Journal of Information Systems Engineering and Management
Journal of Information Systems Engineering and Management Journal of Information Systems Engineering & Management is published quarterly ISSN (Online): 2468-4376 DOI: https://doi.org/10.55267/iadt
Assessing Readiness for AI and Blockchain Integration in Indonesia's Coconut Ecosystem: A Multi-Stakeholder Analysis
Introduction: Indonesia, a leading coconut producer, faces supply chain inefficiencies, traceability challenges, and limited value-added processing. While AI and blockchain offer solutions, successful adoption requires a comprehensive readiness assessment. Objectives: This study evaluates Indonesia's coconut ecosystem readiness across six dimensions: knowledge, infrastructure, human capital, regulatory frameworks, financial resources, and process maturity. Methods: A qualitative survey of 18 stakeholders (government officials, processors, collectors, suppliers, farmers, breeders, and associations) was conducted from June to October 2025 using thematic analysis. Results: Results reveal a "readiness paradox": high adoption enthusiasm despite significant capability gaps. The Composite Technology Readiness Index scored 2.3/10 (pre-readiness stage), with over 80% possessing minimal AI/blockchain knowledge. Critical gaps include regulatory frameworks (10% readiness), human capital (20%), knowledge (20%), and process maturity (20%), while infrastructure showed relatively better readiness (30%). Conclusions:Key pain points price volatility, transparency deficits, and traceability inadequacies, align well with technology capabilities. Universal concern for farmer welfare provides a strong foundation for multi-stakeholder collaboration.
A Dynamic and Adaptive Decision Support System for Managerial Time Allocation Under Environmental Uncertainty
Introduction: Managerial time allocation is a critical mechanism through which managers influence organizational performance and adaptability. Existing decision support approaches typically treat time allocation as a static, one-shot optimization problem, despite the fact that managerial priorities, constraints, and environmental conditions evolve continuously. In uncertain and dynamic environments, static recommendations risk becoming misaligned with organizational needs, reducing their practical usefulness. Objectives: The objective of this study is to develop and evaluate a dynamic and adaptive decision support framework for managerial time allocation that explicitly accounts for environmental uncertainty and temporal change. The study aims to assess whether adaptive time-allocation policies outperform static and purely reactive approaches in terms of cumulative value, robustness, and behavioral stability. Methods: Managerial activities are modeled as a time-dependent portfolio, with time allocations revised periodically using a rolling-horizon decision process. Managerial preferences are represented through interval-valued parameters to preserve flexibility, while environmental uncertainty is captured via stochastic states and scenario-based evaluation. The resulting optimization problem is solved using a robustness-aware rolling-horizon evolutionary algorithm. Performance is evaluated through simulation under multiple uncertainty regimes, including low volatility, high volatility, shock-driven, and regime-switching environments, and compared against static and non-robust rolling-horizon benchmarks. Results: The results show that dynamic adaptation provides limited benefits in stable environments but yields substantial performance improvements under volatility, shocks, and regime changes. The proposed adaptive robust model consistently achieves higher cumulative value and lower performance variability than benchmark approaches. Dynamic analysis further reveals that the model selectively adapts to meaningful environmental changes while avoiding excessive oscillations in time allocations. Conclusions: The findings demonstrate that managerial time allocation should be supported by adaptive, uncertainty-aware decision support systems rather than static schedules or purely reactive adjustments. By formalizing time allocation as a dynamic portfolio problem, this work contributes to the decision support and management science literature and provides a foundation for intelligent systems that evolve alongside managerial environments.
BrainFusion: Deep Learning for Early Detection and Grading of Brain Tumors Using Medical Imaging and Clinical Data
Brain tumors are still one of the most dangerous and deadly types of cancer. Early detection and correct grading are very important for effective treatment. This study presents a dual-stage artificial intelligence framework that utilizes advanced transformer-based deep learning architectures, specifically the Swin Transformer and Vision Transformer (ViT), for the early detection and grading of brain tumors through MRI images and clinical metadata. Our approach takes advantage of these transformer models' better feature extraction and contextual representation, which is not the case with traditional CNN-based methods. The framework consists of two core components: (1) a transformer-based classifier for detecting tumor presence in 2D MRI slices, and (2) a multimodal fusion model combining imaging features and clinical data (age, gender, tumor type, grade) to predict tumor grade (HGG vs LGG). We also made a web interface powered by Gradio that lets users upload images and get a diagnosis in real time. This makes the model available to both researchers and doctors. We use publicly available brain MRI datasets like BraTS and TCGA to create and test our model. show that the Swin Transformer is better than current CNN-based methods at finding tumors and classifying grades, with an accuracy of up to 98.3% and 95.2%, respectively. These findings validate the effectiveness and promise of transformer architectures and web-based AI tools in enhancing neuro-oncological diagnosis.
Technology Acceptance and Consumer Behavior: A Study of Online Shopping in Indonesia
Introduction: The growth of this information technology allows every individual to easily involve themselves in legal activities, such as buying and selling transactions. The rapid development of the internet has had a significant impact on various aspects of our lives, facilitating interaction, communication, and trade efficiently and economically, even involving people from all over the world. By using TAM, buying and selling goods is getting easier. Objectives: This study aims to determine the effect of ease of use, trust, web quality, and perceived usefulness on consumer purchasing decisions in the TikTok online shop on Generation Z in Central Java, Indonesia. Methods: This study adopts a descriptive quantitative method, designed to illustrate the qualities (features) of a scenario through an impartial lens. This incorporates gathering and scrutinizing numerical information, alongside the application of statistical assessments. The analysis technique used in this study is Partial Least Squares (PLS), which is processed using Smart PLS software version 4.0. The analysis includes descriptive statistical analysis, outer model testing, inner model testing, and hypothesis testing. Results: For Business Owners/Sellers on TikTok Shop: Trust significantly influences purchasing decisions. Therefore, sellers need to build consumer trust through clear product information, a transparent returns system, and maintaining a reputation through positive customer reviews. For TikTok Platform Managers: Continuous improvements to the user interface (UI) and user experience (UX) are needed to ensure consumers feel comfortable shopping, even if ease of use is no longer a dominant factor. For Consumers: The findings of this study can serve as a basis for consumers to be more selective in choosing online sellers, by considering aspects of platform trustworthiness and quality rather than just ease of use. Consumers are also encouraged to utilize security features and read reviews from other buyers before making a transaction.For Academics and Researchers: This study emphasizes the importance of trust and web quality factors in the online shopping behavior of Generation Z. These implications can be used as a reference for developing digital marketing models that are more focused on building consumer loyalty and a sense of security. Conclusions: This study applies the Technology Acceptance Model (TAM) to analyze the behavior of Generation Z consumers in Indonesia using TikTok Shop, which is still rarely studied in the context of the Indonesian local market. From an academic perspective, this research expands the literature on the application of TAM to online shopping behavior by adding trust and web quality variables. Practically, it provides insights for businesses to prioritize trust and web quality in their e-commerce strategies.
Women’s Empowerment in the IT Sector under Remote Work: The Role of Organizational Commitment, Job Loyalty, and Work–Life Balance Policies
Introduction: Work from home gets the whole world flocked into their home. Everyone has to do their own work confine at home. The work of women increased by manifold. In this situation it was of great interest to know how the working women balances her work and life and whether their job loyalty (JL) and organizational commitment (OC) is influenced by the companies work life balance policies (WLBP). Objectives: The primary objective of this study is to examine how organizational work–life balance policies influence organizational commitment and job loyalty among working women in the software industry during the work-from-home period. Methods: This study is carried out in 3 software firms of India, by using primary data collected online from women respondents through a structured questionnaire. Data are analysed by correlation and regression method. Results: The study explores that there is a significant positive correlation between WLBP with OC and JL. On the basis of the results the study proposes a conceptual model on WLBP, OC and JL. Conclusions: The study has significant practical implication in organisational policy formulation, human resource management and organisational effectiveness.
The Role of Artificial Intelligence in HRM–Marketing Integration: A PLS-SEM–Informed Review
AI is revolutionizing HRM and marketing through data-driven decision-making, predictive analytics, and automated workflows. Academic research on AI-driven HRM–Marketing integration has grown as companies adopt AI-enabled employee and customer strategies. Partial Least Squares Structural Equation Modelling (PLS-SEM) is the leading analytical method for modelling complex, multidimensional, and often formative phenomena, including AI capability, people analytics, customer analytics, and cross-functional performance. However, a systematic evaluation of PLS-SEM in this intersection is absent. This systematic literature review synthesizes 97 peer-reviewed studies (2015–2025) from Scopus and Web of Science using PRISMA 2020. The review highlights internal–external experience alignment, cross-domain predictive analytics, and algorithmic coordination as AI-enabled integration pathways. While PLS-SEM is well-suited for capturing these relationships, findings reveal methodological inconsistencies, such as weak justification, limited algorithm configuration reporting, insufficient predictive assessment, and underuse of advanced techniques (e.g., higher-order constructs, PLSpredict, FIMIX-PLS, endogeneity controls). This paper demonstrates how AI promotes cross-functional HRM–Marketing alignment and offers best practices to improve the rigour, transparency, and predictive value of future PLS-SEM–based research.
Artificial Protozoa Optimizer for Enhanced Robust and Secure Image Watermarking
Digital image watermarking faces significant challenges in balancing imperceptibility and robustness under diverse distortions. With the growing prevalence of deepfake and image manipulation technologies, preserving the authenticity and integrity of digital images has become increasingly critical. This paper presents a novel hybrid optimization-based framework for digital image watermarking, integrating the Arnold Transform (AT), Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), and the Artificial Protozoa Optimizer (APO). The watermark, such as a QR code, is first encrypted using the Arnold Transform and embedded into the low-frequency sub-band via SVD, while the APO determines the optimal scaling factor α through an objective function combining the Structural Similarity Index Measure (SSIM) and Normalized Cross-Correlation (NCC) ensuring an optimal balance between imperceptibility and robustness. Experiments on a diverse set of images, including medical (e.g., MRI and chest X-ray) and standard benchmark images (e.g., Baboon, Peppers), demonstrate that the proposed framework achieves high SSIM and NCC, along with low Learned Perceptual Image Patch Similarity (LPIPS) and Bit Error Rate (BER). These metrics ensure accurate watermark extraction under attacks such as JPEG compression, noise, and rotation, while supporting ownership protection and multimedia authentication.
Automating Healthcare Authorization Letters Through Intelligent Systems
Healthcare organizations face significant challenges in managing authorization correspondence, requiring precise coordination across multiple system components while meeting stringent regulatory requirements and ensuring accessibility for diverse patient populations. Traditional manual processes for generating approval and denial letters create substantial administrative burdens, with physicians dedicating nearly two full business days weekly to prior authorization activities, leading to workflow bottlenecks and inconsistent communication quality. Modern healthcare platforms address these challenges through sophisticated automation frameworks that integrate artificial intelligence technologies to streamline correspondence workflows while maintaining regulatory compliance. The implementation of service-oriented architectures built on health informatics standards enables seamless interoperability between authorization management systems and correspondence engines, facilitating automated extraction and population of member letters with complete accuracy. For denial letters specifically, generative artificial intelligence transforms complex clinical rationale into member-accessible language targeting sixth-grade reading comprehension levels, addressing critical health literacy barriers that impede patient understanding of coverage decisions. Natural language processing technologies analyze clinical documentation and member case histories to generate personalized, contextually relevant explanations that synthesize information across the entire authorization lifecycle. Responsible implementation incorporates human-in-the-loop architectures where qualified supervisors review AI-generated content for accuracy, appropriateness, and cultural sensitivity before final distribution. This hybrid model optimizes resource allocation by automating routine translations while reserving human expertise for complex scenarios requiring professional judgment. The integration of intelligent automation with bidirectional communication patterns creates closed-loop systems that track correspondence from generation through postal delivery, establishing comprehensive audit trails necessary for regulatory reporting and quality assurance initiatives essential for value-based care arrangements.
Strategic Go-to-Market Models in Enterprise Software: Free, Trial, and Proof-of-Concept Approaches
Customer acquisition strategic frameworks through corporate software spending have gradually changed, and nowadays the enterprises decide among one of the three: freemium models, time-limited trials, and proof-of-concept implementations based on the sophistication of the product, the characteristics of the target audience, and the limitations of the operations. Every roll-out plan is functioning under a different control system of restrictions - in the case of a freemium product, the limitations are related to the features or the capacity of the product; the temporal boundaries define the duration of the implementation of the trial; and the scope of the validation criteria for enterprises is what defines a proof-of-concept engagement. Digital transformation agendas have, on the one hand, changed the conventional software delivery methods and, on the other hand, have opened the way for self-service adoption mechanisms and have eliminated the friction points that usually hinder market penetration. The emergence of artificial intelligence-integrated software introduces fundamental shifts in deployment economics, particularly regarding computational resource allocation and cost recovery mechanisms. Traditional customer acquisition paradigms require substantial modification when inference costs and token-based pricing models become primary economic considerations. Transformer architectures process sequences through attention mechanisms that scale computational requirements with context length, creating direct relationships between usage patterns and operational expenses. Freemium models remain viable for AI applications, but demand careful capacity management through token allocation limits and query frequency restrictions. Proof-of-concept engagements experience the most substantial impact due to extended validation durations and intensive computational workload patterns characteristic of enterprise requirements, driving preference for paid validation structures with explicit cost recovery mechanisms and contractual commitment frameworks. In AI-native enterprise software, every user interaction burns GPU cycles and can execute actions in external systems. This changes how thinking about Free, Trial, and POC motions should proceed. In AI-native enterprise software, go-to-market strategy is no longer just a question of friction and conversion—it is also a question of inference cost, safety, and trust. Each user interaction consumes non-trivial GPU resources and, in the case of AI agents, may trigger actions across external tools and data sources. This article compares freemium models, time-limited trials, and proof-of-concept engagements as GTM entry motions for AI-enhanced enterprise products, with particular emphasis on inference economics, token-based pricing, and safety validation during customer evaluation.
Optimizing Full-Stack Application Performance through End-to-End Observability and Real-Time Diagnostics
In full-stack applications, performance problems are often not limited to the frontend or backend but can affect the entire stack as distributed architectures become increasingly common. When an issue arises in one component, it may quickly cascade throughout the application stack, leading to performance degradation. Customary application monitoring tools tend to provide visibility into different system layers in isolation. Because distributed systems can involve many interconnected components, end-to-end observability seeks to overcome these shortcomings by providing a single view of the application throughout the entire application stack. Telemetry is typically divided into three groups: logging, metrics, and distributed tracing, which provide different views of system behavior. Standardized telemetry collection allows interoperability between heterogeneous technology stacks, and effective instrumentation is the foundation of observability. Frontend instrumentation provides real user monitoring, whereas backend instrumentation provides server-side metrics, such as request latency distribution and the percentage of requests that experience failures. By relating user-reported latency to the backend processing time, targeted optimizations can be performed. Observability platforms use machine learning algorithms to uncover patterns that are overlooked by threshold-based alerts. Large language models are promising candidates for automating root cause analysis in complex cloud systems.
Examining the Economic and Business Impact of Cloud-Based Disaster Recovery
Cloud-based disaster recovery has emerged as a transformative alternative to conventional infrastructure protection strategies. Traditional disaster recovery models demanded substantial capital investments in redundant physical facilities. Secondary data centers remained idle during normal operations while consuming significant resources. The shift toward cloud-native recovery architectures addresses fundamental inefficiencies inherent in legacy protection frameworks. Consumption-based pricing models eliminate upfront hardware procurement requirements. Operational expenses replace capital expenditures through pay-per-use service arrangements. Site reliability engineering practices allow for automation in failure detection and response. Structures that can be allotted geographically throughout different availability zones can resist a regional disruption. Non-stop replication of statistics enables in reduction of the threat of information loss for the duration of a failover situation. Resource provisioning that is elastic can be compared to a dynamic capacity which is changing along with the business requirements. Scaling operations proceed without procurement delays or fixed capacity constraints. Resource scheduling algorithms optimize workload distribution across available infrastructure. Investment evaluation requires consideration of direct cost savings alongside risk mitigation value and strategic benefits. Decision support frameworks assist organizations in navigating adoption complexity through structured assessment mechanisms. The economic advantages of cloud-based disaster recovery extend beyond simple cost reduction to encompass improved organizational agility and enhanced competitive positioning.
Resource Optimization and Cost Management in Kubernetes Clusters: A Framework for Container Orchestration Platforms
The use of container orchestration platforms has changed the application deployment patterns, but cost management has remained an operational issue. Kubernetes environments are often resource inefficient due to over-provisioning, node fragmentation, workload imbalance, too much storage allocation, and high network transfer costs. This framework introduces systematic approaches to cost optimization in Kubernetes clusters, leveraging built-in visibility strategies, configuration tuning, and workload-based optimization strategies. The proposed approach integrates a cluster telemetry system and allocation tracking to create end-to-end cost visibility for containerized infrastructure. This optimization of resources can be achieved by right-sizing using utilization signals, vertical scaling policy, horizontal scaling policy, and bin-packing efficiency-enhancing techniques that minimize idle capacity by using dynamic provisioning policies. Mixed-instance node fleet deployment, the adoption of spot instances with disruption-aware scheduling, and long-term capacity planning based on savings plans are among the compute cost reduction methods. These strategies and practical tips help companies using Kubernetes to consistently save on infrastructure costs while ensuring that their workloads remain reliable, perform well, and meet compliance requirements during production.
Modern Data Platform Evolution: From Legacy Warehouses to Cloud-Native Ecosystems
The transition from monolithic, infrastructure-bound data platforms to modern, cloud-native designs signals a model shift toward higher levels of scalability, flexibility, and enterprise trust. Conventional, on-premises data warehouse techniques rely on tightly coupled architectures and batch processing, often leading to operational and performance challenges. Distributed computing frameworks enabled horizontal scaling and polyglot persistence patterns but added important complexity to the ecosystem. Cloud-native architectures improved the data platform by separating storage and computing, allowing for flexible resource use and the availability of managed services to reduce operational burdens. The lakehouse architecture brings together the advantages of data lakes, like flexibility, and the benefits of data warehouses, such as ACID transactions, schema enforcement, and detailed access control, while also allowing teams to manage their own data areas in line with company rules through federated data governance. Modern architectures use new technologies like embedded AI, streaming, and metadata-driven pipeline creation to help organizations build reliable, scalable, and affordable data systems that speed up decision-making, support various advanced analytics, and align infrastructure costs with business results across the distributed enterprise.
The Autonomous Financial Advisor: A Framework for AI-Powered Personal Finance Management
The article provides an overview of the framework to create an AI-based Personal Finance Advisor (AIPFA) that would democratize access to advanced financial advice. Conservative financial advice has long been limited to people with a great deal of wealth, and the available digital financial advice systems are mostly descriptive as opposed to prescriptive. The AIPFA system proposed changes to this paradigm by a multi-layered architecture, which involves the aggregation of data, an advanced AI engine based on a four-stage analytical maturity model, and personalization features in addition to a conversational interface. The key aspect in this framework is a well-developed ethical governance model with fairness, transparency, and user-centric optimization. The article describes every element of the architecture, discusses how descriptive analytics transforms into prescriptive analytics, sets ethical standards in autonomous financial advice, and provides a roadmap for gradual implementation. Through this framework, financial institutions will be able to provide personalized and proactive financial advice at scale and enhance customer relationships and close key financial wellness gaps among underserved people.
Expanding Ad Inventory Through New Surface Monetization: A Technical Analysis of Discovery Surface and Creator Content Surface Implementation
This article examines the technical implementation of advertisement integration into previously unmonetized surfaces within a major social media platform ecosystem. It details the engineering approaches, system modifications, and performance optimization techniques employed to monetize these surfaces while maintaining positive user engagement metrics successfully. The article investigates how the engineering team extended the advertisement rendering system to accommodate diverse creative formats tailored to unique display requirements, implemented backend eligibility framework extensions for surface-specific rule implementation, and established comprehensive real-time monitoring capabilities. Additionally, the article explores the sophisticated revenue attribution mechanisms developed for content creator monetization, including creator revenue sharing and eligibility determination algorithms. Through cross-functional technical integration across finance systems, organic surface protection mechanisms, and infrastructure scaling solutions, the implementation achieved successful monetization expansion without compromising user experience quality, demonstrating how careful technical planning, cross-functional collaboration, and data-driven decision-making can balance revenue objectives with platform integrity.
Identity and Access Management (IAM) Expertise and Organizational Success Stories
Identity and Access Management (IAM) has evolved into a strategic security discipline that manages digital identities and controls resource access across complex enterprise environments. This article examines core IAM components including identity lifecycle management, access governance frameworks, authentication mechanisms, and Privileged Access Management (PAM) systems that provide comprehensive security capabilities. Implementation success requires orchestrated organizational strategies addressing technical complexities, stakeholder engagement, and change management while establishing measurable metrics across security, compliance, and operational dimensions. PAM emerges as a critical component requiring enhanced security controls and behavioral analytics to protect high-value accounts. Regulatory frameworks including HIPAA, SOX, and GDPR significantly influence IAM design and operational procedures through sophisticated audit processes. Emerging technologies such as AI-driven behavioral analytics, cloud-native architectures, and zero trust models reshape IAM capabilities while creating challenges in hybrid cloud environments, IoT proliferation, and API security. Healthcare case study analysis demonstrates that successful implementations require executive support, phased approaches, and alignment with digital transformation initiatives. Organizations treating IAM as a strategic enabler rather than compliance obligation achieve greater value realization and competitive advantages. Future IAM success depends on continuous adaptation to technological evolution, threat landscape changes, and regulatory developments while maintaining focus on business objectives and user productivity requirements.
Synergizing Human Expertise and Enterprise Architecture: Collaborative Innovations in Healthcare, Manufacturing, and Scientific Research
The unprecedented technological complexity confronting companies nowadays calls for advanced frameworks that link day-to-day operational reality with infrastructure capabilities. Connecting technological systems with corporate objectives across several industries, Enterprise Architecture has become an essential discipline. This article examines how Enterprise Architecture works synergistically with specialized domain knowledge in healthcare, manufacturing, and scientific research settings. Healthcare institutions face significant hurdles when deploying Electronic Health Record systems, requiring a careful balance between technical specifications and clinical workflow practicalities through a collaborative design that incorporates clinician perspectives throughout the architectural development process. Manufacturing industries undergo digital transformation through Industry 4.0 technologies, with cyber-physical systems and intelligent production frameworks gaining substantial advantages when production experts contribute to establishing monitoring parameters, alert thresholds, and analytics capabilities. Flexible yet organized architectures that support exploratory workflows are necessary in scientific study environments; these structures also enable broad data sharing and the allocation of computational resources across institutional barriers. Instead of secondary stakeholders, the collaborative model treats domain experts as active participants in architectural design, thereby producing answers that demonstrate both technical strength and practical applicability. Companies that utilize human-centric Enterprise Architecture techniques consistently demonstrate significantly better implementation success rates, higher user adoption, fewer post-deployment adjustments, and improved alignment of technical capabilities with operational demands. Sociotechnical systems theory and participatory design methodologies furnish the theoretical foundation for collaborative architectural development, acknowledging the interdependence of social and technical components within organizational settings. Effective enterprise architecture deployment requires constant communication between domain experts and technical architects, ensuring that technical frameworks serve human needs while leveraging computational power. This cooperative integration transforms Enterprise Architecture from a purely technological discipline into a strategic catalyst for operational excellence and organizational innovation, spanning healthcare, manufacturing, and research fields. While this article focuses on healthcare, manufacturing, and scientific research as primary case studies, the collaborative EA principles and methodologies discussed are broadly applicable across diverse industries, including finance, telecommunications, retail, government, energy, transportation, and education sectors.
Federated Learning for Industrial IoT Networks: Privacy-Preserving AI Across Distributed Manufacturing and Energy Systems
Industrial Internet of Things networks spread across manufacturing facilities and energy infrastructure generate enormous operational data streams. Traditional centralized processing creates bandwidth constraints and raises serious concerns about proprietary production information. Federated learning provides a transformative paradigm for collaborative model development without centralizing raw operational data. Data sovereignty remains protected while collective intelligence flourishes through distributed training mechanisms. Operational technology environments present unique challenges distinct from consumer-oriented applications. Equipment heterogeneity spans multiple manufacturers and machine generations. Data distributions vary significantly based on facility-specific operational patterns. Stringent cybersecurity demands require robust protection against adversarial manipulation within multi-party collaborative frameworks. The article presents comprehensive federated learning architectures engineered for industrial constraints and organizational complexities inherent within manufacturing and energy sector deployments. Hierarchical designs mirror natural industrial network structures through coordinated layers spanning edge devices, facility coordinators, and enterprise aggregation platforms. Advanced aggregation algorithms handle non-independent and identically distributed data patterns prevalent across manufacturing settings. Personalized learning mechanisms and clustering-based techniques address statistical heterogeneity challenges effectively. Differential privacy methodologies safeguard sensitive operational variables while maintaining model utility through adaptive noise calibration strategies. Blockchain-based coordination systems ensure transparent governance and equitable incentive distribution within industrial consortia. Byzantine fault-tolerant security protocols defend against malicious participants attempting to corrupt collaborative model development. Edge computing integration enables localized training and inference within resource-constrained industrial hardware deployments. Technical foundations emerge for privacy-preserving distributed intelligence supporting predictive maintenance, quality control optimization, and energy consumption prediction across diverse industrial ecosystems.
The Critical Role of Data Engineering in Modern Analytics
Data engineering serves as the cornerstone of contemporary analytics infrastructures, enabling organizations to efficiently collect, process, and deliver data throughout enterprise ecosystems. This technical article explores how data engineering has evolved from traditional batch processing paradigms to sophisticated real-time architectures that support mission-critical business operations across industries. The global datasphere continues expanding at unprecedented rates, with creation and replication significantly outpacing available storage capacity, creating both challenges and opportunities for data professionals. As organizations increasingly depend on timely, accurate insights for competitive advantage, robust data engineering practices have become essential strategic assets rather than merely technical capabilities. The article examines implementation methodologies, including pipeline automation, ETL/ELT processes, integration frameworks, orchestration platforms, and scalability considerations that form the architectural foundation of modern data ecosystems. Further sections explore cloud-based data engineering's transformational impact on operational economics, artificial intelligence's dependence on well-structured data infrastructure, and the quantifiable business impacts of mature versus underdeveloped data engineering capabilities. For organizations navigating digital transformation initiatives, understanding these fundamental principles and applications provides the foundation for leveraging data as a strategic asset.
Intelligent Trade Lifecycle Orchestration Using Agentic AI for End-to-End Trade Automation
The securities trading around the world is facing an increased complexity challenge of fragmented infrastructures, dependence on manual oversight, and exception-based management systems. The global trading ecosystem continues to undergo rapid digitization driven by algorithmic execution, cross-venue liquidity fragmentation, real-time settlement mandates, and increasingly complex regulatory obligations. Traditional process automation—consisting of process automation, static workflows, and rule-driven validation—offers productivity gains but lacks the adaptive intelligence to manage the dynamic, interdependent, and high-variability nature of trade lifecycles. Intelligent Trade Lifecycle Orchestration introduces an Agentic AIs orchestrating framework that operates autonomously and manages the establishment of trade, verification and matching, settlement and reconciliation, using self-learning, collaborating agents. The novelty of the framework lies in multi-agent architecture, dynamic policy learning, and predictive exception orchestration, making it possible to make changes in the workflow to move it to cognitive coordination. Multi-agent systems exhibit distributed problem-solving ability in which autonomous agents interact with each other via negotiations, cooperation, and competition to accomplish group goals that are beyond the reach of each agent. Deep learning reinforcement algorithms allow agents to improve policies as they are applied by observing the result of the policies, and find out the best strategies by learning them based on experience instead of adhering to rules. Asynchronous training techniques can enable the interaction of multiple agents with distinct instances of the operational environment at the same time, and can gain experience in a very short time frame with steady convergence characteristics. Event streaming systems' low-latency process high data volumes of trade events, updating market data, trade announcements, and settlement instructions as they occur. The Trade Ontology Layer defines standard semantics of trade status, settlement instructions, identities of counterparties, and regulatory requirements, so the interpretation of information can be common to all agents. Experimental assessments of anonymized data on institutional trading indicate that the trade exceptions, settlement latency, and the need to use manual intervention are significantly reduced relative to traditional robotic process automation and predictive machine learning models. The framework has a high level of audit traceability and has been proven to be more effective during market stress times due to its adaptive learning abilities. ITLO offers frameworks for building self-regulating financial ecosystems that are flexible, interpretable, and regulatory sufficient in changing market terms. Unlike classical automation (e.g., RPA, workflow scripts, rule-based engines), ITLO employs autonomous agents capable of negotiation, cooperation, competition, long-horizon reasoning, real-time learning, and cross-domain decision orchestration.
Developing Scalable Technical Readiness Frameworks: An Approach to Worldwide Engineer Capability Building
Multinational support operations frequently encounter difficulties maintaining uniform technical capabilities among dispersed engineering teams. This article presents actionable methodologies for constructing, expanding, and sustaining effective readiness initiatives targeting cloud engineering professionals. The article examines proven practices from extensive enablement programs, with particular attention to blended learning approaches, worldwide subject matter expert coordination, and iterative enhancement processes. A comprehensive readiness structure is introduced, merging modular educational components, practical scenario laboratories, and evidence-based feedback systems to guarantee flexibility and quantifiable learning achievements.
Event-Driven Platform Engineering: Building Always-On Consumer Systems with Exactly-Once Guarantees
Event-driven platform engineering has emerged as the foundation for modern always-on consumer systems across travel booking, ride-hailing, e-commerce, and financial services sectors. This article explores architectural principles for building platforms capable of processing high-volume event streams with sub-second latency, strict ordering guarantees, and exactly-once processing semantics essential for business-critical operations. Moving beyond traditional monolithic and batch processing paradigms, event-driven architectures provide loose coupling, temporal decoupling, horizontal scalability, and built-in resilience. The article explores log-based event ingestion, idempotent processing patterns, schema evolution strategies, and mechanisms for handling late-arriving data. It presents implementation techniques for achieving exactly-once semantics in distributed environments, including the transactional outbox pattern and change data capture pipelines. A reference architecture demonstrates these principles in action, with practical implementation guidelines covering design, deployment, and operational considerations.
Camera-Based Depth Perception for Precision Agriculture: A Software-Defined Approach to 3D Scene Understanding
Autonomous agricultural systems need strong three-dimensional perception skills to carry out specific tasks such as selective crop intervention, weed detection, and avoidance of obstacles. Existing solutions are based on active depth sensing (LiDAR/radar) that provides accuracy at the cost of significant economic, operational, and environmental factors limiting its use in a variety of farming environments. As this article will show, passive camera-based depth perception can perform equally as well at a fraction of the cost in hardware, power, and environmental sensitivity. The fundamental novelty is the systematic combination of multi-view stereo geometry, learning-based refinement, and confidence-conscious orchestration into a software-based system that is optimized to work in agricultural settings. This identification of passive imaging as the main sensing modality, as opposed to supporting active sensors, allows the ongoing performance improvement via algorithmic improvement and retrofit integration to already existing machinery, where redesigning hardware is economically infeasible. Multicrop species validation over several growth cycles using camera-only systems has shown that these systems can be used to provide acceptable depth accuracy, high temporal stability in the presence of mechanical vibration, high crop detection accuracy, and long field operation with long deployment cycles. These performance attributes, together with the huge economic benefits, make camera-based depth perception a viable and scalable alternative to active depth sensing as a means of precision agriculture.
Active-Active DNS Resiliency Across Two Providers: Architecture, Performance, and Operational Efficiency
Digital service delivery has undergone substantial transformation as organizations extend their operational footprint across international markets. Contemporary users anticipate seamless connectivity independent of geographic position, network volatility, or underlying infrastructure complications. Conventional single-provider or Active-Passive DNS failover methodologies demonstrate increasing inadequacy when addressing these requirements, primarily attributed to record propagation latencies, initialization challenges, and concentrated provider reliance. Active DNS implementation utilizing dual autonomous DNS providers resolves these constraints through concurrent traffic servicing from multiple operational endpoints while preserving provider-tier redundancy. Rather than establishing a primary DNS provider designation with secondary backup infrastructure, both providers simultaneously respond to queries, allocate traffic loads, and execute routing determinations. This dual-provider Active-Active framework achieves near-instantaneous recovery intervals, eliminates DNS provider dependency, and advances international performance through proximity-driven routing. Additionally, this article guarantees that complete deployed infrastructure participates in production operations rather than maintaining dormant status. This article investigates how dual-provider Active-Active DNS reinforces availability parameters, advances performance characteristics, and strengthens operational effectiveness through sophisticated routing mechanisms and resilient architectural frameworks.
Autonomous Platform Engineering: A Framework for Eliminating Human-Induced Latency in Cloud Operations
Many enterprise cloud operations today still rely on human remediation or ticketing, which can lead to increased time to resolution. This architectural effort is to move enterprise cloud operations from human-driven DevOps approaches to policy-driven, closed-loop autonomous operations. It seeks to eliminate talent shortages and queuing within hyperscale cloud growth by enabling self-healing autonomous infrastructure through agentic AI systems. These are goal-driven reasoning agents acting within a governance policy frame. Platform engineering is more than automating rules to remove manual tasks. Platform engineering enables intelligent agents to autonomously determine and execute scaling, security mitigation and compliance as a function of defined scenarios. Further, it separates autonomous versus human-in-the-loop configurations, and quantifies smart behavior. It establishes operational patterns with levels of performance that no human could achieve. By eliminating human-induced latency for routine operational activities and limiting human involvement to high-level decision making and non-routine situations, it enables operational velocity and operational reliability that are impossible with customary approaches. This article helps organizations prepare their environment to safely and effectively use autonomous systems against production workloads, including providing technical and governance frameworks.
Modernizing U.S. Federal Reserve Reporting Through Infrastructure-Led Transformation
The U.S. Federal Reserve reporting systems are a critical part of the financial infrastructure. They have to be perfectly accurate, strictly timely, fully auditable, and massively scalable. They should also absorb regulatory changes and infrastructure impacts. This article describes an infrastructure-led transformation model for Federal Reserve reporting platforms. Therefore, improvements made in the compute resources, data pipelines, storage infrastructure, and operational controls determine platform resiliency more than the reporting logic. These changes have resulted in substantial increases in processing throughput, reductions in reporting cycle time, and increased data accuracy levels. Furthermore, reduced unplanned downtime during peak regulatory reporting periods. Infrastructure engineering is an important driver of regulatory improvement and national economic viability. Modern platform architecture allows for much better accuracy, reliability, and growth in meeting national regulations, making compliance a built-in feature instead of just a series of tasks. Financial institutions gain capabilities to meet today's and future federal regulatory requirements and become better able to withstand operational disruptions with infrastructure-first architecture principles.
Agentic AI for Recruiting: Designing, Evaluating, and Governing Autonomous Hiring Workflows
Contemporary recruiting systems are reactive, relying on constant human labor for candidate outreach, interview coordination, and relationship building. Agentic AI offers revolutionary potential by providing autonomous agents that perform end-to-end hiring workflows while remaining compliant with organizational policies and legal constraints. The overall architecture decomposes recruiting into specialized agents responsible for sourcing, outreach, interview orchestration, screening analysis, feedback synthesis, and offer coordination, operating over shared memory with auditable actions and explicit guardrails. Retrieval-augmented generation grounds decisions in policy and role specification, while structured tool calls enable integration with applicant tracking systems and scheduling platforms. Human-in-the-loop controls support approval gates, uncertainty escalation, and bias-aware constraints. Evaluation includes efficiency metrics, quality assessments, compliance monitoring, and candidate experience measurement. Characteristic failure modes include hallucinated justifications, over-automation removing necessary human judgment, and unfair exclusion amplifying demographic disparities. The governance mechanisms are policy-as-code, end-to-end logging, end-to-end fairness, and minimalism of data. The framework can enable the scalable and reliable use of recruiting agents and accountability, transparency, and equity considerations required to support responsible automation in high-stakes employment situations.
AI Governance for Third-Party Models: The Compliance Blind Spot in Vendor AI
As organizations rapidly deploy AI to govern many core business processes, third-party and vendor AI models are increasingly used in healthcare, financial services, manufacturing, and public sectors. While organizations invest large budgets in governing their internally developed AI, vendor AI often lacks any governance, creating a compliance blind spot which can result in many risks and violations. This governance gap is particularly critical from a cybersecurity perspective, as vendor AI systems introduce unique attack surfaces including adversarial manipulation vulnerabilities, data exfiltration risks through model APIs, and supply chain security weaknesses that traditional security controls cannot adequately address. Regulatory regimes fully assign liability to organizations deploying AI, regardless of whether the organization developed the AI in-house, or purchased it. Because organizations lack technical access to the underlying components and external behavior of vendors' AI, they cannot scrutinize vendor AI for security vulnerabilities, model poisoning attempts, or backdoor exploits. This creates an intrinsic paradox: Organizations fully own responsibility for systems that they cannot fully control, audit for security flaws, nor understand. We propose a Third-Party AI Governance Framework based on systematic vendor AI classification based on decision criticality and regulatory ramifications, shared accountability architectures based on vendor-client responsibility, audit-ready transparency standards that require full documentation of all model attributes including security testing results, and continual oversight mechanisms beyond procurement-time evaluation. Organizations that cannot establish sufficient control over vendor AI security posture are likely to experience regulatory enforcement actions, security breaches, lawsuits, and loss of stakeholder trust. By contrast, showing and proving control of vendor AI through third-party AI governance may allow an organization to survive and even give it a competitive edge.
AI-Driven Predictive Analytics for Ethical and Efficient Credit Union Collections: Explainability, Fairness, and Member-Centric Recovery Optimization
Delinquent account portfolios present a major operational challenge in consumer finance businesses, requiring the trade-off of risk management and member experience. Static rules-based collections solutions focusing on retrospective (rather than potential) risk profiles often do not fit with an evolving risk profile, leading to inefficient use of resources and a poor member experience. This article describes how AI-based predictive analytics can improve credit union collections by more accurately predicting delinquency, using behavioral segmentation, and better targeting interventions in an ethical, regulatory-compliant, and responsible way. It outlines probability estimates, progression analysis, and actionable segmentation and intervention as foundational modeling approaches for creating predictive models. Explainable AI approaches have been advocated to ensure transparency and auditability, fairness monitoring protocols for algorithmic bias, and human-in-the-loop decision-making to maintain professional discretion and accountability. The evidence demonstrates that institutions achieve better operational results through their essential need to balance accurate predictions with their governance requirements. The operational improvements benefit members by creating better financial access and trust in the organization through its fair and open repayment methods, which focus on member needs.
From Monolith to Microservices: Architectural Strategies for High-Availability Database Re-platforming
The transition from monolithic architectures to microservices-based systems represents a fundamental paradigm shift in enterprise software development, particularly for organizations managing complex financial applications and database infrastructures. This article examines comprehensive architectural strategies for migrating legacy monolithic applications to distributed microservices architectures while simultaneously modernizing database platforms to achieve high availability and operational resilience. The article explores domain-driven decomposition methodologies that identify natural business boundaries and bounded contexts within existing systems, enabling effective service granularity decisions that balance independence benefits against distributed system complexity. Containerization and cloud-native technologies emerge as critical enablers, providing lightweight deployment mechanisms, orchestration capabilities, and dynamic resource allocation that significantly enhance system scalability and operational efficiency. Performance optimization techniques including query optimization, automated indexing strategies, polyglot persistence patterns, and infrastructure-as-code practices are analyzed to demonstrate how organizations can maintain optimal database performance across distributed microservices while managing data consistency and service dependencies. The article synthesizes research findings and empirical evidence to provide practitioners with actionable strategies for navigating the complexities of architectural transformation, addressing technical challenges in service orchestration, data architecture redesign, and resource management that are essential for successful microservices adoption in production environments.
Theoretical Frameworks for API-First and Shift-Left Quality Engineering in Microservices Architectures
Microservices architectures have shifted the model of how quality is assured. Specifically: end-to-end screening strategies used in monolithic architectures are not viable because UI centric approaches have delays in defect discovery; and resolving architectural defects post integration requires orders of magnitude more effort than resolving defects found at the level of service contracts. API first testing provides mechanisms for coping with the unmanageable exponential growth in possible test scenarios that arise from service dependencies in microservice architectures and other distributed systems. Contract-driven development specifies service interfaces in an executable way as provider-consumer contracts and generates testable contracts that enforce contract-test-first as an independent way to evolve and deploy services. Consumer-driven contract testing breaks up validating a contract into discrete steps, where consumer tests generate compatibility specifications as contract files and provider tests replay requests and responses against APIs. Defect seeding experiments show that an important percentage of integration defects can be detected in the consumer, provider, and HTTP-structural categories, although value range changes are a limitation of this method. The underlying economic rationale for shift-left quality activities is an exponential relationship between defect discovery delay and defect cost. Early-discovered defects are cheaper to fix than those found in a late phase of the software life cycle (after product deployment). Empirical studies suggest that shift-left quality activities can considerably lower defect levels, cost of rework, and lead-time, if a design-time validation philosophy is substituted for system test-time verification.
Secure Multi-Cloud Architectures for State Governments: A Governance-First Approach
State governments increasingly adopt multi-cloud strategies to enhance service delivery and reduce vendor dependencies, yet security governance remains fragmented across platforms. This article presents a governance-first architecture framework specifically designed for government multi-cloud environments that establishes centralized identity management, unified policy enforcement, and integrated compliance monitoring as foundational elements preceding workload deployment. The framework addresses critical security challenges, including fragmented identity and access management systems, inconsistent logging and monitoring capabilities, policy drift across platforms, and complex regulatory compliance requirements. Implementation follows a structured four-phase deployment strategy encompassing foundation establishment, identity integration, policy harmonization, and advanced governance capabilities. The governance-first architecture transforms traditional reactive security approaches into proactive governance-centered strategies that treat identity as the primary control plane for all multi-cloud operations. Technical implementation leverages cloud-native mechanisms while maintaining consistency through centralized governance policies that automatically translate into platform-specific configurations. Cost-benefit evaluation demonstrates substantial long-term operational benefits, including reduced security incidents, improved compliance audit efficiency, and decreased administrative overhead despite significant upfront investment requirements. The article concludes that governance-first architecture enables State governments to maintain security consistency while leveraging diverse cloud capabilities, ultimately supporting enhanced citizen services while meeting stringent regulatory requirements.
When Systems Are Uncertain: Human Reliability Engineering in AI-Driven Production
Existing work on reliability engineering is generally based on a deterministic model of the world, and interpretable modes of failure. Such assumptions are not valid for production AI systems, motivating the discussion of the model change from machine-centric to human-centric reliability constraints. The latter are defined by human operators' cognitive load, trust calibration, and interpretation of probabilistic signals. Challenges include cognitive overload from non-deterministic failure diagnosis, trust in automated incident response, alert design for probabilistic systems, investigation of opaque failures, and maintaining human agency in automation. These challenges illustrate that the reliability of AI systems requires a focus on human-machine cognitive collaboration, rather than simply a focus on performance. In this context, observability, alerting and automation can be reengineered to the cognitive limits of humans and to prevent skill loss. From a sociotechnical perspective, reliability can be understood as an emergent property of human-AI interaction. This provides the new basis for managing uncertainty in production environments beyond the limits of deterministic models.
Cognitive Middleware Orchestration: A Human-AI Framework for Distributed Data Consistency
With the advent of distributed microservices-based database architectures, polyglot persistence (where services are expected to be mostly independent from each other), and heterogeneous data stores, consistency across data stores has become a fundamental problem that existing tools do not adequately solve, particularly for larger context-driven business and compliance processes. This article develops a cognitive middleware orchestration framework for the symbiotic cooperation of artificial intelligence systems and human domain experts for resolving conflicts in data consistency within distributed systems. The proposed framework extends the customary orchestration with a clever middleware layer that observes the data streams and includes machine learning models to detect and classify conflict patterns. In conflict detection, interactive decision interfaces are provided to human experts. Suggested decisions to resolve each conflict are offered via the AI, and the human expert provides business justification for their chosen decision to support adaptive learning engines. As a result, the framework realizes progressive autonomy models, which increase the autonomy of the AI based on the assessment of the AI performance while keeping humans in the loop for the most critical decisions. The contributions advance the theory of consistency management and realize learning-enabled service meshes within cloud-native environments.
Digital Transformation of Mortgage Lending: Emerging Frameworks for Fractional Ownership, Subscription Payments, and Embedded Finance
Technological innovation is fundamentally transforming the residential mortgage business by removing conventional obstacles of accessibility of homes and service provision. Management systems based on gamification use the principles of behavioral economics for mortgage servicing and turn the routine payment into an interactive process that allows increasing borrower involvement and financial literacy and creates significant behavioral data. Fractional ownership models implemented with blockchain and distributed ledger technology and smart contracts enable the democratization of real estate investment by alleviating capital requirements and establishing markets of liquid property interests in small portions but still customized to regulatory frameworks and governance infrastructure. The payment systems of subscriptions combine the mortgage payments with the property taxes and insurance into fixed monthly payments, resembling models of consumer-oriented technologies services, which attract demographics that want to have stable housing expenses. Platform-based mortgage services directly integrate financing experiences into property discovery experiences via API-based integrations, creating a less transactional friction homebuying experience and speeding up the homebuying timeline. All of these innovations transform the nature of competition in mortgage industries, present new models of risk distribution, and are more digital infrastructures, which are driving the wider trends of financial technology with an emphasis on user experience, accessibility, and automated service provision.