Sustainable Digital Economy Transformation Through Intelligent Automation: A Multi-Environmental Framework for Strategic Decision-Making

Abstract

Organizations implement intelligent automation across diverse operational contexts but often lack comprehensive frameworks for strategic decision-making and cross-domain integration. The existing literature frequently examines isolated applications with limited implementation guidelines addressing environmental interdependencies. This study conducts a systematic review of 69 publications (2019–2024) using thematic analysis to examine automation patterns across six environmental domains: social, economic, educational, scientific, technological, and ecological. The analysis identifies three implementation patterns: efficiency-focused domains (economic, technological) emphasizing operational optimization; capability-focused domains (social, educational) prioritizing human augmentation; innovation-focused domains (scientific, ecological) developing transformative applications. Cross-domain analysis reveals integration opportunities and sustainability considerations. The study proposes a strategic decision-making framework incorporating environmental assessment tools, quality enhancement mechanisms, and planning capabilities. This framework supports organizations in selecting domain-appropriate strategies while addressing sustainable transformation objectives. The research provides systematic environmental categorization of intelligent automation applications and offers implementation guidelines for practitioners pursuing coordinated digital transformation across organizational contexts.

1. Introduction

Contemporary digitalization has significantly reshaped organizational approaches to intelligent automation across diverse operational contexts. Fernandez and Aman [1] identify implementation challenges that require organizational changes in global business services, demonstrating the complexity beyond simple technology adoption, while Kuzior et al. [2] establish how the informatization of innovative technologies significantly increases macroeconomic indicators and socio-economic development processes in circular economy conditions. Ng et al. [3] provide systematic evidence that intelligent automation combining RPA, AI, and soft computing transcends traditional decision-making and spans multiple organizational contexts. Organizations increasingly recognize that automated systems represent strategic enablers rather than mere operational tools.

Strategic automation implementation requires sophisticated approaches that consider multiple environmental factors simultaneously. Traditional management frameworks prove insufficient for modern technological complexity, as demonstrated by Kuzior et al. [4] regarding AI-enhanced organizational models and Rossmann et al. [5] concerning AI-powered service management. Contemporary research demonstrates that systematic three-pillar frameworks (task automation, cognitive enhancement, and human–machine symbiosis) provide structured approaches to intelligent automation implementation in Industry 5.0 contexts [6]. This complexity necessitates integrated decision-making models that support organizations in achieving both transformation objectives and sustainability commitments. Recent sector analysis demonstrates that hybrid AI–RPA systems achieve significantly higher process efficiency compared to traditional implementations across multiple sectors [7].

Despite growing consensus on smart automation benefits, several controversial aspects remain unresolved, creating challenges for practitioners and researchers alike. Key debates include the ongoing debate over automation’s net employment impact versus job creation potential, with studies such as Dwivedi et al. [8] examining AI’s transformative potential for augmentation and potential replacement of human tasks across industries including finance, healthcare, and manufacturing. Kandaurova et al. [9] identify how low-code conversational AI platforms create new opportunities for business process automation adoption, and Mayr et al. [10] suggest that intelligent process automation enables more efficient use of the human workforce through augmented decision-making capabilities rather than wholesale replacement. While automation enhances productivity, research reveals the importance of careful consideration of sustainability metrics in automation design due to potential environmental impacts [11].

Disagreements persist about optimal implementation approaches, with Haase et al. [12] emphasizing the need for interdisciplinary research on human–automation interaction and adaptive training strategies, while Uklańska [13] demonstrates through bibliometric analysis that RPA implementation models lack standardized sequential frameworks across different organizational contexts. Additionally, conflicting evidence exists regarding sustainability outcomes, with research such as that of van der Aalst [14] demonstrating how hybrid intelligence blends human and machine intelligence to combine the best of both worlds, emphasizing that most processes work best using a combination of human and machine intelligence rather than replacing humans entirely. Zelisko et al. [15] showcase sustainable automation implementations in agricultural sectors through smart farming technologies that improve efficiency while reducing environmental resource consumption. These controversies underscore the need for frameworks addressing multiple perspectives while providing implementation guidance.

This study addresses these challenges by developing a systematic approach to intelligent automation implementation that considers key environmental factors. The research significance lies in its environmental analysis methodology and the development of an integrated strategic decision-making model that bridges theoretical understanding with practical application needs. Our investigation reveals that successful digital economy transformation through intelligent automation requires innovative management approaches that can navigate complex environmental interdependencies while achieving sustainable socio-economic impact. Recent digital transformation research emphasizes sustainable computing approaches, with frameworks developed [16] showing how AI and IoT technologies enable predictive analysis for knowledge-based organizations, while supply chain studies [17] demonstrate that Industry 4.0 technologies enhance both operational efficiency and environmental sustainability through strategic process alignment. These developments reinforce the necessity for integrated approaches that simultaneously address technological advancement and environmental sustainability considerations. Recent analysis demonstrates that successful AI-driven automation strategies require integrated approaches encompassing technology selection, phased deployment, and continuous monitoring [18]. Empirical evidence establishes direct connections between digital transformation and green innovation, with systematic automation approaches enhancing green innovation capabilities in manufacturing firms [19]. It is concluded that organizations implementing intelligent automation must adopt multi-dimensional strategic frameworks that simultaneously address technological capabilities, environmental sustainability, and stakeholder requirements. Furthermore, the study establishes that effective automation implementation depends on systematic environmental analysis and strategic decision-making processes that integrate analysis tools, decision quality enhancement mechanisms, and planning capabilities across all environmental domains. This paper pursues four interconnected objectives:

• Systematic analysis of smart automation applications across six environmental domains examining implementation patterns and their strategic implications;
• Development of an integrated decision-making framework for AI-driven automation considering environmental sustainability factors;
• Identification of key strategic dimensions: analysis tools, quality enhancement mechanisms, and planning capabilities;
• Provision of practical implementation guidelines for organizations pursuing sustainable digital transformation.

This study addresses four interconnected research questions that guide our systematic investigation:

• How do intelligent automation implementations manifest across different environmental domains, and what patterns emerge from cross-domain analysis?
• What strategic decision-making frameworks are needed for successful automation implementation that integrate environmental sustainability considerations?
• What are the key dimensions of strategic decision support in multi-domain automation contexts, and how do they interact across organizational boundaries?
• How can organizations systematically integrate environmental sustainability considerations into automation decision-making processes while maintaining operational effectiveness?

Research on the digital economy and green innovation demonstrates that high-quality economic development requires integrated approaches combining digital transformation with environmental sustainability considerations [20]. This research contributes to existing knowledge by developing a systematic environmental analysis framework for smart automation applications across six domains and proposing a strategic decision-making model that integrates sustainability factors. The study identifies key dimensions of strategic decision support—analysis tools, quality enhancement mechanisms, and planning capabilities—while providing practical implementation guidelines for organizations pursuing sustainable digital transformation through AI-driven automation.

2. Materials and Methods

This research combines systematic literature review methodology [21] with thematic analysis to examine smart automation implementations across diverse environmental domains. The systematic review provides a framework for literature identification and selection, while thematic analysis enables systematic identification of implementation patterns and contexts.

Thematic analysis methodology was employed as the primary analytical approach, following Braun and Clarke’s six-stage framework [22]. This method was selected for its systematic rigor in identifying patterns across large datasets and flexibility in handling complex technological implementations. The initial stage involved data familiarization through detailed reading of all publications, noting initial patterns in automation implementations and applications. This was followed by systematic coding of implementation contexts, automation types, and outcomes, with each publication coded for technological approaches, application domains, and results.

The third stage involved grouping initial patterns into potential themes, with particular attention paid to implementation contexts and environmental impacts. These preliminary themes were then refined through iterative review, leading to the identification of six environmental domains as primary organizing frameworks. The fifth stage involved clearly defining and characterizing these environmental domains based on specific implementation patterns and technological approaches. The final stage comprised analysis of how intelligent automation manifests within each environmental domain, supported by specific implementation examples.

This systematic approach to data collection and analysis enabled robust identification of both implementation patterns and theoretical frameworks, while maintaining methodological rigor throughout the research process. The resulting environmental domain framework provided a structured foundation for analyzing intelligent automation implementations across different contexts.

The systematic literature review methodology was selected because intelligent automation implementation across multiple environmental domains represents a rapidly evolving field with distributed insights across diverse academic publications. This approach addresses the research complexity through systematic knowledge synthesis for three key reasons.

First, field fragmentation characterizes current research across multiple disciplines with limited cross-disciplinary integration. Our analysis reveals a domain-specific focus: social research emphasizes workforce transformation, as demonstrated by Chakraborty et al. [23] who examine AI and RPA confluence with concluding analysis of automation applications. Economic studies focus on manufacturing optimization, with Banța et al. [24] providing systematic analysis of Industry 4.0 manufacturing processes. Ecological research addresses sustainability in isolation, as shown by van der Aalst [14] who demonstrates hybrid intelligence approaches blending human and machine capabilities. This fragmentation leaves organizations without systematic guidance for coordinated implementation.

Second, significant theoretical gaps exist regarding unified frameworks for automation implementation across environmental domains. Ng et al. [3] provide systematic evidence that intelligent automation spans multiple organizational contexts but lacks environmental domain categorization. Fernandez and Aman [1] identify implementation challenges in global business services without addressing multi-domain integration. No existing research provides frameworks addressing all six environmental domains simultaneously.

Third, organizations require evidence-based guidance for sustainable automation implementation, yet existing research provides isolated case studies rather than systematic frameworks. Our methodology bridges this gap by synthesizing implementation patterns into actionable decision-making models that provide a foundation for future empirical research across environmental domains.

The systematic search was conducted using the Scopus database, focusing on intelligent automation implementations and their environmental impacts. The search strategy combined three key domains through specific search terms: artificial intelligence (“AI,” “machine learning,” “deep learning”), business process management (“BPM,” “process management,” “business process”), and automation (“robotic process automation,” “RPA,” “process automation,” “intelligent automation”). These terms were searched within titles, abstracts, and keywords of publications from 2019 to 2024, yielding 69 relevant publications. The corpus consists entirely of peer-reviewed articles and book chapters in English.

Quality assessment procedures involved systematic evaluation based on four key dimensions: methodological quality (research design appropriateness, data collection rigor, analytical validity), relevance evaluation (degree of intelligent automation implementation within environmental contexts), adequacy of data presentation (comprehensiveness of implementation details and outcomes reporting), and theoretical contribution (advancement of understanding regarding environmental domain applications).

These systematic protocols ensured only publications meeting established quality thresholds were included, maintaining methodological rigor while ensuring coverage of intelligent automation implementations across all environmental domains.

The selection process followed systematic protocols with clearly defined inclusion and exclusion criteria. Studies were evaluated based on methodological quality, relevance to intelligent automation implementation, and theoretical contribution to the field. Bibliometric analysis was conducted using VOSviewer software 1.6.20 [25] to validate our thematic approach and identify research patterns within the selected literature. Two complementary analyses were performed: keyword co-occurrence analysis identified 22 key terms with a minimum occurrence threshold of 3, revealing four main research clusters, while citation network analysis using document-level examination revealed core publications and research evolution patterns within the corpus. The keyword co-occurrence network is presented in Figure 1, while the citation network analysis results are shown in Figure 2.

The search initially yielded 266 records. These were then filtered by publication year (2019–2024), resulting in 202 records. Subsequently, document type filtering was applied to include only peer-reviewed articles and book chapters, excluding conference papers (n = 104), conference reviews (n = 19), books (n = 6), and reviews (n = 2). One additional study was excluded due to limited thematic relevance to intelligent automation applications, and one article was excluded due to retraction. The final corpus consisted of 69 publications: 42 peer-reviewed articles, and 27 book chapters, all published in English during the specified timeframe. The systematic search process is illustrated in Figure 3, which presents the PRISMA flow diagram showing the complete selection procedure from initial identification through final inclusion.

The selection process followed systematic protocols with clearly defined inclusion and exclusion criteria. Studies were evaluated based on four key dimensions: (1) methodological quality, (2) relevance to intelligent automation implementation, (3) adequacy of data presentation, and (4) theoretical contribution to understanding environmental domain applications.

The methodological integration followed Snyder’s systematic review guidelines [21] while incorporating thematic analysis to identify and organize implementation patterns. This combined approach enabled both systematic coverage of the literature and detailed analysis of implementation contexts. The systematic review methodology guided the identification and organization of the relevant literature, ensuring coverage of intelligent automation implementations. Simultaneously, thematic analysis provided the analytical framework for identifying patterns and relationships within this literature.

Through this integrated approach, each publication was systematically analyzed for implementation context and characteristics, recurring patterns and themes, environmental domain alignment, implementation outcomes and impacts, and cross-domain relationships. The analysis revealed patterns in how intelligent automation manifests across different environmental domains.

This dual methodological approach enabled robust identification of environmental domains while maintaining systematic rigor in literature analysis. The resulting framework combines the comprehensiveness of systematic review with the pattern recognition capabilities of thematic analysis, providing a robust foundation for understanding intelligent automation implementation across different environmental contexts. The analysis culminated in the development of tables mapping intelligent automation applications across six environmental domains, and the formulation of a strategic decision-making model for implementation. All analyzed publications were sourced from the Scopus database and remain publicly available through their respective publishers. No proprietary datasets were created during this study, as all analysis was conducted on publicly available academic literature. Contemporary developments in AI-driven automation and real-time information flow systems [29] further validate the need for systematic frameworks that can address the complexity of multi-domain implementation contexts.

3. Results

The initial search yielded 69 relevant publications from 2019 to 2024. Bibliometric network analysis revealed strong connections between core concepts: ‘robotic process automation’ (23 occurrences), ‘business process management’ (29 occurrences), ‘AI’ (33 occurrences), and ‘machine learning’ (19 occurrences), as illustrated in Figure 1. Citation network analysis identified five core publications forming the theoretical foundation: Siderska [30], Rizk et al. [31], Moderno et al. [32], Engel et al. [33], and Mayr et al. [10], demonstrating evolution from basic automation toward sophisticated AI-integrated systems, as shown in Figure 2. The analysis reveals a significant upward trend in research interest. The field has seen substantial growth, particularly in recent years, with 24 publications in 2024 and 17 in 2023, representing over 57% of the total literature in these two years. This marked increase from 9 publications in 2019, 6 in 2020, 10 in 2021, and 5 in 2022 indicates not only growing academic interest but also the maturing of smart automation as a research domain.

Thematic analysis of the publications reveals an evolution in research focus over this period. The early publications (2019–2020) primarily concentrated on foundational aspects of intelligent automation implementation and technical integration within business processes. From 2021 onwards, the research scope expanded to include more sophisticated applications across different environmental domains. The recent publications (2023–2024) show increased attention to environmental impacts, strategic decision-making frameworks, and cross-domain implementation patterns. This thematic progression reflects the field’s maturation from technical implementation concerns to broader strategic and environmental considerations in intelligent automation adoption.

Analysis reveals patterns of smart automation implementation across six environmental domains. In the social environment, publications demonstrate varied implementation approaches: Chakraborty et al. [23] examine the confluence of AI and RPA integration concluding with analysis of various RPA applications, while Chakraborty et al. [34] provide an intelligent automation framework introduction that integrates AI and RPA to move beyond rule-based approaches, and Urbani et al. [35] develop managerial frameworks specifically for evaluating AI chatbot integration in customer service contexts. The economic environment emphasizes manufacturing transformation with Banța et al. [24] demonstrating RPA-ERP integration in automotive Industry 4.0 and Schmitz et al. [18] analyzing smart automation implementation barriers. Financial innovations focus on Barta and Kumar [36] developing AI risk frameworks and Gul [37] applying genetic algorithms for process automation. Banking operations show sophistication with Tailor and Sharma [38] implementing RPA for loan-sanctioning processes. Educational domain publications concentrate on cognitive learning systems, with Engel et al. [33] specifically examining cognitive automation applications for enhanced learning experiences and establishing theoretical foundations for AI-enhanced educational processes. This domain shows advancement in automated assessment tools, with Polančič et al. [39] demonstrating the effectiveness of optical character recognition combined with machine learning for automatically transforming hand-drawn business process diagrams into digital format using TensorFlow-based solutions, while Ilieva et al. [40] develop intelligent decision support platforms that provide data-driven insights for educational improvement and strategic educational management, with significant developments in learning support systems [41]. Scientific domain implementation patterns reveal a focus on research methodology enhancement through human–machine research systems [42] and automated analysis platforms [43]. Research efficiency advances through automated knowledge extraction tools [37] and collaborative BPM platform evolution toward AI integration [43], indicating scientific automation maturity. The technological environment publications demonstrate coverage of RPA development [13] and integration frameworks [44]. Process optimization advances through strategic RPA frameworks [32] and AI-enhanced ERP systems [45], while ecological sustainability emerges via hybrid intelligence approaches [14] and agricultural AI implementations [15]. Environmental monitoring and analytics platforms show significant advancement through Bavaresco et al. [46] who develop machine learning-based automation for accounting services with environmental impact tracking, Chakraborty et al. [23] who examine AI and RPA confluence with concluding remarks on automation applications, and Chakraborty et al. [34] who provide intelligent automation framework introductions applicable to monitoring contexts. Agricultural automation demonstrates increasing sophistication with Tariq [47] exploring intelligent automation and blockchain integration in E-Business 5.0 frameworks for enhanced operational efficiency.

Environmental Analysis of Intelligent Automation Application

The analysis of intelligent automation applications reveals several key areas of implementation in social environments, as presented in

Table 1. Social environment applications of intelligent automation: customer analytics and workforce development.

Category	Application	Description	Sources
Social dynamics
	AI-driven customer analytics	Implementation of AI systems for customer interaction analysis and service optimization	[23,34,35]
	Human–machine interaction	Integration of social aspects in automation processes	[42]
	Cognitive process optimization	Enhancement of social processes through intelligent automation	[33]
	HR transformation systems	Implementation of AI and RPA in recruitment processes	[48,49,50]
Labor and workforce
	Automation adoption systems	Frameworks for implementing automation in organizations	[9,10]
	Interdisciplinary integration	Cross-functional automation implementation approaches	[12,48]
	Digital workforce development	Integration of automation for workforce enhancement	[49]
		Future perspectives of workforce digitalization	[50,51,52]
	Workforce management systems	RPA applications for Industry 4.0 workforce management	[53]

, which demonstrates the evolution from basic automation to sophisticated social integration.

The integration of intelligent automation in social environments has evolved to encompass more sophisticated applications. In the domain of social dynamics, AI-driven customer analytics serves as a foundational technology for business transformation, with Chakraborty et al. [23] examining the confluence of AI and RPA integration concluding with analysis of various RPA applications, Chakraborty et al. [34] providing an intelligent automation framework introduction that integrates AI and RPA to move beyond rule-based approaches, and Urbani et al. [35] developing managerial frameworks specifically for evaluating AI chatbot integration in customer service contexts. These findings are further reinforced by Rossmann’s analysis of chatbot impact on customer service performance [5]. This is enhanced by human–machine interaction considerations, thoroughly explored by Kreuzwieser et al. [42] and supported by Shneiderman’s framework for human-centered AI design [54], setting the foundation for social aspects of automation.

The cognitive aspects of process optimization have gained prominence, with Engel et al. [33] providing integrated conceptualization of cognitive automation that extends deterministic business process automation through probabilistic automation of knowledge and service work. HR transformation systems represent a significant advancement in the social domain, with Bajzikova et al. [55,56] examining how RPA and AI implementation improves recruitment processes in multinational organizations through automated candidate screening and evaluation, while Poisat et al. [57] investigate human resource managers’ perceptions of AI’s impact on workforce management and recruitment practices. Global forecasts regarding the development of Industry 4.0 are a premise for implementing new work patterns and thus changing the paradigm of the concept of human resources management. Robots will increasingly become not only assistants cooperating with employees but will also act as digital/virtual employees themselves. The new reality requires the creation of an HRR department, i.e., responsible not only for managing human resources, but also robot resources [58]. In the workforce domain, automation adoption has become more systematic, as shown by Mayr et al. [10] and Kandaurova et al. [9], who present frameworks for successful implementation. The interdisciplinary nature of automation is emphasized by Haase et al. [12] and Engel et al. [48], demonstrating the need for cross-functional approaches within social environments, an aspect further elaborated by Dwivedi et al. [8] in their multidisciplinary analysis of AI challenges and opportunities.

Digital workforce development has emerged as a crucial focus area, with Ghouse and Sipos [49] examining RPA progression and futuristic aspects of automation implementation. These findings are reinforced by Brynjolfsson and Mitchell’s [59] seminal analysis of machine learning’s workforce implications and potential for task augmentation versus replacement. This evolution extends to future workforce perspectives, as thoroughly examined by William et al. [51,52] and Moorthy et al. [50], who analyze the trajectory of workforce digitalization. The implementation of workforce management systems, as demonstrated by Sathya et al. [53], shows practical applications in Industry 4.0 contexts.

The economic transformation through intelligent automation encompasses various sectors and operational domains, as detailed in

Table 2. Economic environment applications of intelligent automation: manufacturing, financial operations, and market dynamics.

Category	Application	Description	Sources
Production and manufacturing
	Smart factory automation	Integration of robotics and IoT for Industry 4.0 manufacturing	[18,24]
	Process efficiency systems	Implementation of intelligent automation for operational optimization	[32,60]
	Supply chain intelligence	Advanced analytics for supply chain enhancement	[61]
Financial operations
	Risk management systems	AI-driven financial risk assessment and control	[36,37]
	Banking process automation	Intelligent automation in banking operations	[38]
	Business model innovation	Integration of AI for financial service transformation	[62,63]
Market dynamics
	Financial risk management	AI-driven analysis of financial risks and returns	[64,65]
	Sales automation systems	AI-powered sales management solutions	[66]
	Performance management	AI enablers for business performance	[67]
Process transformation
	Digital transformation systems	Implementation of transformation strategies	[30,66,68]

, highlighting the progression from traditional processes to AI-enabled business operations.

The economic environment has witnessed significant transformation through intelligent automation integration. In manufacturing, smart factory automation has become increasingly sophisticated, with Banța et al. [24] providing systematic analysis of manufacturing processes and system architecture in automotive Industry 4.0 contexts, while Schmitz et al. [18] examine smart automation as an enabler of digitalization by reviewing RPA/AI potential and implementation barriers in manufacturing environments. This transformation is further supported by Soori et al.’s analysis of IoT implementation in smart factories [69]. Process efficiency has been enhanced through implementation cases, as shown by Danner et al. [70] and Moderno et al. [32], building upon the social transformations discussed in Table 1.

Supply chain intelligence has evolved substantially, with Hartley and Sawaya [62] demonstrating how predictive analytics and intelligent automation enhance supply chain operations. This evolution connects to broader market dynamics, where financial risk management has gained prominence through Sachan et al. [65] who examine human–AI collaboration frameworks for mitigating decision noise in financial underwriting and FinTech innovation, while Gupta and Sagar [64] analyze anomalies in risks and returns specifically following AI investment announcements, providing empirical evidence for AI impact on financial performance. This financial transformation is further elaborated by Lacity et al.’s case study of RPA implementation in financial operations [71].

The sales domain has been transformed through AI-powered sales management solutions, as demonstrated by Alshurideh et al. [70], while performance management has been enhanced through AI enablers, as shown by Sharma et al. [67]. In financial operations, risk management has become increasingly sophisticated through AI integration. Barta et al. [36] and Gul [37] explore risk assessment methodologies and parameter estimation in financial contexts.

Banking process automation has shown significant advancement, as evidenced by Tailor and Sharma [38], who examine automation in loan processing and customer satisfaction. Business model innovation through intelligent automation is thoroughly explored by Priya et al. [63] and Met et al. [62], who investigate the transformation of financial services. The process transformation domain has been enriched by digital transformation systems, with Balabanov [66] examining data management transformation in enterprises under digital influence, Tang [68] providing conceptual foundations for understanding digital transformation scope and definitions, and Siderska [30] demonstrating RPA’s role as a driver of digital transformation through empirical analysis of implementation outcomes, with practical implications detailed in Davenport and Ronanki’s analysis of AI implementation in business contexts [72].

The educational sector demonstrates significant adoption of intelligent automation technologies, as shown in

Table 3. Educational environment applications of intelligent automation: learning systems and knowledge development.

Category	Application	Description	Sources
Educational processes
	Cognitive learning systems	Implementation of AI for enhanced learning experiences	[48]
	Decision support platforms	Intelligent systems for educational decision making	[35]
	Language learning automation	Automated systems for language acquisition and support	[41,73]
Knowledge development
	Automated assessment tools	AI-driven evaluation and feedback systems	[39]
	Educational analytics platforms	Data-driven insights for educational improvement	[40]
	Digital transformation tools	Technologies for modernizing educational systems	[41,74]

, which captures the transformation from traditional educational processes to AI-enhanced learning environments.

The educational environment has experienced significant transformation through intelligent automation integration. In educational processes, cognitive learning systems have become increasingly sophisticated, as demonstrated by Engel et al. [33] and Skrynnyk et al. [75], building upon the technological advances outlined in Table 2. Decision support platforms have evolved to enhance educational management, with Ilieva et al. [40] showing how intelligent systems improve decision-making processes. Language learning automation has emerged as a significant area, as evidenced by Esselink [73] and Chyzhevska et al. [41], who examine automated approaches to language acquisition. In knowledge development, automated assessment tools have shown remarkable advancement. Polančič et al. [39] demonstrate how AI enhances evaluation processes, complemented by insights from Akyuz [76] on intelligent tutoring systems’ effects on personalized learning. Educational analytics platforms have become crucial tools, as shown by Ilieva et al. [40], who explore data-driven approaches to educational improvement. The digital transformation of educational systems is thoroughly examined by Hujran et al. [74] and Chyzhevska et al. [41].

The scientific domain reflects integration of intelligent automation across research methodologies and innovation systems, as illustrated in

Table 4. Scientific environment applications of intelligent automation: research methodologies and innovation systems.

Category	Application	Description	Sources
Research methodologies
	Human–machine research systems	Integration of human–machine interaction in research	[12,42]
	Process automation frameworks	Systematic approaches to research automation	[31,46]
	Research efficiency tools	AI-enhanced research optimization systems	[48,77]
Innovation systems
	Cognitive research platforms	AI-driven research development platforms	[33,78]
	Automated analysis systems	Implementation of automated research analysis	[23,34,79]
	Collaborative research tools	AI-powered research collaboration platforms	[43,80]
Research processes
	Cognitive research systems	Development of cognitive modeling approaches	[81]
	Process development research	Analysis of automation development lines	[82]

, which maps the evolution from traditional research practices to AI-enhanced scientific processes.

The scientific environment has undergone significant transformation through intelligent automation integration. In research methodologies, human–machine research systems have become increasingly sophisticated, as demonstrated by Kreuzwieser et al. [42] and Haase et al. [12], supported by Taylor et al.’s insights into AI innovation [83] in simulation and Hey et al.’s analysis of machine learning [84] in scientific data processing.

Process automation frameworks have evolved significantly, with Rizk et al. [31] and Bavaresco et al. [46] examining end-to-end automation approaches in research contexts. The development of cognitive research systems, as shown by Naidu and Vedavathi [81], alongside process development research by Scheer [82], provides crucial insights into automation evolution, further enriched by Dwivedi et al.’s multidisciplinary analysis of AI research challenges and opportunities [8].

Research efficiency tools have shown remarkable advancement, as evidenced by Engel et al. [48] and Garcia-Garcia et al. [77], who investigate cognitive automation assessment models. In innovation systems, cognitive research platforms have demonstrated a substantial impact, with Engel et al. [33] and de Moraes et al. [78] exploring systematic approaches to research automation. Automated analysis systems have become crucial research tools, with Chakraborty et al. [23] providing concluding remarks on AI and RPA confluence across various application domains, while Chakraborty et al. [34] establish intelligent automation framework foundations using AI and RPA integration, and Cho et al. [79] create specific multimodal frameworks for understanding unstructured financial documents using RPA combined with transformer-based models for multilingual document processing. The development of collaborative research tools is thoroughly explored by Szelagowski et al. [43] and Jena et al. [80], complemented by McCreadie et al.’s work on data-driven infrastructure management [85].

The technological landscape demonstrates implementation of intelligent automation across multiple domains, as detailed in

Table 5. Technological environment applications of intelligent automation: implementation frameworks and process automation.

Category	Application	Description	Sources
Technology implementation
	RPA development systems	Frameworks for robotic process automation	[13]
	Architectural solutions	Design patterns for automation implementation	[44,86]
	Process optimization platforms	Advanced systems for process enhancement	[32,87]
Integration frameworks
	Security automation systems	Implementation of automated security measures	[88,89]
	Performance enhancement tools	AI-driven system optimization tools	[33]
	Technology advancement platforms	Frameworks for technological evolution	[18,43]
Process automation
	Blockchain automation systems	Integration of intelligent automation in blockchain	[90]
	Workflow automation tools	Implementation of automated workflow systems	[91,92]
	Enterprise automation frameworks	Automation solutions for enterprises	[45,93]
	RPA implementation criteria	Standards and methodologies for RPA selection	[94]
Innovation and development
	Industry 4.0 solutions	Evolution of RPA and AI in industrial context	[52]
	Document processing systems	Hybrid approaches for document automation	[95]
	Management systems automation	Transformation of accounting and control systems	[96]
	Robotics innovation	Development of automation solutions	[97]

, which illustrates the progression from basic automation tools to sophisticated AI-integrated systems.

The technological environment has experienced substantial transformation through intelligent automation integration. In technology implementation, RPA development systems have become increasingly sophisticated, as demonstrated by Uklańska [13] and Ghouse and Sipos [49], with implementation strategies enriched by Lacity et al.’s foundational case studies of RPA deployment [71].

Architectural solutions have shown significant advancement, with Firmansyah and Arman [44] and Tayab and Li [86] exploring generic solution architectures and warehouse management applications. Additionally, Liu et al. [98] provide comprehensive frameworks for autonomous vehicle systems development. These developments are further supported by Serey et al.’s analysis of pattern recognition technologies [99].

Process optimization platforms have demonstrated remarkable capabilities, as evidenced by Moderno et al. [32] and Zhu et al. [87]. Enterprise automation frameworks have been established by Watson III and Schwarz [45] and Kumar and Afza [93], while RPA implementation criteria have been standardized through Yadav and Panda [94]. This standardization is reinforced by Taherdoost and Madanchian’s review of AI implementation strategies [100]. This research is complemented by Mohammed et al.’s work on automated waste management systems [101].

The integration of blockchain automation systems, demonstrated by Srilatha [90], and workflow automation tools developed by de Jager and Nel [91] and Fettke and Loos [92], mark significant technological advances. Innovation and development have expanded through Industry 4.0 solutions explored by William et al. [51,52], document processing systems enhanced by Guha and Samanta [95], and management systems automation transformed by Rautenstrauch et al. [96]. Pransky [97] further advances these developments through robotics innovation, with theoretical foundations strengthened by Shneiderman’s framework for human-centered AI [54].

The ecological domain represents a critical application area for intelligent automation technologies, as presented in

Table 6. Ecological environment applications of intelligent automation: environmental management and sustainability systems.

Category	Application	Description	Sources
Environmental management
	Sustainable automation systems	Integration of AI for environmental sustainability	[14,15]
	Agricultural automation tools	Smart systems for agricultural processes	[15,47]
Monitoring frameworks
	Environmental analytics platforms	AI-driven environmental monitoring systems	[23,34,46]
	Sustainability assessment tools	Automated environmental impact evaluation	[32]
	Energy management systems	Intelligent energy optimization platforms	[33,102]
Resource management
	Supply chain automation	Implementation of robotics in logistics and transportation	[103]
	Data management systems	Enterprise data transformation and automation	[104]

, which demonstrates the integration of AI and automation solutions in environmental management and sustainability initiatives.

The ecological environment has undergone significant transformation through intelligent automation integration. In environmental management, sustainable automation systems have become increasingly sophisticated, as demonstrated by van der Aalst [14] and Zelisko et al. [15], complemented by Barakat et al.’s analysis of AI’s role in ecological transition [105].

Resource optimization has been advanced through Jha et al.’s analysis of AI applications in renewable energy [106]. Agricultural automation tools have demonstrated a significant impact, as evidenced by Zelisko et al. [15] and Tariq [47], who investigate smart technologies in agricultural processes.

In monitoring frameworks, environmental analytics platforms have become crucial tools. Chakraborty et al. [23,34] and Bavaresco et al. [46] examine how machine learning enhances environmental monitoring. Supply chain automation has evolved significantly, with Kamal et al. [103] implementing robotics in logistics and transportation, while data management systems have been transformed through Gao et al.’s enterprise data automation approaches [104].

Sustainability assessment tools have evolved substantially, with Moderno et al. [32] exploring hyperautomation in industrial applications. Energy management systems are thoroughly examined by Engel et al. [33] and Zebec et al. [102], completing the overview of intelligent automation applications across all domains.

4. Strategic Decision-Making Model for Intelligent Automation Implementation

The effectiveness of strategic decision-making in intelligent automation implementation depends on the careful consideration of environmental factors. The proposed model demonstrates how different environmental aspects—social, economic, educational, scientific, technological, and ecological—influence each phase of the decision-making process. This approach ensures that organizations can make informed decisions while considering all relevant factors and potential impacts.

Based on the analysis of intelligent automation applications across different environments, we propose a conceptual model for strategic decision-making in intelligent automation implementation. The model in Figure 4 integrates the findings from the environmental analysis and provides a structured approach to decision-making.

Figure 4 illustrates the key phases of the strategic decision-making process for intelligent automation implementation. The process begins with strategic needs identification and moves through analysis, implementation, and evaluation phases, with specific decision points that allow for iteration and adjustment.

The environmental factors framework demonstrates how different environmental factors influence the decision-making process for intelligent automation implementation. This framework illustrates the interconnections between social, economic, educational, scientific, technological, and ecological factors that organizations must consider in their strategic planning. The following systematic presentation outlines how each environmental domain contains specific factors structured according to three strategic phases: analysis, implementation, and evaluation.

The social environment addresses human and organizational dimensions through workforce digital literacy assessment, automation acceptance evaluation, and organizational change readiness during analysis. Implementation focuses on employee training programs, change management strategies, and resistance management. Evaluation centers on user adoption metrics, employee satisfaction, and productivity improvements.

The economic environment encompasses financial considerations including implementation costs calculation, ROI projections, and market competitiveness analysis during the analysis phase. Implementation involves budget allocation, resource optimization, and performance monitoring. Evaluation includes cost-benefit analysis, efficiency metrics, and financial impact assessment.

The educational environment addresses training needs assessment, learning infrastructure evaluation, and knowledge gap identification during analysis. Implementation focuses on training program development, knowledge transfer systems, and skill development initiatives. Evaluation measures learning effectiveness and competency assessment.

The scientific environment covers technology maturity assessment, research capability evaluation, and innovation potential analysis. Implementation involves research integration, innovation management, and scientific collaboration. Evaluation focuses on research impact, innovation metrics, and scientific value assessment.

The technical environment addresses infrastructure readiness, system compatibility, and security assessment during analysis. Implementation encompasses technical deployment, system integration, and security measures. Evaluation monitors technical performance, system reliability, and integration success.

The ecological environment emphasizes environmental impact assessment, sustainability analysis, and resource efficiency evaluation. Implementation focuses on sustainable practices integration and environmental monitoring. Evaluation tracks environmental impact metrics and sustainability achievements.

The analysis of AI tools implementation across different environments reveals three key dimensions of strategic decision support: strategic analysis tools, decision quality and speed enhancement, planning and forecasting capabilities.

Strategic analysis tools demonstrate varying capabilities across different domains. AI-driven customer analytics, as shown by Urbani et al. [35] and Chakraborty et al. [23,34], enables deep understanding of customer behavior and preferences, providing crucial insights for strategic decision-making. Process efficiency systems, examined by Danner et al. [60] and Moderno et al. [32], facilitate operational optimization through intelligent automation. Educational decision support platforms, analyzed by Ilieva et al. [40], enhance strategic planning in educational contexts. The development of automated analysis systems, as demonstrated by Chakraborty et al. [23,34] and Cho et al. [79], provides frameworks for understanding complex data patterns.

Decision quality and speed enhancement have been significantly improved through various AI implementations. Performance management systems, as described by Sharma et al. [67], provide AI enablers for business performance optimization. Educational analytics platforms, explored by Ilieva et al. [40], deliver data-driven insights for educational improvement. Research efficiency tools, investigated by Engel et al. [48] and Garcia-Garcia et al. [77], optimize research processes through cognitive automation. Performance enhancement tools have shown remarkable advancement in system optimization, as evidenced by [77].

Planning and forecasting capabilities have evolved through sophisticated AI applications. Supply chain intelligence, examined by Hartley and Sawaya [61], demonstrates how predictive analytics enhance supply chain operations. Environmental analytics platforms, investigated by Chakraborty et al. [23,34] and Bavaresco et al. [46], provide AI-driven environmental monitoring systems.

The integration of these various factors and tools in the decision-making process is crucial for successful intelligent automation implementation. The proposed model provides a framework that organizations can use to:

• systematically evaluate their automation needs;
• consider relevant environmental factors;
• select appropriate AI tools for implementation;
• monitor and adjust their automation strategies.

The effectiveness of strategic decision-making in intelligent automation implementation depends on the careful consideration of environmental factors. The model demonstrates how different environmental aspects influence each phase of the decision-making process, ensuring that organizations can make informed decisions while considering all relevant factors and potential impacts.

This approach to strategic decision-making enables organizations to make informed decisions about intelligent automation implementation while considering the full range of environmental impacts. The framework supports organizations in selecting appropriate tools and technologies, monitoring and evaluating implementation effectiveness, and adjusting strategies based on continuous feedback. By integrating these capabilities, organizations can navigate the complex landscape of automation implementation more effectively, ensuring that their strategic choices align with both operational objectives and sustainability commitments while maintaining the flexibility to adapt to evolving technological and environmental conditions.

The model thus serves as a practical guide for organizations navigating the complex landscape of intelligent automation implementation, while ensuring consideration of all relevant environmental factors and available technological solutions.

5. Discussion

5.1. Managerial Insights

Organizations implementing intelligent automation must adopt multi-dimensional strategic frameworks that simultaneously address technological capabilities, environmental sustainability, and stakeholder requirements. The strategic decision-making model provides managers with practical tools for navigating complex automation landscapes through integrated analysis tools, decision quality enhancement mechanisms, and planning capabilities.

Strategic planning recommendations for multi-domain automation include establishing cross-functional teams that represent all six environmental domains, developing implementation roadmaps that account for interdependencies between domains, and creating feedback mechanisms that monitor environmental impacts throughout the automation lifecycle. Risk management considerations for environmental sustainability demand that managers integrate environmental impact assessment into every phase of automation decision-making.

Implementation sequencing strategies across organizational contexts should follow the three-dimensional framework integrating analysis tools, decision quality enhancement mechanisms, and planning capabilities. This approach enables organizations to navigate complex automation landscapes while maintaining strategic coherence across all environmental domains.

5.2. Theoretical and Practical Insights

The findings align with and extend previous research on intelligent automation implementation while revealing new insights into environmental domain considerations. Our identification of six environmental domains aligns with Ng et al.’s [3] systematic literature review findings that intelligent automation applications span multiple organizational contexts and builds upon their work by providing a structured environmental framework. The nature of our environmental analysis corroborates Fernandez and Aman’s [1] observations about the complexity of implementing robotic process automation in global business services, particularly regarding the need for multi-dimensional strategic approaches.

The strategic decision-making model developed in this research builds upon existing frameworks while addressing gaps identified in the current literature. Recent advances in AI solution search methods, as demonstrated by Mahdi et al. [107], further support the systematic approach to intelligent automation implementation, particularly in developing robust decision-making frameworks for complex organizational contexts. Our three-dimensional approach integrating strategic analysis tools, decision quality enhancement, and planning capabilities extends beyond traditional technology adoption models by incorporating environmental sustainability considerations throughout the decision-making process. This aligns with recent work by Koibichuk et al. [108] on digitalization and innovation transfer in education, who emphasized the importance of systematic frameworks for technology implementation, while expanding the scope to encompass all environmental domains identified in our analysis.

Notably, our findings regarding the interconnected nature of environmental domains support the growing consensus in the digital economy transformation literature about the need for holistic approaches to intelligent automation. However, our research diverges from studies that focus primarily on single-domain implementations by demonstrating that successful automation initiatives require simultaneous consideration of multiple environmental factors. This finding challenges the prevalent incremental implementation approaches suggested in earlier literature and supports more integrated strategic planning methodologies.

The theoretical contributions to the digital transformation literature include validation that environmental considerations must be integrated throughout automation decision-making processes, not treated as secondary factors. Practical applications of the environmental domain framework enable organizations to systematically evaluate automation opportunities while considering all relevant stakeholder impacts.

5.3. Research Implications and Validation

The research questions have been systematically evaluated through our analysis. Our analysis revealed that smart automation implementations demonstrate patterns across different environmental domains was strongly supported by the evidence. The analysis revealed clear differentiation in how automation manifests across social, economic, educational, scientific, technological, and ecological environments, with each domain exhibiting unique characteristics, challenges, and strategic requirements.

Our investigation confirmed that organizations require structured decision-making frameworks for successful intelligent automation implementation which was validated through our development and application of the strategic decision-making model. The model’s three-dimensional structure proved effective in addressing the complexity of automation decisions while incorporating environmental considerations throughout the process. This confirms our expectation that traditional decision-making frameworks would prove insufficient for the multifaceted challenges of modern intelligent automation implementation in digital economy contexts.

Our hypotheses regarding the need for innovative management approaches of different environmental domains were partially challenged by the findings. While we initially expected technological and economic domains to dominate intelligent automation applications, the analysis revealed that social and educational domains demonstrate equally sophisticated implementations with significant strategic implications. This unexpected finding suggests that organizations are adopting more holistic approaches to automation than previously documented in the literature, indicating a maturation of the field beyond purely technical or economic optimization toward socio-economic transformation.

The investigation regarding the need for innovative management approaches in digital economy transformation was conclusively supported by our findings. The research demonstrates that successful intelligent automation implementation requires management practices that can navigate complex environmental interdependencies while achieving sustainable socio-economic impact, validating our expectation that traditional management frameworks would prove inadequate for contemporary automation challenges.

This paper provided a systematic analysis of intelligent automation implementation across six environmental domains, examining the patterns, challenges, and strategic implications of automation adoption. The analysis revealed two primary contributions: mapping of intelligent automation applications across environmental domains, and development of a strategic decision-making model for implementation.

The environmental domain analysis uncovered patterns of implementation. In the social environment, implementations centered on human–machine interaction and workforce development. The economic domain demonstrated transformation through smart factory automation and financial process optimization. Educational applications focused on cognitive learning systems and automated assessment tools. The scientific domain showed evolution in research methodologies and automation frameworks. Technological implementations revealed advancement in RPA development and integration approaches, while ecological applications emphasized sustainable automation and resource optimization.

The strategic decision-making model emerged from this analysis, offering a structured approach to intelligent automation implementation that considers environmental factors throughout the decision process. The model integrates three key dimensions: strategic analysis tools for implementation context understanding, decision quality and speed enhancement mechanisms, and planning and forecasting capabilities. This framework provides organizations with practical guidance for navigating the complex landscape of intelligent automation implementation while ensuring consideration of environmental impacts.

5.4. Limitations and Future Research Directions

The primary limitation of this study lies in its data source constraints. The analysis included only publications, peer-reviewed articles, and book chapters, from the Scopus database and in the English language. While Scopus provides extensive coverage of the academic literature, it may not encompass all relevant sources in the field. Additionally, the exclusive focus on English-language publications may have excluded valuable research published in other languages, potentially limiting the global perspective on intelligent automation implementation. The research scope was further limited by the timeframe (2019–2024), which, while capturing recent developments, may not fully reflect the historical evolution of intelligent automation. Furthermore, the environmental domain categorization, while comprehensive, may not capture all nuances of implementation contexts and their interconnections.

The methodological limitations include potential biases inherent in thematic analysis interpretation, the exclusion of grey literature and industry reports that might provide additional implementation insights, and the focus on academic publications which may not fully capture practitioner perspectives on intelligent automation implementation.

Future research directions should focus on several key areas to address these limitations and expand understanding of intelligent automation implementation. First, deeper investigation of implementation patterns across multiple environmental domains could reveal important synergies and success factors. Second, research should examine the long-term impacts of automation implementation on different environmental domains, including the development of impact measurement frameworks and integration of sustainability considerations. Third, the strategic decision-making model should be validated across different organizational contexts, with development of domain-specific implementation guidelines and integration of emerging technological capabilities. These research directions would contribute to both theoretical understanding and practical application of intelligent automation while addressing the crucial need for environmentally conscious implementation strategies in organizational contexts.

Future research should prioritize the development of real-time monitoring systems for environmental impact assessment, the creation of industry-specific implementation guidelines, and the integration of emerging AI technologies into the strategic decision-making framework. Additionally, longitudinal studies examining the long-term sustainability outcomes of intelligent automation implementations across different organizational contexts would provide valuable insights for both theory and practice. Cross-cultural validation of the environmental domain framework represents another crucial research direction, particularly given the global nature of digital transformation initiatives.

6. Conclusions

This research contributes to the understanding of smart automation implementation in digital economy transformation by providing a systematic environmental analysis framework and strategic decision-making model. The study demonstrates that successful automation adoption requires innovative management approaches that simultaneously address technological capabilities, environmental sustainability, and socio-economic impact across six environmental domains.

The strategic decision-making model developed in this research offers organizations practical guidance for navigating complex automation landscapes through integrated analysis tools, decision quality enhancement mechanisms, and planning capabilities. This framework helps connect theoretical understanding with practical implementation needs, enabling organizations to leverage cognitive automation as a driver of sustainable digital economy transformation rather than merely as an operational efficiency tool.

Key contributions include the identification of implementation patterns across social, economic, educational, scientific, technological, and ecological environments, and the development of innovative management practices essential for multi-domain automation success. The research provides both academic insights and practical tools for organizations seeking to achieve positive socio-economic impact through intelligent automation while maintaining environmental consciousness and sustainable development commitments.

Organizations can utilize our framework through four fundamental capabilities: systematic assessment of automation readiness across environmental domains, identification of optimal implementation sequences, integration of sustainability considerations throughout the automation lifecycle, and monitoring of cross-domain impacts using the proposed evaluation metrics.

Domain-specific recommendations address unique contextual requirements. Social domain implementations should prioritize human–machine interaction optimization through AI-driven customer analytics and HR transformation systems. Economic domains require smart factory automation and financial process optimization while maintaining supply chain intelligence. Educational contexts demand cognitive learning systems and automated assessment tools emphasizing personalized learning enhancement. Scientific applications require human–machine research systems and automated analysis platforms. Technological implementations should establish RPA development frameworks with robust security measures. Ecological strategies must deploy sustainable automation systems integrating resource optimization with environmental monitoring.

Framework application follows a systematic five-phase approach: environmental assessment across all domains, strategic planning with domain-specific sequences, implementation using three-dimensional approaches (analysis tools, quality enhancement, planning capabilities), monitoring through continuous feedback mechanisms, and optimization based on cross-domain impact assessment.

Risk mitigation strategies address primary implementation challenges. Cross-domain integration requires phased rollout with pilot testing. Sustainability conflicts necessitate environmental impact assessment protocols. Stakeholder resistance demands change management with domain-specific communication strategies. Technology obsolescence risks require flexible architectures accommodating emerging capabilities.

Success metrics encompass operational efficiency improvements, environmental sustainability indicators (energy optimization, waste reduction, resource effectiveness), stakeholder satisfaction measures (employee engagement, customer satisfaction, community impact), economic performance indicators (ROI, cost reduction, revenue enhancement), and innovation capacity assessments (capability development, competitive advantage creation).

The strategic framework provides managers with tools for technology selection addressing multi-domain automation complexity. Environmental domain prioritization matrices enable systematic evaluation of organizational objectives and alignment with institutional goals. Technology readiness assessment tools facilitate capability evaluation within each domain, ensuring implementation strategies match available resources.

Investment prioritization frameworks incorporate sustainability alongside economic returns, enabling decisions that balance operational benefits with environmental responsibilities. Implementation sequencing algorithms provide systematic approaches to resource allocation, ensuring automation initiatives maximize organizational learning while minimizing risks. Risk-benefit analysis frameworks enable outcome evaluation within each domain, while resource allocation models balance sustainability and efficiency objectives. Stakeholder impact assessment tools support implementation planning, and performance monitoring dashboards enable real-time decision support across environmental domains.

Several important limitations must be acknowledged. Our analysis relied exclusively on the Scopus database and English-language publications, potentially excluding valuable research from other databases and languages. While thematic analysis provides robust pattern identification capabilities, future research could benefit from incorporating grey literature and industry reports to complement academic perspectives on intelligent automation implementation. The temporal scope of 2019–2024 limits longitudinal perspective on implementation outcomes.

Our environmental domain categorization represents one possible framework; alternative schemes might reveal different patterns. The research scope was further limited by the focus on peer-reviewed academic literature, which may not fully capture rapidly evolving industry practices and emerging implementation approaches. Finally, the strategic decision-making model requires empirical validation across different organizational contexts, industries, and geographic regions to confirm its practical applicability and effectiveness.

Several important research avenues emerge from this study. First, deeper investigation of implementation patterns across multiple environmental domains could reveal important synergies and success factors that optimize automation outcomes. Second, longitudinal studies examining the long-term impacts of automation implementation on different environmental domains should develop measurement frameworks that capture both quantitative metrics and qualitative organizational changes.

Third, the strategic decision-making model should be validated across diverse organizational contexts, with development of domain-specific implementation guidelines and integration of emerging technological capabilities. Fourth, research should examine how emerging technologies such as advanced AI, quantum computing, and edge computing can be integrated into the environmental framework. Fifth, studies focusing on sustainability integration throughout the automation lifecycle should develop frameworks for optimizing environmental impact while maintaining economic viability.

Finally, comparative studies across national and cultural contexts could provide valuable insights into how different regulatory environments and cultural factors influence the effectiveness of automation strategies, contributing to more globally applicable implementation frameworks.

These findings position smart automation as a strategic enabler of digital economy transformation, requiring sophisticated management approaches that integrate environmental considerations throughout the implementation process. The framework developed in this study can support organizations in making informed automation decisions that better align with sustainability objectives while optimizing technological and economic benefits.

For practitioners, this research provides actionable guidance for systematic automation implementation that considers multiple environmental factors simultaneously. The strategic decision-making model offers a structured approach to navigating the complexity of intelligent automation adoption while ensuring consideration of sustainability impacts and stakeholder requirements.

For researchers, the environmental domain framework offers a foundation for future investigations into the relationships between technology adoption, environmental sustainability, and organizational transformation. The identification of three key strategic dimensions—analysis tools, quality enhancement mechanisms, and planning capabilities—provides a theoretical foundation for further development of automation implementation theories.

The research demonstrates that organizations implementing intelligent automation must adopt multi-dimensional strategic frameworks that simultaneously address technological capabilities, environmental sustainability, and stakeholder requirements, positioning intelligent automation as a catalyst for digital economy transformation rather than isolated technological advancement.