Unlike the Industry 4.0 narrative, which celebrated automation for its own sake and measured progress in throughput, uptime and cost reduction, Industry 5.0 is defined by a deliberate rebalancing. The framework, first articulated prominently by the European Commission, calls for industrial systems that are human-centric, sustainable and resilient. The new volume embraces that framing directly. According to the publisher, the book examines the evolving relationship between people and intelligent technologies, emphasizing innovation, sustainability and organizational resilience, and draws on conceptual analyses, systematic reviews and practical perspectives to explore how digital tools can support human-centric workplaces, sustainable business practices and adaptive organizational strategies.

The technical scope of the book is broad. Its chapters cover unified communication technologies, artificial intelligence, the Internet of Things, big data, machine learning and blockchain, alongside their applications in business transformation and sustainable development. That combination is significant because these technologies are rarely discussed as a single interacting system. In an Industry 5.0 setting, they are best understood as layers of one architecture: IoT devices generate streams of operational data, big data platforms aggregate and store those streams, machine learning models extract patterns and predictions from them, AI systems act on those predictions in ways that can augment or displace human judgment, blockchain provides tamper-resistant records of transactions and provenance, and unified communication tools bind the whole loop back to the human workers who must supervise, correct and learn from it.

Human-computer interaction sits at the center of that loop, and the book’s emphasis on it reflects a growing recognition in the research literature that the success of intelligent systems depends less on raw algorithmic performance than on the quality of the interface between machine and operator. A predictive maintenance model that flags a failing bearing is only useful if the technician who receives the alert can understand why it was raised, trust it enough to act, and feed back information when it is wrong. Interaction design, explainability, cognitive load and trust calibration are therefore not cosmetic concerns but determinants of whether human-centric automation actually functions in practice.

The volume also extends beyond the factory floor. Among the topics listed by the publisher are service flexibility, marketing strategies, green innovation in higher education, sustainable banking, innovation management in volatile environments and leadership for risk management in what strategists call a VUCA world, an environment marked by volatility, uncertainty, complexity and ambiguity. That range signals an important feature of the Industry 5.0 debate: the same technological stack that reorganizes manufacturing is now reorganizing services. Banks deploy machine learning for credit assessment and fraud detection while under pressure to demonstrate sustainable finance credentials; universities pursue green innovation while digitizing teaching; marketing organizations use AI-driven analytics to reshape customer relationships in ways that raise questions about autonomy and manipulation.

Sustainability receives particular attention throughout the book. The link between digital transformation and environmental performance is neither automatic nor simple. Data centers, sensor networks and blockchain systems carry real energy and material costs, and poorly governed digitalization can simply accelerate consumption. The chapters on green innovation and sustainable development engage with the counterargument that, when designed deliberately, digital technologies can reduce waste, optimize energy use, shorten supply chains and make environmental impacts measurable and auditable. The book’s framing suggests that whether digitalization becomes an ecological liability or an asset depends on organizational strategy and governance rather than on the technologies themselves.

Resilience is the third pillar of the Industry 5.0 triad, and the book’s treatment of leadership and risk management in VUCA environments addresses it head-on. The past several years have given organizations repeated stress tests: pandemic disruptions, geopolitical shocks, supply chain breakdowns and the sudden arrival of generative AI. In such conditions, rigid, efficiency-maximized systems tend to fail brittlely, while organizations that combine technological adaptability with empowered, well-informed human decision-makers absorb shocks and recover faster. The volume’s chapters on innovation management in volatile environments and leadership for risk management argue, in effect, that resilience is a designed property, built through communication infrastructure, distributed intelligence and organizational culture, not a fortunate accident.

The book is edited by Dr. Sonal Trivedi, an Associate Professor at Manav Rachna University in India, who brings more than fourteen years of teaching and research experience across institutions including VIT Bhopal, Chitkara University and SVNIT. She holds a Ph.D. from the University of Kota and has authored or edited more than a dozen Scopus-indexed books with publishers including Springer Nature, CRC Press, Emerald, Taylor & Francis and Bentham Science, in addition to over thirty book chapters and numerous Scopus-indexed journal papers. She also holds eight granted patents and serves as a reviewer for several international journals, including those published by Wiley and Emerald. Her editorial background spans management, information technology and applied engineering, which helps explain the volume’s unusually wide disciplinary reach.

The intended readership is correspondingly broad. The publisher identifies researchers, graduate students, policymakers and professionals working in management, information technology, digital innovation and sustainable development as the target audience. That mix matters, because the questions raised by Industry 5.0 do not respect disciplinary boundaries. A policymaker drafting rules for algorithmic workplace monitoring, a chief information officer selecting an IoT platform, a sustainability officer auditing a bank’s green credentials and a graduate student modeling human trust in automation are all, in different ways, working on the same underlying problem: how to keep human beings meaningfully in command of systems that increasingly learn, decide and act on their own.

The release comes at a time when the human-centric turn in industrial policy is gaining institutional weight. The European Commission’s Industry 5.0 framework has been followed by research funding calls, academic conferences and corporate sustainability reports that adopt its vocabulary of human-centricity, resilience and sustainability. Critics have asked whether Industry 5.0 is a genuine paradigm shift or a rebranding of Industry 4.0 with better public relations, and the honest answer may depend on implementation. If organizations deploy AI and IoT purely to cut labor costs, the human-centric label is hollow. If they use the same technologies to reduce physical strain, expand worker skills, shorten feedback loops and make environmental performance transparent, the label describes something real. Books like this one, which assemble conceptual analysis, systematic literature review and practitioner perspectives across sectors, serve as early maps of that implementation landscape, and they will help determine whether the next industrial era is remembered for its machines or for the people who worked alongside them.

Subject of Research: Human-computer interaction and human-centric digital technologies in Industry 5.0 workplaces

Article Title: Human-Computer Interaction in Industry 5.0: Innovation, Sustainability and the Future of Work

Article References: Human-Computer Interaction in Industry 5.0: Innovation, Sustainability and the Future of Work. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Industry 5.0, human-computer interaction, artificial intelligence, Internet of Things, big data, machine learning, blockchain, sustainability, green innovation, future of work, organizational resilience, VUCA leadership

The study, authored by Irlenys Josefina Tersek Rodríguez and published open access in October 2026, develops a construct called AI-supported decision structures: the formal organizational arrangements through which AI-generated cybersecurity inputs become admissible, reviewable, and actionable within decision processes. The framing deliberately shifts attention away from the AI tool itself and toward the decision architecture that surrounds it. In cybersecurity, decisions about escalating an alert, isolating a host, deferring a patch, accepting residual risk, or invoking crisis procedures are made under time pressure and uncertainty, and they carry legal, operational, reputational, and continuity consequences. Cybersecurity decision-making, the paper insists, is therefore not merely technical problem solving but a governance process in which evidence, authority, accountability, and coordination must be aligned.

To build the framework, the study draws together several research streams that have rarely been integrated. Information systems governance explains how organizations allocate decision rights and design structural, procedural, and relational mechanisms such as committees, policies, and escalation procedures. Cybersecurity governance research emphasizes board oversight, risk ownership, and incident escalation. Responsible AI scholarship contributes principles of transparency, accountability, explainability, and human oversight, now reinforced by regulation such as the European Union’s AI Act, which ties high-risk AI systems to risk management, documentation, record keeping, and monitoring. Research on AI and organizational decision-making shows that AI reshapes search, speed, scale, explainability, and delegation, while resilience research describes how organizations anticipate, cope with, and adapt to adversity. What remains underdeveloped, the paper argues, is an account of how AI-generated analytical inputs move from machine output to legitimate organizational evidence.

The gap is sharpest in cybersecurity itself, which the author describes as a particularly demanding context for AI-supported governance for five reasons. Decisions are time-sensitive, since delayed action may allow an intrusion to expand while premature action may disrupt operations. Evidence is probabilistic and incomplete, requiring interpretation of alerts, logs, and threat indicators. Decisions cut across security operations, infrastructure, legal, compliance, business continuity, and senior management. Actions must be retrospectively defensible to auditors, regulators, boards, customers, or courts. And the domain is surveillance-intensive, depending on continuous monitoring of users, endpoints, networks, identities, and behavior. In this setting, AI may increase detection and prioritization capacity, but it can also produce false positives, false negatives, automation bias, alert fatigue, opaque evidence trails, and unclear responsibility.

The proposed construct comprises five interrelated dimensions, which the author presents as a governance configuration rather than a checklist. Validation refers to routines that filter AI-generated inputs for reliability and contextual relevance before they influence authorization, including checks on data quality, model suitability, confidence thresholds, and review by qualified personnel. Control refers to rules governing how inputs may be used, challenged, escalated, or overridden, including permissions, review gates, segregation of duties, and exception procedures. Institutional embedding incorporates AI outputs into recurring routines such as vulnerability review meetings, security operations center triage forums, risk committees, and post-incident reviews. Accountability ensures that someone remains responsible for the final decision and that the influence of AI-generated inputs can later be reconstructed. Substantive human oversight, finally, preserves the meaningful capacity of qualified actors to interpret, contest, contextualize, and override machine outputs.

Each dimension operates through a distinct theoretical mechanism. Validation works through evidentiary filtration, reducing the probability that unreliable or decontextualized outputs enter formal decisions as credible evidence. Control works through procedural constraint, clarifying when AI inputs may influence decisions and when human or committee review is required. Institutional embedding works through routinized coordination, turning isolated analyst artifacts into shared signals that support collective prioritization and learning. Accountability works through responsibility preservation, preventing responsibility from being displaced onto the AI system or diffused across technical and managerial actors. Oversight works through judgmental correction, mitigating automation bias when outputs are incomplete, misleading, or organizationally inappropriate. The paper formalizes these mechanisms in five propositions linking the dimensions to decision quality, decision legitimacy, coordinated response, and organizational resilience.

Crucially, the framework introduces theoretical tension rather than a simple prescription that more governance is always better. Stronger validation may improve reliability while slowing response. Stronger control may improve legitimacy while reducing improvisational flexibility during a live incident. Institutional embedding may improve coordination while normalizing surveillance and unquestioned reliance on executive dashboards. Accountability may improve defensibility while encouraging defensive documentation. Human oversight may correct AI outputs while becoming purely symbolic when workload, hierarchy, or time pressure prevents meaningful challenge. The effect of each dimension is also conditional: validation matters most when threat conditions are ambiguous, control matters most in regulated or audit-intensive settings, and oversight matters most when reviewers possess genuine domain competence and actual authority to override the machine.

The study also connects cybersecurity to the broader politics of digital surveillance. Because organizations protect digital assets partly by observing systems, networks, identities, and behavior, AI-supported security tools intensify organizational visibility by classifying behavior, detecting deviations, and producing prioritized alerts. The author, drawing on recent work on digital surveillance governance, argues that this does not make security monitoring illegitimate, but it does mean governance must address the dual character of AI-generated inputs: they can support anticipation and rapid response while simultaneously expanding surveillance capacities, encoding classifications of risky behavior, and shaping how employees or events are treated. AI-supported decision structures, on this reading, must specify not only how AI outputs support security action but also how the surveillance capacities underlying those outputs remain transparent, bounded, reviewable, and accountable.

Organizational resilience serves as the framework’s core outcome, understood through the capability-based triad of anticipation, coping, and adaptation. AI-supported decision structures contribute to anticipation when validated inputs improve early warning, vulnerability prioritization, and risk visibility. They contribute to coping when control mechanisms and embedding connect signals to escalation paths, response authority, and coordinated action during disruption. They contribute to adaptation when accountability and documentation make it possible to reconstruct how AI inputs influenced decisions and to revise models, thresholds, procedures, and training after incidents. But the resilience claim is explicitly conditional: the same structures can weaken resilience when they produce overreliance, brittle procedures, excessive centralization, or surveillance practices that erode trust and reduce reporting behavior.

The practical implications are pointed. AI adoption, the paper warns, should not be confused with governance maturity: organizations may invest heavily in models, data pipelines, and AI-enabled platforms while underinvesting in the structures required to validate, challenge, document, and justify the outputs those systems produce. Managers should specify who may rely on AI-generated inputs, what review is required before action, how disagreement is handled, what must be documented, when escalation is mandatory, and where final responsibility remains. The framework is most applicable to governance-intensive, high-stakes settings such as cybersecurity, financial fraud detection, clinical decision support, credit-risk assessment, and critical infrastructure monitoring, and less relevant to low-stakes uses like routine chatbots. As a conceptual contribution, it does not empirically test its propositions, and the author acknowledges that the five dimensions may overlap in practice and that validated measurement instruments remain future work. Even so, the study offers a precise foundation for one of the defining governance questions of the AI era: how machine-generated judgment becomes accountable human decision.

Subject of Research: AI-supported cybersecurity governance and organizational resilience

Article Title: Toward an integrative theoretical model of AI-supported cybersecurity governance and organizational resilience

Article References: Rodríguez, I. J. T. (2026). Toward an integrative theoretical model of AI-supported cybersecurity governance and organizational resilience. Discover Artificial Intelligence, 6(1), Article 1322. https://doi.org/10.1007/s44163-026-02050-0

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02050-0

Keywords: artificial intelligence, cybersecurity governance, AI governance, decision structures, human oversight, organizational resilience, digital surveillance, accountability, incident response, EU AI Act, automation bias, risk management

The core of the paradox lies in what efficiency-oriented redesign typically demands. Streamlined processes favor standardization, rigid control points, and predictable, repeatable transaction flows. Automation engines and shared service centers perform best when inputs are uniform and exceptions are rare. Yet financial flexibility—the capacity of an organization to redirect funds quickly, renegotiate commitments, restructure obligations, or exploit unexpected opportunities—thrives on the opposite qualities: optionality, slack resources, and processes that can absorb irregularity. When a company engineers its finance function purely for throughput, the study argues, it tends to eliminate exactly the slack and adaptability that would allow it to respond to shocks, opportunities, or shifting strategic priorities.

This tension is not merely theoretical. Consider the finance function of a multinational firm that consolidates payment processing into a single global hub. Transaction costs per invoice plummet, error rates fall, and headcount requirements drop substantially. But the same consolidation often imposes fixed service agreements, standardized credit terms, and tightly sequenced approval chains that cannot be bent when a subsidiary needs to disburse emergency funds during a supply disruption or prepay a supplier to lock in scarce inventory. The reengineered process delivers efficiency in ordinary times and rigidity in extraordinary ones. The study’s analysis suggests that organizations routinely measure the first effect and ignore the second, because flexibility has no line item on the income statement until the moment it is missing.

Technically, the research situates this paradox within established frameworks from operations management and corporate finance. Process reengineering, descending from the business process reengineering movement of the early 1990s, treats workflows as candidate objects for fundamental redesign rather than incremental improvement. Its canonical metrics—cycle time, cost per transaction, first-pass yield, straight-through processing rates—reward the removal of human intervention, redundant authorization, and buffer capacity. Financial flexibility, by contrast, is typically operationalized in the corporate finance literature through cash holdings, unused debt capacity, access to revolving credit facilities, and the structural ability to adjust capital allocation without friction. The study’s contribution is to show that these two constructs are coupled: many of the design choices that maximize the first set of metrics mechanically degrade the second.

The coupling operates through several identifiable mechanisms. First, standardization reduces the variety of financial instruments and payment arrangements a firm can deploy. A treasury operation tuned to one set of standardized instruments loses fluency in alternatives—supply chain finance, dynamic discounting, bespoke hedging structures—that become valuable under stress. Second, centralization concentrates decision rights in ways that lengthen the effective distance between the point where a financial need arises and the point where authority to act resides. Third, automation embeds business logic into systems that are expensive and slow to modify, so that adapting to a new regulatory regime, a new tax structure, or an acquisition requires reengineering the reengineered process. Fourth, the elimination of slack—excess capacity in finance teams, buffer cash positions, unallocated budget envelopes—removes the shock absorbers that historically allowed organizations to operate through turbulence without renegotiating their entire financial architecture.

The research frames these mechanisms as a governance problem as much as an engineering one. Executives who sponsor reengineering programs are typically accountable for cost metrics that appear within one or two budget cycles, whereas the flexibility costs of redesign surface only in rare, hard-to-attribute events—a market dislocation, a supplier failure, a sudden regulatory shift. This asymmetry in visibility creates a systematic bias: managers rationally optimize for what is measured and rewarded, even when they understand, at some level, that optionality has value. The study suggests that the paradox persists not because leaders are unaware of the trade-off, but because organizational incentive structures make it rational to ignore it. Flexibility is, in effect, an unpriced insurance policy that reengineering programs quietly cancel.

Methodologically, the study draws on the interdisciplinary territory of Humanities and Social Sciences Communications, blending process management theory with insights from organizational sociology and financial economics. Rather than treating finance as a neutral plumbing system, the analysis treats financial processes as social and institutional structures that encode relationships—with suppliers, lenders, regulators, and internal business units. When those structures are flattened for efficiency, the relational capital embedded in them deteriorates. A long-standing banking relationship nurtured through flexible, negotiated transactions, for instance, may deliver little measurable value in a dashboard and yet prove decisive when credit markets freeze and only trusted counterparties can access funding. Reengineering, by replacing negotiated relationships with standardized interfaces, liquidates this relational capital without recording the loss.

The practical implications for practitioners are significant. The research points toward design principles that acknowledge the trade-off rather than deny it. Organizations might deliberately preserve targeted pockets of redundancy—retained decision authority for time-critical disbursements, dual-sourced banking arrangements, modular automation architectures whose business rules can be reconfigured without full redevelopment. They might also introduce flexibility metrics into reengineering business cases, explicitly valuing the option to redirect capital, reprice commitments, or resequence obligations under defined stress scenarios. Real options reasoning, long applied to capital investment, could be extended to process design: a standardized workflow and a semi-flexible one should be compared not only on steady-state cost but on the value of the choices each preserves. The study implies that firms which do this accounting honestly will often choose less aggressive reengineering than pure cost analysis recommends.

The findings also carry implications for how scholars understand organizational resilience more broadly. In recent years, research on supply chain resilience and operational robustness has converged on a similar conclusion: efficiency and adaptability are not independent dimensions that can be maximized simultaneously but competing objectives that must be actively balanced. The finance function, often the last stronghold of standardized, centralized operations, is now shown to obey the same law. This suggests that the popular corporate aspiration of a ‘frictionless’ finance department—touchless invoices, algorithmic budget approvals, continuous automated close—may be self-defeating at the margins, because friction in financial processes is sometimes the visible expression of the optionality that keeps an organization maneuverable.

Ultimately, the study’s paradox is best read as a caution against single-objective optimization in domains that exist to manage uncertainty. Financial processes serve two masters: they must execute the routine flow of money with minimal waste, and they must preserve the organization’s capacity to act when the routine breaks. Reengineering programs that acknowledge both mandates—and that treat flexibility as an asset with a real, estimable value rather than as waste to be eliminated—stand a better chance of building finance functions that are not only lean in calm markets but dependable in stormy ones. The efficiency paradox, on this reading, is not an argument against redesign but a demand that redesign be measured against the full spectrum of what finance is for.

Subject of Research: The trade-off between efficiency gains and reduced financial flexibility in financial process reengineering

Article Title: The paradox of financial process reengineering: efficiency gained vs. financial flexibility reduced

Article References: The paradox of financial process reengineering: efficiency gained vs. financial flexibility reduced. (n.d.). https://doi.org/10.1038/s41599-026-09058-y

Image Credits: AI Generated

DOI: 10.1038/s41599-026-09058-y

Keywords: financial process reengineering, financial flexibility, business process reengineering, efficiency, corporate finance, organizational resilience, automation, treasury management, governance, standardization, operational risk, humanities and social sciences