International Journal of Intelligent Systems and Applications in Engineering

A Hybrid Deep Learning Approach for Predicting Patient Health Outcomes in Mobile Healthcare Applications

Akhil Tirumalasetty — Sat, 14 Feb 2026 00:00:00 +0000

Along with mobile health care apps, deep learning has transformed health monitoring and prediction. A hybrid approach based on deep learning for mobile health systems for precise patient health outcome prediction is proposed in this paper. It exploits Convolutional Neural Networks (CNN) to extract the features followed by Long Short Term Memory (LSTM) networks to learn from the sequential pattern for efficient analysis of the patients' vitals, past medical history and real-time sensor data. Also Attention Mechanism plays very significant role in highlighting important health parameters thus interprets and explains levels of data which helps in decision improvement through the model. We train the hybrid model on heterogeneous healthcare data and test it with accuracy, precision, recall and F1-score. The experimental results demonstrate significant benefits in terms of predictive consistency and real-time flexibility than traditional deep learning models. This framework could change the base of mobile healthcare applications to initiate early disease detection, personal treatment recommendations, and timely involvement in the patient journey that would facilitate healthier and more effective healthcare.

Efficient Large-Scale Data based on Big Data Framework using Critical Influences on Financial Landscape

Bhanu Prakash Paruchuri — Sat, 14 Feb 2026 00:00:00 +0000

One of the most recent commercial and technological concerns in the technological era is big data. Hundreds of millions of events occur on an ongoing basis. The financial sector is significantly involved in the computation of big data events. As a result, hundreds of millions of financial transactions occur in the financial industry each day. Financial practitioners and analysts perceive it as an emerging challenge in the data administration and analytics of a variety of financial products and services. In addition, financial services and products are significantly affected by big data. Determining the financial concerns that big data significantly affects is, thus, an important topic to research with the impacts. This paper used these concepts to show the current state of finance and how big data affects financial markets, institutions, internet finance, financial management, internet credit service companies, fraud detection, risk analysis, financial application management, and more. The connection between big data and economic aspects can be better understood by doing an exploratory literature review of secondary data sources. Because big data in finance is a relatively new concept, further research directions will be proposed at the end of this study.

A Physics-Informed Neural Network Framework for MHD Casson Ternary and Tetra Hybrid Nanolubricant Flow

Praveen Kumar U M, Venkata Sundaranand Putcha — Sat, 14 Feb 2026 00:00:00 +0000

The heat and mass transport properties of Casson hybrid nanofluids flowing across a stretched surface in the presence of thermal radiation, Joule heating, and a magnetic field are examined in this work. We look at two sophisticated nano-lubricant arrangements. , ZnO, and SiC nanoparticles suspended in engine oil make up the first ternary hybrid nanofluid. Graphene nanoplatelets (GNPs) are added to the ternary mixture to create the second tetra hybrid nanofluid. Comparing the effects of nanoparticle composition on energy dissipation mechanisms, flow behavior, and thermal conductivity is the aim. Joule heating, radiative heat flux, thermo-diffusion, and chemical reaction effects are all included in the mathematical formulation. The controlling nonlinear partial differential equations are reduced to a linked system of ordinary differential equations by means of appropriate similarity transformations. A Physics Informed Neural Network (PINN) method designed especially for nanofluid lubrication systems is used to solve these equations. By directly integrating the governing physical laws into the loss function, the suggested PINN architecture enables the simultaneous elimination of boundary condition errors and equation residuals. Computational efficiency and solution stability are improved by this two-way optimization. Also wed did Numerical Validation of the PINN Solver Comparing the tetra hybrid nanofluid to the ternary formulation, numerical results show that the former offers noticeably greater thermal enhancement and lower entropy generation. GNPs' remarkable heat conductivity and enormous surface area are primarily responsible for this performance enhancement. On the other hand, the ternary hybrid nanofluid shows moderate temperature gradients and comparatively constant viscosity behavior. For complicated nonlinear thermal-fluid problems in lubrication applications, the PINN framework provides a dependable computational tool with good convergence and prediction accuracy overall.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8084

Distributed AI Systems: Building Scalable and Safe LLM Orchestration Layers

Sahil Agarwal — Sat, 14 Feb 2026 00:00:00 +0000

Distributed artificial intelligence systems, a new model for integrating large language models with enterprise infrastructure, require orchestration layers to coordinate large models across heterogeneous computing environments. These orchestration frameworks address issues such as retrieving context, controlling execution, managing system state, and ensuring observability, improving the overall effectiveness of the deployment. Retrieval-augmented generation (RAG) is a major model for LLMs to complement model output with grounded information to reduce hallucinations, using hybrid retrieval architectures combining lexical and dense retrieval with multi-agent coordination patterns, organising specialised autonomous agents to decompose compositional reasoning problems into subproblems, and enabling efficient pinpointing of semantically relevant documents. Policy-aware execution mechanisms implement security functionalities, such as authorization gates and context sanitization pipelines, that respect zero-trust principles during inference via mutual authentication and encryption protocols. Fault tolerance mechanisms address probabilistic failures unique to language model inference, including token truncation and semantic coherence degradation. Scalability patterns employ horizontal and vertical strategies to maintain performance under variable workloads while preserving tenant isolation boundaries. This article presents architectural patterns, performance benchmarks, and governance frameworks for production-ready language model systems that meet enterprise goals for reliability, security, and regulatory compliance. This work is informed by production deployment patterns and operational metrics observed in large-scale enterprise language model systems, emphasizing practical applicability over purely theoretical analysis.

Numerical Solution of the 2D Cauchy–Riemann System Using Classical and Quantum-Inspired Finite Difference and Crank–Nicolson Schemes

Mitat Uysal, Aynur Uysal — Thu, 26 Feb 2026 00:00:00 +0000

The Cauchy–Riemann (CR) equations form the fundamental condition for analyticity in complex analysis and arise in potential theory, fluid mechanics, and electromagnetic field modeling. In this study, the two-dimensional Cauchy–Riemann system is solved numerically under prescribed Dirichlet boundary conditions using four approaches: (i) Finite Difference (FD), (ii) Quantum-Inspired Finite Difference (QI-FD), (iii) Crank–Nicolson (CN), and (iv) Quantum-Inspired Crank–Nicolson (QI-CN). Full mathematical derivations of discretization schemes are provided. The quantum-inspired schemes introduce amplitude-modulated update operators motivated by quantum probability dynamics. Comparative simulations demonstrate convergence behavior, stability properties, and error characteristics. Multiple graphical outputs including surface plots, contour maps, error heatmaps, and convergence curves are presented.

Designing Reliable Event-Driven Enterprise Platforms Using Apache Kafka

Chandramouli Holigi — Tue, 24 Mar 2026 00:00:00 +0000

Enterprise platforms in domains such as digital payments, supply chains, and customer engagement increasingly leverage event-driven architectures to achieve real-time data propagation, service decoupling, and horizontal scalability. Apache Kafka has emerged as a foundational element to build high-throughput, fault-tolerant messaging systems that can sustain event streams across distributed architectures. Kafka-based systems require discipline across delivery semantics, partitioning, consumer group coordination, back pressure, and schema evolution. Exactly-once semantics are achieved through idempotent producers and transactional APIs to avoid duplicate processing with throughput that is sufficient for production workloads at enterprise scale. Partition keys that match business rules help keep the order of transactions, while adjusting the number of consumers based on lag and controlling producer access help maintain system stability during different load levels. Schema compatibility enforcement via registry-driven governance keeps producers from accidentally publishing incompatible breaking changes to production topics. Together, these architectural and operational principles provide the durability, correctness, and resilience required from enterprise-grade event processing in the modern system of record when building Kafka-based platforms.

From Sampling to Population Testing: Continuous Audit Analytics for ICFR Effectiveness

Karishma Velisetty — Tue, 24 Mar 2026 00:00:00 +0000

Internal control over financial reporting has historically depended on periodic, sample-based testing methods that create measurable coverage gaps across high-volume transaction populations. The transition to continuous audit analytics represents a fundamental shift in assurance architecture—from discrete, interval-driven sampling to automated, population-level control testing executed in real time. This article examines the structural drawbacks of conventional sampling models, proposes a three-layer continuous audit architecture integrating deterministic testing, anomaly detection, and behavioral analytics, and redefines key controls within the context of algorithmic execution and machine learning-driven fraud detection. An implementation pathway progressing through foundation, build, operate, and optimize phases is presented alongside the operational governance metrics required to sustain continuous ICFR effectiveness. The convergence of enterprise resource planning infrastructure, big data analytics, and artificial intelligence has rendered full-population testing operationally deployable, compressing control failure detection timelines and strengthening the reliability of financial reporting assurance in ways that periodic audit cycles are structurally unable to achieve.

Designing High-Performance Distributed Systems for In-Memory Secure Data Processing in Cloud Security Analytics

Akhil Karrothu — Wed, 25 Feb 2026 00:00:00 +0000

The surge of cloud-based apps and advanced cyber threats has led to a huge demand for high-powered security analytics that can ingest and process enormous amounts of data in real time. Conventional disk-based centralized security analysis systems tend to have high latency, limited scalability and insufficient privacy of sensitive data. To cope with these issues, we introduce in this paper the design of a high-performance distributed in-memory secure data processing system for cloud security analytics. The proposed model employs distributed in-memory computing, parallel processing and secure data management techniques to support us with low-latency threat analysis and real-time analytics. Advanced security features, such as data encryption in memory, secure access management, and isolation across distributed nodes are included to maintain the confidentiality and integrity of data during analytics processing. The system is deployable in a scalable form factor across cloud platforms, and yet also achieves fault tolerance and resource efficiency. Experimental results show large savings in terms of processing, types response and scalability of traditional disk-centric security analytics platforms. The results show in-memory distributed processing can provide a viable platform for next-generation cloud security analytics, leading to faster threat identification, increased operation efficiency, and strengthened data protection in the ever-evolving cloudy world.

AI-Based Predictive Maintenance for General Aviation Aircraft

Sam Suseelan — Sat, 28 Feb 2026 00:00:00 +0000

Advances in artificial intelligence have brought many new possibilities into predictive maintenace. this happens especialy on general aviation. Predictive maintenance, which uses artificial intelligence to predict when machinery will break down, is revolutionizing how work needs to be done. It allows us to focus less on reparing things we've already broken and focus more on keeping things up and running smoothly. This paper analyzes how artificial intelligence is integrated into predictive maintenance systems with the goal of doing away with orderly current aircraft, analyzing methodologies that use data analytics and also predictive machine learning to predict the failure of components and schedule maintenance accordingly. This article talks about the large amount of benifits AI has on the aviation inditrly. Such as better sefety, less expence, and it makes everything run smoother. The Essay coves the issue on what challanges thease companies are facening they are facening challanges on trying to get their systems work. One thing that the AI Advanced PdM System does is it introduces future possibilities for technological advancements in PdM including, but not limited to, edge computing, real time data prediction, and autonomous maintanance. This paper delves deep into what the future holds for maintenance in the state of GA aviation with the use of AI.

Leveraging AI for Predictive Technical Debt Management in SAP Development Ecosystems: Case Studies and Future Prospects

Vamsi Krishna Talasila — Sat, 28 Feb 2026 00:00:00 +0000

Technical debt (TD) acts as the silent killer in massive, integrated SAP ecosystems and is often the main reason projects crash and burn. We simply can’t afford to be reactive anymore; we need to get ahead of the problem with Predictive Technical Debt Management (PTDM). This paper proposes a PTDM framework that uses Artificial Intelligence (AI) to handle three critical jobs: predicting what will break, prioritizing what to fix, and keeping the deployment line moving. We use a binary classification model (Algorithm 1) to guess the odds of an ABAP object failing, and we apply Natural Language Processing (NLP) to support tickets to figure out which bugs are actually hurting the business (Algorithm 2). By wrapping this in a Continuous PTDM Loop (Algorithm 3), we automate the creation of remediation tasks. Our operational case studies like an S/4HANA migration triage and continuous performance forecasting (Algorithm 4) show that this AI-driven approach speeds up custom code cleanup and stabilizes the system by calculating the "interest rate" of debt before it becomes too expensive to pay off. We wrap up by discussing future research into Deep Learning for semantic debt detection and managing debt in cloud-native SAP landscapes.

Adaptive AI Governance in Regulated Enterprise Data Platforms: A Trust-Calibrated Automation Framework

Suman Reddy Gaddam — Fri, 27 Feb 2026 00:00:00 +0000

Artificial intelligence (AI) has become foundational to enterprise data platforms in regulated industries, including financial services, healthcare, and compliance-sensitive digital ecosystems. While AI automation improves spotting unusual patterns, making predictions, and scaling operations, giving more decision-making power to algorithms adds challenges in governance, regulatory risks, and overall system safety. Traditional governance methods that depend on fixed rules or after-the-fact checks are not enough for environments where AI is making decisions, as they fail to account for the dynamic nature of AI systems and the need for real-time oversight and adaptability to changing circumstances, particularly in light of the complex challenges posed by algorithmic bias and regulatory compliance in sectors like healthcare and finance. The Trust-Calibrated Automation (TCA) Framework provides a clear method for handling AI that changes how much automation is used based on the specific risks, rules, and financial importance of different decision-making situations. The framework has various control levels, a method to assess overall risks, systems that focus on important issues based on trust, and elements that make sure the design fixes known problems in AI systems, like algorithmic bias that led to a 50% lower identification of high-need Black patients compared to equally sick White patients in healthcare risk prediction.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8126

Multi-Version Infrastructure for Privacy-Preserving AI/ML Inference at Scale

Jay Bankimchandra Desai — Thu, 26 Mar 2026 00:00:00 +0000

As the number of regulatory regimes, multi-stakeholder data relationships, and compliance requirements grows, privacy becomes an increasing architectural concern for large-scale AI/ML systems for data inference. Inference pipelines that apply a single, globally cast restrictive data policy to every inference context incur a measurable decrease in model performance. To avoid degrading model performance through globally restrictive policies while also avoiding potential policy violations introduced by dynamically modifying data usage per request, our multi-version architecture explicitly maintains multiple versions of user and participant information at the feature and embedding levels. In conjunction, context-aware version selection mechanisms deterministically map the metadata describing an incoming request to the appropriate data usage policy at runtime. In turn, versioned feature vectors are generated from superset representations of available signals, with the appropriate version selected based on the incoming request context and its corresponding data usage policy. Model-specific embeddings are derived from their privacy-compliant feature vectors to ensure end-to-end compliance. Rule-based selection schemes, implemented as abstractions decoupled from inference execution code, allow rapid regulatory adaptation without requiring service redeployment. Continuous monitoring helps validate selection quality and detect performance regressions in production environments. The computational overhead introduced by generating and maintaining multiple feature and embedding versions can be reduced through centralized build-once orchestration, shared feature storage schemas, and hybrid offline–online embedding generation within internet-scale latency budgets. Beyond privacy, this architectural pattern generalizes to fairness-aware inference, multi-tenant data isolation, and auditable policy enforcement, enabling versioned features and embedding representations as a foundational primitive for developing trustworthy, policy-compliant AI/ML systems.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8129

AI-Assisted Workflow Orchestration in Regulated Healthcare Contact Centers: Architecture, Governance, and Human-in-the-Loop Design Patterns

Mohammad Jakeer Mehathar — Thu, 26 Mar 2026 00:00:00 +0000

Healthcare contact centers managing medication access, prior authorization, and benefit coordination operate under sustained pressure—balancing administrative complexity, regulatory obligation, and the expectation of timely, accurate patient support. Artificial intelligence offers meaningful potential to augment these environments, yet the stakes involved demand architectural discipline that many early deployments have underestimated. This article presents a reference architecture and accompanying framework for AI-assisted workflow orchestration in regulated healthcare contact centers that deliberately positions machine learning as an augmentative layer within saga-orchestrated, event-driven architectures rather than as a surrogate for human judgment. Drawing on design patterns from responsible AI, distributed systems architecture, and healthcare interoperability standards, the framework addresses human-in-the-loop orchestration, explainable AI integration, continuous model governance, fairness auditing, and regulatory alignment across FDA, CMS, and emerging international requirements. Operational evidence from specialty pharmacy contact center implementations demonstrates that well-governed AI assistance improves agent decision quality, accelerates therapy access timelines, and supports measurable medication adherence gains in high-risk patient cohorts—without ceding accountability over consequential decisions to autonomous systems. Data governance emerges consistently as the foundational prerequisite determining AI readiness and model performance. Taken together, these architectural patterns, governance mechanisms, and evaluation findings position AI-assisted workflow orchestration in regulated healthcare contact centers as a distinct domain within enterprise healthcare systems architecture, providing a concrete reference model for organizations seeking to modernize contact center platforms and medication access workflows without compromising oversight, equity, or human judgment. The framework is positioned explicitly within the domain of enterprise healthcare systems architecture, with a focus on regulated contact center platforms and workflow orchestration, providing a reusable foundation for organizations seeking to operationalize AI responsibly in high-stakes patient access workflows.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8130

Alexa Smart Home: Pioneering Voice‑Driven Smart Home Integration

Anil Mankali Masakal — Mon, 30 Mar 2026 00:00:00 +0000

The emergence of voice assistants represents one of the most significant paradigm shifts in human–computer interaction since the graphical user interface. Among these, Amazon Alexa played a foundational role in bringing voice assistants from experimental systems to mass-market consumer adoption. This article presents a scholarly analysis of how voice assistants evolved to market readiness, how Alexa pioneered large-scale smart home integration, and how standardized, cloud-based integration frameworks enabled rapid ecosystem growth. It further documents my original technical and organizational contributions as a founding engineering manager in the Alexa Smart Home organization, focusing on the design of the Smart Home Skill API, capability interface taxonomy, and lifecycle architecture that became the industry's dominant model for voice-controlled Internet of Things (IoT) systems.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8132

Digital Equity and Embedded AI: Ensuring Accessibility in Smart City Infrastructure

Ishan Pardesi — Mon, 30 Mar 2026 00:00:00 +0000

An array of embedded AI systems becoming widespread in the urban infrastructure puts society at a critical point of juncture with a promise of significantly enhancing the quality of life of all citizens and, at the same time, promoting the worsening of the current inequalities. This detailed review of how algorithm-based implementation of embedded AI use in traffic management, community safety, and utility systems will inevitably introduce or exacerbate social divisions by being biased or not truly algorithmic, by being digital and not designed to be user-friendly. The article provides actionable frameworks that the embedded system architecture can apply to provide equitable benefits of smart cities, based on experiences of successful implementation in digitally inclusive cities. Among the important findings, it can be stated that strategic platform architecture choices, general design principles, and community-oriented development procedures play a crucial role in developing actually smart urban systems that act as bridges to an opportunity instead of barriers to involvement. The article addresses some crucial issues, such as the fact that there is a problem of algorithmic bias in the field of facial recognition and pedestrian detection and the need to design a multi-sensory interface that would accommodate a wide range of abilities. The article also highlights the fact that digital equity is not an additional feature of smart city development but a mandatory condition of sustainable urban change and indicates that inclusive embedded AI platforms offer high technical quality and equitable deliverables.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8133

Best Practices in Process and Digital Transformation: A Cross-Industry Framework for Scalable Impact

Swapna Chimanchodkar — Mon, 06 Apr 2026 00:00:00 +0000

Mounting cross-industry pressure to improve efficiencies at scale has made process and digital transformation a unified strategic imperative. Effective transformation requires reengineering workflows, governance, and data infrastructure, not technology investment alone. When process discipline and digital capability are developed together, organizations shift from reactive, fragmented operations to adaptive, predictive models that deliver sustainable value. Evidence from healthcare, manufacturing, education, and neurology confirms that durable transformation outcomes depend on process discipline, human capability, and purposeful technology deployment. In healthcare, AI-assisted early warning systems integrated with standardized sepsis protocols, including the Hour-1 Bundle, have produced clinically meaningful reductions in sepsis-related mortality. In manufacturing, IoT-enabled predictive maintenance, digital traceability systems, and robotic automation have reduced unplanned downtime, improved yield, and strengthened supply chain resilience. In education, adaptive AI platforms and hybrid learning models have improved outcomes for underserved populations by enabling personalized, student-centered learning at scale. In neurology, wearable monitoring and machine learning models are enabling earlier detection of mild cognitive impairment, while integrated care platforms are reducing fragmentation across dementia care providers. Generative AI and digital twins represent the next frontier, with applications across clinical decision support, autonomous production, and knowledge work already demonstrating measurable productivity gains. Transparent governance frameworks will be essential to ensure these advances are deployed responsibly, equitably, and with clear accountability.

Human-in-the-Loop UI Design: Evaluating Co-Creation with Generative AI Tools

Sonali Priya — Sat, 14 Feb 2026 00:00:00 +0000

The use of generative artificial intelligence in user interface design is changing the way people and AI work together, making processes more efficient but also creating some challenges in how to implement it. Human-in-the-Loop UI design is a socio-technical approach that sees AI as a partner rather than a replacement for human knowledge. This approach necessitates careful integration of technological capabilities, human cognitive processes, and organizational constraints. The evaluative framework created includes metrics for semantic fidelity, design system compliance, cognitive load assessment, and trustworthiness that measure both technical performance and how well people work together. Implementation challenges include technical issues like unclear meanings and inconsistent visuals; concerns about people relying too much on technology and losing skills; and complications within organizations related to rules and responsibilities. The article shows that successfully using HITL relies on clear strategies to reduce problems, such as setting design rules, creating easy-to-understand AI interfaces, having ongoing human supervision, and providing thorough training. Enterprise-specific factors include the need for accurate data visualization, meeting accessibility standards, and ensuring security. These factors require special evaluation methods that combine numbers with personal opinions. The framework highlights the importance of keeping human creativity intact while using AI to improve efficiency by carefully assigning tasks and checking results. Effective collaboration models include AI-suggestive systems where artificial intelligence provides recommendations while humans maintain decision-making authority. Structured template approaches offer another viable model that balances creative exploration with organizational governance requirements. The socio-technical perspective reveals that advanced technology alone cannot guarantee implementation success. Organizations must also address human factors considerations and assess organizational readiness for integrating AI capabilities into existing design workflows.

Hybrid Classical–Quantum Optimization of Wireless Routing Using QAOA and Quantum Walks

Sat, 28 Mar 2026 00:00:00 +0000

Routing in wireless communication networks is shaped by mobility, interference, congestion, and competing service requirements, making route selection a high-dimensional constrained optimization problem rather than a simple shortest path task. This paper investigates the use of hybrid classical–quantum methods for wireless routing, focusing on the Quantum Approximate Optimization Algo-
rithm (QAOA) and quantum walks as candidate mechanisms for exploring complex routing spaces. The paper examines how wireless routing can be expressed as a constrained graph optimization problem in which routing objectives, flow constraints, connectivity requirements, and interference effects are mapped into quantum-compatible Hamiltonian representations. It then discusses how these approaches can be integrated into a hybrid architecture in which classical systems perform network monitoring, graph construction, pre-processing, and deployment, while quantum subroutines are used for selected optimization components. The analysis shows that the potential value of quantum routing lies primarily in the treatment of difficult combinatorial subproblems rather than end-to-end replace-
ment of classical routing frameworks. The paper also highlights practical limitations arising from state preparation, constraint encoding, oracle construction, hardware noise, limited qubit resources, and hybrid execution overhead. It is argued that any meaningful near-term advantage will depend on careful problem decomposition, compact encoding, and tight classical–quantum integration.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8149

Probabilistic Attribution Models for Digital Out-of-Home Advertising: A Design Science Approach to Bridging Physical Exposure and Digital Behavior

Muthupalaniappan Ramanathan — Fri, 20 Mar 2026 00:00:00 +0000

However‚ since no end-user interaction such as a click or impression exists within a public digital Out-of-Home advertising environment‚ the article presents a probabilistic attribution framework for linking offline advertisement exposures to observable end-user digital behavior through defined geographical regions of exposure․ Using DSR methodology‚ we construct and validate a spatial-temporal modeling framework that utilizes geolocation signals‚ sensor data harvested from devices of subjects and privacy-aware inference algorithms․ Within this framework‚ a probabilistic viewability fence concept introduces spatial and temporal constraints on the inferred exposure while employing quality filters including dwell time‚ device orientation‚ and movement patterns․ Comparative validation with attribution modeling against benchmarks set by location-based and machine learning models shows that multi-dimensional probabilistic exposure inference is applicable and effective․ The framework thus turns DOOH into an accountable advertising medium‚ as opposed to the pure brand building medium‚ allowing cross-channel comparison of campaigns and data-driven actions by marketers․ This article contributes to the probabilistic attribution theory for non-interactive environments and gives a practical architecture to connect sample-based behavior offline and online․

Retail Data Engineering as a Fraud & Security Control Plane: A Reference Architecture and Design Patterns

Vikas Sripathi — Wed, 18 Mar 2026 00:00:00 +0000

Data engineering is increasingly a frontline security capability in retail and CPG because fraud detection, incident investigation, and compliance reporting depend on trustworthy, timely, and attributable data. This article makes three contributions. First, it defines a domain-specific reference architecture for retail data engineering—ingestion, storage, processing, serving, and governance—explicitly mapping each layer to control objectives such as integrity, auditability, privacy, and resilience. Second, it formalizes five canonical design patterns (loyalty personalization, multi-touch attribution, inventory automation, enterprise financial migration, and executive reporting) and specifies the operational controls needed in each pattern, including data contracts, identity resolution, and tiered latency. Third, it synthesizes empirical evidence from prior literature to show repeatable outcomes while clarifying the trade-offs between latency, cost, interpretability, and audit requirements. The result is a prescriptive, security-aware blueprint that helps practitioners design retail data platforms that are not only scalable but defensible.

Reliable Multimodal AI for Structured Knowledge Extraction and Study Material Generation in Real Classrooms: A Transparent Scoping Survey, Taxonomy, Benchmarks, and Research Roadmap

Soma Kiran Kumar Nellipudi, Nidhibehen Patel — Wed, 18 Mar 2026 00:00:00 +0000

Educational knowledge in real classrooms is distributed across speech, slides, whiteboards, handwritten mathematics, code, and ad hoc diagrams. This makes accurate and persistent study support difficult even when recordings are available. Recent multimodal models and large language model (LLM) systems can summarize lectures and generate notes, but real deployment remains limited by alignment drift, OCR and ASR noise, incomplete extraction of formal STEM content, and hallucinations that can silently corrupt study artifacts. This paper presents a transparent scoping survey of a balanced 100-paper corpus organized into five clusters: multimodal lecture understanding, educational artifact generation, structured knowledge extraction, reliability and hallucination control, and benchmarks and evaluation. We explicitly treat the last two clusters as a transfer toolkit layer for classroom AI rather than as classroom-native systems. Beyond synthesis, the paper contributes: (1) a review protocol with an explicit audit trail and descriptive-count caveats; (2) a reliability-first classroom pipeline in which alignment is the operational core; (3) an operational intermediate representation (IR) with typed fields, evidence granularity, verification records, and abstention behavior; (4) a worked micro-example that carries a 30-second lecture snippet into evidence-linked flashcards; (5) a lecture-grounded versus resource-grounded verification matrix; and (6) a reviewer-ready multimodal faithfulness protocol for mixed evidence such as noisy board crops, OCR, and ASR. The result is a sharper, more operational roadmap for trustworthy classroom AI.

Five Critical Mistakes Organizations Make When Implementing Data Mesh

Naveena Kumari Nandale Vadlamudi — Wed, 18 Mar 2026 00:00:00 +0000

The new architectural model of data mesh is poorly understood and implemented in many organizations. The article describes five primary pitfalls of a data mesh transformation. The pitfalls are rooted in (1) mistaken perception of data mesh as a technology migration instead of a model shift for organizational change, (2) centralized ownership structures while supporting domain ownership, (3) lack of platform enablement for data products and self-service, (4) absence of data product contracts and interoperability agreements, and (5) weak federated governance and accountability models. These drawbacks have in common that they don't take into account that data mesh is a socio-technical change, requiring systemic change to organizational design, decision rights, culture, and governance. The article then shows how misalignment of technical adoption and organizational design substantially reduces return on investment and results in domains without genuine autonomy. Inadequate self-service platforms with high cognitive and technical overhead for domain teams, as well as a lack of interoperability standards, result in exponentially increasing integration costs as the number of domains increases. This article describes an ideal design comprising aligned organization, true decentralization, effective self-service platforms, federated contracts, and balanced governance for independence and accountability in exactly the right ways. It makes the case that by using this approach and avoiding the main problems, businesses can transform data from a centralized asset into a product capability that can be effectively dispersed throughout the organization and then used much more strategically.

Lineage, Traceability, and Reproducibility as Reliability Requirements in Enterprise AI Systems

Divya Bonthala — Wed, 15 Apr 2026 00:00:00 +0000

Artificial intelligence is being applied to key business and compliance choices by more systems in the enterprise. One of the most common systems is concerned with the accuracy of the model and does not factor in the reliability aspect, like the lineage or traceability, or reproducibility. In this paper, we obtain these three aspects as fundamental reliability expectations of enterprise AI. The study was a real enterprise AI applied in 12 months with a before and after quantitative design. Lineage coverage, version control and reproducibility controls were introduced thus, the lineage coverage rose to 0.91 and the success of reproducibility rose to 92% after these tools were applied on structured lineage. Rapid time to incident investigation was less by 66%, audit preparation was also less by 62% and compliance findings were also less by 75%. Monte Carlo simulation also indicated that the risk variability was smaller when the lineage controls had been incorporated. This observation is in full agreement with the results that indicated that integrating lineage, traceability, and reproducibility into AI platforms enhances reliability, audit readiness, and trust in AI results.

Efficient Incremental Data Modeling in Apache Iceberg-Based Analytical Pipelines: Partitioning and Snapshot Optimization Strategies

Guruprasad Raghothama Rao — Wed, 15 Apr 2026 00:00:00 +0000

Lakehouse relies on Apache Iceberg to efficiently handle big data analytics in a reliable and scala-able way. But inefficient incremental modeling has the capacity of decreasing the speed of queries and hiking the cost of storage in the long run. This paper gives a quantitative assessment of the partitioning and snapshot retention and compaction policies in terms of Monte Carlo simulations. Findings indicate that scans shrink percentage was increased day to day using partitioning (0.61 to 0.82) and reaction savings were decreased (18.4 seconds to 13.4 seconds). Snapshot expiration policies decreased metadata to data ratio (0.18 to 0.07) and reduced the overall query response (19.3 seconds to 15.8 seconds). Threshold based and daily compaction ensured that average file sizes were above 240 MB and overall efficiency score increased by 0.032 as compared to 0.051. Connected optimization minimized the overall latency by 34 per cent and storage fragmentation by 41 per cent. The results offer viable suggestions in the development of robust and viable Iceberg analytical pipelines.

From Requirements to Resilience: Architecting a Digital Thread Across Engineering and Supply Chain Using MBSE and PLM

Jasleen Singh Saini — Wed, 25 Mar 2026 00:00:00 +0000

Modern engineering enterprises invest heavily in CAD environments and PLM platforms, yet supply chains continue to fail at the point where design decisions meet operational execution. The root cause is rarely a logistics breakdown — it is an architectural one. Most enterprises begin the digital thread in CAD, after system intent has already been established informally, without structured traceability. The absence of Model-Based Systems Engineering (MBSE) at the origin of this thread means that requirements, functional allocations, and supply chain constraints never enter the product lifecycle in a machine-readable, queryable form. By the time geometry is committed, the decisions behind it are invisible to any governance mechanism. This paper proposes an enterprise architecture blueprint that repositions MBSE as the authoritative anchor of the digital thread, establishes a formal Semantic Mapping Framework to bridge the logical-to-physical boundary between MBSE and CAD, and uses PLM as the backbone that synchronizes both layers across the full product lifecycle. A RACI-based governance model enforces data ownership at every thread boundary. A five-level Digital Thread Maturity Model provides a structured adoption roadmap. The central argument is that supply chain resilience cannot be achieved operationally when it has not first been built architecturally — and that architecture begins in MBSE, not in CAD.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8172

Human-AI Collaborative Architecture for Enterprise Financial Platforms

Ravindra Rajasekhar Kavuru — Wed, 25 Mar 2026 00:00:00 +0000

Co-branded credit card platforms combine high-volume consumer software with stringent financial regulation, creating architectural challenges that standard design approaches cannot adequately address. This paper presents a human-AI collaborative architecture built around five interlocking design commitments: an event-driven core that captures every state transition as an immutable, replayable domain event; regulation-aware caching that restricts sensitive data domains to narrow read surfaces; cryptographic boundaries with key isolation scoped to the service and regulatory domain; a Zero Trust posture that enforces continuous authentication on every inter-service request; and a tiered human-AI collaboration model that is policy-governed rather than autonomous. The central argument is that compliance is not an external control overlay but a first-class structural property of data models, service boundaries, and event schemas from the outset of design. The resulting platform demonstrates that regulatory requirements and platform innovation are structurally complementary when encoded from the beginning of the architecture.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8173

Leveraging AI-Driven Predictive Analytics for Effective Program Management in Retail Supply Chains: A Program Manager's Perspective

Cijin Lonappan Kappani — Wed, 25 Mar 2026 00:00:00 +0000

Large retail firms are now leveraging AI-driven PA tools to improve demand forecasting, inventory routing, workforce planning, and disruption recovery. In this article, we will discuss how PA tools can be effectively incorporated into retail SCM program management from a software technology program manager’s perspective, highlighting that forecast accuracy improves much faster than organizational decision adoption, thus making change management a critical success factor. This is because production readiness is built on the foundation of data observability, stress validation, and human-in-the-loop governance. Sustainability is achieved through the recognition of the importance of treating predictive analytics as an end-to-end solution, as opposed to an island-like solution. The article discusses the challenges and provides ways to mitigate them. The article discusses recoverability‑optimized architectures, curated feature stores, shadow testing, and confidence‑based overrides. Governance with clear decision rights and escalations/compliance is highlighted as a requirement to scale predictive analytics in various retail operational contexts. By leveraging technical innovation and program management discipline, predictive analytics can be integrated into retail supply chains as a strategic element. The insights provided here are intended to serve as a roadmap for program managers to effectively integrate technical innovation with organizational realities to improve service levels, reduce costs, and improve supply chain resiliency in a dynamic retail environment.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8174

A Governance-First and Systems-Theoretic Framework for Scalable Enterprise Cloud Integration Architecture

Chakra Dhari Gadige — Wed, 25 Feb 2026 00:00:00 +0000

Enterprise cloud integration has traditionally been approached as a collection of discrete interfaces and data pipelines connecting heterogeneous systems. Such linear integration models are effective at a limited scale, but they might fail when it comes to nonlinear behavior, feedback effects and governance risks, which grow with the growth of enterprise complexity. This article explains enterprise cloud integration architecture as a complex adaptive system made of interacting and evolving subsystems, state dependencies, and governance boundaries. The article proposes a governance-first systems-theoretic framework that focuses on formal separation of control and data planes, embedded compliance and security mechanisms, predictive scalability modeling, and observability-driven feedback mechanisms. By treating governance as a stabilizing architectural invariant rather than a reactive constraint, the framework enables sustainable scalability, resilience, and long-term adaptability in enterprise cloud ecosystems.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8175

Secured Credit Cards: A Strategic Partnership Model for Financial Inclusion and Customer Development

Avaneendra Kanaparti — Thu, 16 Apr 2026 00:00:00 +0000

This article examines the secured credit card market as a critical gateway to financial inclusion for approximately 45 million credit-invisible Americans and proposes a transformative partnership model between financial institutions and their secured card customers. The article identifies significant gaps in the current industry approach, which treats secured cards primarily as risk mitigation tools rather than customer development opportunities, resulting in minimal educational support and high attrition rates. Through an analysis of customer demographics, industry limitations, and behavioral economics principles, this article advocates for a collaborative framework that positions banks as trusted advisors in their customers' credit-building journeys. The proposed partnership model incorporates comprehensive onboarding programs, behavioral nudging techniques, milestone-based rewards, and strategic partnerships with employers and community organizations. By shifting from transactional to relationship-based approaches, financial institutions can create aligned incentives that benefit all stakeholders while addressing fundamental gaps in financial literacy and inclusion. The implementation strategies leverage digital transformation, personalized support systems, and data analytics to create scalable solutions that guide customers toward positive financial behaviors and successful transitions to mainstream credit products.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8179

Offloading Network Policy Enforcement to Data Processing Units

Satya Sagar Reddi — Sat, 14 Feb 2026 00:00:00 +0000

General-purpose server CPUs in modern data centers bear a dual burden: executing application workloads while simultaneously enforcing network policies. This split responsibility introduces computational overhead, cache contention, and latency variability that degrade both application throughput and network performance. This article examines the architectural case for offloading policy enforcement, connection tracking, firewall operations, and traffic metering to Data Processing Units (DPUs)—purpose-built accelerators integrated directly into the network data path. By relocating these functions from host CPUs to dedicated silicon, organizations recover substantial compute headroom while achieving deterministic, sub-microsecond network performance. The article analyzes the bottlenecks of CPU-based network processing, the architectural design of modern DPUs, the role of open standards in enabling portable policy management, and the operational benefits across diverse deployment scenarios. Results demonstrate measurable gains in resource utilization, energy efficiency, and latency consistency for latency-sensitive workloads, establishing hardware-accelerated network processing as a foundational shift in data center architecture.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8180

Convergence of AI and Zero Trust: Enabling Continuous Verification Across Hybrid Cloud Environments

Barinder Pal Singh — Wed, 15 Apr 2026 00:00:00 +0000

Contemporary organizations confronting sophisticated threat actors across distributed hybrid cloud environments cannot maintain the velocity required for continuous verification of millions of daily authentication decisions through manual security operations. Artificial intelligence integration within Zero Trust frameworks enables operationally viable continuous verification across hybrid cloud infrastructures through systematic literature synthesis and conceptual framework development. Four contributions address existing gaps: (1) five-layer reference architecture explicitly integrating AI components (data collection, analytics, policy decision, enforcement, orchestration) with Zero Trust pillars across hybrid cloud platforms, (2) three-phase implementation framework with quantified metrics synthesized from eight documented enterprise deployments, (3) cross-sectoral deployment analysis across five industries with operational KPIs, (4) evidence-based mitigation strategies validated through expert consensus with twelve chief information security officers. Synthesized findings demonstrate measurable improvements detailed in Section VI, including significant reductions in misconfiguration incidents, detection time improvements, automated incident response capabilities, and substantial operational savings. Cross-sectoral results reveal industry-specific improvements ranging from 30-75% across manufacturing, financial services, healthcare, retail, and energy sectors. The integrated framework addresses documented gaps in AI-Zero Trust technical architectures for hybrid cloud continuous verification, providing actionable implementation guidance for organizations transitioning from perimeter-based defenses to AI-powered continuous authentication and authorization systems.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8181

Hallucination Is a Retrieval Problem: Diagnosing Structural Confabulation in LLMs and a Path Forward via Grounded Belief Representations

Sai Manoj Jayakannan — Wed, 15 Apr 2026 00:00:00 +0000

Hallucination in large language models (LLMs), the confident generation of factually incorrect or unsupported content, remains one of the most consequential unsolved problems in the field. Despite an enormous volume of empirical work, the community lacks a mechanistic consensus on why models hallucinate even when ground-truth information resides in training corpora. This article argues that hallucination is fundamentally a retrieval failure, not a knowledge failure: the parametric weights encode sufficient information, but the inference-time process of locating and conditioning on that information is unreliable. This framing redirects blame from the knowledge store toward the access mechanism and suggests that retrieval-augmented approaches are not merely useful patches but are architecturally necessary. Four structural limits of the dominant decoder-only transformer paradigm are diagnosed: superposition-induced interference, attention dilution in long contexts, RLHF overconfidence calibration, and benchmark saturation that together explain why scaling alone cannot resolve confabulation. Three concrete research directions are then proposed: (1) Belief-Grounded Decoding, which separates knowledge retrieval from language generation via an explicit epistemic state; (2) Structured Knowledge Integration for RAG, replacing flat retrieved text with relational subgraphs; and (3) Domain-Divergent Hallucination Benchmarks that test generalization across knowledge-distribution shift. Minimal proof-of-concept experiments executable within 12–18 months are outlined, and the critical failure modes of the proposed approaches are identified.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8182

AI-Optimized Real-Time Decision Systems for Digital Advertising

Sai Dheeraj Guntupalli — Wed, 15 Apr 2026 00:00:00 +0000

Real-time bidding architectures powering programmatic advertising face simultaneous demands across latency, privacy, and decision quality that no single prior system has addressed within a unified engineering framework. The deprecation of third-party cookies, platform-level tracking restrictions, and evolving data protection regulation under GDPR have fundamentally altered the identity infrastructure that behavioral targeting depends upon, while exchange-imposed rigorous deadlines continue to constrain every component of the serving pipeline. Four concrete contributions are presented: a sub-50 ms AI inference pipeline built on distributed edge caching and SLO-aware gradient-boosted scoring; a federated identity framework achieving privacy-compliant personalization through rotating session tokens and cohort-based identifiers; a hybrid multi-agent reinforcement learning and large language model bidding optimizer delivering substantial revenue improvement over rule-based baselines; and a systematic experimental evaluation framework reporting latency, throughput, and CTR prediction accuracy synthesized from peer-reviewed production-scale benchmarks. End-to-end P95 latency remains within the exchange deadline at production DSP throughput, CTR prediction AUC reaches 0.776 for gradient-boosted models, and coordinated multi-agent RL bidding achieves 19,501 CNY platform revenue versus 5,347 CNY for hand-crafted rules. Zero-knowledge verification mechanisms address the measurement attribution gap introduced by identifier deprecation, while legally grounded privacy design satisfies GDPR requirements as system properties rather than post-hoc compliance overlays.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8183

AI-Driven Dynamic Pricing, Fee Optimization, and Incentive Intelligence Across the Transaction Lifecycle

Satheesh Kumar Kumara Chinnaian — Wed, 15 Apr 2026 00:00:00 +0000

Payment processors have historically relied on static billing models and broad merchant segmentation, creating structural inefficiencies in an increasingly dynamic digital commerce environment. Transaction-level costs, risks, and strategic value vary materially with context—channel, geography, funding source, payout timing, merchant behavior, and dispute outcomes—yet legacy pricing systems treat these dimensions as uniform. This article presents a modern pricing architecture that transforms the pricing engine into a real-time economic decision layer, combining transaction-level cost and loss forecasting, competitive and elasticity-aware optimization, continuous post-settlement learning, and an integrated incentive layer for promotions and merchant-funded campaigns. The platform employs machine learning for predictive components and large language models for unstructured signal extraction, enabling a pricing system that remains auditable, adaptive, and aligned with long-term network health. Implementation through governed architectural layers, deterministic fee construction with explainable components, event-driven lifecycle data contracts, and closed-loop learning mechanisms demonstrate how economic precision and transparency can coexist. Evaluation methods combining controlled experimentation, causality validation, and lifecycle measurement ensure that pricing decisions improve both processor profitability and merchant experience without sacrificing either regulatory compliance or competitive positioning.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8184

Cloud Modernization and High Availability Architecture: Strategic Foundations for Enterprise Digital Transformation

Rajesh Kumar Balusu — Wed, 15 Apr 2026 00:00:00 +0000

Database infrastructure modernization and cloud migration have become one of the main calculated initiatives for most organizations today. This is largely due to the need to remain competitive in the digital age, increase operational resilience, and implement strong business continuity. This article explains the principles of migrating from an on-premises database system to a cloud-native database solution with high availability, redundancy, automated failover, and a distributed architecture. To achieve 99.999 percent uptime and smooth scale, improved resiliency, and efficiency, organizations will need to adopt architectural elements such as microservices, infrastructure as code, and a cloud-native approach; deliberate migration programs; and structured planning approaches to migration and modernization (including discovery, assessment, prioritization, optimization, and operational excellence), such as the six Rs and cloud adoption frameworks from cloud service providers. Modern cloud environments (public, private, and hybrid) provide distributed computing resources while guaranteeing high availability for mission-critical systems. For example, technologies such as active data guard, zero-downtime migration strategies, real application clusters, and automated disaster recovery can be used to deliver near-zero downtime and scalability in highly available systems. Container orchestrators, elastic scaling processes, tiered storage mechanisms, observability, and all other components of a data infrastructure form an increasingly flexible and scalable platform that supports the accelerating growth of businesses and innovation across the world. Data infrastructure literacy, secure infrastructure-as-code, and continuous optimization are keys to maintaining this competitive advantage, as they help to increase the reliability and automated recovery of business-critical processes.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8185

The Algorithmic Engine of American Resurgence: Catalyzing Labor Productivity through AI-First ERP Orchestration

Srikanth Gadde — Wed, 15 Apr 2026 00:00:00 +0000

Something structural has shifted in the global economy, and it is not just about technology getting faster. Labor shortages are biting in ways that feel permanent rather than cyclical. Workforces are aging. And despite enormous investment in digital infrastructure, American businesses are not getting the productivity returns that investment was supposed to generate. The gap between what enterprise technology promises and what organizations actually extract from it has become one of the more costly open problems in modern business—and closing it requires more than upgrading software. It requires rethinking the architecture entirely. Autonomous Resource Orchestration (ARO) aims to change how we use Enterprise Resource Planning (ERP) by making it an active system that connects Human Capital Management (HCM) platforms with Financial Management Systems (FMS) using a built-in generative AI layer, which can manage resources, identify problems, and start workflows instantly without needing a manager's input. Comparative evidence across smart factory and knowledge-intensive service environments suggests the productivity lift is real and substantial—administrative time drops sharply, workforce reallocation that once took weeks happens in hours, internal talent mobility triples, and forecasting accuracy tightens to a degree that changes how confidently organizations can plan. None of these improvements requires replacing workers. It requires stopping the waste of their time on tasks that systems should handle automatically—and redirecting that recaptured capacity toward the creative, relational, high-judgment work that actually drives growth.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8186

Integration of Autonomous Artificial Intelligence within Established Enterprise Resource Planning Financial Infrastructure

Pradeep Narayanan — Wed, 15 Apr 2026 00:00:00 +0000

Today’s enterprise artificial intelligence is changing a lot, moving from just answering questions to becoming self-sufficient agents that can observe their surroundings, make plans, and carry out tasks. This academic study examines the integration of autonomous AI with existing Enterprise Resource Planning systems, focusing on the monitoring of financial transactions and regulatory frameworks. The article covers basic ideas about how autonomous systems work, ways to combine them with ERP systems, methods for continuous learning, and the management structures needed for them to operate independently in regulated financial settings. Through observation of current processes and developing implementation configurations, this scholarship reveals pathways toward anticipatory financial supervision infrastructures that amplify rather than substitute human discernment. This scholarship, by analyzing current research and emerging implementation models, identifies routes for developing proactive financial oversight systems that enhance, rather than replace, human judgment. The metamorphosis from conventional batch-oriented analytics toward instantaneous, occurrence-activated agent implementation signifies a revolutionary transformation in how establishments administer intricate operational workflows. Key improvements include using layered agent setups, advanced learning to change deceptive patterns, learning from human reactions, and ERP systems that work across different platforms, which are the technical foundations for practical use. While obstacles endure in interpretability, synchronization, and institutional acceptance, the coalescence of numerous technological progressions renders autonomous AI amalgamation both practicable and progressively imperative for sustaining productive fiscal regulations at magnitude.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8187

The Metrology Imperative: The Necessity of Robust Evaluation Frameworks and Comprehensive Automated Judges in Generative AI

Ankur Partap Kotwal — Wed, 15 Apr 2026 00:00:00 +0000

Across the past several years, the accelerating advancement of Large Language Models (LLMs) and generative artificial intelligence has quietly produced a crisis that much of the field has been slow to name directly—a breakdown in the ability to evaluate what these systems can and cannot actually do. Traditional, static benchmarking methodologies have proven structurally inadequate, collapsing under the combined weight of rapid benchmark saturation, pervasive data contamination, and the kind of systematic overfitting that emerges whenever commercial incentives are tied too tightly to leaderboard rankings. This brief argues, with considerable urgency, that building robust and dynamic evaluation frameworks alongside sophisticated automated judges—most prominently through the LLM-as-a-Judge paradigm—is not an optional enhancement to existing practices but an absolute prerequisite for the continued, safe, and value-aligned development of AI systems. Through a careful examination of where current evaluation practices fail, an analysis of the architectural requirements governing automated multi-agent juries, and a survey of multi-dimensional safety assessment approaches, a coherent pathway toward genuinely reliable AI metrology is charted here. The arguments and architectural outlines presented across these sections are intended to serve as a structured foundational blueprint for a full-length 40-page journal article that will pursue the theoretical, empirical, and architectural dimensions of this problem in considerably greater depth.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8188

Standing Out Early in Enterprise Web Engineering: Practical Ownership Strategies for Platform and API Professionals

Dreema Patel — Wed, 15 Apr 2026 00:00:00 +0000

The API economies and distributed platform ecosystems that are a natural outcome of cloud-native applications have changed the baseline competencies for entry-level enterprise web engineering talent․ Programming is expected as a threshold skill․ Engineers who can show delivery maturity‚ production responsibility‚ and cross-functional collaboration are some of the most sought after․ There is a gap between the task-oriented focus of current technical training and the ownership mindset needed to succeed in modern engineering culture․ This article proposes a set of concrete frameworks for early-career engineers‚ spanning ownership‚ engineering quality‚ observability‚ experimentation‚ and inclusive platform delivery․ We discuss how pro-active problem solving, design-aware delivery, telemetry-driven development, and accessibility-first engineering work together to enable early-career engineers to deliver enduring value. In an environment of saturated hiring markets for engineering talent, the competitive edge for technical excellence increasingly appears to be the disciplined excellence across the end-to-end delivery lifecycle.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8189

Conversational AI Agents for Financial Operations with Escalation-Aware Handoff Protocols: Designing Intelligent Human-AI Collaboration Systems

Gautham Paspala — Wed, 15 Apr 2026 00:00:00 +0000

Conversational artificial intelligence (AI) provides a model shift from deterministic rule-based process automation to context-aware, always-on learning systems for financial operations. Toward that goal, this article presents a framework for escalation-aware conversational AI in financial operations, including a multi-dimensional signal architecture that leverages linguistic, behavioral, transactional, and relationship signals to make real-time, probabilistic escalation decisions for customers and service agents of financial institutions. Another key concept is the collaboration zone, where artificial intelligence and a human agent are processing in parallel, having distinct skills, and there is no explicit handoff of control between the agents. The curriculum builds on the human agents' reasoning to discover human-like reasoning paths and extend the AI competency frontier. It uses a high rate of automation while also ensuring highly satisfactory customer experiences similar to those of human agents. Other considerations include implementation architecture; the transformation of the workforce; QA and continuous improvement operations; as well as quests for proactive engagement, multimodal interaction, and federated learning; as well as the evolution of autonomous agents.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8190

Ethical Imperatives in Enterprise Statistical Modeling: Navigating Bias, Opacity, Surveillance, and Governance in Organizational Data Analytics

Ranjeet Sharma — Wed, 15 Apr 2026 00:00:00 +0000

Enterprise data analytics has undergone a structural transformation over the past decade, with statistical modeling systems now embedded in organizational decisions that carry profound consequences for employees, consumers, and broader society. From algorithmic hiring tools that screen thousands of candidates in seconds to credit-scoring models that determine financial access for millions, the enterprise deployment of predictive analytics has outpaced the ethical and governance frameworks needed to oversee it responsibly. This article examines four interconnected dimensions of that oversight gap. First, it traces how algorithmic bias originates and propagates through organizational data pipelines — from historically skewed HR records to proxy variables that reconstruct protected attributes — and documents the feedback mechanisms through which biased outputs institutionalize inequality over successive model iterations. Second, it analyzes the fundamental tension between predictive accuracy and equitable treatment, arguing that impossibility results in fairness. Mathematics confirms these trade-offs as value-laden choices demanding democratic deliberation rather than technical resolution. Third, it confronts the opacity problem inherent in complex enterprise models, evaluating both technical explainability methods and the institutional accountability structures—independent auditing, contestation mechanisms, and regulatory mandates such as the EU AI Act—that technical transparency alone cannot substitute. Fourth, it examines how the data demands of statistical modeling have normalized pervasive workplace and consumer surveillance, introducing risks of inferential discrimination that existing legal frameworks are ill-equipped to address. Across all four dimensions, the analysis converges on a central argument: ethical governance of enterprise statistical modeling requires multidisciplinary oversight structures, ethics-by-design development practices, and the organizational courage to decline deployment where no technically sophisticated solution can resolve a fundamentally impermissible application.

Retail Core Evolution Under Uptime Constraints

Retail Core Evolution Under Uptime Constraints — Wed, 15 Apr 2026 00:00:00 +0000

The systems themselves (order management, inventory allocation, pricing engines, store systems) are often the most critical components of the business, and classic modernization techniques do not apply. The omnichannel imperative, the need for near real-time data, and composable digital experiences are overwhelming, but uptime requirements on legacy platforms make disruptive replacements unrealistic. The framework addresses this contradiction with the architectural patterns library: continuous availability enforces continuous operation as a non-negotiable architectural requirement. Edge-first experience replacement separates the part of the system that users interact with from the transaction logic, making it possible to enhance the user interface or try out new options without risking the current transaction processing. In addition to scheduled batch jobs, event-shadowed batch patterns allow the event-driven interfaces to bring inventory/order and production data very close to real-time without prematurely retiring the relevant interfaces. Progressive store rollout disciplines the application of change across diverse retail environments in a way that minimizes risk and maximizes proof points from the live experience before going to full rollout. De-risked cutover patterns drive the transformation of legacy to modernized flows through parallel operation, quantitative validation, and rehearsed rollback plans. When used together, these three patterns allow retail organizations to make the transition from tightly coupled, batch-oriented architecture to API-first, adaptive architecture in a disciplined and incremental manner, without adversely affecting the operational continuity that all retail businesses depend on every day.

AI-Enabled Digital Experience Design for Enterprise Applications and Platforms: Advancing Inclusion, Equity, and Societal Participation

Muthu Saravanan Ramachandran — Wed, 15 Apr 2026 00:00:00 +0000

Enterprise digital platforms have become the foundational infrastructure through which citizens access healthcare, government services, education, and financial systems, yet across these critical domains, complex interfaces routinely exclude the populations most dependent on them through inaccessible design, cognitively demanding workflows, and content calibrated to narrow assumptions about user capability. People with disabilities, limited digital literacy, non-native language backgrounds, aging-related cognitive changes, and low-bandwidth connectivity constraints face systemic obstacles and. these barriers deprive them of access to healthcare, financial stability, and civic engagement services. The latter failures are not design oversights within their particularity; they are indicative of the structural inability of the traditional experience design methods to deal with population-level diversity at enterprise scale. The digital experience design based on AI can provide a radically different and scalable answer by augmenting human design knowledge with automated behavioral pattern recognition, perpetual accessibility testing, plain language testing, and intelligent quick prototyping. Such abilities turn the inclusive design into a dream and a structured and operationally viable practice in healthcare access, civic participation, educational equity, and financial inclusion. Ongoing automated accessibility checking during platform development minimizes WCAG compliance violations in ways that human review alone cannot achieve. Additionally, population-stratified behavioral analysis reveals failure patterns that aggregate usability metrics obscure and that small-scale testing cannot uncover. Platforms redesigned through AI-supported inclusive design show significantly higher task completion among underserved groups and also demonstrate decreased citizen support escalations, enhanced patient portal engagement, and increased financial service adoption among historically excluded populations. The major point of view that this body of evidence advocates is that AI is not a replacement for human design skills, but it enhances the potential of empathy-driven, morally based designers to reach the entire gamut of human diversity that digitally dependent societies need.

Energy Conservation in Autonomous Vehicles: Challenges, Technologies, and Future Directions

Chandra Sekhar Kollapudi — Wed, 15 Apr 2026 00:00:00 +0000

Although considered a main theme of the fourth industrial revolution, the introduction of AVs is a major hindrance to establishing a sustainable transport system. Additional driving automation levels mean increases in on-board energy consumption for sensors, computing, communication and actuator units. The energy share of AV automation is expected to be considerable, with perception, sensor fusion and real-time decision-making placing a large computational burden on the vehicle's energy, affecting the vehicle's range and emissions at the grid level. However, the performance of technology such as regenerative braking optimization, vehicle-to-everything (V2X) systems, predictive energy management, and adaptive equivalent consumption minimization strategies may achieve net savings. Due to the clear gains in powertrain efficiency and energy recovery from reinforcement learning and model predictive control systems, these control advantages are being integrated. Each of these discoveries reveals that on-road deployment of automated cars designed with energy efficiency as a co-equal engineering requirement has the potential to dramatically reduce GHG emissions from the transportation sector over a few decades.

Generative AI for Data Engineering: A Seven-Stage Orchestration Framework for LLM-Powered Code Generation

Mosaic Basha Syed — Wed, 15 Apr 2026 00:00:00 +0000

Data engineering organizations have encountered difficulties with productivity, with platform complexity and the requirement to use multiple technologies, programming languages, and frameworks increasing the effort required to develop data pipelines. Maintaining existing pipelines is challenging and costly with changing requirements, modernization, and poor documentation relative to implementation, complicating the transfer of knowledge and debugging. We propose a seven-stage orchestration architecture to apply LLMs in enterprise data engineering workflows to close the divide between LLMs' theoretical code generation capabilities and practical deployments of such systems in strictly regulated environments․ The architecture implements a process that leverages specification ingestion, retrieval augmented generation (RAG), multi-stage code generation with semantic validation, auto documentation writing, multi-layer security scanning, confidence-gated human-in-the-loop review, CI/CD deployment, and reinforcement feedback-based continuous learning to govern the LLMs. We adopt enterprise guardrails like data classifications‚ metadata-only retrieval‚ generation scope limits‚ and immutable audit trails to ensure security‚ regulatory compliance‚ and motivated assurance․ Recent article are in code generation literature show that multi-turn synthesis, bidirectional context modeling, and human feedback can substantially improve generation effectiveness, which informs our design choices. We propose a thorough architecture for responsibly deploying LLMs for enterprise data engineering and plan to validate this approach with production deployment of LLMs in banking data platforms.

Human-in-the-Loop Autonomous Networking: Designing Safe Artificial Intelligence–Assisted Infrastructure Systems

Vijaya Bhaskar Methuku — Wed, 15 Apr 2026 00:00:00 +0000

Hyperscale network environments have grown far beyond the thresholds where manual operational models remain viable, driving a structural transition from scripted automation toward genuinely autonomous remediation systems that observe, diagnose, and act without waiting for human commands. While this evolution resolves longstanding scalability constraints, it simultaneously introduces categories of systemic risk that have no precedent in deterministic automation architectures. This article proposes a human-in-the-loop autonomy framework built around three interlocking principles: bounded mitigation authority governed by a risk-tiered classification model, confidence-based execution thresholds derived from multi-layer telemetry and signature matching, and a three-stage safety loop that treats rollback capability as a first-class design requirement rather than an afterthought. A controlled testbed evaluation conducted across 127 managed network endpoints over twelve weeks validated the framework's core safety claims, achieving a 95.3% autonomous success rate, a 4.7% rollback rate with zero cascading failures, and a 19.6x improvement in mean time to remediate relative to manual intervention baselines—representing the first framework to combine risk-tiered authority classification with mandatory pre-execution rollback verification as coequal structural requirements. A structured governance layer ensures that human engineers evolve from reactive troubleshooters into strategic supervisory architects who validate, calibrate, and continuously improve autonomous system behavior. The framework argues that safe autonomy is a structural engineering imperative for the coming decade, not a discretionary enhancement, and that the most resilient infrastructure environments will be those that combine machine speed with human judgment through principled, transparent, and reversible collaboration.

AI-Driven DevOps in Cloud-Native Environments: Opportunities, Architectures, and Challenges

Mahesh Yadlapati — Sat, 14 Feb 2026 00:00:00 +0000

The growing complexity of cloud-native systems — built around microservices, containers, and dynamic orchestration platforms like Kubernetes — has stretched traditional DevOps practices to their limits. While CI/CD pipelines, infrastructure as code, and observability tooling have dramatically improved how software gets built and shipped, these approaches still lean heavily on static rules and human judgment that struggle to keep up with the pace and unpredictability of modern distributed environments. This article investigates how artificial intelligence and machine learning techniques can be woven into DevOps workflows to close that gap — a convergence widely known as AIOps. It presents a layered conceptual architecture for AI-enabled DevOps and examines AI applications across three critical operational dimensions: anomaly detection, incident response, and CI/CD pipeline optimization. A synthetic dataset modeled on realistic cloud telemetry was used to evaluate three detection approaches—rule-based thresholds, Random Forest classification, and LSTM-based deep learning. The LSTM model achieved the strongest results with a 94% accuracy rate and an F1-score of 92.5%, outperforming both alternatives by a significant margin. AI-driven incident response cut average resolution time to 10 minutes from the 45 minutes typical of manual workflows, while AI-enhanced pipelines completed delivery cycles roughly 40% faster without sacrificing deployment reliability. Beyond the results, the article candidly addresses persistent challenges around data quality, model interpretability, integration overhead, and adversarial security risks. It concludes by outlining future research paths, including explainable AI, reinforcement learning for adaptive resource management, and the long-term vision of fully autonomous, self-healing DevOps systems.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8212

Data Quality Frameworks in Educational Assessment: Ensuring Scoring Integrity at Scale

Venkatesan Kandavelu — Sat, 14 Feb 2026 00:00:00 +0000

Educational assessment systems generate complex, high-volume data that must meet rigorous standards of accuracy and fairness before informing high-stakes decisions. This article examines structured data quality frameworks as a foundational requirement for scoring integrity in large-scale assessment environments, where manual validation methods are insufficient to address the scale and diversity of errors that emerge across distributed data pipelines. Drawing on literature spanning data governance, psychometric measurement, streaming validation architectures, and process data analysis, the article characterizes the principal categories of assessment data failure including range violations, categorical inconsistencies, timestamp anomalies, and duplicate identifiers and traces the mechanisms through which these errors propagate into subgroup reporting and equity metrics. A layered validation methodology is presented, encompassing ingestion-level data validation, cross-system reconciliation, statistical anomaly detection, and psychometric integration, with particular attention to the diagnostic transparency and field-level auditability that high-stakes reporting environments demand. The article further addresses the transition from error detection to systematic remediation, arguing that automated correction pipelines embedded within governance architectures, followed by iterative revalidation, are essential for producing defensible, accurate, and complete assessment records at the scale modern programs require.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8213

Engineering Quality in Hybrid Physical–Digital Fitness Platforms

Mani Deep Reddy Singireddy — Sat, 14 Feb 2026 00:00:00 +0000

The convergence of IoT-enabled fitness equipment, biometric assessment hardware, mobile applications, and cloud-based training services has produced a new class of enterprise platform that operates simultaneously in physical and digital environments. Sustaining reliable, consistent behavior across this hybrid infrastructure is a non-trivial engineering challenge. Conventional application-level testing frameworks, designed for bounded software systems, are structurally inadequate for environments where sensor data crosses network boundaries, member state must remain synchronized across heterogeneous surfaces, and transactional workflows span commerce, access control, and real-time activity pipelines in parallel. This article examines how structured quality engineering frameworks address these challenges at the ecosystem level. It analyzes the architectural complexity introduced by multi-channel interaction surfaces and physical-to-digital data flows, including the measurement standardization requirements of internationally deployed device fleets. It then develops a principled treatment of data integrity in biometric-integrated platforms, covering device-to-platform schema governance, accuracy validation, and automated reconciliation pipelines. The article further addresses reliability engineering for edge-to-cloud streaming systems, where connectivity variance in physical club environments introduces failure modes not captured by conventional test environments. The article looks at workflow orchestration in high-end digital training settings, such as eligibility gating, journey-level validation, and commerce transactional integrity. It shows that quality assurance must work across multiple layers of business logic and asynchronous state transitions. Finally, the article examines how continuous integration pipelines, site reliability engineering disciplines, and feedback-driven automation architectures constitute the operational backbone through which ecosystem-level quality is enforced and evolved.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8214

From Compliance Burden to Competitive Advantage: Leveraging RPA and AI for Streamlined Compliance Documentation and Audits

Sapna Nishant Pillai — Sat, 14 Feb 2026 00:00:00 +0000

With the growing complexity of regulatory requirements, the need for compliance with national and international standards has increased significantly for organizations across different sectors. Conventionally, compliance management has been viewed as a resource-intensive duty that takes away important resources from core business goals. However, this perception is being drastically changed by integrating RPA with AI technologies. These technologies digitally enable the automation and intelligent optimization of compliance processes, helping organizations transition to proactive, data-driven governance models from reactive management approaches, thereby creating quantifiable value. This article examines how RPA and AI enhance the efficiency of compliance documentation, prepare organizations better for audits, and present regulatory compliance as a competitive advantage in today's complex business environments. It analyzes the current landscape of compliance, explores how RPA streamlines rule-based compliance activities, investigates AI cognitive capabilities for processing unstructured regulatory data, and quantifies the multidimensional benefits of technology-enabled compliance. The article also addresses critical implementation considerations that are necessary for the successful deployment of automated compliance systems.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8215

Intent-Driven Fleets: An Agentic AI Framework for Cloud Elasticity

Somdutt Brajaraj Patnaik — Sat, 14 Feb 2026 00:00:00 +0000

The evolution of cloud infrastructure toward hyper-scale deployments has exposed the fundamental inadequacy of reactive, threshold-based auto-scaling mechanisms. As digital services grow to serve global user bases during concentrated seasonal demand windows, the gap between high-level business objectives and low-level infrastructure execution has widened into a structural operational failure. Intent-Driven Fleets (IDF) address this gap through an autonomous orchestration framework that coordinates specialized AI agents, Commander, Forecasting, Provisioner, and Efficiency via the Model Context Protocol (MCP), enabling infrastructure to reason about goals rather than execute pre-written rules. The framework proposes the Plan-Execute-Observe-Reflect (PEOR) cycle, a formalized iterative process in which infrastructure anticipates demand, breaks business intent down into dependency-based execution plans, accepts business-layer telemetry to provide a context in which decisions are made, and continually optimizes provisioning behaviour via long-term memory. Security is also achieved by a deterministic guardrail layer, which is directly implemented as part of the MCP server that ensures that agent actions are not unlimited financially by permitting only signed authorization tokens, which are verified prior to each tool call. Individually identified engineering issues of this architecture are: context window congestion when receiving full telemetry, tool-call latency buildup amidst multi-region provisioning sequences, and concurrency conflicts necessitating distributed intent locking. The IDF framework establishes intent as the most effective abstraction for managing hyper-scale cloud environments, pointing toward a genuinely autonomous operational paradigm where global infrastructure responds directly to business goals without requiring continuous human translation at every execution step.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8220

Augmenting Retail Intelligence: A Human–AI Framework for Operational Decision-Making and Digital Governance

Sanjay Basu — Sat, 14 Feb 2026 00:00:00 +0000

Structural shifts in retail have brought artificial intelligence from peripheral experimentation into the operational core of commercial enterprises worldwide. Yet accumulated deployment experience has surfaced a consistent finding: systems configured for maximal automation frequently underdeliver relative to those that distribute decision authority deliberately between algorithmic processes and human practitioners. The gap between technical capability and organizational utility, it turns out, is bridged less by model sophistication than by interaction architecture — the degree to which AI-generated outputs are made accessible, interpretable, and genuinely actionable for the people who must apply them. This article examines that gap directly, constructing a structured three-layer framework for Human-in-the-Loop retail intelligence and tracing its application across merchandising, demand planning, pricing strategy, inventory coordination, and e-commerce infrastructure operations. Large language models receive focused attention as the interface mechanism through which operational practitioners engage with machine-generated insight without requiring analytical or technical specialization. Governance structures, explainability requirements, and ethical conditions are treated as integral components of the collaboration model rather than supplementary considerations. The article concludes that augmented intelligence — specifically, the deliberate structuring of complementary human and machine contributions — constitutes the configuration at which retail AI systems generate their most durable and organizationally meaningful value.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8221

Artificial Intelligence for Accessibility and Performance Auditing: Automated Findings with Human Judgment

Harshit Sunilkumar Vora — Sat, 14 Feb 2026 00:00:00 +0000

Automated accessibility and performance auditing tools have become integral to modern web development pipelines, yet systematic evidence shows that treating their outputs as definitive conformance verdicts leads to programs that are overconfident in coverage and underinvest in expert judgment. Deterministic rule engines reliably surface structural defects at scale but remain fundamentally constrained in their ability to evaluate success criteria requiring semantic interpretation, contextual reasoning, or natural language understanding. Established standard frameworks—structured around principles of perceivability, operability, understandability, and robustness—provide the normative foundation against which both automated and human findings must be mapped to remain institutionally credible and legally defensible. Performance auditing presents a structurally parallel set of challenges, where threshold-based metrics require human disambiguation before remediation decisions can be responsibly made. The empirical boundaries of automated detection are quantified through mutation testing and coverage analysis, confirming that no single tool is sufficient and that tools are structurally complementary rather than interchangeable. Artificial intelligence augmentation extends automated coverage into semantically demanding criteria, achieving meaningful detection rates that conventional rule engines cannot approach, while introducing anchoring risks that demand carefully designed human-in-the-loop workflows. A continuous auditing pipeline with graded confidence tiers — separating high-confidence structural findings, medium-confidence semantic assessments, and low-confidence interaction-dependent evaluations — provides the operational architecture necessary to allocate expert attention proportionally, measure program quality over time, and produce findings that are auditable, reproducible, and defensible across tool versions and evaluation cycles.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8222

Autonomous Optimization of Business Intelligence Platforms Through Multi-Agent Systems

Mallikarjun Reddy Ramasani — Sat, 14 Feb 2026 00:00:00 +0000

Despite being a core piece of enterprise decision-making, Business Intelligence (BI) optimization is still reactive and limited to isolated automation scripts that cannot adapt to the growing complexity of today's analytics landscape in a continuous fashion. This article introduces a novel Multi-Agent Autonomous Optimization Framework (MAAOF), a distributed and highly adaptable approach based on cooperating and competing autonomous agents to continuously optimize the performance, data quality, query execution, and governance of all layers of a BI architecture. The combination of reinforcement learning, the distributed coordination of agents, and metadata-driven intelligence supports increasing levels of autonomous adaptation while keeping human oversight available. Compared to the benchmark industry standard, experiments conducted on simulated enterprise settings of a regulated banking infrastructure show MAAOF's ability to improve query latency, data pipeline efficiency, anomaly detection, and regulatory compliance. The work presents an integrated method that builds upon agentic artificial intelligence theory, autonomous systems theory, and self-healing data architecture. It helps establish scalable, adaptive, and resilient analytics infrastructures in contemporary enterprises.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8223

Zero Trust Segmentation for Cloud-Native and AI Service Architectures: An Intelligent Policy Enforcement Framework to Minimize Lateral Movement

Navaneeth Komirisetty — Fri, 01 May 2026 00:00:00 +0000

Cloud-native architectures break the concept of a perimeter, making lateral movement a focus of concern for distributed enterprise systems. AI services add to the attack surface via east-west traffic. As every workload, every pipeline, and every model-serving endpoint is a potential attack pivot point, layering zero-trust segmentation controls across identity, network, workload, and data planes provides a complementary strategy that restricts lateral movement in modern cloud-native and AI environments. The paper proposes a micro-segmentation model, disassociating policy decision and policy enforcement components in the context of securing data flow networks. The proposed model leverages workload identity, explicit allow-listing of communication patterns, and a service mesh to achieve micro-segmentation. Another AI-specific segmentation model addresses the introduction of the LLM tool chain‚ vector databases‚ and agentic services into a system's trust boundaries․ This model adopts operational governance‚ evidence generation‚ and alignment with the NIST SP 800-207 and AI Risk Management Framework as early design requirements for the implementation and operation of zero trust segmentation in regulated and critical services contexts․ It allows security architects and platform leaders to implement solutions through structured evidence generation.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8224

Human–AI Collaboration as Critical Digital Infrastructure: Hybrid Impact on Enterprise Operations and Quality Engineering

Shreelekha Ramabadran — Fri, 01 May 2026 00:00:00 +0000

Collaboration with AI has crossed the divide from curiosity in the lab to a core expectation of enterprise automation, especially in regulated and high-reliability systems. AI assistants based on LLMs, RAG, and agentic AIOps are all indispensable tools for engineers and support professionals when it comes to knowledge retrieval, drafting, incident triage, root cause analysis, and workflow automation. However, quality engineering teams can rely upon human-in-the-loop (HITL) collaboration to accelerate test design, understand defects, and assess safety for release. This can lead to operational hazards, including hallucinations (confabulation), automation bias, model drift, exposure of private training data, and governance issues, which can erode trust in AI outputs when they're assumed to be correct. This summarizes the state of the art in human-AI interaction models, including the HITL pipeline, mixed-initiative control sharing, and symbiotic teaming, and their supporting toolchains, benefits, and challenges. To inform future governance directions, moving on to discussing NIST AI RMF 1.0 and Generative AI Profile (NIST AI 600-1), ISO/IEC 23894, and ISO/IEC 42001. A hybrid governance approach is proposed. It introduces principles for evidence-based grounding, risk-based autonomy, and traceable decision-making. Lastly, it introduces a vision of collaborative adaptation where AI initiatives based on confidence, impact, and policy constraints maintain human accountability while achieving scalability of productivity and reliability.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8225