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Independently, generative AI and low-code software are two highly desirable technologies. But experts say the two mesh in a way that accelerates innovation beyond the status quo.
Low code development allows people to create applications with minimal need for hard code, rather than using visual tools and other development paradigms. While the intersection of low code and AI seems natural, it is critical to consider nuances such as data integrity and security to ensure meaningful integration.
Microsoft Low code signals 2023 The report says that 87% of CIOs and IT professionals believe that “increased AI and automation built into low-code platforms will help them better use the full range of capabilities.”
According to Dinesh Varadharajan, CPO at Low Code/No Code Work Platform Kissflow, the convergence of AI and low code enables systems to manage work rather than humans having to work in systems.
In addition, instead of the AI revolution replacing low code, Varadarajan said, “One does not replace the other, but the power of the two will bring a lot of possibilities.”
Varadarajan notes that with AI and low-code technologies coming together, the development gap is being closed. Low-code software increases development accessibility across organizations (often to so-called citizen developers) while generative AI increases organizational efficiency and compliance.
Faster innovation
According to Jim Rose, CEO of an automation platform for software delivery teams called CircleCI, these large language models that serve as the basis for generative AI platforms will eventually be able to transform low-code language. Instead of creating an app or website with a visual design format, Rose said, “What you can do is query the templates themselves and say, for example, ‘I need an easy-to-manage e-commerce store for vintage shoes.'” “
Technology hasn’t quite reached that point, Rose agrees, in part because “you have to know how to talk” to generative AI to get what you’re looking for. Keseflo’s Varadharajan says he can see AI taking over tasks within a year, and possibly cutting through low-code in a more meaningful way soon after.
Governance and innovation go hand in hand
Like anything related to AI, there are a lot of nuances that business leaders need to consider for successful implementation and low AI-powered code redundancy.
Don Schurman, chief technology officer of enterprise software company Pega, prioritizes what he calls a “responsible and ethical AI framework.”
This includes the need for transparency. In other words, can you explain how and why the AI makes a certain decision? Without that clarity, he says, companies could end up with a system that fails to serve end users in a fair and responsible manner.
He added that this merges with the need for bias testing. “There are latent biases that are ingrained in our society, which means there are latent biases that are built into our data,” he said. “This means that AI will pick up on those biases unless we explicitly test and protect against them.”
Schurman is a proponent of “keeping the human in the loop,” not just for checking for errors and making changes, but also for looking at what machine learning algorithms have yet to master: customer empathy. By prioritizing customer empathy, organizations can maintain systems and recommend products and services that are actually relevant to the end user.
For Varadarajan, the biggest challenge he anticipates with the convergence of AI and low code is change management. Enterprise users, in particular, are used to working a certain way, he says, which could make them the last segment to adopt the AI-powered low-code shift.
Whatever risks a company takes, maintaining a layer of governance is what will help leaders keep pace with the evolution of AI. “So far, we are still grappling with the possibilities of what generative AI can do,” Varadharajan said. “As humans, we will also evolve. We will figure out ways to manage risk.”
New starting point
While many generative AI platforms stem from open-source models, CircleCI’s Rose says a successor of a different kind is coming. “The next wave is closed-loop models that are trained against private data,” he said.
Proprietary data and closed-loop models still have to factor in the need for transparency, of course. However, organizations’ ability to keep data secure in this small pattern could rapidly transform generative AI capabilities across industries.
Generative AI and low-code software put innovation on a highway, experts said, as long as organizations don’t compromise on responsibility. In modern times, speed of innovation is a must in order to be competitive. Just look at coldan Adobe-Google offering that is set to compete with OpenAI’s ChatGPT in the generative AI space.
According to Schurman, with the AI and the low-code, “I’m starting to work more than before.” By shortening the path between idea, experimentation and ultimately to a live product, he said low-code, powered by AI, accelerates the speed of innovation.
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