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Combining low code with emerging AI technologies: Can users truly create compelling apps?

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Enterprises are no strangers to disruptions, with uncertainty lurking around every corner. In this dynamic environment, adaptability and resilience aren’t just admirable qualities but essential for business survival. The recent geopolitical and economic unpredictability combined with the need to do more with fewer resources, has nudged businesses towards flexible solutions, such as low-code platforms enabling organizations to make a rapid recovery.

Low-code platform development provides enterprises with the agility to design workflows without investing in large and expensive software development teams. This liberates enterprises from traditional, time-consuming software development processes. However, many low-code platforms have not lived up to the hype with a higher level of complexity and dependence on technical staff to create compelling apps. The emergence of Generative AI (Artificial Intelligence) acts as a transformative force, bridging the gap between software and ‘citizen’ developers while automating various elements of the software development life cycle.

Combining low-code and AI can enable non-IT employees to launch workflows, create great user experiences, develop interactive reports, and generate enterprise applications quickly with higher levels of complexity than what was possible before.

Importance of AI-led democratization of application development in enterprises

Democratization, the process of making software development more accessible to a wider audience, including non-programmers, has become a necessity with growing software requirements and the need for enhanced digital experiences in composable enterprises. While low-code development provides the right environment to design applications, it can still be expensive and slow. However, integration of low-code with AI through user-friendly interfaces can enable business analysts, marketing experts, and other non-IT users – who will constitute ~80% of the user base for low-code development tools by 2026 to build innovative applications. Moreover, numerous up-and-coming AI technologies are propelling the low-code landscape.

Emerging AI technologies in the low-code landscape

Advanced AI technologies are reshaping application development by accelerating code generation and comprehending natural language commands. It is estimated that 70% of professional developers will use AI-powered coding tools by 2027. AI automates large sections of low-code development–a visual approach to software development with simple drag-and-drop features, wizard-based interfaces, and many other additional benefits.

Benefits of combining low-code with emerging AI technologies

Embracing AI in low-code development improves agility while delivering tangible business value. It helps businesses with:

  1. Increased accessibility for non-technical users: Integrated platforms reduce dependence on specialized IT skills by empowering non-technical users to participate in application development, including automated text completion, building a UI from a drawing, generating automated workflows, and self-service analytics, to name a few.
  2. Faster and more efficient development: Generative AI can auto-complete code, detect errors, and suggest fixes in real-time, significantly expediting the development process.
  3. Improved quality and functionality: AI-driven tools assist in generating high-quality code, ensuring adherence to best coding practices, and optimizing performance.

With AI revolutionizing the low-code development process, generative AI stands at the forefront of this transformation, facilitating efficient application development. By harnessing machine learning algorithms, it speeds up delivery cycle time and suggests relevant code fragments that meet functional and operational requirements. Enabling developers to build complex applications even without extensive coding expertise, generative AI has showcased its phenomenal capabilities in the real world as well.

Use cases of low-code and generative AI

Many organizations have already ventured into the realm of AI-powered low-code application development. Here are a few notable examples:

  • Appian’s AI Copilot: Appian has leveraged generative AI tools to express application designs with prompts while enabling humans to understand and visually refine what the AI has created.
  • Google’s AutoML: By leveraging generative AI in low-code platforms, Google’s AutoML enables developers to create custom models tailored to their business needs.
  • Microsoft Power Platform: This low-code platform provides the ability to quickly build applications, automate and optimize workflows, and turn data into engaging reports rapidly from user prompts.
  • Pega Infinity ‘23: Utilizes generative AI-powered boosters to automate and simplify the development process in low-code environments, enabling teams to focus on high-priority tasks.

Challenges in implementing AI-driven low-code platforms

The alliance between AI and low-code looks promising and is already yielding excellent results. However, it comes with its own set of challenges:

  1. User education and training on AI: Users need to understand how to use AI tools responsibly, including AI concepts, their limitations, and how to avoid misuse.
  2. Bias and discrimination: AI systems can perpetuate biases present in trained data. It’s crucial to train AI models on diverse data and regularly audit for bias.
  3. Tool limitations and trade-offs: Users may encounter trade-offs in terms of flexibility, customization, or specific types of applications they can build.
  4. Complexity: The introduction of AI can add complexity to the development process, requiring users to understand the intricacies of AI models and their deployment.

Addressing these challenges is essential to harness the full potential of AI within low-code platforms to develop future-oriented, ethical, and efficient applications.

Harmonizing the future of low-code and AI

The global low-code development platform is estimated to witness a growth of USD 148.5 billion by 2030. The integration of AI and low-code platform development is going to further drive this growth to produce:
  • Conversational applications generation and BI/augmented analytics: AI-powered low-code platforms enable users to describe their requirements in natural language. Augmented BI empowers enterprises to generate valuable insights.
  • Domain-specific low-code platforms: These platforms will offer pre-built components and templates tailored to the unique needs of different industries.
  • Automatic codebase updates: low-code platforms will automatically update their codebase, reducing the burden of manual maintenance.
  • Astounding real-world applications: AI-enabled low-code development spans from streamlining telemedicine application development in healthcare to advanced recommendation systems in retail and fraud detection applications in finance.

Pursuing AI-led excellence in the low-code landscape

AI’s remarkable capabilities in code generation and operational efficiencies play a pivotal role in delivering tailored experiences. It facilitates seamless integration between business applications, cloud services, third-party APIs, and databases, ensuring the efficient flow of data. As AI becomes more accessible to non-technical users, there will be a growing emphasis on its ethical development, ensuring the systems are accountable, transparent, and capable of preventing potential risks and tackling biases.

At HTCNXT, we are steadfast in our mission to modernize enterprises into AI-first organizations. Despite the potential of low-code and AI to empower users in creating compelling apps, there remain challenges that need to be addressed. Our AI expertise and ecosystem, including our platform MAGE, take center stage in this transformative journey. Through the synergy of low-code and AI, MAGE is unlocking substantial value in the low-code landscape by guiding enterprises to fully embrace the potential of low-code and AI, enabling them to overcome challenges and foster innovation in an ever-evolving technological landscape.


Rajeev Bhuvaneswaran

Vice President, Digital Transformation and Innovation Services







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