AI Automation v Traditional Low-Code Workflow Automation

Introduction

The landscape of enterprise computing solutions is rapidly evolving, with both AI automation and traditional low-code workflow automation emerging as powerful approaches to streamline business processes. As businesses pursue digital transformation initiatives, understanding the distinct capabilities and use cases of these technologies becomes crucial for making informed strategic decisions.

Understanding AI Automation

AI automation represents the convergence of artificial intelligence capabilities with workflow automation. Unlike traditional automation, AI automation can handle complex tasks that require decision-making capabilities in dynamic, data-rich environments. This technology utilizes machine learning, natural language processing, and data analysis to create intelligent systems that can adapt to changing conditions.

At its core, AI automation is designed to tackle both repetitive tasks and complex workflows with speed and intelligence. The introduction of large language models has significantly enhanced these capabilities, enabling AI systems to not only predict or analyze but also create solutions.

“AI automation uses both machine learning and natural language processing (NLP), which is able to understand and respond to human language, can analyze large swaths of datasets and make intelligent decisions,” notes a recent industry report. This allows for autonomous analysis of results and adaptation of processes to achieve more relevant outcomes.

AI Application Generators

One notable manifestation of AI automation is the AI application generator – tools that leverage artificial intelligence to create functional applications with minimal human input. For example, Jotform’s AI App Generator allows users to “turn ideas into apps without coding” by simply describing what they want. These systems represent a significant advancement in how enterprise products are developed and deployed.

Enterprise AI Integration

Enterprise AI, defined as “the integration of advanced AI-enabled technologies and techniques within large organizations to enhance various business functions,” is transforming how companies operate. It encompasses routine tasks such as data collection and analysis, plus more complex operations like automation, customer service, and risk management.

Understanding Traditional Low-Code Workflow Automation

Traditional low-code workflow automation offers a structured approach to application development with minimal coding requirements. These platforms typically feature pre-designed templates, intuitive drag-and-drop interfaces, and reusable components that streamline the development process.

Evolution of Low-Code Platforms

Low-code platforms have evolved substantially, with Gartner projecting that by 2025, 70% of new applications created by organizations will use at least four low-code development tools, up from 25% in 2020. These platforms generally fall into three categories:

1. Low-code Application Platform as a Service

2. Low-code Rapid Application Development tools

3. Low-code Enterprise Architecture Solutions

These platforms enable users to “design, build, and deploy applications without diving deep into traditional coding,” making application development accessible to a broader range of users within an organization.

Key Features of Low-Code Platforms

Modern low-code platforms offer a versatile set of features, including:

• Drag-and-drop interfaces for visual application design

• Visual modeling tools for workflow and data models

• Pre-built templates for faster development

• Cross-platform compatibility for diverse device support

• Integration capabilities with existing systems

• Scalability for handling increasing workloads

• Security features for protecting applications

• Automated testing tools for quality assurance

• Reporting and analytics for performance tracking

• Rapid prototyping capabilities

Comparing AI Automation and Traditional Low-Code Workflow Automation

Development Process and Tools

AI Automation: Development typically involves training AI models on large datasets and configuring autonomous systems that can make decisions and adapt based on feedback. AI application generators can create applications based on natural language descriptions, significantly reducing development time.

Traditional Low-Code: Development relies on visual interfaces, drag-and-drop components, and pre-built templates. This approach still requires manual configuration of workflows and business rules, albeit with minimal coding.

Capabilities and Limitations

AI Automation:

• Can handle complex, dynamic tasks requiring decision-making

• Offers scalability through machine learning and cloud computing

• Provides faster response times through autonomous agents

• Demonstrates high accuracy in data processing and pattern recognition

• Capable of tackling multi-layer work requiring real-time decisions

Traditional Low-Code:

• Excels at rule-based, repetitive tasks in stable environments

• Limited ability to adapt to changing conditions without reconfiguration

• Typically follows predefined workflows rather than learning from data

• May face challenges with highly complex or unpredictable processes

Integration with Enterprise Systems

Both approaches interact differently with existing enterprise systems and enterprise business architecture.

AI Automation: AI automation can be deeply integrated with enterprise information systems (EIS), enhancing their ability to improve business processes through intelligent data analysis and decision-making. This integration allows enterprise systems to evolve from simple data management tools to sophisticated platforms that drive strategic decision-making.

Traditional Low-Code: These platforms typically offer built-in connectors for seamless data integration and automation across organizations. They can work within existing enterprise business architecture to align business processes with IT systems on a large scale.

Role of Citizen Developers and Business Technologists

The democratization of application development is a key benefit of both approaches but manifests differently.

Citizen Developers: Defined as “non-IT employees who create applications or enhance existing systems, often without formal training in software development,” citizen developers leverage both AI automation and low-code platforms to build solutions that meet business needs. With traditional low-code platforms, they follow a more structured development approach, while AI application generators allow them to create solutions through natural language descriptions.

Business Technologists: These professionals “work outside of traditional IT departments” and focus on “crafting innovative technological solutions and analytical capabilities tailored to internal and external business needs”. They play a crucial role in implementing both AI automation and low-code solutions, applying innovative tools to enhance business operations, drive growth, and facilitate informed decision-making.

Enterprise Context and Implementation Considerations

Enterprise Resource Planning and Systems

Enterprise resource planning (ERP) software helps enterprises integrate management aspects of inventory, accounting, CRM, human resources, and more. Both AI automation and low-code platforms can enhance ERP systems, with AI offering more adaptive capabilities and low-code providing customization options.

The Enterprise Systems Group plays a crucial role in orchestrating technological transformation, leveraging advanced technologies such as AI application generators, low-code platforms, and enterprise resource systems to streamline operations and align processes with enterprise business architecture.

Software Bill of Materials (SBOM)

An SBOM, which is “a list of all of the components used to build and run an application,” becomes increasingly important in both AI automation and low-code development. For AI automation, tracking components becomes more complex due to the inclusion of AI models and datasets, while for low-code platforms, the SBOM typically includes pre-built components and libraries.

Open-Source Considerations

Organizations can leverage open-source solutions for both AI development and low-code platforms, enabling democratized AI development while maintaining governance and quality. This approach can be particularly beneficial for organizations with limited resources.

Types of Technologists in the Automation Landscape

Different types of technologists contribute to the implementation and management of both AI automation and low-code solutions:

• Analyst Technologists: Analyze data to provide insights for technology products, crucial for AI automation implementations

• Builder Technologists: Develop and construct technology solutions through coding, engineering, and assembling

• Facilitator Technologists: Ensure smooth project execution by coordinating teams and resources

• Designer Technologists: Focus on user experience and aesthetic aspects of technology products

• Businessperson Technologists: Integrate technology solutions to drive business success

AI Assistance in Enterprise Products

The question of whether all enterprise products should incorporate AI assistance is increasingly relevant. While AI integration offers substantial benefits like enhanced decision-making, operational efficiency, and customer engagement, the implementation must be strategic rather than indiscriminate.

Organizations should consider AI assistance as a strategic capability to be deployed where it adds genuine value and aligns with business objectives. This decision should be guided by thorough assessment of specific use cases, organizational readiness, and expected returns.

Digital Transformation Impact

Both AI automation and low-code workflow automation serve as catalysts for digital transformation. However, they contribute differently:

AI Automation: Drives transformation through intelligent automation of complex processes, enabling organizations to reimagine their operations with AI-powered capabilities that can analyze data, make decisions, and continuously improve.

Traditional Low-Code: Accelerates transformation by enabling rapid application development and deployment, empowering non-technical users to contribute to digital initiatives and reducing dependency on specialized IT resources.

Conclusion: Strategic Selection for Business Success

The choice between AI automation and traditional low-code workflow automation should be guided by organizational needs, use cases, and strategic objectives rather than simply following market trends.

AI automation excels in scenarios requiring adaptive intelligence, complex decision-making, and learning capabilities. It represents the cutting edge of business enterprise software, enabling organizations to tackle challenges that were previously unaddressable through automation.

Traditional low-code workflow automation remains valuable for structured processes, rapid application development, and empowering citizen developers to create solutions with minimal IT involvement. It continues to play a critical role in democratizing application development and accelerating digital transformation initiatives.

Many organizations will benefit from a hybrid approach that leverages both technologies appropriately, with enterprise business architecture providing the framework for strategic technology integration. The Enterprise Systems Group, business technologists, and citizen developers will collaborate to implement these solutions, ensuring alignment with business objectives and maximizing return on technology investments.

As technology transfer between these domains continues, we can expect further convergence of AI capabilities and low-code platforms, creating even more powerful tools for enterprise automation and transformation.