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Communication

Democratizing Digital Transformation: A Multisector Study of Low-Code Adoption Patterns, Limitations, and Emerging Paradigms

by
Zhengwu Shi
,
Junyu Dong
and
Yanhai Gan
*
The Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(12), 6481; https://doi.org/10.3390/app15126481
Submission received: 16 April 2025 / Revised: 4 June 2025 / Accepted: 5 June 2025 / Published: 9 June 2025
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Abstract

Low-code development platforms (LCDPs) have emerged as transformative tools for accelerating digital transformation across industries by enabling rapid application development with minimal hand-coding. This paper synthesizes existing research and industry practices to explore the adoption, benefits, challenges, and future directions of low-code technologies in key sectors: automotive, equipment manufacturing, aerospace, electronics, and energy. Drawing on academic literature, industry reports, and case studies, this review highlights how low-code bridges the gap between IT and domain experts while addressing sector-specific demands. The study emphasizes the significant impact of LCDPs on operational efficiency, innovation acceleration, and the democratization of software development. However, it also identifies critical challenges related to customization, interoperability, security, and usability. The paper concludes with a discussion of emerging trends, including enhanced AI/ML integration, edge computing, open-source ecosystems, and sector-specific platform evolution, which are poised to shape the future of low-code development. Ultimately, this research underscores the potential of low-code platforms to drive sustainable digital transformation while addressing the complex needs of modern industries.

1. Introduction

Low-code development platforms (LCDPs) represent a paradigm shift in software engineering, offering a visual, model-driven approach to application design that significantly reduces reliance on traditional hand-coding. By leveraging drag-and-drop interfaces, prebuilt templates, and modular components, these platforms empower both professional developers and non-technical users (referred to as “citizen developers”—business users without formal programming training who build applications to solve domain-specific problems) to rapidly build applications tailored to specific business needs (as shown in Figure 1). This democratization of software development aligns with the growing demand for digital agility across industries, particularly in sectors such as manufacturing, aerospace, and energy, where domain expertise often outweighs formal programming skills [1,2,3,4,5,6,7,8,9].
At its core, low-code technology bridges the gap between operational requirements and IT capabilities. For instance, platforms like Microsoft Power Apps [10,11,12] and Mendix [13,14,15] enable users to design workflows by assembling preconfigured logic blocks, thereby accelerating development cycles compared to conventional methods [16]. This efficiency is further amplified by the integration of domain-specific modules—such as IoT connectors for industrial equipment or compliance templates for aerospace regulations—which ensure that applications align with sector-specific standards and infrastructure [17]. The rise of low-code is also reshaping organizational dynamics. By decentralizing development tasks, enterprises can reduce bottlenecks caused by overburdened IT departments [18,19]. For example, automotive manufacturers utilize platforms like Siemens’ MindSphere to prototype predictive maintenance systems in weeks rather than months, enabling real-time sensor data integration and dynamic fault detection [20]. Similarly, pharmaceutical companies employ low-code tools to streamline regulatory compliance workflows, automating documentation processes that previously required extensive manual intervention.
However, the adoption of low-code is not without challenges. While its visual interface lowers entry barriers, studies highlight persistent usability gaps, particularly for non-technical users unfamiliar with logical abstractions. Additionally, reliance on proprietary platforms raises concerns about vendor lock-in and interoperability, as organizations risk becoming dependent on closed ecosystems that limit customization and scalability [21]. These issues underscore the need for governance frameworks, such as the Adaptive Integrated Digital Architecture Framework (AIDAF), which harmonize low-code initiatives with enterprise-wide IT strategies to mitigate risks and ensure long-term adaptability [22].
In summary, low-code development is both a technological innovation and a cultural enabler, fostering collaboration between domain experts and technologists. Its value lies not only in accelerated delivery but also in its capacity to translate complex industrial requirements into actionable digital solutions. As industries increasingly prioritize resilience and agility, low-code platforms are poised to become indispensable tools for driving sustainable digital transformation.
To guide this study, we pose the following core research questions:
RQ1: How are low-code development platforms currently being adopted across diverse industrial sectors such as automotive, aerospace, energy, electronics, and equipment manufacturing?
RQ2: What are the primary technical and operational limitations encountered in deploying low-code platforms in mission-critical industrial environments?
RQ3: What emerging trends and innovations are shaping the future evolution of low-code technologies?
To address these questions, this study synthesizes insights from academic literature, industry reports, and real-world case studies. The primary contributions of this paper are threefold: (1) a sector-specific analysis of current adoption patterns of low-code platforms; (2) a detailed examination of existing limitations categorized into technical, operational, and human-centered dimensions; and (3) a forward-looking synthesis of emerging paradigms, including AI integration, edge computing, open-source strategies, and domain-specific adaptations. This work thus aims to provide a comprehensive and structured foundation for future research and practical deployment of low-code systems in industrial digital transformation contexts. To provide a structured understanding of this work, the remainder of this article is organized as follows: Section 2 examines the sector-specific applications of low-code platforms across five major industries—automotive, equipment manufacturing, aerospace, electronics, and energy. Section 3 explores key technical and operational challenges that hinder broader adoption, including limitations in customization, interoperability, security, usability, and scalability. Section 4 outlines emerging trends and innovations that are shaping the future of low-code technologies, such as AI/ML integration, edge computing, open-source ecosystems, and sector-specific platform evolution. Section 5 offers a cross-sector discussion synthesizing strategic insights, and Section 6 concludes the paper by summarizing key findings and suggesting directions for future research.

2. Sector-Specific Applications

LCDPs are redefining industrial digitalization by enabling domain experts to rapidly prototype, deploy, and iterate applications tailored to sector-specific challenges [23,24,25,26,27]. This section explores how low-code technologies address critical needs in automotive, equipment manufacturing, aerospace, electronics, and energy industries, emphasizing their transformative role in bridging operational gaps and accelerating innovation. To better illustrate the LCDPs and their suitability across various industrial scenarios, Table 1 presents a comparative analysis of several widely adopted platforms. This comparison includes key features, target domains, underlying technical architectures, strengths, and notable limitations. The selected platforms—ranging from enterprise-grade solutions like OutSystems and Mendix to specialized tools such as Huawei AppCube and UI Bakery—reflect the diversity of LCDP offerings in both functionality and industrial focus. By providing a side-by-side overview, this table helps contextualize the sector-specific applications discussed in subsequent sections and highlights the trade-offs organizations face when selecting a low-code solution.

2.1. Automotive Industry

The automotive sector, characterized by complex supply chains and dynamic production environments, has embraced low-code platforms to enhance agility and operational efficiency [28]. LCDPs empower engineers to design applications for predictive maintenance, real-time data integration, and workflow automation without relying on traditional software development cycles [29,30,31,32,33,34,35,36]. For instance, predictive maintenance systems built on platforms like Siemens MindSphere integrate IoT sensor data from assembly lines to detect equipment anomalies in real time, reducing unplanned downtime [17]. These systems leverage WebSocket protocols to dynamically update maintenance schedules based on sensor telemetry, ensuring alignment with production targets. Furthermore, by leveraging a low-code platform, Thinkmoney achieved the development of a novel banking application in merely 14 weeks and finalized its online banking system in 26 weeks, reducing the R&D timeline by a significant margin. In response to the pandemic, New York City leveraged Unqork’s low-code platform to deploy a COVID-19 information portal in just 72 h, underscoring the agility of low-code solutions in crisis scenarios [37].
Table 1. Comparative analysis of low-code development platforms.
Table 1. Comparative analysis of low-code development platforms.
Low-Code
Platform		Core FeaturesTarget
Domains		Technical
Architecture		StrengthsLimitations
OutSystemsFull-stack development, AI-assisted code generation, multi-cloud deployment, CI/CD pipeline integrationEnterprise systems, mission-critical applicationsCloud-native architecture with hybrid deploymentHigh scalability, GDPR compliance, enterprise-grade governanceHigh licensing costs, steep learning curve for non-technical users
MendixCollaborative visual modeling, multi-experience apps, agile project managementCross-team enterprise collaborationModel-driven development with microservices supportRapid prototyping, extensive community resourcesLimited customization for complex business logic, dependency on proprietary tools
Microsoft Power AppsOffice 365 ecosystem integration, lightweight app development with Power BI analyticsSME departmental apps, data visualizationLow-code + pro-code hybrid modelSeamless Microsoft ecosystem compatibility, AI model embeddingWeak external system interoperability, performance bottlenecks
UI BakeryDrag-and-drop UI builder, JavaScript embedding, Git version control, 80+ prebuilt componentsInternal toolsComponent-based architecture with SQL/NoSQL database connectivityDeveloper flexibility, dark mode/theme customizationLimited community support, unsuitable for consumer-facing apps
Huawei AppCubeIndustrial IoT connectors, multi-screen fusion, AI/5G integrationSmart manufacturing, government digitalizationHybrid cloud architecture with Huawei Cloud ecosystem integrationHigh-security compliance, 5× faster deploymentVendor lock-in risks, limited internationalization
AppMasterVisual BP designer, automated source code generation (Go/Vue/Kotlin), Swagger API documentationCross-industry scalable applicationsServer-driven architecture with PostgreSQL compatibilityEliminates technical debt via blueprint regeneration, 10× faster developmentLimited offline capabilities, steep pricing for enterprise features
RetoolDeveloper-first internal tools with JS/API control, RBAC, audit logsStartups/mid-sized teamsModular architecture with REST/GraphQL integrationHigh customization, 400+ native integrationsRequires coding expertise, lacks mobile app support
A key advantage of low-code in the automotive sector lies in its ability to unify fragmented IT ecosystems. Legacy systems such as ERPs and CRMs are often siloed, but platforms like vf-OS [38,39,40] provide open APIs to seamlessly connect these systems with IoT devices and machine learning modules [28]. This integration supports customized workflows for quality control, where non-technical staff can visually design dashboards to monitor defect rates or optimize inventory management. For example, Scania’s engine assembly facility utilized a low-code solution to streamline real-time production tracking, reducing manual reporting efforts while maintaining compliance [29].
However, challenges persist in scaling low-code solutions for mission-critical applications. While platforms such as Mendix demonstrate strong capabilities in prototyping, automotive manufacturers exhibit reservations regarding their security and interoperability. These concerns highlight key areas for future development and improvement in LCDPs [29].

2.2. Equipment Manufacturing

Equipment manufacturers face unique challenges, including legacy system modernization and the need for rapid prototyping [41,42]. Low-code platforms address these by offering drag-and-drop process design and prebuilt templates for industrial automation. For example, the vf-OS platform enables manufacturers to model production line workflows visually, integrating CAD designs with real-time machine data to simulate and validate automation sequences before deployment. This reduces development time compared to traditional PLC programming methods [17,43]. Opto 22’s case study demonstrates that Node-RED’s low-code platform has enhanced the development efficiency of industrial automation projects by over 50%, significantly reducing project delivery time and enabling rapid ROI through real-time data integration and protocol conversion, as reported by the company.
A notable application is in legacy system integration. Manufacturers often operate with outdated ERP or MES systems that lack IoT connectivity. Low-code tools like Apache Activiti [44] provide connectors to harmonize data flows between legacy infrastructure and modern IIoT devices, enabling predictive analytics for equipment health monitoring. Case studies in collaborative manufacturing environments demonstrate that such integrations improve production yield through real-time anomaly detection [28,43]. Additionally, low-code fosters cross-departmental collaboration. Business analysts and shop-floor operators can co-develop applications using platforms like OutSystems [45,46,47,48], aligning IT solutions with operational needs.
In summary, low-code platforms streamline the integration of legacy systems with modern IIoT devices, enhancing real-time data processing and predictive analytics. By facilitating cross-departmental collaboration, these tools not only reduce development time but also improve production yields and operational efficiency, making them indispensable for equipment manufacturers aiming to modernize and optimize their processes.

2.3. Aerospace Industry

In the aerospace sector, where mission-critical systems and stringent regulatory compliance are paramount, low-code platforms are increasingly deployed to balance agility with security. These platforms enable rapid development of applications for supply chain management, compliance tracking, and real-time operational analytics.
A key advantage lies in automated compliance workflows. Aerospace manufacturers must adhere to standards like AS9100 [49] and DO-178C [50], which demand meticulous documentation and traceability. Low-code platforms such as OutSystems allow engineers to design applications that automatically generate audit trails and validate design changes against regulatory checklists, reducing manual verification efforts [51]. Additionally, an open-source low-code solution supports mission-critical systems by enabling developers to embed custom security protocols into applications while maintaining scalability for large-scale simulations [52,53,54].
However, challenges persist in integrating low-code solutions with legacy avionics systems. Hybrid architectures combining low-code interfaces with traditional embedded software are often necessary to meet real-time processing demands for flight control systems.

2.4. Electronics

In electronics manufacturing, low-code accelerates firmware testing and IoT device management. A study by Coutinho et al. [55] highlights how platforms like App Builder enable automated test generation for embedded systems, reducing validation cycles compared to manual scripting. For instance, TSMC employs a low-code framework to simulate semiconductor fabrication workflows, integrating AI models for defect prediction directly into visual dashboards [56].
Low-code also addresses the complexity of cross-platform IoT integration. Schneider Electric’s EcoStruxure platform uses drag-and-drop tools to unify PLCs, sensors, and ERP systems, enabling non-technical staff to design energy-efficient production lines. This aligns with Wang et al. [28], who note that low-code’s modular architecture reduces interoperability challenges in heterogeneous IoT ecosystems.

2.5. Energy

Energy sectors leverage low-code for predictive maintenance and grid optimization. A notable application is ENEL’s use of Siemens’ MindSphere to monitor renewable energy assets. By deploying low-code apps on edge devices, field technicians receive real-time alerts for turbine anomalies, cutting downtime. Similarly, Rokis et al. [54] propose a low-code knowledge graph framework for smart grids, enabling dynamic load balancing through visual rule configuration [57].
Low-code also enhances SCADA system adaptability [58,59]. The hybrid model resolves the tension between low-code’s speed and traditional coding’s precision [20].

3. Technical and Operational Challenges

Technical architecture classification summary: From a technical architecture perspective, low-code platforms are mainly divided into three categories: model-driven, Form-Driven, and Hybrid-Driven. Model-driven platforms center on business models, defining relationships between data entities, processes, and interfaces through metadata modeling tools, and generating executable code via model parsing engines. They excel in complex data modeling, BPMN process orchestration, and dynamic interface rendering, suitable for building enterprise-level applications like ERP and CRM, with support for multi-terminal adaptation and deep business logic extension. Form-Driven platforms focus on form design, rapidly creating data entry interfaces through drag-and-drop tools and integrating simple workflow engines for process automation. They feature short development cycles and low learning curves, ideal for lightweight scenarios such as expense approval and data collection, though with limited flexibility in data modeling and support for complex logic. Hybrid-Driven platforms combine the strengths of both, adopting a hierarchical architecture where the core data and business logic are built via model-driven approaches at the bottom, while the front-end uses form/page designers for quick interface generation with custom code extensions. This “dual-engine” architecture meets both the need for fast process building and the deep customization requirements of medium-sized enterprises’ complex businesses, emerging as a mainstream choice balancing efficiency and flexibility.
While LCDPs offer significant advantages in accelerating digital transformation, their adoption in mission-critical industries such as aerospace, energy, and automotive faces persistent technical and operational hurdles [60,61,62]. This section synthesizes challenges identified in academic research and industry practices, emphasizing the need for domain-specific adaptations and governance frameworks.

3.1. Limited Customization for Complex Workflows

Low-code platforms excel in standardizing repetitive tasks but struggle to accommodate highly specialized or computationally intensive workflows. For example, in aerospace systems requiring real-time fault tolerance, prebuilt components often lack the granularity to model safety-critical algorithms, necessitating hybrid architectures that combine low-code interfaces with manually coded safety layers. Similarly, semiconductor manufacturers report limitations in using low-code tools for advanced defect detection in wafer fabrication, where proprietary machine learning models demand custom integrations beyond standard AI modules.
Academic studies highlight this tension between flexibility and simplicity. Coutinho et al. [55] note that while platforms like App Builder automate test generation for embedded systems, they fail to address edge cases requiring dynamic parameter adjustments, forcing developers to revert to traditional coding for critical subsystems. This aligns with observations in automotive industries, where low-code-driven predictive maintenance systems often require supplemental scripting to handle heterogeneous sensor data formats.

3.2. Interoperability and Vendor Lock-In

The proprietary nature of many low-code platforms creates interoperability barriers, particularly in industries reliant on legacy systems. For instance, energy grid operators using SCADA systems face challenges integrating low-code dashboards with decades-old control protocols, as platforms like Mendix prioritize modern REST APIs over legacy communication standards. A case study by Frank et al. [63] reveals that aerospace firms spend most of their low-code project budgets on custom connectors to bridge avionics data silos.
Moreover, vendor lock-in risks emerge when organizations depend on platform-specific features. For example, Siemens’ MindSphere offers robust IoT integration for automotive production lines but lacks compatibility with competing cloud ecosystems like AWS IoT Core, limiting scalability. Open-source initiative [54] knowledge graph frameworks aim to mitigate this by enabling cross-platform data harmonization, yet adoption remains limited due to immature tooling.
In contrast, open-core platforms like AppMaster and vf-OS demonstrate how architectural flexibility can mitigate interoperability hurdles. AppMaster, a low-code solution designed for enterprise backend development, emphasizes compatibility with heterogeneous systems by supporting both modern APIs and legacy protocols like FTP and SOAP. This enables manufacturers to integrate newly developed low-code applications with decades-old ERP systems without extensive custom coding. Meanwhile, the vf-OS project, an open-source low-code ecosystem focused on industrial automation, provides a standardized interface layer that connects proprietary PLCs and IoT devices through a unified data model.

3.3. Security and Compliance Gaps

Industrial applications demand stringent data security, yet many low-code platforms prioritize ease of use over robust safeguards. In energy sectors, grid monitoring apps built with drag-and-drop tools often expose vulnerabilities in data encryption, as highlighted by Wang et al. [28] in their analysis of edge computing deployments. Similarly, aerospace compliance workflows require adherence to standards like DO-178C, but most platforms lack built-in audit trails for automated documentation, forcing manual validations that negate efficiency gains.
Emerging solutions propose hybrid models. Duke Energy’s hybrid SCADA system combines Mendix for real-time dashboards with Python -based anomaly detection scripts, achieving ISO 27001 [64] compliance while retaining low-code’s agility. However, such approaches require specialized expertise, undermining low-code’s promise of democratization.

3.4. Usability and Training Barriers

Despite claims of citizen developer accessibility, studies reveal persistent usability gaps. Luo et al. [20] found that some non-technical users in manufacturing struggled with logical abstractions in platforms like OutSystems, particularly when configuring conditional workflows or data mappings. This aligns with observations in electronics manufacturing, where engineers at TSMC required time for training to effectively utilize low-code tools for firmware testing—a timeline comparable to traditional coding.
The root cause lies in platform design. While tools like Zoho Creator simplify UI design, they abstract away critical debugging features, leaving users ill-equipped to troubleshoot runtime errors. Sanchis et al. [17] propose adaptive training programs that pair visual interfaces with context-sensitive guidance, yet implementation remains rare in practice.

3.5. Scalability and Performance Trade-Offs

Low-code platforms often sacrifice performance for rapid deployment. In automotive edge computing, apps built with drag-and-drop tools exhibit latency spikes when processing real-time telemetry from autonomous vehicles, as noted in Frank et al.’s analysis [63] of CAN bus integrations. Similarly, energy grid simulations using low-code models show higher computational overhead compared to native C++ implementations, limiting their use in high-frequency trading systems.
To address this, Coutinho et al. [55] advocate for just-in-time (JIT) compilation techniques in low-code platforms, dynamically optimizing generated code for specific hardware architectures. Early adopters like Lockheed Martin report success with this approach, achieving a performance boost in satellite telemetry systems while retaining visual development benefits [1].

3.6. Technical and Maintainability Challenges

Low-code platforms, by design, promote rapid application assembly through visual modeling and pre-built components. However, this speed often comes at the expense of sound architectural practices, leading to the accumulation of technical debt. As Domingues et al. observe, “higher productivity and reduced costs of low-code development could be achieved at the expense of quality management practices established for software engineering” [65]. In this context, shortcuts—such as bypassing rigorous design reviews or deferring refactoring—become entrenched, mirroring Cunningham’s original metaphor that “shipping first-time code is like going into debt… The danger occurs when the debt is not repaid”. Without frequent audits and disciplined repayment strategies, these expedient choices incur “interest” in the form of escalating maintenance effort, brittle integrations, and hidden defects that can jeopardize long-term system health.
When compared to traditional hand-coded systems, low-code solutions often exhibit lower flexibility and robustness as they scale, with direct implications for maintainability. Jun Cui’s analysis [66] highlights that, while low-code platforms “enhance development efficiency by reducing coding time and supporting agile practices, they may limit flexibility and robustness in large-scale implementations, posing risks to long-term quality and maintainability”. Similarly, a recent head-to-head benchmarking study finds that, although low-code environments enforce consistent design patterns—thereby reducing entropy—they impose “vendor lock-in and limited customization,” making core behavior modifications cumbersome and driving teams back to traditional coding to address complex requirements [67]. As a result, organizations must weigh the short-term gains in speed against potential increases in effort and cost during later phases of maintenance and evolution.
Despite these technical and operational challenges, low-code platforms have demonstrated substantial efficiency gains and cost reductions in diverse sectors. Before exploring future trends, we highlight representative cases of such measurable impacts. We present empirical evidence showcasing the tangible benefits of low-code adoption across different organizational contexts. Table 2 summarizes real-world case studies where low-code platforms have significantly improved development efficiency, reduced costs, and enabled broader participation from non-technical staff. These cases span multiple sectors—including education, automotive, public services, and government IT—demonstrating the versatility and economic potential of low-code solutions. The metrics provided serve as practical benchmarks for organizations evaluating the return on investment (ROI) of low-code initiatives.

4. Future Directions

The evolution of LCDPs is poised to address existing limitations while unlocking novel capabilities in industrial digitalization. This section synthesizes emerging trends and research priorities, emphasizing the integration of advanced technologies and sector-specific innovations to advance the next generation of low-code solutions.

4.1. Enhanced AI/ML Integration for Autonomous Decision-Making

Future low-code platforms will increasingly embed artificial intelligence (AI) and machine learning (ML) modules to automate complex workflows [69,70,71]. For instance, AI-driven code generation tools, such as SAP Build Code’s Joule, enable developers to auto-generate data models and logic flows, reducing manual coding efforts in ERP customization projects. Academic studies highlight the potential of generative AI to dynamically adapt low-code templates based on real-time user behavior. For example, Coutinho et al. [55] propose a framework where AI agents analyze historical application usage patterns to recommend optimal component configurations, improving user productivity in IoT device management scenarios. Figure 2 illustrates the temporal milestones of AI and ML adoption across diverse industries. Early pilot projects for AI/ML in quality control and predictive maintenance emerged as early as 2020. These sectors prioritized initial exploration of AI-driven supply chain optimization and ML-based fault detection in 2021, followed by small-scale pilot implementations of intelligent diagnostic systems in 2024, with plans for broader industry-wide adoption by 2025. It demonstrates that while manufacturing and energy sectors have achieved relatively advanced deployment, high-tech industries like aerospace are prioritizing incremental pilot expansions, reflecting diverse strategic approaches to technological innovation.
However, challenges persist in balancing automation with transparency. Research [1] underscores the need for explainable AI (XAI) in low-code platforms to ensure compliance with regulatory standards, particularly in aerospace and healthcare sectors where decision-making processes require auditability.

4.2. Edge Computing and Real-Time Industrial Analytics

The convergence of low-code with edge computing is critical for latency-sensitive applications, such as autonomous vehicle telemetry and energy grid monitoring. Platforms like Siemens MindSphere now support edge-native (designed to run directly on edge devices with minimal reliance on cloud resources) low-code apps, enabling real-time fault detection in automotive production lines. A recent study by Frank et al. [63] demonstrates how low-code apps deployed on edge devices reduced computational overhead in smart grid simulations, achieving energy efficiency improvements.
Future research should prioritize adaptive resource allocation algorithms to optimize low-code app performance in resource-constrained environments, such as remote oil rigs or satellite systems. For example, Wang et al. [28] propose a dynamic compilation framework that adjusts code generation based on hardware specifications, ensuring compatibility with heterogeneous IoT ecosystems.

4.3. Open-Source Ecosystems and Interoperability Standards

To mitigate vendor lock-in, open-source low-code frameworks are gaining traction. Platforms like vf-OS exemplify this trend by providing modular architectures that integrate with legacy systems and third-party tools. Academic efforts [54] enable cross-platform data harmonization, reducing integration costs in collaborative manufacturing environments. These developments are critical for increasing system flexibility and reducing long-term dependencies on proprietary solutions, which can otherwise impose constraints on scalability and system evolution.
Standardization initiatives are also critical. The Adaptive Integrated Digital Architecture Framework (AIDAF), proposed in Miyake et al. [16], establishes governance models for hybrid low-code architectures, ensuring compliance with industry-specific regulations like AS9100 and ISO 27001. Notably, AIDAF integrates Design Thinking and Agile Software Development (ASD) to align low-code application development with enterprise-wide digital strategies. In practice, as demonstrated in the pharmaceutical case study, AIDAF’s four-phase adaptive cycle—Prototype Development, Contextualization, Architecture Review, and Realization—enables structured risk management, addressing concerns such as vendor lock-in, interoperability, and operational continuity. For example, Power Apps, though a proprietary solution, was strategically evaluated and approved via AIDAF’s Architecture Board review, with specific mitigation procedures implemented to counter long-term lock-in risks. Thus, frameworks like AIDAF not only promote technical standardization but also embed governance and compliance deeply into the development lifecycle of citizen-built applications.

4.4. Human-Centric Design and Usability Optimization

Despite advancements, usability gaps remain a barrier for non-technical users. Future platforms must adopt context-aware interfaces that dynamically adjust complexity based on user expertise [72,73,74]. For example, Luo et al. [20] propose a tiered training system where citizen developers receive real-time guidance via AI-powered tooltips, reducing onboarding time in electronics manufacturing.
Additionally, Sanchis et al. [17] advocate for collaborative development models, where domain experts and developers co-design applications using shared visual dashboards. Case studies in equipment manufacturing show this approach reduces requirement misalignment.

4.5. Sector-Specific Platform Evolution

Tailored solutions for niche industries will dominate future development. In aerospace, platforms like Pega are integrating digital twin capabilities to simulate mission-critical systems, enabling predictive maintenance for satellite constellations [75]. Case studies have shown that organizations adopting Pega platform’s low-code development framework attained a 598% return on investment (ROI) within three years, while recovering implementation costs in fewer than three months [37]. Similarly, energy sectors require low-code tools that interface with SCADA systems and renewable energy APIs. Duke Energy’s hybrid platform, combining Mendix with Python-based analytics, exemplifies this trend, achieving faster response to grid fluctuations.
Academic research [1] emphasizes the need for domain-specific language (DSL) support in low-code platforms, allowing users to define industry-specific logic without coding. For example, a DSL for semiconductor manufacturing could automate defect detection workflows, reducing validation cycles.

4.6. Summary and Outlook

Across sectors, the future of low-code development platforms hinges on their ability to address current limitations while adapting to domain-specific requirements. Enhanced AI/ML integration promises to automate increasingly complex tasks, while edge-native deployments expand low-code’s reach into latency-sensitive environments. Simultaneously, open-source ecosystems and standardization efforts aim to reduce vendor lock-in and bolster interoperability. Human-centric design innovations seek to close usability gaps and broaden participation among non-technical users. Ultimately, the evolution of low-code platforms is not linear but domain-driven—each sector will adopt and shape these technologies based on unique operational, regulatory, and technical priorities. As such, future research and development must strike a balance between general-purpose innovation and tailored, sector-specific solutions to fully realize the democratizing potential of low-code.

5. Discussion

This study advances the understanding of low-code platform adoption by systematically synthesizing sector-specific challenges, benefits, and strategic trade-offs across industries with distinct technical and regulatory demands. Unlike prior works that generalize low-code applicability, our analysis reveals nuanced adoption patterns tied to domain-specific operational realities. In automotive and equipment manufacturing, LCDPs demonstrate transformative potential in bridging legacy systems with modern IoT ecosystems, enabling rapid prototyping and cross-departmental collaboration. However, our findings challenge the assumption that low-code democratization universally translates to mission-critical readiness. In highly regulated sectors like aerospace and energy, organizations prioritize hybrid architectures—combining low-code agility with traditional coding rigor—to reconcile compliance requirements with development speed. This insight refines existing narratives by emphasizing context-dependent adoption strategies over one-size-fits-all solutions.
A key contribution lies in identifying the symbiotic relationship between low-code platforms and emerging technologies such as AI and edge computing. While prior research acknowledges these trends in isolation, our sectoral analysis demonstrates how their integration addresses specific limitations: AI-driven automation mitigates low-code’s customization gaps in complex workflows, while edge-native deployments resolve latency bottlenecks in real-time industrial analytics. For instance, automotive predictive maintenance systems leveraging edge-enabled low-code apps achieve faster anomaly detection compared to cloud-centric approaches, a distinction underexplored in existing literature.
The study also redefines the discourse on technical debt in low-code development. Contrary to frameworks that equate rapid deployment with maintainability risks, our observations highlight governance-driven success cases—such as Duke Energy’s hybrid SCADA system—where structured oversight balances citizen developer empowerment with architectural integrity. This underscores the need for adaptive governance models tailored to industry-specific maturity levels, a dimension often overlooked in generic low-code evaluations.
Operational efficiency gains, while significant, are contingent on overcoming interoperability and usability barriers. For example, our comparative workflow analysis illustrates how low-code reduces manual coding efforts but introduces new dependencies on platform-specific ecosystems. Organizations like Scania and ENEL mitigate these risks through open-source integrations and tiered training programs, strategies that provide actionable blueprints for practitioners navigating vendor lock-in dilemmas. These examples advance the field by translating theoretical interoperability challenges into pragmatic mitigation pathways.
Ultimately, the research positions low-code not merely as a development tool but as a catalyst for organizational evolution. By enabling domain experts to co-design applications with IT teams, LCDPs foster a culture of iterative innovation—evident in TSMC’s AI-integrated semiconductor workflows and ENEL’s edge-native grid optimizations. This cultural shift, coupled with technical advancements, redefines low-code’s role from a tactical accelerator to a strategic enabler of sustainable digital transformation.

6. Conclusions

This paper provides a multisector analysis of low-code development platform (LCDP) adoption, revealing both their disruptive potential and their current limitations. By exploring real-world implementations across five key industries, we identified common benefits—such as rapid development cycles, improved collaboration, and increased IT accessibility—as well as shared obstacles, including limited customization, vendor lock-in, and security concerns.
Our findings indicate that low-code is not a one-size-fits-all solution. Its adoption is contingent on the technical demands and regulatory environments of each sector. Notably, hybrid approaches that combine low-code agility with traditional coding robustness offer a promising compromise for mission-critical applications. The value of LCDPs is further amplified when paired with AI-driven automation and edge-native deployment, although these innovations require deliberate governance and domain-specific adaptations.
The discussion has also highlighted an emerging tension: the balance between empowering non-technical users and maintaining long-term system integrity. As low-code platforms continue to evolve, it is critical for organizations to adopt structured evaluation frameworks and invest in cross-functional training to maximize benefits while minimizing long-term risks.
Future research should explore metrics for assessing technical debt in low-code projects, develop methodologies for cross-platform interoperability, and investigate the role of explainable AI in regulated domains. Ultimately, low-code platforms are poised to be a cornerstone of digital transformation—but their success depends on thoughtful, domain-sensitive implementation.

Author Contributions

Writing—original draft preparation, Z.S.; writing—review and editing, Y.G.; project administration, J.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Comparative workflow: low-code vs. traditional software development lifecycles.
Figure 1. Comparative workflow: low-code vs. traditional software development lifecycles.
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Figure 2. Timeline of Al/ML adoption in low-code by industry.
Figure 2. Timeline of Al/ML adoption in low-code by industry.
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Table 2. Low-code development impact on cost and efficiency [68].
Table 2. Low-code development impact on cost and efficiency [68].
CompanyApplication
Scenario		Manpower ReductionTime SavingsCost Savings
New Oriental (Beijing, China)WeChat-based study room booking system50% reduction, 80% backend labor saved via component reuse50% faster delivery, 30–50% maintenance time reduction$72,000 development cost savings, $50,000+ annual IT budget reduction
Mercedes-Benz (Lisbon, Portugal)Customer complaint management system66% reduction, 50% development team downsizing60% faster deployment, 50% third-party collaboration time reduction$116,000 annual labor cost savings, $290,000+ annual loss prevention
MDEC (Cyberjaya, Malaysia)Integration of 50+ legacy government systems70% reduction, 60% non-technical staff participation93% faster per-application delivery, 10× overall efficiency improvement$1.6M annual maintenance cost reduction, $1.4M+ outsourcing cost savings
MassHousing (Boston, MA, USA)Affordable housing loan approval platform70% reduction, 80% business user involvement83% faster development, 50% loan processing time reduction$375,000 development cost savings, $3M+ annual revenue growth
% reduction calculated based on pre-/post-low-code metrics. Currency conversions use 2023 average exchange rates.
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Shi, Z.; Dong, J.; Gan, Y. Democratizing Digital Transformation: A Multisector Study of Low-Code Adoption Patterns, Limitations, and Emerging Paradigms. Appl. Sci. 2025, 15, 6481. https://doi.org/10.3390/app15126481

AMA Style

Shi Z, Dong J, Gan Y. Democratizing Digital Transformation: A Multisector Study of Low-Code Adoption Patterns, Limitations, and Emerging Paradigms. Applied Sciences. 2025; 15(12):6481. https://doi.org/10.3390/app15126481

Chicago/Turabian Style

Shi, Zhengwu, Junyu Dong, and Yanhai Gan. 2025. "Democratizing Digital Transformation: A Multisector Study of Low-Code Adoption Patterns, Limitations, and Emerging Paradigms" Applied Sciences 15, no. 12: 6481. https://doi.org/10.3390/app15126481

APA Style

Shi, Z., Dong, J., & Gan, Y. (2025). Democratizing Digital Transformation: A Multisector Study of Low-Code Adoption Patterns, Limitations, and Emerging Paradigms. Applied Sciences, 15(12), 6481. https://doi.org/10.3390/app15126481

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