The Future of Intelligent Automation: How Low-Code/No-Code Platforms are Transforming AI Decisioning

Abstract

Artificial intelligence (AI) has significantly advanced in recent years, driving intelligent automation across various industries. However, traditional AI development often demands specialized coding expertise, prolonged development cycles, and high costs, limiting its accessibility and widespread adoption. The emergence of low-code and no-code (LCNC) platforms is revolutionizing AI decisioning by enabling non-technical users and citizen developers to create and deploy AI-driven applications with minimal programming knowledge. These platforms offer intuitive drag-and-drop interfaces, pre-built AI models, automated workflows, and seamless integrations with existing enterprise systems, thereby accelerating AI deployment, reducing costs, and democratizing AI capabilities across different sectors. This study explores how LCNC platforms enhance AI decision-making processes by simplifying model development, improving operational agility, and fostering rapid innovation. By comparing traditional AI development approaches with LCNC-based AI solutions, this paper highlights key efficiency gains, cost reductions, and performance enhancements. Through a comprehensive industry analysis, we examine the role of LCNC platforms in healthcare, finance, retail, supply chain, and manufacturing, demonstrating their impact on workflow automation, decision intelligence, and predictive analytics. Furthermore, this paper investigates the challenges and limitations associated with LCNC AI, including customization constraints, security concerns, and scalability limitations, while also discussing emerging trends such as AutoML, Edge AI integration, and advanced security mechanisms to mitigate these challenges. The findings suggest that as LCNC platforms continue to evolve, they will play an increasingly critical role in AI-driven business transformation, bridging the gap between AI capabilities and enterprise automation needs. Ultimately, this research underscores that low-code and no-code AI platforms are not just simplifying AI development but are also reshaping the future of intelligent automation, enabling businesses to optimize processes, enhance decision-making, and gain a competitive edge in the digital economy.

Full text

www.ijecs.in
International Journal Of Engineering And Computer Science
Volume 13 Issue 01 January 2025, Page No. 26758-26772
ISSN: 2319-7242 DOI: 10.18535/ijecs/v14i01.4946
Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26803
The Future of Intelligent Automation: How Low-Code/No-Code
Platforms are Transforming AI Decisioning
Vamsi Viswanadhapalli
Senior Manager - Software development
Verizon USA
Abstract
Artificial intelligence (AI) has significantly advanced in recent years, driving intelligent automation across
various industries. However, traditional AI development often demands specialized coding expertise,
prolonged development cycles, and high costs, limiting its accessibility and widespread adoption. The
emergence of low-code and no-code (LCNC) platforms is revolutionizing AI decisioning by enabling non-
technical users and citizen developers to create and deploy AI-driven applications with minimal
programming knowledge. These platforms offer intuitive drag-and-drop interfaces, pre-built AI models,
automated workflows, and seamless integrations with existing enterprise systems, thereby accelerating AI
deployment, reducing costs, and democratizing AI capabilities across different sectors.
This study explores how LCNC platforms enhance AI decision-making processes by simplifying model
development, improving operational agility, and fostering rapid innovation. By comparing traditional AI
development approaches with LCNC-based AI solutions, this paper highlights key efficiency gains, cost
reductions, and performance enhancements. Through a comprehensive industry analysis, we examine the
role of LCNC platforms in healthcare, finance, retail, supply chain, and manufacturing, demonstrating their
impact on workflow automation, decision intelligence, and predictive analytics.
Furthermore, this paper investigates the challenges and limitations associated with LCNC AI, including
customization constraints, security concerns, and scalability limitations, while also discussing emerging
trends such as AutoML, Edge AI integration, and advanced security mechanisms to mitigate these
challenges. The findings suggest that as LCNC platforms continue to evolve, they will play an increasingly
critical role in AI-driven business transformation, bridging the gap between AI capabilities and enterprise
automation needs.
Ultimately, this research underscores that low-code and no-code AI platforms are not just simplifying AI
development but are also reshaping the future of intelligent automation, enabling businesses to optimize
processes, enhance decision-making, and gain a competitive edge in the digital economy.
Keywords: Low-Code/No-Code Platforms, AI Decisioning, Intelligent Automation, Workflow Automation,
Machine Learning, Robotic Process Automation, Business Intelligence, Edge AI, Predictive Analytics, AI-
Driven Decision-Making, Digital Transformation.
1. Introduction
1.1 Background
Artificial intelligence (AI) has transformed industries by enabling businesses to automate processes, enhance
decision-making, and improve efficiency. However, the adoption of AI remains challenging due to the

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26804
complexity of developing and deploying AI models, which traditionally requires extensive programming
expertise, large datasets, and computational resources. This dependency on skilled developers and data
scientists has created a barrier to entry for many organizations, limiting AI‘s broader impact.
To address this challenge, Low-Code/No-Code (LCNC) platforms have emerged as an innovative solution
that democratizes AI by allowing users with minimal technical expertise to develop AI-driven applications.
These platforms provide graphical user interfaces (GUIs), drag-and-drop tools, prebuilt AI models, and
automated workflows, enabling business professionals, analysts, and non-technical users to create AI-
powered solutions with little or no coding.
LCNC platforms bridge the gap between AI capabilities and business needs by providing a simplified, user-
friendly approach to AI deployment. Companies can now integrate AI-driven decision-making into their
operations without needing to build models from scratch or hire extensive AI development teams. This shift
is revolutionizing AI adoption across industries, making it more accessible and cost-effective.
1.2 Importance of Intelligent Automation in AI Decisioning
AI decisioning refers to the ability of AI systems to autonomously process data, analyze patterns, generate
insights, and make decisions without direct human intervention. This capability is a key enabler of
intelligent automation, which integrates AI with robotic process automation (RPA), business rules engines,
and machine learning algorithms to drive efficiency and scalability.
Traditional AI decisioning required manual programming of rules and extensive machine learning model
training, making it a time-consuming and resource-intensive process. LCNC platforms streamline AI
decisioning by providing preconfigured AI models, intuitive workflow builders, and automation templates
that reduce the complexity of implementing AI-driven solutions.
The benefits of intelligent automation in AI decisioning include:
 Faster Decision-Making: AI can process and analyze data in real time, allowing organizations to
make quicker, data-driven decisions.
 Improved Accuracy: AI models reduce human errors by consistently applying rules and insights.
 Operational Efficiency: AI-driven automation minimizes manual intervention, streamlining repetitive
and rule-based tasks.
 Scalability: AI decisioning systems can handle large volumes of data and adapt to dynamic business
environments.
LCNC platforms play a crucial role in this transformation by enabling organizations to leverage AI for
decision-making without requiring deep technical knowledge. As a result, businesses can rapidly deploy AI-
driven solutions that enhance productivity, improve customer experiences, and optimize operational
efficiency.
1.3 Evolution of Low-Code/No-Code Platforms
The concept of low-code and no-code development has been evolving for over two decades, initially
emerging as a solution for simplifying software development. Early LCNC platforms were focused on
workflow automation, business process management (BPM), and rule-based decision-making systems.
However, advancements in AI, cloud computing, and API-driven architectures have expanded their
capabilities, allowing them to support AI model development and intelligent automation.
Key technological advancements that have driven the evolution of LCNC platforms include:
1. Cloud-Based Infrastructure: LCNC platforms leverage cloud computing to provide on-demand
access to AI models and computational resources without requiring local installations.
2. Prebuilt AI Models and AutoML: Automated Machine Learning (AutoML) capabilities allow users to
train and deploy AI models without manual tuning or programming.
3. Graphical Interfaces and Visual Programming: LCNC tools provide drag-and-drop environments,
prebuilt AI templates, and code-free customization options.

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26805
4. API Integration and AI Services: Modern LCNC platforms integrate seamlessly with third-party AI
services, data sources, and enterprise systems, enabling organizations to embed AI decisioning into
existing workflows.
These advancements have made it possible for organizations to accelerate AI adoption by reducing the time,
cost, and expertise required to develop AI-driven applications. As a result, LCNC platforms have become a
key enabler of AI democratization, allowing businesses of all sizes to harness the power of AI for decision-
making and automation.
1.4 The Need for AI Democratization
One of the biggest challenges in AI adoption is the shortage of skilled AI professionals, which limits the
ability of businesses to develop and deploy AI solutions. AI democratization aims to make AI accessible to a
broader audience, including business users, analysts, and domain experts. LCNC platforms serve as a
catalyst for AI democratization by providing tools that enable non-technical users to build AI-powered
applications without requiring programming expertise.
The key benefits of AI democratization through LCNC platforms include:
 Empowering Business Users: Non-technical professionals can create AI-driven solutions using visual
tools and prebuilt components.
 Reducing AI Development Costs: Organizations can deploy AI solutions without hiring large AI
development teams, significantly lowering costs.
 Accelerating Innovation: Faster development cycles enable businesses to iterate and deploy AI
solutions more quickly.
 Enhancing Cross-Functional Collaboration: Business analysts, IT teams, and AI experts can work
together more efficiently using intuitive LCNC tools.
By lowering the barriers to AI adoption, LCNC platforms enable businesses to rapidly implement AI
decisioning solutions, allowing them to gain a competitive advantage and drive digital transformation.
1.5 Objectives of the Paper
This paper explores the transformative impact of LCNC platforms on AI decisioning and intelligent
automation. The study aims to address the following key objectives:
1. Analyze the role of LCNC platforms in AI decisioning and how they simplify the development and
deployment of AI-driven applications.
2. Compare traditional AI development with LCNC-based AI applications, highlighting differences in
cost, speed, and ease of implementation.
3. Examine real-world industry applications of LCNC AI platforms, focusing on use cases in
healthcare, finance, retail, and manufacturing.
4. Discuss the challenges and limitations of LCNC platforms in AI-driven automation, including issues
related to scalability, security, and customization.
5. Explore future trends and innovations in LCNC AI decisioning, including advancements in AutoML,
edge AI, and explainable AI (XAI).
By addressing these objectives, the paper provides a comprehensive analysis of how LCNC platforms are
shaping the future of AI decisioning, making AI more accessible, efficient, and scalable for businesses
worldwide.
1.6 Structure of the Paper
The remainder of this paper is structured as follows:
 Section 2 provides an overview of AI decisioning and intelligent automation, detailing their key
components.
 Section 3 explores the fundamental principles of LCNC platforms and their core features.
 Section 4 examines how LCNC platforms are revolutionizing AI decisioning, with a comparative
analysis of traditional AI vs. LCNC-based AI.

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26806
 Section 5 presents real-world applications of LCNC AI decisioning across various industries.
 Section 6 discusses challenges and future directions for LCNC-based AI automation.
 Section 7 concludes with key insights and recommendations for businesses adopting LCNC AI
platforms.
2. Understanding AI Decisioning and Intelligent Automation
2.1 Introduction to AI Decisioning
AI decisioning is the process by which artificial intelligence (AI) systems analyze data, apply logic, and
generate insights to make automated decisions. It integrates machine learning (ML), deep learning (DL),
natural language processing (NLP), and business rules engines to create intelligent systems that can
automate decision-making processes with speed and accuracy.
2.1.1 The Evolution of AI Decisioning
Traditionally, decision-making in organizations relied on human expertise and structured rule-based systems.
However, the rise of AI and machine learning has shifted decisioning processes from predefined rules to
data-driven insights. The evolution of AI decisioning can be categorized into three stages:
 Rule-Based Decision Systems (Pre-2000s): Early AI systems relied on manually programmed logic
(if-then rules) to automate business processes. These systems were rigid and could not handle
unstructured data.
 Machine Learning-Based Decisioning (2010s-Present): AI decisioning evolved to use statistical
models that learn from data, enabling more adaptive and predictive decision-making.
 AI-Driven Automation with LCNC (2020s-Present and Beyond): The introduction of low-code/no-
code (LCNC) platforms has allowed non-programmers to leverage AI decisioning tools, increasing
adoption across industries.
2.1.2 Key Characteristics of AI Decisioning
AI decisioning systems are designed to mimic human intelligence while improving decision accuracy, speed,
and efficiency. The core characteristics of AI decisioning include:
 Autonomy: AI systems make decisions without human intervention.
 Scalability: AI decisioning processes can handle large volumes of data and transactions.
 Accuracy and Consistency: AI models minimize human bias and errors.
 Self-Learning Capabilities: ML-based decisioning systems continuously improve by learning from
new data inputs.
2.1.3 Key Components of AI Decisioning
AI decisioning consists of multiple interconnected technologies that work together to process data, extract
insights, and make automated decisions.
Table 1: Key Components of AI Decisioning
Component
Description
Examples
Machine Learning
Algorithms that analyze
historical data to make
predictions
Neural Networks, Decision
Trees
Natural Language Processing
(NLP)
AI techniques for text and
speech analysis
Chatbots, Sentiment Analysis
Robotic Process Automation
(RPA)
Automates repetitive rule-
based tasks
Invoice Processing, Data
Entry
Business Rules Engines
Defines structured logic for
automated decisions
Loan Approval Systems
Predictive Analytics
Uses statistical models to
predict future outcomes
Customer Churn Prediction
Computer Vision
AI-driven image and video
recognition
Quality Control, Surveillance

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26807
2.2 Intelligent Automation and Its Role in AI Decisioning
Intelligent automation (IA) is the fusion of AI decisioning, robotic process automation (RPA), and data
analytics to create systems that can automate complex workflows with minimal human involvement.
2.2.1 How Intelligent Automation Works
Intelligent automation operates by integrating three core technologies:
 Artificial Intelligence (AI): Enables systems to learn, reason, and make decisions without predefined
rules.
 Robotic Process Automation (RPA): Automates repetitive, rule-based tasks such as data extraction,
document processing, and transaction approvals.
 Workflow Orchestration: Connects AI models with business processes to create end-to-end
automation.
2.2.2 The Relationship Between AI Decisioning and Intelligent Automation
AI decisioning acts as the brain behind intelligent automation, while RPA and workflow management
systems serve as the execution layer. AI decisioning ensures that automated processes adapt dynamically to
new data, exceptions, and changing business environments.
For example, in financial fraud detection, AI decisioning models analyze real-time transactions for
anomalies, and an RPA bot automatically blocks suspicious accounts based on AI-generated risk scores.
2.2.3 Key Benefits of Intelligent Automation
The combination of AI decisioning and intelligent automation offers several benefits:
Enhanced Decision-Making Accuracy
 AI-driven decisioning reduces human errors in complex decision-making processes.
 Predictive models improve risk assessment, fraud detection, and customer insights.
Operational Efficiency and Cost Savings
 Automating repetitive tasks with RPA reduces manual labor costs.
 AI decisioning speeds up data-driven processes, improving overall efficiency.
Scalability and Flexibility
 AI-powered automation adapts to changing market conditions.
 Businesses can scale AI-driven operations without major infrastructure changes.
Real-Time Insights and Adaptive Learning
 AI continuously learns from new data streams, improving decision-making over time.
 Real-time AI analytics help businesses react to trends immediately.
2.3 Applications of AI Decisioning and Intelligent Automation
AI decisioning and intelligent automation are transforming industries by enhancing productivity, reducing
costs, and increasing accuracy.
2.3.1 Healthcare
 AI-assisted diagnostics: AI decisioning analyzes patient data, medical imaging, and genetic
sequences to assist doctors.
 Automated claims processing: RPA automates insurance claim approvals based on AI-driven
assessments.
2.3.2 Finance
 Fraud detection: AI analyzes transactional patterns to flag fraudulent activities.
 Automated credit scoring: AI models assess creditworthiness based on customer data.
2.3.3 Retail & E-Commerce
 Personalized product recommendations: AI decisioning tailors customer experiences based on
behavior.
 Inventory management automation: AI predicts demand and automates restocking.
2.3.4 Manufacturing & Supply Chain

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26808
 Predictive maintenance: AI detects machine failures before they occur.
 Automated quality control: AI-powered image recognition identifies defects.
Table 2: Industry Applications of AI Decisioning & Intelligent Automation
Industry
Application
Impact
Healthcare
AI-assisted medical imaging
interpretation
Faster, more accurate
diagnoses
Finance
AI-powered fraud detection
Reduction in fraudulent
transactions
Retail & E-Commerce
AI-driven personalized
product recommendations
Increased sales & customer
retention
Manufacturing
Predictive maintenance for
machinery
Reduced downtime & repair
costs
Supply Chain
Automated inventory
management
Improved logistics & reduced
waste
2.4 Challenges and Considerations
Despite its advantages, AI decisioning and intelligent automation face challenges that must be addressed for
widespread adoption.
2.4.1 Data Privacy & Security Risks
 AI decisioning relies on large datasets, making it vulnerable to data breaches.
 Stricter regulatory compliance (GDPR, CCPA) is needed for AI-powered automation.
2.4.2 AI Model Bias and Ethical Concerns
 AI decisioning may inherit biases from training data.
 Ethical concerns arise in areas like AI-driven hiring, credit scoring, and surveillance.
2.4.3 Integration Complexity
 Legacy systems may not seamlessly integrate with AI-driven automation.
 Businesses must restructure existing workflows for effective AI deployment.
Graph 1:
A bar chart comparing AI adoption rates across industries, including Healthcare, Finance, Retail, and
Manufacturing.

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26809
AI decisioning and intelligent automation are revolutionizing industries by improving accuracy, efficiency,
and scalability. While challenges such as security risks and ethical concerns must be addressed, future
advancements in AI and LCNC platforms will further enhance automated decision-making capabilities.
Organizations that successfully integrate AI decisioning into their business models will gain a competitive
edge in an increasingly data-driven world.
3. The Rise of Low-Code/No-Code Platforms
3.1 Understanding Low-Code/No-Code (LCNC) Platforms
Low-code and no-code (LCNC) platforms represent a significant shift in the way software applications,
automation workflows, and AI-driven decision systems are developed and deployed. These platforms
eliminate the need for extensive manual coding by providing graphical user interfaces (GUIs), pre-
configured modules, and drag-and-drop functionalities. By simplifying the development process, LCNC
platforms democratize AI and automation, making it accessible to business users, domain experts, and
citizen developers.
 Low-code platforms allow users to develop applications with minimal coding by providing pre-built
templates, reusable components, and API integrations. These platforms are ideal for developers who
need to accelerate application development while maintaining customization flexibility.
 No-code platforms eliminate coding requirements entirely, allowing non-technical users to build
applications through visual development tools. These platforms enable rapid prototyping, workflow
automation, and AI decision-making without needing programming expertise.
Table 3: Comparison of Low-Code and No-Code Platforms
Feature
Low-Code Platforms
No-Code Platforms
Target Users
Developers and business
analysts
Business users and non-
technical teams
Customization Level
Moderate to high
Limited to predefined options
Coding Required?
Minimal
None
Development Speed
Faster than traditional coding
Instant development
Scalability
Highly scalable with coding
Limited by platform

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26810
constraints
Use Cases
AI automation, enterprise
applications
Workflow automation,
customer portals
3.2 The Growth of LCNC in AI and Intelligent Automation
The adoption of low-code/no-code platforms in AI decisioning has grown significantly in recent years.
Several factors have driven this surge:
1. Growing Demand for AI-driven Automation:
 Enterprises across industries seek AI-enhanced decision-making to improve efficiency, accuracy, and
customer experience.
 Traditional AI development is time-consuming and costly, requiring data scientists, ML engineers,
and software developers.
 LCNC platforms bridge this gap, allowing businesses to implement AI-driven automation without
deep technical expertise.
2. Shortage of Skilled AI Developers:
 A global shortage of AI engineers and data scientists has made AI development expensive and
inaccessible to many companies.
 LCNC platforms empower business users to build AI applications without relying on specialized
developers.
3. Accelerated Digital Transformation Post-Pandemic:
 The pandemic accelerated the need for digital automation, particularly in sectors like finance,
healthcare, and retail.
 Companies sought faster AI implementation to optimize operations, reduce costs, and enhance
customer experiences.
 LCNC platforms enabled rapid AI-driven automation without long development cycles.
4. Cloud and API-Driven Ecosystems:
 Modern LCNC platforms integrate with cloud computing, API-driven services, and third-party AI
models.
 This allows businesses to deploy scalable AI automation without heavy infrastructure investments.
Graph 2:
A line graph showing the global market growth of low-code and no-code platforms from 2015 to 2025,
highlighting key growth phases.
3.3 Key Features of Low-Code/No-Code AI Development

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26811
Modern LCNC platforms provide a variety of features that make AI-driven decision-making accessible and
scalable:
1. Drag-and-Drop Visual Development
 Users can create AI workflows and automation without coding using intuitive drag-and-drop tools.
 Example: A business analyst can build an AI-powered customer service chatbot without writing
Python or JavaScript.
2. Prebuilt AI and ML Models
Many LCNC platforms include pre-trained AI models for use cases like:
 Predictive analytics
 Natural language processing (NLP)
 Computer vision
Users can apply these models without training new AI algorithms from scratch.
3. API Integrations and Cloud Deployment
LCNC tools seamlessly integrate with:
 Cloud AI services (AWS, Google Cloud, Azure)
 Enterprise systems (CRM, ERP, databases)
Example: An e-commerce company can integrate a no-code AI model with its customer database to
automate personalized product recommendations.
4. Business Rule Engines for AI Decisioning
 AI decision-making requires rule-based logic to process information effectively.
 LCNC platforms allow users to configure if-then decision rules, automating complex workflows.
5. Automated Testing and Deployment
 LCNC platforms provide built-in testing and debugging tools to verify AI workflows.
 Automated version control and deployment capabilities enable businesses to scale AI applications
rapidly.
Graph 3:
A bar chart comparing the adoption rate of different LCNC platform features, such as prebuilt AI models,
API integrations, and business rule engines.

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26812
3.4 Benefits of LCNC for AI Decisioning and Automation
The integration of low-code/no-code platforms with AI decisioning provides several advantages for
businesses:
1. Faster AI Development and Deployment
 Traditional AI model development takes weeks or months, whereas LCNC AI tools can deploy AI
workflows in hours or days.
 Businesses can test and iterate AI models quickly without a long coding process.
2. Cost Savings and Accessibility
 Traditional AI development requires data scientists and software engineers, increasing costs.
 LCNC platforms allow business users and analysts to build AI-driven automation, reducing reliance
on expensive technical teams.
3. Scalability for Business Growth
 LCNC AI applications can scale without rewriting large amounts of code.
 Enterprises can expand AI-driven automation across multiple departments efficiently.
4. Democratization of AI
 Empowers non-technical users to build and deploy AI-driven applications.
 Example: A hospital administrator can use a no-code AI tool to automate patient scheduling and
optimize doctor availability.
5. Real-Time Decision-Making and Intelligent Automation
 AI-powered LCNC platforms enable real-time data processing and intelligent automation.
 Example: In supply chain management, an LCNC AI model can predict inventory shortages and
automatically reorder stock.

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26813
Table 4: Key Benefits of LCNC AI Platforms
Benefit
Description
Rapid Deployment
AI solutions can be built and launched
quickly
Lower Development Costs
Reduces reliance on specialized AI engineers
Greater Accessibility
Enables business users to create AI
applications
Enhanced Scalability
AI-driven applications can scale across
departments
Improved Decision-Making
AI models provide real-time insights for
business decisions
3.5 The Role of LCNC in Future AI Development
As AI decision-making becomes increasingly central to business processes, low-code and no-code platforms
will play a vital role in shaping the future of AI. Several emerging trends highlight the growing impact of
LCNC AI platforms:
1. AI-Augmented Low-Code Platforms
 Next-generation LCNC platforms will integrate AI-driven suggestions and automation to guide users.
 Example: AI will auto-generate workflows based on past data patterns.
2. Expansion into Edge Computing
 LCNC platforms will extend AI capabilities to edge devices, enabling real-time decision-making for
IoT, healthcare, and smart city applications.
3. Hyperautomation and Autonomous AI Systems
 Hyperautomation combines AI, RPA, and LCNC workflows to create fully autonomous AI-driven
decision systems.
Graph 4:
A futuristic trend graph showcasing the predicted adoption of LCNC AI platforms across industries by 2030.
Low-code and no-code platforms are transforming AI decision-making and automation, making AI
development more accessible, cost-effective, and scalable. By integrating prebuilt AI models, drag-and-drop

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26814
development, and cloud-based automation, LCNC platforms enable businesses to implement intelligent
automation without requiring extensive programming knowledge. As these platforms continue to evolve,
they will drive the next wave of enterprise AI adoption, democratizing innovation across industries.
4. Transforming AI Decisioning with Low-Code/No-Code Platforms
4.1 Speed and Agility: Reducing AI Development Timelines
One of the most transformative aspects of Low-Code/No-Code (LCNC) platforms in AI decisioning is the
drastic reduction in the time required to develop, deploy, and refine AI models. Traditional AI development
involves multiple stages, including data preprocessing, model selection, hyperparameter tuning, and coding
integration, all of which require extensive expertise and months of work. In contrast, LCNC platforms
accelerate the process through automation, prebuilt components, and user-friendly interfaces.
How LCNC Enhances Speed and Agility
1. Graphical AI Model Builders: Unlike traditional development, which requires extensive
programming, LCNC platforms enable users to build AI models using drag-and-drop functionalities.
2. Prebuilt AI Models: Many LCNC platforms come with ready-made AI models for common tasks like
fraud detection, sentiment analysis, and forecasting, reducing the need for custom training.
3. Automated Data Processing: Built-in data connectors, cleaning tools, and feature engineering
modules eliminate the manual effort required for data preparation.
4. No Manual API Integration: LCNC platforms automatically integrate AI models into enterprise
systems, whereas traditional AI development requires manual API connections.
Real-World Example: AI-Driven Loan Approval System
Consider a financial institution deploying an AI-based loan approval system. Traditionally, this would take:
 3-6 months for model development and integration.
 Additional time for testing, validation, and deployment.
With an LCNC platform, the system can be operational in a few weeks using:
 Prebuilt credit scoring models.
 Automated risk assessment workflows.
 Drag-and-drop integrations with existing banking systems.
Table 5: AI Development Time Comparison – Traditional vs. LCNC
Process
Traditional AI Development
LCNC AI Development
Data Preparation
Weeks (manual data cleaning)
Automated (prebuilt
connectors)
Model Selection & Training
Months (requires expertise)
Pre-trained models available
Integration & Deployment
Months (manual API setup)
Drag-and-drop integration
Total Time Required
3-6 months
2-6 weeks
4.2 Democratization of AI: Empowering Citizen Developers
A significant limitation of traditional AI development is its reliance on highly skilled data scientists, machine
learning engineers, and software developers. This creates a bottleneck where only technical teams can
develop AI solutions. LCNC platforms eliminate this dependency by allowing non-technical users, or
―citizen developers,‖ to create AI models without extensive programming skills.
Key Aspects of AI Democratization with LCNC
 No Need for Coding Knowledge: Business analysts, HR teams, and finance departments can deploy
AI solutions without IT support.
 AI Adoption Across Non-Tech Sectors: LCNC tools allow sales, marketing, and operations teams to
build AI-driven applications for automation and decision-making.
 Visual AI Training Interfaces: Users can train AI models through intuitive dashboards rather than
writing complex Python or R scripts.

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26815
 Increased SME Participation: LCNC enables small and medium-sized enterprises (SMEs) to
implement AI without hiring costly AI teams.
Case Study: HR Recruitment Automation
An HR department in a multinational corporation wanted to build an AI-powered recruitment assistant.
Traditional development required months of data engineering and machine learning expertise. Using an
LCNC platform, the team was able to:
 Upload hiring data and apply pre-trained AI models for candidate evaluation.
 Use an intuitive drag-and-drop interface to design an AI-powered recruitment workflow.
 Deploy the AI assistant in two weeks without needing data scientists.
Table 6: Technical Expertise Required – Traditional vs. LCNC AI
Aspect
Traditional AI Development
LCNC AI Development
Required Expertise
Machine learning,
programming
No programming required
AI Model Training
Requires ML engineers
Automated, one-click training
Deployment Complexity
Manual integration required
Plug-and-play solutions
Time to Production
Months
Days to weeks
Graph 5: A pie chart showing the increasing adoption of LCNC AI platforms by non-technical teams, such
as HR, marketing, finance, and sales.

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26816
4.3 Cost Efficiency: Lowering AI Implementation Costs
Developing AI systems using traditional methods is expensive due to:
1. High Salaries for AI Engineers: The average salary for a machine learning engineer is $150,000–
$200,000 per year.
2. Infrastructure Costs: Traditional AI development requires expensive cloud computing, GPUs, and
data storage.
3. Ongoing Maintenance and Upgrades: AI models require continuous monitoring, retraining, and
integration support.
LCNC platforms drastically reduce these costs by:
 Eliminating the need for expensive AI specialists.
 Using cloud-based AI services, reducing on-premise infrastructure costs.
 Automating model maintenance, reducing long-term operational expenses.
Cost Comparison: Traditional AI vs. LCNC AI
According to Gartner (2023):
 Traditional AI projects cost $100,000–$500,000 per deployment.
 LCNC platforms reduce costs by 50–70% due to prebuilt AI components and automation.
Table 7: Cost Analysis – Traditional vs. LCNC AI Development

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26817
Cost Factor
Traditional AI Development
LCNC AI Development
AI Engineer Salaries
High ($150K+ per engineer)
Minimal (no AI team needed)
Cloud Infrastructure
Expensive GPU clusters
Cloud-based pay-as-you-go
model
Maintenance & Updates
Requires dedicated engineers
Automated updates
Total Cost
$100,000–$500,000
50-70% cost reduction
4.4 Scalability and Adaptability: Expanding AI-Driven Automation
For businesses, AI decisioning must be scalable to handle increasing data loads, support multiple use cases,
and adapt to evolving requirements. LCNC platforms provide built-in scalability and flexibility, allowing
organizations to expand their AI automation capabilities without additional coding or infrastructure changes.
Scalability Benefits of LCNC AI Platforms
 Cloud-Native Infrastructure: Most LCNC platforms run on AWS, Google Cloud, or Microsoft Azure,
enabling automatic scalability.
 Modular AI Components: Businesses can easily add or modify AI models as needed.
 AutoML Integration: Some LCNC platforms include AutoML features, allowing users to train AI
models with minimal input.
 Cross-Industry Adaptability: LCNC tools come with industry-specific templates for finance,
healthcare, retail, and logistics.
Case Study: AI-Driven E-Commerce Personalization
A global e-commerce company scaled its AI-driven recommendation engine using an LCNC platform:
 The company deployed an AI model to personalize product recommendations for millions of users.
 The system automatically scaled based on website traffic.
 The result was a 30% increase in conversion rates.
Table 8: Scalability Comparison – Traditional vs. LCNC AI
Scalability Factor
Traditional AI Development
LCNC AI Development
Cloud Scalability
Manual optimization needed
Auto-scaling
Adaptability
Requires code modifications
Drag-and-drop adjustments
Industry Use Cases
Custom solutions per sector
Prebuilt industry templates
Graph 6: A bar chart showing the scalability improvements in AI deployment times for traditional AI vs.
LCNC platforms.

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26818
LCNC platforms are revolutionizing AI decisioning by enabling faster deployment, democratization, cost
reduction, and scalability. While traditional AI development remains valuable for complex models, LCNC
platforms are making AI more accessible, efficient, and cost-effective for businesses of all sizes.
5. Industry Applications of LCNC AI Decisioning
Low-code/no-code (LCNC) platforms are increasingly being adopted across various industries to streamline
AI decisioning and automation processes. These platforms enable businesses to leverage AI capabilities
without requiring deep programming expertise, thus accelerating innovation, reducing costs, and improving
decision-making accuracy. This section explores the transformative impact of LCNC AI decisioning in key
industries, including healthcare, finance, retail & e-commerce, supply chain & manufacturing, and customer
service.
5.1 Healthcare
The healthcare industry is undergoing a digital transformation, where AI-driven predictive analytics,
diagnostics, and workflow automation are playing a crucial role. LCNC platforms are making it easier for
hospitals, clinics, and pharmaceutical companies to implement AI models without extensive coding
knowledge.
Key Applications in Healthcare:
1. AI-Powered Diagnostics and Predictive Analytics:
 LCNC AI platforms enable healthcare professionals to develop custom predictive models for
diagnosing diseases such as cancer, diabetes, and cardiovascular disorders.
 Example: A hospital can use an LCNC platform to create an AI model for early-stage cancer
detection using patient imaging data.
2. Automated Patient Triage & Chatbots:
 AI-based chatbots built using LCNC platforms help automate initial patient consultations, symptom
checking, and appointment scheduling.
 Example: Babylon Health‘s AI chatbot triages patients by asking health-related questions and
recommending appropriate next steps.
3. Drug Discovery & Clinical Trials Optimization:
 Pharmaceutical companies are leveraging LCNC AI tools to analyze biomedical data, genetic
sequencing, and patient responses to drugs.

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26819
 Example: A pharma company can quickly develop and test AI models for predicting drug efficacy
without needing a team of AI engineers.
4. Remote Monitoring and IoT Integration:
 LCNC AI tools can integrate with wearable devices to provide real-time patient monitoring, alerting
doctors in case of abnormal readings.
 Example: An LCNC AI model can detect early signs of a heart attack by analyzing ECG data from a
smartwatch.
5.2 Finance
Financial institutions are rapidly adopting LCNC AI solutions to improve fraud detection, risk assessment,
customer engagement, and compliance processes.
Key Applications in Finance:
1. Fraud Detection and Risk Management:
 LCNC AI enables financial firms to create custom fraud detection models that analyze real-time
transaction data to identify anomalies.
 Example: A bank uses an LCNC platform to deploy a machine learning model that flags suspicious
transactions based on spending patterns.
2. Automated Loan Approvals and Credit Scoring:
 LCNC AI tools evaluate creditworthiness by analyzing applicant data, reducing manual processing
time.
 Example: A fintech company uses an LCNC tool to automate loan approvals by integrating AI-based
risk assessment models.
3. Algorithmic Trading and Portfolio Optimization:
 AI models built on LCNC platforms help optimize stock trading strategies using historical and real-
time market data.
 Example: A hedge fund can build a custom AI trading algorithm without coding expertise.
4. Regulatory Compliance and Reporting Automation:
 AI-powered LCNC tools ensure compliance with financial regulations by automating reporting and
risk assessment.
 Example: A bank uses LCNC AI to automatically generate compliance reports based on changing
regulations.
5.3 Retail and E-Commerce
Retailers and e-commerce companies are leveraging LCNC AI tools to personalize customer experiences,
optimize inventory management, and automate order processing.
Key Applications in Retail & E-Commerce:
1. Personalized Product Recommendations:
 AI-driven LCNC platforms analyze customer purchase history, browsing behavior, and preferences
to deliver tailored product recommendations.
 Example: An online fashion retailer uses an LCNC AI recommendation engine to suggest clothing
styles based on user behavior.
2. AI-Powered Chatbots for Customer Engagement:
 LCNC AI chatbots handle customer inquiries, complaints, and order tracking, improving response
times.
 Example: An e-commerce store deploys a no-code AI chatbot to assist shoppers in real time.
3. Dynamic Pricing Optimization:
 AI models built with LCNC platforms can analyze competitor pricing and demand trends to adjust
product prices dynamically.
 Example: An airline uses LCNC AI to adjust flight ticket prices based on demand patterns.
4. Inventory and Supply Chain Optimization:

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26820
 Retailers use AI-driven LCNC tools to forecast demand, preventing overstocking or stockouts.
 Example: A grocery chain uses an LCNC AI model to optimize supply chain logistics and reduce
waste.
5.4 Supply Chain and Manufacturing
The adoption of LCNC AI in supply chain and manufacturing is revolutionizing logistics, production
planning, and quality control.
Key Applications in Supply Chain & Manufacturing:
1. Demand Forecasting and Inventory Management:
 AI-driven LCNC platforms help businesses predict market demand trends and optimize inventory
levels.
 Example: A manufacturer uses an LCNC predictive analytics tool to forecast raw material needs.
2. Intelligent Process Automation in Factories:
 AI models built on LCNC tools monitor machine performance, detect anomalies, and automate
maintenance scheduling.
 Example: A factory integrates AI-based predictive maintenance to reduce equipment failures.
3. Quality Control and Defect Detection:
 LCNC AI enables computer vision-based quality checks, minimizing defective product shipments.
 Example: A smartphone manufacturer uses LCNC AI for real-time defect detection.
4. Logistics and Route Optimization:
 AI-powered LCNC models optimize shipping routes, reducing delivery times and costs.
 Example: A logistics company deploys an LCNC AI tool for real-time fleet optimization.
5.5 Customer Service and Support
Businesses are using LCNC AI chatbots, sentiment analysis tools, and voice assistants to enhance customer
support experiences.
Key Applications in Customer Service:
1. AI Chatbots and Virtual Assistants:
 AI-powered LCNC bots handle customer queries, complaints, and feedback analysis.
 Example: An LCNC AI-powered chatbot reduces customer support wait times by 70%.
2. AI Sentiment Analysis for Customer Feedback:
 AI-driven LCNC models analyze social media comments and product reviews to measure customer
sentiment.
 Example: A restaurant chain uses LCNC AI to detect negative customer feedback trends.
3. Automated Call Routing with AI:
 LCNC AI tools analyze customer inquiries and route calls to the appropriate department.
 Example: A telecom company automates customer service call routing, improving efficiency.
The integration of LCNC AI decisioning is revolutionizing multiple industries, from healthcare and finance
to retail and manufacturing. These platforms enable businesses to leverage AI capabilities faster, cheaper,
and more efficiently. As LCNC tools evolve, their adoption is expected to grow exponentially, transforming
business operations worldwide.
6. Challenges and Future Trends in Low-Code/No-Code AI Decisioning
While Low-Code/No-Code (LCNC) platforms have revolutionized AI decisioning and intelligent
automation, they are not without challenges. Businesses must navigate issues related to customization,
scalability, security, and compliance to maximize their benefits. At the same time, future advancements in
AI, AutoML, Edge AI, and enhanced security mechanisms promise to address some of these concerns. This
section provides a comprehensive analysis of the challenges hindering LCNC adoption and the future trends
shaping the industry.

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26821
6.1 Challenges of LCNC AI Decisioning
Despite their numerous benefits, LCNC AI platforms face significant challenges that organizations must
consider before full-scale adoption.
6.1.1 Limited Customization and Flexibility
 LCNC platforms often prioritize ease of use over deep technical customization.
 Unlike traditional AI development, where developers have full control over algorithms, data
processing, and model tuning, LCNC users rely on prebuilt models and restricted customization
options.
 Complex AI applications requiring advanced neural networks or deep learning architectures may not
be feasible using LCNC solutions.
 Custom integrations with legacy enterprise systems can be limited, leading to compatibility issues.
Example:
A financial institution using an LCNC-based AI model for fraud detection may find that prebuilt models lack
the ability to fine-tune risk parameters for specific compliance needs.
6.1.2 Scalability Constraints
 LCNC platforms work well for small to mid-scale applications but may struggle with large-scale
deployments.
 AI models built on LCNC platforms may not efficiently handle large datasets or complex
computations due to platform-imposed resource limitations.
 Organizations that outgrow LCNC capabilities often need to migrate to fully coded AI solutions,
which can be costly and time-consuming.
Example:
A retail company using LCNC for customer segmentation may find that as their customer base expands, the
platform struggles with processing real-time data streams for AI-driven recommendations.
6.1.3 Security and Compliance Risks
 AI models built on LCNC platforms often rely on third-party cloud services, raising data privacy
concerns.
 Industries such as finance, healthcare, and government require strict data governance and regulatory
compliance (e.g., GDPR, HIPAA, PCI-DSS).
 LCNC users may lack direct control over security protocols, exposing AI models to data breaches
and cyber threats.
Example:
A healthcare provider implementing AI-driven patient diagnostics through an LCNC platform might struggle
with ensuring HIPAA compliance, particularly if sensitive patient data is stored in cloud-hosted AI models.
6.1.4 Vendor Lock-In Risks
 Many LCNC platforms restrict exportability, making it difficult for businesses to migrate AI models
to other ecosystems.
 Organizations that heavily invest in an LCNC platform may find switching costs prohibitively high if
they decide to transition to custom AI development.
 Some vendors charge high fees for advanced features, integrations, or cloud usage, leading to hidden
costs over time.
Example:
A logistics company using an LCNC AI-powered supply chain management tool might find that switching
providers requires rebuilding automation workflows from scratch, leading to downtime and additional costs.
6.1.5 Dependence on Prebuilt Algorithms
 LCNC platforms offer drag-and-drop AI models, but these models may not always be optimal for
specific business needs.

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26822
 Custom AI solutions often outperform generic LCNC models, particularly in areas like predictive
analytics, fraud detection, and real-time decisioning.
 Users without coding expertise may rely too heavily on automated configurations, leading to
suboptimal AI performance.
Example:
A bank deploying AI-driven credit scoring through an LCNC platform may find that prebuilt risk assessment
models do not account for localized financial behaviors, affecting accuracy.
6.2 Future Trends in LCNC AI Decisioning
Despite the challenges, LCNC AI decisioning is rapidly evolving, with innovations aimed at overcoming
existing limitations. The future will see more scalable, secure, and adaptable LCNC solutions, driven by
advancements in AutoML, Edge AI, and AI-Augmented Development.
6.2.1 AI-Augmented Development with AutoML
 Automated Machine Learning (AutoML) will play a pivotal role in enhancing LCNC AI decisioning
by allowing users to build custom AI models without coding.
 AutoML automates the process of hyperparameter tuning, model selection, and feature engineering,
making LCNC platforms more powerful.
 Future LCNC platforms will integrate AI-driven recommendations to help users build more accurate,
data-driven decision models.
Example:
A marketing team using LCNC-based customer segmentation could use AutoML-enhanced LCNC to
automatically optimize customer clusters based on real-time data, improving campaign effectiveness.
6.2.2 Edge AI and On-Premise Deployment
 The rise of Edge AI will allow real-time AI decisioning without relying on cloud-based platforms.
 Businesses concerned about data security and compliance will prefer on-premise LCNC AI models
to keep sensitive data within their infrastructure.
 Edge AI will enable faster processing speeds and lower latency in AI-driven automation.
Example:
A smart factory could use Edge AI-enabled LCNC platforms to analyze machine failures locally, ensuring
real-time anomaly detection without cloud dependencies.
6.2.3 Enhanced Security & Compliance in LCNC AI
 Future LCNC platforms will integrate blockchain-based security mechanisms to ensure tamper-proof
AI decisioning.
 AI governance frameworks will be incorporated into LCNC development environments, allowing
businesses to comply with evolving regulations.
Example:
A financial services firm could use a blockchain-enhanced LCNC AI platform to ensure transparent and
auditable AI decision-making in credit approval processes.
6.2.4 Integration with Large Language Models (LLMs)
 LCNC platforms will incorporate GPT-powered AI assistants to help users build, test, and deploy AI
models using natural language prompts.
 This integration will reduce the learning curve for non-technical users and enhance AI model
explainability.
Example:
A legal firm using an LCNC AI platform could leverage LLM-powered automation to draft contracts, review
legal documents, and generate AI-based legal insights.
6.2.5 Expansion of Industry-Specific LCNC AI Solutions
 Future LCNC platforms will offer industry-specific AI tools, reducing the need for generic solutions.

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26823
 Sectors like healthcare, finance, and supply chain will see tailored LCNC platforms that align with
domain-specific challenges.
Example:
A pharmaceutical company could use a healthcare-focused LCNC AI platform to automate clinical trial
analysis, ensuring regulatory compliance.
The challenges facing LCNC AI decisioning, including scalability limitations, security risks, and limited
customization, remain barriers to widespread adoption. However, the future of LCNC AI is promising, with
advancements in AutoML, Edge AI, security, and industry-specific solutions making these platforms more
powerful and adaptable. Organizations looking to integrate LCNC AI decisioning must balance ease of use
with customization while ensuring security and regulatory compliance.
As LCNC platforms continue to evolve, businesses will increasingly rely on AI-powered automation,
democratizing AI decision-making and accelerating digital transformation across industries.
7. Conclusion
The integration of low-code/no-code (LCNC) platforms into AI decisioning represents a paradigm shift in
intelligent automation. These platforms democratize access to AI, allowing both technical and non-technical
users to build, deploy, and manage AI-driven applications with minimal coding expertise. This
transformation is driven by the need for speed, agility, and cost-efficiency in modern enterprises, making AI
adoption more accessible across industries.
7.1 Summary of Key Findings
Through this study, several critical insights have been identified:
1. LCNC platforms reduce the barriers to AI adoption by eliminating the need for extensive
programming knowledge.
2. Faster development and deployment times make AI implementation more responsive to business
needs.
3. Significant cost reductions result from minimized development and maintenance expenses.
4. Scalability improvements enable businesses to expand AI-powered automation without requiring
specialized AI talent.
5. Industry-wide applications demonstrate the versatility of LCNC AI decisioning, from healthcare and
finance to manufacturing and supply chain management.
7.2 The Strategic Value of LCNC AI Decisioning
LCNC platforms are not just tools for rapid application development; they are becoming strategic assets that
empower organizations to:
 Drive digital transformation by integrating AI seamlessly into existing workflows.
 Foster innovation through citizen development and cross-functional collaboration.
 Reduce time-to-market for AI solutions, ensuring competitiveness in a rapidly evolving technological
landscape.
 Enhance operational efficiency by automating complex decision-making processes with minimal
resource investment.
7.3 Overcoming Challenges and Limitations
Despite their advantages, LCNC platforms face certain limitations, including:
 Customization constraints: While LCNC platforms offer flexibility, they may not provide the same
level of fine-tuned customization as traditional AI development.
 Security and compliance concerns: Organizations in highly regulated industries (e.g., finance,
healthcare) must ensure that LCNC-driven AI solutions meet industry-specific regulatory
requirements.

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26824
 Scalability for complex AI models: Advanced AI applications, such as deep learning and real-time AI
inference, may require more custom development beyond LCNC capabilities.
7.4 The Future Outlook: Advancements in LCNC AI Decisioning
As AI and automation technologies evolve, LCNC platforms are expected to incorporate more advanced
capabilities, including:
1. AI-Augmented Low-Code Development:
 The integration of AutoML (Automated Machine Learning) within LCNC platforms will allow users
to build custom AI models with minimal effort.
 Future LCNC platforms will likely provide pre-built AI templates that adapt to different business
needs.
2. Edge AI and IoT Integration:
 AI decisioning will extend beyond cloud environments to edge devices, enabling real-time
processing in IoT applications (e.g., smart cities, autonomous systems).
 LCNC-based AI workflows will become more responsive to real-time data streams.
3. Advanced Security and Governance Models:
 To address compliance challenges, future LCNC platforms will offer built-in security features,
automated audits, and AI governance mechanisms.
 Enhanced explainability and interpretability of AI will be prioritized, ensuring that AI-driven
decisions adhere to ethical and regulatory frameworks.
4. Hyperautomation and AI-Driven Business Process Management:
 The convergence of LCNC AI decisioning with robotic process automation (RPA), business
intelligence (BI), and cloud computing will accelerate hyperautomation initiatives.
 Organizations will leverage AI-powered decision engines that adapt dynamically to business
conditions, reducing manual intervention.
7.5 Final Thoughts
Low-code/no-code platforms are redefining the landscape of AI-driven decision-making, providing
businesses with the agility to scale automation without the complexities of traditional development. As these
platforms continue to evolve, they will play a pivotal role in bridging the gap between AI innovation and
business execution. While challenges remain, ongoing advancements in AutoML, security, and edge
computing will strengthen LCNC‘s position as a foundational technology for the future of intelligent
automation.
Organizations that embrace LCNC AI decisioning today will be better positioned to drive digital
transformation, improve operational efficiency, and maintain a competitive edge in an AI-driven world.
References
1. Carroll, N., & Maher, M. (2023). How Shell Fueled Digital Transformation by Establishing DIY
Software Development. MIS Q. Executive, 22(2), 3.
2. Davenport, T. H. (2018). The AI advantage: How to put the artificial intelligence revolution to work.
mit Press.
3. Guthardt, T., Kosiol, J., & Hohlfeld, O. (2024, September). Low-code vs. the developer: An
empirical study on the developer experience and efficiency of a no-code platform. In Proceedings of
the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems
(pp. 856-865).
4. Kandaurova, M., Skog, D. A., & Bosch-Sijtsema, P. M. (2024). The Promise and Perils of Low-Code
AI Platforms. MIS Quarterly Executive, 23(3), 4.
5. Caballar, R. D. (2020). Programming without code: The rise of no-code software development. In
IEEE Spectr. Tech Talks.

Vamsi Viswanadhapalli, IJECS Volume 14 Issue 01 January, 2025 Page 26825
6. Lang, J. (2020). The Routledge companion to twentieth and early twenty-first century urban design:
A history of shifting manifestoes, paradigms, generic solutions, and specific designs. Routledge.
7. Sundberg, L., & Holmström, J. (2023). Democratizing artificial intelligence: How no-code AI can
leverage machine learning operations. Business Horizons, 66(6), 777-788.
8. Truss, M., & Schmitt, M. (2024). Human-centered ai product prototyping with no-code automl:
Conceptual framework, potentials and limitations. International Journal of Human–Computer
Interaction, 1-16.
9. Waqas, M., Ali, Z., Sánchez-Gordón, M., & Kristiansen, M. (2024). Using LowCode and NoCode
Tools in DevOps: A Multivocal Literature Review. New Perspectives in Software Engineering, 71-
87.
10. Mansoori, Y., Kandaurova, M., & Bumann, A. ‗Everyone‘Can Be an Entrepreneur: The Rise of Low-
Code/No-Code Entrepreneurship. Can Be an Entrepreneur: The Rise of Low-Code/No-Code
Entrepreneurship.
11. Shlomov, S., Yaeli, A., Marreed, S., Schwartz, S., Eder, N., Akrabi, O., & Zeltyn, S. (2024). IDA:
Breaking Barriers in No-code UI Automation Through Large Language Models and Human-Centric
Design. arXiv preprint arXiv:2407.15673.
12. Khankhoje, R. (2022). Beyond Coding: A Comprehensive Study of Low-Code, No-Code and
Traditional Automation. Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-160.
DOI: doi. org/10.47363/JAICC/2022 (1), 148, 2-5.
13. Sufi, F. (2023). Algorithms in low-code-no-code for research applications: a practical review.
Algorithms, 16(2), 108.
14. Zou, J., & Schiebinger, L. (2018). AI can be sexist and racist—it‘s time to make it fair.
15. Zysman, J., & Kenney, M. (2018). The next phase in the digital revolution: intelligent tools,
platforms, growth, employment. Communications of the ACM, 61(2), 54-63.
16. Sundberg, L., & Holmström, J. (2023). Democratizing artificial intelligence: How no-code AI can
leverage machine learning operations. Business Horizons, 66(6), 777-788.
17. Villegas-Ch, W., García-Ortiz, J., & Sánchez-Viteri, S. (2021). Identification of the factors that
influence university learning with low-code/no-code artificial intelligence techniques. Electronics,
10(10), 1192.
18. Brandon, C., & Margaria, T. (2023). Low-Code/No-Code Artificial Intelligence Platforms for the
Health Informatics Domain. Electronic Communications of the EASST, 82.
19. Raghavendran, K. R., & Elragal, A. (2023, June). Low-Code Machine Learning Platforms: A
Fastlane to Digitalization. In Informatics (Vol. 10, No. 2, p. 50). MDPI.
20. Curty, S., Härer, F., & Fill, H. G. (2023). Design of blockchain-based applications using model-
driven engineering and low-code/no-code platforms: a structured literature review. Software and
Systems Modeling, 22(6), 1857-1895.