How Generative AI Low Code Will Change App Development?

Now that we know that low code is here to stay, the question becomes, how is your organization preparing for its next chapter? For the last few years, low-code tools like App Builder have targeted various pain points in app building, enabling C-level executives and Dev Team Leaders to accelerate time to market, automate app development from design to code, and optimize development productivity. But things are advancing again with generative AI low code that pushes the boundaries of low-code app development even more.

What Is Generative AI? 

In general, generative AI works as a subset of artificial intelligence that has emerged as a pivotal force along with predictive and prescriptive AI. While predictive AI is used for forecasting the future and prescriptive AI drives informed decisions, generative AI is all about creating all types of content. Think about ChatGBT, for example. It is powered by transformer neural networks and deep learning to deliver humanlike content based on the natural language prompts users feed it with. 

At its core, generative AI relies on techniques like: 

• Transformer Models 

• Large Language Models (LLMs) 

• Diffusion Models 

• Machine Learning Models 

Generative AI revolutionizes product design and development. When combined with low code, it enables organizations and their teams to work faster and more efficiently. Defining a novel concept—generative AI low code—these tools can provide a context (data and parameters) and generate code in minutes. 

People are empowered to train the tools in human-machine collaboration, taking advantage of the new generative AI low-code model. 

What Is Generative AI Low Code? 

Generative AI low code is when low-code tools integrate the principles and capabilities of AI (like App Builder AI) to simulate technological fluency. Managing multimodal inputs and outputs, generative AI simplifies the creation of workflows that combine text processing, image generation, and more, all within low-code platforms. 

As generative AI low-code technology matures, the gap between user intent and software behavior diminishes, with the primary goal of automating even more app development processes faster. Some of the post-effects of the new AI + low code paradigm include: 

• Improved collaboration and empowering a broader range of users (including citizen developers) who can participate in the development process. 

• Faster prototyping and iteration, building components, creating user interfaces, mockups, scalable POCs or MVPs, and more without writing extensive code. 

• The ability to streamline daunting or repetitive tasks like data entry, integrating APIs, and image generation out of plain language prompts. 

Generative AI Low Code in Businesses & App Development 

Having ambitious goals, this technology has been involved in scenarios for building and modernizing mission-critical apps to more specific tasks like improving fraud detection and customer service. However, every time management deliberates on incorporating generative AI low code into a new project, their focus shifts to business outcomes and value. This causes organizations and teams to reevaluate and ask themselves: 

• Does it have high business value? 

• What is the business outcome that will drive revenue? 

• Are there any risks and disadvantages that need to be assessed? 

• Is this solving a critical pain point or addressing a high-priority need? 

• How does this align with long-term goals? 

This discussion centers on implementing generative AI into business practices and already established low-code processes. But AI is not a new concept. It’s more of a new approach. By leveraging generative AI capabilities that fit into common and already determined workflows, CTOs, CIOs, and Development Team Leaders can unlock the potential of more people. 

According to Gartner, “By 2026, more than 80% of enterprises will have used generative artificial intelligence (GenAI) application programming interfaces (APIs) or models, and/or deployed GenAI-enabled applications in production environments, up from less than 5% in 2023.”  

During the ISTA Conference, Jason Beres, Sr. VP of Development Tools at Infragistics, indicated that leading companies “are improving, they’re fine-tuning models, they’re just adding new innovation tools to the edges to make it better for them, and they’re seeing improvements of up to 3% a month in productivity using AI when generating code, building unit tests, creating data sources, and more.” 

The increase is not because these tools replace developers but because they help teams do their jobs better. 

Useful article for C-level executives: 

How To Improve Developer Productivity With Low-Code Tools? 

How Are Businesses Taking Advantage of Generative AI To Deliver Business Value? 

AI-augmented software development is revolutionizing how development teams manage projects and tasks to boost developer productivity and increase developer satisfaction. Jason Beres again mentions, “I hate walking by the desks where my developers are working, and I see them writing all this code in console windows and notepad and all these other tools. Why do we still have to do all this stuff? And that’s where AI can really help out.” 

It aims to improve the dev experience and drive work efficiency. GitHub, for example, tried to quantify GitHub Copilot’s impact on developer productivity and happiness. And here’s what they highlighted. 

(Source: GitHub blog) 

Using it as a signifier of the effect of similar tools; it demonstrates how generative AI doesn’t simply revolutionize engineering best practices and makes it much faster and simpler to build and retain business value. Generative AI also advances the low-code software field. 

Undoubtedly, “generative AI has become a top priority for the C-suite and has sparked tremendous innovation in new tools beyond foundation models,” comments Arun Chandrasekaran, Distinguished VP Analyst at Gartner. “Demand is increasing for generative AI in many industries, such as healthcare, life sciences, legal, financial services, and the public sector.” 

Generative AI Challenges and Considerations 

Although there are many use cases where generative AI low code works as a transformative technology that revolutionizes processes, people still need to understand there are limits and risks. Here are some of the AI low code development challenges that need to be addressed despite the immense potential that’s offered:  

• Robust governance: In the context of app development, understanding, testing, and maintaining large amounts of AI-generated code is still required to ensure quality and compliance. 

• Security risks: To ensure generative AI low-code development enhances accuracy rather than poses vulnerabilities like exposing sensitive data or producing bad code. 

• Dev skill “atrophy”: Over-reliance on generative AI low code may potentially limit basic skills like troubleshooting or debugging. 

• Cost implications: Escalating additional costs of adopting generative AI software development against expected benefits. 

• Job displacement: Reskilling and training may be necessary. 

• Integration limitations: Legacy systems or external APIs can sometimes be incompatible with generative AI low code. 

Useful article for C-level executives: 

Guide: Eliminating Low-Code Challenges and Mitigating Risks 

How To Prepare for the Generative AI Low Code Future? 

The rise of low code and AI initially brought disruption because it challenged many traditional app development models. The process democratized software creation and insisted that businesses rethink how they handle technology and innovation and what culture they cultivate within the company. As AI and low code continue to evolve rapidly, here’s what companies need to reimagine and “refurbish” to be ready for what’s to come. 

Moderating expectations about what generative AI low code can do 

Generative AI low code is not a monolith; it is still evolving. That’s why executives need to be pragmatic about the opportunities and risks. Define the specific tasks that will be assigned to the machine. 

Setting up the stage for AI 

Creating a roadmap and working with a generative AI low-code strategy that covers pillars like AI governance, data management, talent acquisition, technology infrastructure, and ethical considerations. 

Being open to experimentation 

It’s important to eliminate rigid traditional development cycles, transition to agile workflows when applicable, try new approaches and tools, and work toward flexibility. 

Thinking of continuous upskilling 

Investing in training opportunities and planning the budget for skill development initiatives. This way, C-level executives will manage change in IT roles because generative AI will impose a new type of workforce in the low-code development. 

Identifying opportunities for AI and low code in advance 

This involves business process analysis, examining industry benchmarks, engaging all stakeholders to gather insights and see what pain points AI can manage, and conducting feasibility studies. 

Linking emerging technologies to existing strategies 

Considering the IDEA framework—identify, determine, extrapolate, and anticipate. Used regularly, this approach helps leaders and C-level executives evaluate the landscape, discover and eliminate gaps within processes, and plan for the future. 

Conclusion + Article Takeaways 

We can safely say we are in a significant transition in app development where machines and human power coexist to redefine processes and mindsets. We’ve seen the rise of AI independently, but now it converges with low code, introducing an entirely new paradigm for enterprises and teams that want to keep up with innovations. But these two technological pillars are now working together. The impact of generative AI on low-code development changes how apps are built, who can build them, and how fast it can happen. 

As the industry moves forward, companies need to learn how to harness the power of these tools in the most efficient way and with responsibility in mind. The key will be to not only leverage the benefits but also to address the challenges around governance, security, and upskilling. This will result in business value, adequate process optimization, and growth.