International Journal of Technology, Management and Humanities
International Journal of Technology, Management and Humanities (IJTMH) The International Journal of Technology, Management and Humanities (IJTMH) is a peer-reviewed, open-access academic journal dedicated to the advancement of interdisciplinary research across the fields of technology, management, and the humanities. The journal, bearing the electronic International Standard Serial Number (e-ISSN) 2454-566X, was established in 2015 with the aim of fostering intellectual dialogue and innovative thinking across diverse disciplines. IJTMH publishes original research articles, case studies, technical notes, and review papers that contribute to the understanding and integration of technological innovations, management practices, and humanistic values. Its scope covers a wide range of topics, including but not limited to information technology, engineering management, business strategies, cultural studies, ethics, and societal impacts of technological change. Over the years, IJTMH has built a strong reputation for promoting research that bridges the gap between technical expertise and human-centered approaches. The journal welcomes contributions from scholars, practitioners, and industry experts worldwide, encouraging a global exchange of ideas and interdisciplinary collaboration. Committed to maintaining high academic standards, IJTMH ensures a rigorous peer-review process and strives for the timely dissemination of knowledge to its diverse readership, including researchers, academicians, and professionals.
Enterprise-Scale AI Architecture for Secure Mobile Platforms with Governance-Driven Automation Large-Scale Data Warehousing and Machine Learning
Enterprise-scale adoption of artificial intelligence requires architectures that are secure, scalable, and governed across heterogeneous digital ecosystems. This paper presents an enterprise-scale AI architecture designed for secure mobile platforms that integrates governance-driven automation, large-scale data warehousing, and machine learning capabilities. The proposed architecture enables cross-domain intelligence by unifying data ingestion, storage, processing, and analytics while enforcing security, privacy, and compliance policies throughout the AI lifecycle. Large-scale data warehousing serves as the foundation for managing structured and unstructured data, supporting real-time and batch analytics for machine learning model training and inference. Governance-driven automation ensures transparency, auditability, and ethical AI operations through policy enforcement, access control, and continuous monitoring. The architecture supports scalable deployment across enterprise environments, enhances decision intelligence, and enables secure, data-driven automation for modern mobile platforms. This work demonstrates how integrated AI, data, and governance frameworks can address operational complexity and regulatory requirements in enterprise-scale systems.
Evolving Database Leadership for Cloud-Native Automation and Operational Resilience
The rapid expansion of cloud platform adoption and the growing shift toward automated infrastructure created conditions in whichtraditional database administration models were no longer adequate for ensuring reliable and scalable data operations. The present studyexamines how database leadership must evolve within cloud native environments shaped by virtualization, managed database services,configuration automation, resilient architectures, and early DevOps influenced collaboration models. A mixed method approach was used,combining qualitative analysis of operational patterns across organizations adopting cloud hosted data platforms with quantitativeassessment of changes in workload distribution, automation coverage, and reliability indicators following platform oriented transformations. Findings indicate that organizations that reposition database professionals as strategic leaders of automated workflows,resilience planning, and cross functional coordination achieve measurable gains in operational stability and delivery velocity. Evidencealso shows that automation of provisioning, configuration, and schema changes reduces manual interventions and lowers error rates, whileresilience patterns such as replication and controlled failover improve service continuity. The research highlights how databaseresponsibilities expand beyond maintenance toward platform stewardship, architectural decision making, and proactive reliabilitymanagement. These shifts contribute to a more integrated data ecosystem in which database teams collaborate directly with application andoperations groups, improving communication pathways and enabling more predictable release cycles. The study’s contributions lie inidentifying the structural, technical, and organizational adjustments needed to align database leadership with cloud native automationpractices, offering a reference framework for institutions seeking to modernize data operations and strengthen the resilience of theirinformation environments.
Cloud native database platforms, database leadership, infrastructure automation, DevOps collaboration, managed relational services, configuration automation, operational resilience, database provisioning, schema change management, failover strategies, replication patterns, monitoring practices, incident response models, data platform governance, reliability engineering, distributed data environments
Trustworthy and Adaptive AI Systems for Enterprise Analytics Cybersecurity and Decision Optimization Using API-First and Cloud-Native Architectures
The increasing reliance on data-driven decision-making in modern enterprises has accelerated the adoption of artificial intelligence, cloud-native platforms, and API-first architectures. However, challenges related to trust, security, adaptability, and governance continue to hinder the large-scale operationalization of AI systems. This paper proposes a trustworthy and adaptive AI framework designed for enterprise analytics, cybersecurity, and decision optimization using API-first and cloud-native architectural principles. The framework integrates modular AI services, real-time analytics pipelines, and adaptive learning mechanisms to support scalability, resilience, and rapid innovation. Trustworthiness is ensured through explainable AI, policy-driven governance, continuous monitoring, and secure data exchange via standardized APIs. Cybersecurity capabilities are embedded across the architecture using zero-trust principles, AI-driven threat detection, and automated risk assessment. Experimental and conceptual evaluation demonstrates that the proposed approach improves decision accuracy, system interoperability, and security posture while maintaining low latency and operational flexibility. The findings highlight how enterprises can balance performance, trust, and security in complex digital ecosystems by aligning AI design with cloud-native and API-centric strategies.
Trustworthy Artificial Intelligence, Enterprise Analytics, Adaptive AI Systems, Cybersecurity, Decision Optimization, API-First Architecture, Cloud-Native Systems, Explainable AI, Zero Trust Security, Intelligent Enterprise Platforms.
A Multi-Domain AI Framework for Enterprise Agility Integrating Retail Analytics with SAP Modernization and Secure Financial Intelligence
In the era of digital transformation, enterprises face mounting pressure to enhance agility, optimize operational efficiency, and strengthen cybersecurity resilience. Multi-domain Artificial Intelligence (AI) frameworks have emerged as pivotal enablers, integrating insights from retail analytics, SAP modernization initiatives, and cyber defense strategies to deliver actionable intelligence across organizational functions. This study proposes a comprehensive AI framework that bridges the gap between retail decision-making, enterprise resource planning modernization, and proactive cybersecurity measures. By leveraging machine learning, natural language processing, and predictive analytics, the framework enables real-time demand forecasting, personalized customer engagement, automated SAP workflows, and threat detection. The proposed methodology employs a hybrid approach combining cloud-based AI models, edge computing, and data lakehouse architectures to ensure scalability, security, and performance. Empirical results indicate that organizations adopting multi-domain AI frameworks achieve improved operational efficiency, faster decision cycles, and enhanced protection against cyber threats. The research also identifies critical challenges such as data heterogeneity, integration complexity, and governance requirements. This study underscores the transformative potential of multi-domain AI in fostering enterprise agility and provides practical insights for organizations seeking to integrate retail analytics, SAP modernization, and cyber defense within a unified AI-driven ecosystem.
Multi-domain AI, enterprise agility, retail analytics, SAP modernization, cyber defense, predictive analytics, AI frameworks, digital transformation, cloud AI, data integration.
Digital Strategy Alignment and Business Performance in SMEs
This study examines the relationship between digital strategy alignment and business performance in small and medium-sized enterprises (SMEs). The purpose of the study is to assess how aligning digital initiatives with overall business strategies influences organizational performance. A quantitative research approach was adopted, using survey data collected from selected SMEs. The data were analyzed using descriptive and inferential statistical techniques to evaluate the impact of digital strategy alignment on financial, operational, and market performance. The findings reveal that SMEs with a high level of digital strategy alignment experience improved efficiency, enhanced customer engagement, and stronger overall business performance compared to those with weak alignment. The study concludes that digital strategy alignment plays a significant role in driving sustainable performance in SMEs and recommends that SME owners and managers integrate digital planning into their core business strategies to maximize competitive advantage.
Change Management Challenges in SME Digital Transformation Initiatives
This study examines the unique change management challenges faced by small and medium-sized enterprises (SMEs) during digital transformation initiatives. While digital transformation is often approached from a technological perspective, this research emphasizes the human and organizational factors that determine success or failure in resource-constrained SME environments. The primary purpose is to identify, categorize, and analyze the most critical change management barriers specific to SMEs undergoing digital transformation. The study aims to provide actionable insights for SME leaders, consultants, and policymakers to better navigate the people-centric challenges of technological adoption. A qualitative multi-case study approach was adopted, involving semi-structured interviews with owners, managers, and employees across 12 SMEs in various sectors currently undergoing or recently completing digital transformation projects. Supplementary data was collected through document analysis and participant observation. Thematic analysis was used to identify recurring patterns and challenges. Successful digital transformation in SMEs is less dependent on technological sophistication and more on effective, empathetic change leadership. SME-specific advantages—such as organizational agility and closer interpersonal relationships—can be leveraged to overcome challenges. Practical mitigation requires visible leadership, continuous communication framed around employee benefits ("WIIFM"), incremental implementation, and investment in ongoing peer-supported learning. Future research should explore sector-specific frameworks and the role of digital coaching in SME contexts.
Ethical and Regulatory Challenges in Managing AI-Centric Cybersecurity Programs for Critical Infrastructure
Artificial intelligence (AI) has become a prevalent feature of modern cybersecurity programs for critical infrastructure systems such as those used in energy, transportation, healthcare, and industrial control systems. AI-based security solutions can provide more accurate detection, adaptive response, and scalability than traditional rule-based systems. However, the use of these systems in safety-critical and highly regulated sectors of the economy raises significant ethical and regulatory issues which present additional challenges to their effective management. These include risks of adversarial machine learning, explainability limitations of complex models, accountability gaps for autonomous decisions, and the challenge of meeting regulatory requirements for cybersecurity practices and data privacy in AI-driven operations. This paper presents a thorough, literature-based exploration of the ethical and regulatory challenges of program management for AI-focused cybersecurity programs for critical infrastructure. Building on existing research in areas such as adversarial machine learning, ethical AI, and cybersecurity for critical infrastructure, this work takes a program-level perspective to understand how technical, ethical, and regulatory risks manifest and intersect at various stages of the AI lifecycle. By reviewing existing frameworks, standards, and principles in these areas, this study identifies the structural limitations of traditional cybersecurity program governance frameworks when applied to AI-based solutions. By conceptually organizing these issues along several dimensions of responsible AI - namely robustness, transparency, accountability, and regulatory compliance - this study aims to provide an analytically coherent foundation to understand responsible AI in the context of critical infrastructure cybersecurity. The insights presented highlight opportunities for holistic governance approaches that balance innovation, ethics, and legal considerations, while supporting sustainable, long-term trust in AI-enabled cybersecurity solutions.
AI-centric cybersecurity, critical infrastructure protection, ethical AI governance, regulatory compliance, adversarial machine learning, cybersecurity program management.
AI-Enabled Secure Cloud and Network Transformation for SAP Migration and Regulated Healthcare Data
The rapid adoption of cloud technologies in healthcare enterprises has intensified the need for secure, intelligent, and compliant digital transformation strategies, particularly for SAP system migration and regulated data exchange. Healthcare organizations must balance scalability and innovation with stringent security, privacy, and interoperability requirements. This paper proposes an AI-enabled secure cloud and network transformation framework that supports SAP migration while ensuring compliant healthcare data exchange across distributed environments. The framework integrates cloud-native architectures, AI-driven security analytics, and network governance mechanisms to enable proactive threat detection, automated risk assessment, and policy-based access control. By leveraging intelligent monitoring and predictive analytics, the proposed approach enhances migration reliability, optimizes network performance, and ensures adherence to healthcare regulations. The framework also facilitates seamless interoperability between SAP platforms and external healthcare systems through secure APIs and standardized data exchange models. Experimental analysis and architectural evaluation demonstrate improvements in security posture, operational efficiency, and regulatory compliance, making the proposed solution suitable for large-scale healthcare digital transformation initiatives.
AI-Enabled Security, Cloud Transformation, SAP Migration, Healthcare Data Exchange, Network Governance, Regulatory Compliance, Predictive Analytics.
Regulatory Fragmentation in Healthcare AI: A Policy Review of FDA Oversight, HIPAA, and Implementation Barriers
Background: The rapid integration of artificial intelligence (AI) into U.S. healthcare has exposed gaps in existing governance frameworks. Although the FDA has cleared more than 150 AI-enabled devices since 2019, implementation in clinical practice remains slow, uneven, and burdened by regulatory fragmentation. Methods: This study conducted a mixed-methods secondary analysis combining (1) FDA regulatory pathway review of AI/ML medical devices (2019–2023), (2) case study analysis of 72 publicly reported AI implementations in U.S. healthcare systems, and (3) structured review of HIPAA regulations and policy documents. Descriptive statistics summarized regulatory pathways, approval timelines, and post-market surveillance requirements. Thematic synthesis identified recurring compliance challenges and governance gaps. Results: The FDA database analysis revealed that most AI tools (67%) were cleared via the 510(k) pathway, with an average review time of 6.1 months. However, only 34% of devices had explicit post-market surveillance obligations. Case study review showed that 82% of implementations encountered HIPAA-related barriers, particularly around data de-identification and consent for secondary use. Smaller hospitals reported longer implementation delays and higher relative compliance costs, with regulatory uncertainty cited as the primary barrier. Conclusion: Current U.S. healthcare AI governance is fragmented across HIPAA and FDA frameworks, creating operational, legal, and economic burdens for health systems. Harmonized, risk-based oversight that integrates privacy and safety requirements could reduce inefficiencies and improve equitable AI adoption. These findings provide an evidence-based foundation for policy reforms that balance innovation with patient protection.
Artificial Intelligence, Healthcare Regulation, HIPAA, FDA, Policy Analysis, Implementation Barriers
Copyright (c) 2026 International Journal of Technology, Management and Humanities
Predictive AI for Household Hazard Prevention
The increasing frequency and complexity of household hazards, including fire outbreaks, environmental exposure, and infrastructure-related accidents, have intensified the need for proactive and intelligent prevention mechanisms. Predictive artificial intelligence (AI) offers a transformative approach by enabling early identification of risk patterns through the integration of heterogeneous data sources such as sensor streams, environmental indicators, and behavioral signals. This paper examines the application of predictive AI models for household hazard prevention, drawing on established risk prediction frameworks from disaster management, healthcare, transportation safety, and smart infrastructure systems. By synthesizing insights from machine learning–based early warning systems and validated predictive models, the study highlights how AI-driven risk scoring and forecasting can enhance household preparedness and reduce vulnerability. The paper further discusses governance, ethical, and policy considerations, including data privacy, model transparency, and equitable access to predictive safety technologies. The findings underscore the potential of predictive AI to shift household safety strategies from reactive response to anticipatory risk management, contributing to broader resilience and sustainable risk reduction objectives.
Predictive AI, Household Hazard Prevention, Risk Assessment, Machine Learning, Early Warning Systems, Smart Safety, Disaster Risk Reduction, Ethical AI
Emerging Technologies for Sustainable Treatment of Industrial Wastewater
Industrial wastewater management remains a critical environmental and regulatory challenge in the United States due to increasing industrial activity, stringent discharge standards, and growing concerns over water scarcity and ecosystem degradation. Conventional treatment methods, while effective for basic pollutant removal, often fall short in addressing emerging contaminants, energy efficiency, and long-term sustainability goals. This study examines emerging technologies for the sustainable treatment of industrial wastewater, with a specific focus on their applicability within the U.S. industrial and regulatory context. Key technological advancements reviewed include advanced membrane systems, electrochemical and advanced oxidation processes, biotechnology-driven treatments, and nature-based hybrid solutions. In addition, the integration of digital innovations such as artificial intelligence, Internet of Things enabled monitoring, and smart process control is explored as a means to enhance operational efficiency and compliance. Emphasis is placed on sustainability outcomes, including water reuse, resource recovery, reduced carbon footprint, and alignment with circular economy principles. The analysis also considers policy frameworks, economic feasibility, and scalability challenges influencing technology adoption in the United States. Overall, the study highlights the transformative potential of emerging wastewater treatment technologies to support industrial sustainability, regulatory compliance, and long-term water security in the U.S.
Industrial wastewater; Sustainable treatment technologies; Water reuse; Resource recovery; Smart water systems; United States environmental policy.
An AI-Driven Decision Automation Framework for Cargo Consolidation in High-Volume Trade Hubs
The growing complexity of global supply chains and the expansion of high-volume trade hubs have increased operational demands on cargo consolidation companies. These organizations are required to manage large volumes of shipments while ensuring accurate documentation, efficient space utilization, and timely decision-making. Despite ongoing digital transformation efforts in logistics, many cargo consolidation processes remain heavily reliant on manual intervention or loosely integrated information systems. This dependence often results in operational inefficiencies, delays, and elevated error rates, particularly in environments characterized by high variability and regulatory complexity. This study presents the design and implementation of an artificial intelligence driven automation framework aimed at enhancing decision support in cargo consolidation operations. The proposed framework integrates advanced data processing, intelligent decision logic, and automated execution mechanisms to support critical consolidation activities, including documentation validation, cargo space optimization, and real-time operational prioritization. A mixed-methods research approach is employed to evaluate the framework, combining qualitative insights from logistics professionals and artificial intelligence specialists with quantitative analysis of key operational performance indicators such as processing time, documentation accuracy, and resource utilization efficiency. The results demonstrate that the adoption of AI-driven automation leads to significant improvements in decision accuracy, operational speed, and overall efficiency within cargo consolidation workflows. In addition to performance gains, the framework enhances operational resilience and scalability, supporting modern logistics transformation initiatives. By addressing an underexplored area of supply chain automation, this research contributes a practical and transferable framework that advances both academic understanding and industry practice in intelligent logistics systems operating within high-volume trade hubs.
Artificial intelligence, Cargo consolidation, Supply chain automation, Decision support systems, Logistics 4.0, Digital transformation.
Defining Success in Probabilistic Products: Key Performance Indicators and Lifecycle Management for Generative AI Applications in Enterprise
Generative Artificial Intelligence introduces probabilistic behavior into enterprise software systems, fundamentally challenging the deterministic assumptions underlying traditional product management and Software-as-a-Service success metrics. Conventional indicators such as Daily Active Users, churn rate, and uptime fail to capture the economic, operational, and risk dimensions inherent in stochastic model outputs. This paper proposes a novel product management framework for probabilistic AI systems, grounded in enterprise deployments of generative applications launched from zero to production scale. A new class of AI-native Key Performance Indicators is introduced, including Response Accuracy, Hallucination Rate, Token Efficiency, and Human Intervention Rate, alongside a Probabilistic Product Lifecycle model integrating continuous evaluation and human-in-the-loop governance. Through comparative analysis and applied case evidence, the study establishes a new standard for measuring success in enterprise generative AI products, reframing uncertainty from a liability into a measurable and manageable product dimension.
Generative AI, Probabilistic Systems, Product Management, KPIs, LLMOps, Enterprise AI, Lifecycle Management.
AI Driven DevSecOps Security Pipelines for Fraud Detection in SAP Based Business and Marketing Processes
The increasing adoption of SAP-based cloud platforms for enterprise business and marketing processes has amplified the scale, complexity, and sophistication of fraud and cybersecurity threats. Traditional perimeter-based and rule-driven security mechanisms struggle to provide real-time protection across distributed SAP landscapes, particularly in environments governed by continuous integration and continuous deployment (CI/CD) practices. This paper proposes an AI-driven DevSecOps security pipeline that integrates machine learning–based fraud detection, multivariate analytics, and automated threat intelligence into SAP-centric business and marketing workflows. By analyzing heterogeneous data sources—including SAP transaction logs, marketing campaign data, user behavior metrics, and system telemetry—the framework enables early detection of anomalous activities, financial fraud, and process-level abuse. The proposed approach embeds security controls throughout the DevSecOps lifecycle, ensuring continuous risk assessment, policy enforcement, and rapid incident response. Experimental evaluation demonstrates improved fraud detection accuracy, reduced false positives, and enhanced resilience of SAP-enabled enterprise systems operating in cloud-native environments.
Artificial Intelligence, DevSecOps, SAP Security, Fraud Detection, Business Processes, Marketing Analytics, Machine Learning, Cloud Security, Multivariate Analysis, Threat Intelligence
Transforming Public and Enterprise Business Processes with Cloud AI Data Governance SAP and DevOps for Intelligent Secure and Inclusive Digital Ecosystems
Public institutions and enterprises are undergoing rapid digital transformation to improve efficiency, transparency, security, and inclusivity. This paper examines how the integration of Cloud computing, Artificial Intelligence (AI), Data Governance frameworks, SAP enterprise platforms, and DevOps practices can transform public and enterprise business processes to enable intelligent, secure, and inclusive digital ecosystems. Cloud technologies provide scalable and resilient infrastructure, AI enhances decision-making and service automation, and data governance ensures compliance, trust, and ethical data usage. SAP systems enable standardized and interoperable business processes, while DevOps accelerates innovation through continuous delivery and collaboration. By aligning technological capabilities with governance and process reengineering, organizations can deliver citizen-centric and customer-centric services, enhance operational resilience, and support sustainable digital growth across public and private sectors.
Public Governance, Enterprise Digital Transformation, Cloud Computing, Artificial Intelligence, Data Governance, SAP, DevOps, Business Process Transformation, Security, Inclusivity, Digital Ecosystems.
Advancing Data Consistency and Control Across Global Financial Institutions by Enterprise Master Data Platforms
This research examines how enterprise master data platforms enable data consistency and operational control across global financial institutions operating in complex, highly regulated environments. As financial institutions expand across jurisdictions, products, and legal entities, fragmented client and reference data has emerged as a persistent source of operational risk, reporting inconsistency, and governance failure. The purpose of this research is to examine enterprise master data management not as a technical integration initiative, but as a strategic control capability that underpins trusted business operations. The study adopts a qualitative, evidence-based approach, combining architectural analysis with structured evidence mapping derived from large-scale implementations within global financial institutions. The analysis identifies recurring design patterns in master and reference data platforms, including governance structures, stewardship models, control workflows, and lineage mechanisms, that collectively enable consistent enterprise-wide data representation. Key findings suggest that institutions achieving high levels of data consistency experience reduced reconciliation effort, improved auditability, and stronger alignment between operational processes and regulatory expectations. The study further demonstrates that operational control is strengthened when data governance is embedded into platform workflows and business decision points rather than managed as an isolated compliance function. From an academic perspective, this research extends existing data governance literature by explicitly linking enterprise data consistency to operational control outcomes. From a strategic industry perspective, it provides a practical framework for designing, governing, and sustaining enterprise master data platforms that support risk management, regulatory confidence, and decision integrity. The study concludes that enterprise master data platforms constitute a foundational capability for resilient, transparent, and controllable operations in global financial institutions.
Enterprise master data management, enterprise data governance, data consistency and control, global financial institutions, reference data standardization, client data integration, enterprise data platforms, operational risk management, regulatory reporting integrity, data lineage and traceability, data stewardship models, risk data aggregation.
Enterprise-Scale Data Stewardship Enablement Using Workflow-Driven Governance Mechanisms in Financial Services
Large financial enterprises face persistent challenges in governing reference data assets due to organizational fragmentation, regulatory pressure, and increasing dependency on shared data domains across business lines. This study examines how workflow-driven governance mechanisms can be operationalized to enable scalable and sustainable data stewardship models within complex financial services environments. Drawing on evidence from the BNY Mellon Reference Data Hub and QBE Insurance implementations using enterprise business glossary and metadata platforms, the paper analyzes how structured workflows, role-based accountability, and policy-driven controls contribute to improved data quality, lineage transparency, and governance enforcement. The study adopts a qualitative, design-oriented approach, synthesizing implementation artifacts, governance operating models, and stewardship processes observed across these large enterprises. Findings indicate that workflow-centric governance architectures significantly enhance stewardship effectiveness by formalizing decision rights, reducing manual intervention, and aligning business and technology stakeholders around shared data standards. The study contributes to a generalized enterprise stewardship framework that integrates workflow orchestration, reference data lifecycle management, and governance escalation models. This framework offers practical guidance for financial institutions seeking to mature their data governance capabilities while meeting regulatory expectations and operational scalability demands. The paper positions workflow-enabled stewardship not as a tooling initiative but as an institutional capability critical to enterprise data reliability and risk management.
Data stewardship, workflow governance, reference data management, financial services data governance, enterprise data hubs, metadata management, business glossary, policy enforcement, stewardship operating model, data quality control, regulatory compliance, master data governance
The Economic Impact of Intermittent Fasting on Workforce Productivity in the United States
Intermittent fasting (IF) has already received considerable scientific interest as a potentially health-promoting diet, but there has been little discussion about the economic aspects of its feasibility to workforce productivity. In this paper, the researcher will analyze the correlation between IF and labor efficiency in the United States with reference to the effects of fasting protocols on cognitive performance, energy levels, absenteeism and the overall output of employees. Based on the findings of nutritional science, behavioral health, and labor economics, the study compares the possible economic benefits and expenses in terms of mass implementation of IF among working adults. The findings are expected to guide organizational wellness interventions, as well as, the policy on the role of fasting in the overall health of the population to inform the opportunities and constraints to implementing the fasting practice in the corporate health program. The research results add up to an emerging evidence concerning the connection of food consumption with economic productivity in modern labour markets.
Intermittent fasting, workforce productivity, labor efficiency, employee health, economic impact, United States
Ethical and Regulatory Challenges of AI in Finance
The recent accelerated incorporation of artificial intelligence (AI) in the financial industry has revolutionized the fundamental operations in determining creditworthiness, fraud detection, algorithmic trading and customer interaction. Although such applications can result in considerable efficiency, accuracy, and scalability, they also present difficult ethical and regulatory issues that should be criticized closely. This paper focuses on the ethical issues that are entailed in AI-based financial systems, such as algorithmic discrimination, absence of explainability and transparency, threats to the privacy of personal data, and whether automated decisions are accountable. Concurrently, it evaluates regulatory problems, which are posed due to the constraints of the current financial and data protection standards, cross-jurisdictional discrepancies, and the impossibility of regulating unclear and evolving AI models. The abstract displays the conflict between the promotion of innovation and consumer protection, financial stability, and trust on the part of the population. By summarizing the existing controversies and regulation strategies, the paper highlights the importance of powerful codes of ethics, dynamic regulation frameworks, and proper oversight systems to make AI implementation in finance fair, transparent, and socially accountable.
Artificial intelligence; financial services; ethics; regulation; algorithmic bias; transparency; data privacy; AI governance
Artificial Intelligence Product Management - Identifying Opportunities and Developing AI Product Solutions
Artificial Intelligence (AI) is becoming a new reality of product management, as it will make possible the development of innovative solutions to solve difficult business and customer issues. Management of AI products should entail the identification of high-impact opportunities, conception of AI-based solutions, and the development and implementation of the solutions. It involves a thorough knowledge of market requirements, technical practicality and ethical aspects along with cross-functional teams that involve data scientists, engineers and stakeholders. Through a systematic integration of AI capabilities and business goals, companies are able to design products that can provide quantifiable value, improve user experiences and remain competitive. The current paper discusses the identification of opportunities, designing AI solutions, and overcoming the challenges in AI product management, illustrating the best practices and frameworks that enable to achieve a successful AI product lifecycle.
Artificial Intelligence, Product Management, AI Product Development, Opportunity Identification, AI Solutions, Ethical AI, Data-Driven Innovation.
Clinical Decision Support Systems and AI Regulation: Balancing Innovation, Patient Safety, and Legal Responsibility
Artificial intelligence (AI)-based Clinical Decision Support Systems (CDSS) are changing the face of healthcare by offering evidence-based information to improve the quality of diagnosis and treatment planning. Nevertheless, the fast adoption of AI in clinical practice poses some serious issues concerning patient safety, ethical duty, and legal liability. This study will be based on the regulatory frameworks of AI-enabled CDSS, and the way in which innovation can be achieved in balance with risk management and compliance needs. By comparing the available regulations, case analyses on the clinical implementation, and the opinions of the experts, the research points out the missing links in the current oversight systems and suggests the methods of responsible AI implementation in medicine. The results will inform the policymakers, healthcare professionals and developers in terms of ensuring that AI based decision support systems can drive innovative approaches in medical care without jeopardizing the well-being of patients and the legal requirements.
Clinical Decision Support Systems, Artificial Intelligence, Healthcare Regulation, Patient Safety, Legal Responsibility, AI Ethics, Risk Management
Governing High-Risk AI in Healthcare: Aligning Technical Robustness with Ethical and Legal Accountability
Clinical decision-making, diagnostics, and patient management have been transformed by the integration of high-risk artificial intelligence (AI) systems in healthcare, and complex technical, ethical, and legal issues have been generated simultaneously. These systems are typically implemented in situations involving safety-critical use, where a governance model is required that both provides technical resilience and ethical integrity and legal responsibility. This paper focuses on the regulation of high-risk AI in healthcare through the lens of the relationship between system reliability, transparency, and regulatory accountability. It discusses the main aspects of technical robustness, such as accuracy, explainability, bias reduction, and cybersecurity, and determines how these technical norms are in conflict with the ethical foundations of patient autonomy, fairness, and human control. The paper also evaluates current legal and regulatory frameworks to define accountability, liability and compliance loopholes in AI-driven healthcare applications. The research provides a combined governance framework that harmonizes the technical, ethical, and legal approaches to innovations by synthesizing the three aspects of action to attain patient safety, and trust in the population. The results add to the discussion of responsible adoption of AI in healthcare and offer the policy-related implications to the regulators, healthcare facilities, and AI developers aiming to establish trustful and responsible AI systems.
High-risk AI; Healthcare governance; Technical robustness; Ethical accountability; Legal regulation; Trustworthy AI
Zero Trust Security Architecture: Principles and Early Adoption
The rising complexity of cyber threats and the shortcomings of the old paradigm of security models in the form of perimeter security has compelled organizations to look into other security paradigms. Zero Trust Security Architecture (ZTSA) is one solution that can be viewed as a proactive solution, focusing on rigid identity checking, least privilege access, and constant patrol, instead of trusting things within network boundaries. This study will look at the core concepts of Zero Trust such as micro-segmentation, identity and access management, and device security, and its initial implementation history within the enterprise setting. The analysis of implementation strategies, challenges, and case studies help the study to identify practical considerations that organizations need to realize when implementing Zero Trust frameworks. The results indicate that technical integration and organizational preparedness are problematic, but early adoption shows great potential of reducing security risks and resiliency. The article offers a theoretical background to organizations that are interested in adopting Zero Trust that can be added to the growing debate on the topic of contemporary cybersecurity strategies.
Zero Trust Security, Cybersecurity, Identity and Access Management, Micro-Segmentation, Continuous Monitoring, Early Adoption, Enterprise Security.
Cybersecurity Risks and Threats in the Era of Pandemic-Induced Digital Transformation
The phenomenal pace of digital transformation occasioned by the prevalent disruptions greatly transformed how organizations operated, communicated, and delivered services within industries. Although it has allowed organizations to remain operational and sustained, the introduction of remote work patterns, cloud computing, and online platforms also predisposed companies to increased cybersecurity threats and novel threats. The given paper explores the qualities and extent of the cybersecurity risks and threats, which facilitated during the pandemic-driven digital transformation, focusing on the increased attack surfaces, human-factor vulnerabilities, and the vulnerabilities in the digital infrastructure. The study is a synthesis of the available literature and reports on the industry to determine the common cyber threats like phishing, ransomware, malware propagation, and data breaches, as well as the systemic problems, including governance, policy implementation, and user awareness. Moreover, the research paper shows that these threats affect key sectors, such as healthcare, finance, education, and social institutions, in a disproportionate way. The studies highlight adaptive cybersecurity frameworks, sound risk management, and ongoing user education, which is the responsiveness of the research through the analysis of mitigation measures and best practices implemented during this time. The results can be used to better understand the restructuring of the cybersecurity environment under the actions of accelerated digital transformation and offer a point of view to build more resilient and secure cyber-ecosystems in future crises-related settings.
Cybersecurity risks; Digital transformation; Pandemic-induced disruption; Cyber threats; Remote work; Information security; Risk management]
From Public Confidence to Civic Technology: Designing the Next Generation of Governance Analytics
The trust to governmental institutions in the society is now the critical factor to determine good governance where the citizens determine the level of legitimacy, accountability and transparency. Due to the rapid changes in civic technology, there are new possibilities to reinforce this relationship with the help of data-driven understandings and participatory digital solutions. This paper examines how to shift away from the conventional trust-building processes to the superior governance analytics that are able to track the real-time population mood, in constituency work, and patterns of civic participation. It offers a new-generation analytics system, combining ethical AI, people-centered design, and multidimensional data streams, to help to achieve responsive, transparent, and anticipatory governance. Analyzing the main design principles, possible applications, and systemic issues, the work gives the roadmap of how civic technology can be used to enhance the trust in the people and allow them to have more inclusive and intelligent governance systems.
Public Confidence, Civic Technology, Governance Analytics, Trust, Transparency, Ethical AI, Digital Participation, Citizen-Centric Design
Humanizing Automation: Lessons from Amazon’s Workforce Transition to Robotics
Automation and robotics have altered the previously known workplaces and organizations are struggling to strike a balance between efficiency and human practices. This paper will look at the employment shift of Amazon to the use of robotics and the social, operational, and ethical consequences of mass automation. The study, based on the review of workforce experiences, training programs, and organizational practices, draws major lessons in humanizing automation, such as the necessity of open communication, reskilling schemes, and employee involvement. The results highlight the importance of the fact that successful automation is not just a technological task but a socio-technical process that needs to carefully consider human needs. These lessons can be used by companies that want to implement robotics without interfering with the well-being and productivity of employees.
Automation, Robotics, Workforce Transition, Human-Centered Design, Employee Engagement, Upskilling, Organizational Change
The Economics of Trust: Why Institutional Confidence Is the New Currency of Governance
Modern societies have come up with institutional trust as a key factor in proper governance. Due to the ineffectiveness of conventional economic indicators in capturing the dynamics of the population belief, trust is becoming a crucial asset of socio-economic capital. This paper examines the role of institutional confidence in the outcome of governance through compliance, lower transaction cost and stable policy environment. It looks at what drivers bolster or undermine trust, including transparency, accountability, equity in service delivery, and information integrity and looks at the impact of levels of trust on social cohesion, economic performance, and state legitimacy. In a conceptual analysis and using illustrative examples, the paper suggests that trust has become a new currency of governance, which is needed to overcome complicated situations in society and create robust relations between states and citizens. In sum, the results indicate the necessity of trust-based governance systems that emphasize ethical leadership and participatory procedures as well as stable institutional performance.
Institutional trust, governance, public confidence, transparency, accountability, social cohesion, policy effectiveness, trust economy.
Predictive Analytics for Reducing Title V Deviations in Chemical Manufacturing
The Title V operating permits require chemicals manufacturing facilities to comply with stringent air emissions limits, monitoring, recordkeeping, and reporting provisions. Despite robust Environmental Management Systems (EMS), deviations continue to occur due to manual monitoring limitations, complex regulatory obligations, and the variability of industrial processes. This study proposes a predictive analytics framework designed to anticipate Title V deviations before they occur. Using statistical modeling, machine learning (ML), historical deviation data, continuous emissions monitoring system (CEMS) inputs, and operational parameters, the framework estimates the probability of future deviations and identifies key drivers. Case simulations demonstrate that predictive modeling can reduce deviations by 30 55% by enabling proactive operational adjustments, improved recordkeeping, and targeted corrective actions. The approach is designed to complement existing facility specific permit conditions. Results show that predictive analytics can shift air compliance programs from reactive detection to proactive prevention, improving compliance consistency across multi unit chemical operations.
Title V, predictive analytics, emissions compliance, machine learning, environmental management, air permitting, EPA, data driven compliance
SAP-Based Digital Banking Architecture Using Azure AI and Deep Learning for Real-Time Healthcare Predictive Analytics
The digital transformation of banking has led to increasingly complex operational environments where security, real-time insight, and enterprise system integration are paramount. This research proposes a Secure Digital Banking Framework that tightly integrates core SAP systems with real-time AI-driven predictive analytics to enhance risk management, fraud detection, customer personalization, and operational efficiency. Leveraging SAP’s enterprise capabilities (e.g., SAP S/4HANA, SAP Fiori, SAP Cloud Platform) combined with streaming data platforms and machine learning models, the framework supports real-time data ingestion, contextual analytics, secure access control, and regulatory compliance. The architecture incorporates role-based security, end-to-end encryption, audit trails, and continuous monitoring, ensuring robust protections for sensitive financial data. Predictive analytics modules use advanced machine learning and deep learning techniques to forecast credit risk, detect anomalous transactions, and model customer lifetime value. A prototype implementation demonstrates that integrating SAP data sources with real-time AI models significantly improves predictive accuracy and response times compared to traditional batch analytics. Results indicate improvements in fraud detection precision, reduced false positive rates, and better operational visibility. The proposed framework provides a blueprint for financial institutions to modernize digital banking infrastructure with secure, integrated, and intelligent features that support compliance, resilience, and customer satisfaction.
Secure digital banking, SAP integration, predictive analytics, AI, real-time analytics, fraud detection, enterprise systems, risk management, machine
Text Classification: A Comprehensive Survey of Methods, Applications, and Future Directions
Text classification stands as a fundamental task in natural language processing, involving the automated assignment of predefined categories to textual documents, sentences, or phrases. This comprehensive survey examines the evolution of text classification methodologies from traditional machine learning approaches through deep learning innovations to contemporary transformer-based architectures. We analyze the progression from feature-engineered methods like Naïve Bayes and Support Vector Machines, through representation learning with CNNs and LSTMs, to pre-trained language models including BERT, RoBERTa, and GPT variants. Our analysis encompasses benchmark datasets, evaluation metrics, and quantitative performance comparisons across methodologies. We explore emerging paradigms including prompt-based learning, few-shot classification, and multilingual adaptation while addressing critical challenges in interpretability, fairness, and computational efficiency. The survey provides practitioners with decision frameworks for selecting appropriate approaches based on task requirements, data availability, and resource constraints. We conclude by identifying open research questions and future directions that will shape the next generation of text classification systems.
Text Classification, Natural Language Processing, Machine Learning, Deep Learning, Transformers, BERT, Neural Networks, Sentiment Analysis, Topic Categorization