholder collaboration. Future research should focus on longitudinal studies to assess the
long-term impact of AI on educational outcomes, as well as the development of teacher training programs and
ethical frameworks to ensure AI is used responsibly and equitably in the classroom.

Generative AI can be described as an
evolution of business consulting, which is
characterized by record innovation, productivity, and
decision-making. Employing superior sophisticated
models that can produce, interpret, and combine
information, consulting firms are recasting the
conventional paradigms to match the ongoing
business environment. The possibility of generative
AI as a force of change in the consulting proposition
is discussed in this paper in terms of several operative
themes, namely efficiency, client relations, and
future vision. Another example of generative AI is its
application in machinery consulting services to
minimize or cut the time and expenses required for
consulting interventions because it automates laborintensive processes like data analysis and processing,
report generation, and strategy modeling. Thus, it
can enable firms to provide a large amount of
complicated data, make better decisions, and improve
the efficiency of predictions. In addition, by creating
value by replicating market conditions, evaluating
different strategies, and exploring latent
opportunities and potentialities, AI-driven tools
increase innovation rates. Practice examples
illustrate how generative AI improves reasoning in
real-life situations connected to market entry
strategies, supply chain management, and customer
journey customization. The technology has been
proven to deliver significant advantages, yet
drawbacks include ethical issues, possible prejudice,
and the requirement to remain supervised by people.
Consulting activities that relate to generative AI must
incorporate technological aspects with professional
features proportionately. As this sector increasingly
adopts this shift as the wave of the future. Generative
AI can establish new standards for value creation
and new ways of working and partnering with clients.
This paper, hence, calls for responsible AI adoption,
ethical practices, and encouraging more innovation
to realize the potential of the technology in its highest
capacity

AI has impacted STEM education and expanded employment by implementing new approaches to standard problems. Pre-interview assessment sites employing artificial intelligence enable candidates to prepare for STEM job interviews while saving cost and time. These platforms incorporate tools, including NLP and machine learning algorithms, to estimate technical and behavioral performance constructively and objectively in real-time. In contrast to conventional mock interviews that are generally subjective, available in limited numbers, and expensive to administer, AI platforms result in more effective interview simulations based on the candidate's skills and the requirements for the specific job. These call for coding excellence, system design questions, and soft skills whereby applicants are tested and allowed to demonstrate problem-solving, communication, and leadership. Utilizing interview questions and answers based on a candidate's performance provides flexibility that the market has yet to embrace fully. The extensibility and availability of the platforms also do well in fulfilling the shortage of mock interviews for candidates, especially where they are in regions or have limited access to such services. However, some key challenges still exist, including bias in the AI systems and data privacy issues. Adjusting the machine learning model's data sets, including interview data and user feedback, is crucial for enhancing precision and pertinence. When applied and adopted, AI can enhance the production of interview preparation content for STEM graduates across the globe, thereby equalizing chances for talented and deserving candidates while reinventing the recruitment process.

This study explores the transformative impact of Generative AI on business consulting, examining its current
capabilities, applications, and challenges. Generative AI, driven by advancements in natural language processing
and machine learning, offers unprecedented opportunities for automating data analysis, enhancing decision-making,
and supporting innovation in consulting practices. However, the integration of AI also raises significant ethical
concerns related to transparency, bias, and privacy. The discussion emphasizes the balance between leveraging AI
for operational efficiency and retaining the human-centric aspects of consulting, providing insights into how AI is
reshaping the future of business consulting

The business consulting industry is at the cusp of a revolution mainly precipitated by strides in generative artificial
intelligence systems. Drawing from the generated AI in this paper, the paper proposes a framework that integrates
generative AI in consulting to improve the choice of strategy and deliver value. By examining the state-of-the-art
approaches and authentic success stories, the current work reveals how AI may enhance consulting businesses, increase
client satisfaction, and stimulate innovation. Due to the high speed of data processing, generative AI helps consultants
break complex processes into easily understandable steps and enhance the accuracy of the forecasts and insights they
provide. Moreover, by using big data and means of AI, the following characteristics are introduced: Customization of
solutions specific to the needs of each particular client is provided, which improves the clients' satisfaction, and
consolidation of the relation between the two parties is bolstered. The technology also helps consultants with new
trends and decide on emerging trends that will allow the client to compete well. Because consulting firms are likely to
incorporate AI tools in their work, they can solve the complexity that accompanies today’s business problems by being
more efficient in service delivery. Finally, the paper explores potential scenarios of how generative AI could evolve and
its effects on consulting businesses, pointing out that generative AI is a powerful tool from which consulting services
might change the course. AI’s role will remain necessary and evolve, providing firms with what they require to improve
strategic choices and create lasting client value in an increasingly fluctuating market.

Applying AI to software engineering processes puts software development life cycles in an intelligent automation
era, thus creating a paradigm shift. This paper seeks to demystify how AI tools and techniques have
revolutionized software development to be faster, more reliable, and more productive. More conventional
mechanisms that are tedious, time-consuming, and occasionally error-prone are increasingly giving way to AIfirst approaches to code generation, debugging and testing, and continuous integration and deployment. This
results in superior quality in developed software and enhanced project management since AI cuts out a lot of
time that would otherwise be spent on repetitive tasks, could involve a lot of errors, and cost a whole lot more to
undertake. The paper also presents real-life examples of AI implementation and its most efficient use in
different fields. These case studies show how the application of AI can address the persistent issues of software
engineering with real examples that have evolved as solutions While providing perspective on the barriers of AI,
including technical ones, ethical issues, and the idea of human supervision. Furthermore, possible trends for the
hypothetical future new product include characteristics such as integrating predictive analytics and AI in
project management. In the future, as the software engineering world grows, AI-based intelligent automation
will become a new standard for development life cycles, providing an opportunity for improvement. This paper
offers a systematic review of this transition; it presents information on both the current and potential use of AI
in software engineering processes and explores its role in changing the current development lifecycle into
intelligent systems

This study investigates the multifaceted perceptions of cybernetics students toward integrating artificial intelligence (AI) in educational environments. This research uncovers the students' knowledge levels, preferred sources of information, perceived benefits, and significant students through a comprehensive analysis of survey data combined with recent scholarly insights. Detailed visual analyses underscore key trends, offering a clear perspective on how these students view the future of AI in their academic journey. The findings suggest that while students appreciate AI's utility, they express valid concerns about job security and the potential reduction in human engagement. This study contributes critical insights for educators and policymakers aiming to align AI practices with student needs and expectations.

The integration of traditional medicine into orthodox healthcare systems is an essential step towards improving healthcare delivery in West Africa. Traditional medicine, which includes herbal remedies and indigenous healing practices, continues to be widely utilized, especially in rural areas where access to formal healthcare is limited. However, the divide between traditional and orthodox medicine creates challenges in ensuring comprehensive patient care. This article explores the digitization of traditional medicine as a potential solution to bridge this gap. Through case studies in Nigeria, Ghana, and Senegal, it examines how digital platforms and mobile health applications can standardize traditional practices, preserve indigenous knowledge, and foster collaboration with orthodox healthcare providers. The article highlights key figures from investigations across West Africa, showing that up to 80% of rural populations rely on traditional medicine, with countries making strides towards integration. Despite the promising benefits of digitization, challenges such as standardization, regulation, and digital literacy must be addressed. The findings suggest that the digitization of traditional medicine has the potential to complement orthodox healthcare, offering a more holistic and culturally relevant healthcare approach for the region.

This paper delves into the evolving role of Prompt Librarians in the digital age, tracing their transformation from traditional librarians to AI and Machine Learning (ML)empowered information retrieval specialists. It dissects the responsibilities and expertise expected of them while navigating ethical challenges. The exploration encompasses a comprehensive overview of Prompt Librarianship, drawing parallels between librarians and prompt engineers, elucidating their role, highlighting their influence in information retrieval, contrasting traditional and prompt librarians, assessing the impact of AI and ML, and addressing challenges and ethical dimensions. The findings underscore the transformative impact of AI and ML, showcasing Prompt Librarians as AI-integrated navigators enhancing search capabilities and ethical practices. This study pioneers the recognition of Prompt Librarians as ethical advocates in a tech-driven world, emphasizing their potential for innovation and expansion.

The comprehensive review article examines the historical evolution, current trends, and future possibilities of educational technology, with a particular emphasis on the incorporation of artificial Intelligence (AI). The study emphasizes for transformative impact of technology on teaching and learning practice, beginning with an exploration. The review examines the scholarly literature and address a wide range of educational technology applications, including mobile learning, blended learning, virtual reality, and AI-powered tools.

This article examines the evolving landscape of sports journalism in the context of artificial intelligence (AI) integration. As AI continues to permeate various sectors, its impact on journalistic practices, particularly within sports reporting, warrants careful consideration. Drawing upon scholarly literature and industry insights, this study explores four key dimensions of AI's influence on sports journalism: its role as a transformative tool in the workplace, its potential to enhance audience engagement while mitigating human error, its current limitations in creativity, and its unique impact within the field of sports journalism. We observe that while AI offers opportunities to streamline processes and improve efficiency, concerns persist regarding its ability to replicate the depth and nuance of human storytelling. Through critical examination of AI's implications for sports journalists, this paper seeks to contribute to a deeper understanding of the challenges and opportunities presented by AI integration in the journalistic profession.

Artificial Intelligence (AI) integration in the workplace represents a transformative force that is reshaping traditional business paradigms. This article embarks on a thorough exploration of AI adoption, focusing on a range of case studies and distilling best practices to illuminate successful strategies for seamless integration. Through a detailed examination of real-world implementations, the article showcases instances where AI technologies have not only optimized operational efficiency but also revolutionized decision-making processes. By drawing lessons from these case studies, organizations can gain valuable insights into the practical applications of AI across diverse workplace scenarios. This article delves into key best practices essential for effective AI integration, covering aspects such as stakeholder engagement, robust data governance, ethical frameworks, and ongoing training initiatives. Serving as a practical guide, it caters to decision-makers, IT professionals, and stakeholders recognizing the competitive advantage of leveraging AI in the workplace. By distilling lessons and offering actionable insights, the article contributes to the ongoing conversation on responsible and impactful AI integration, shaping a future where technology enhances the modern work environment.

This paper delves into the evolving role of Prompt Librarians in the digital age, tracing their transformation from traditional librarians to AI and Machine Learning (ML)-empowered information retrieval specialists. It dissects the responsibilities and expertise expected of them while navigating ethical challenges. The exploration encompasses a comprehensive overview of Prompt Librarianship, drawing parallels between librarians and prompt engineers, elucidating their role, highlighting their influence in information retrieval, contrasting traditional and prompt librarians, assessing the impact of AI and ML, and addressing challenges and ethical dimensions. The findings underscore the transformative impact of AI and ML, showcasing Prompt Librarians as AI-integrated navigators enhancing search capabilities and ethical practices. This study pioneers the recognition of Prompt Librarians as ethical advocates in a tech-driven world, emphasizing their potential for innovation and expansion.