Independently, generative artificial intelligence and low-code software are two highly sought-after technologies. But experts say that together, the two harmonize in a way that accelerates innovation beyond the status quo.
Low-code development allows people to build applications with minimal need for hard code, instead using visual tools and other models to develop. While the intersection of low-code and AI feels natural, it’s crucial to consider nuances like data integrity and security to ensure a meaningful integration.
Microsoft’s Low-Code Signals 2023 report says 87% of chief innovation officers and IT professionals believe “increased AI and automation embedded into low-code platforms would help them better use the full set 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 the work rather than humans having to work for the systems.
Additionally, rather than the AI revolution replacing low-code, Varadharajan said, “One doesn’t replace the other, but the power of two is going to bring a lot of possibilities.”
Varadharajan notes that as AI and low-code technology come together, the development gap closes. Low-code software increases the accessibility of development across organizations (often to so-called citizen developers) while generative AI increases organizational efficiency and congruence.
According to Jim Rose, CEO of an automation platform for software delivery teams called CircleCI, these large language models that serve as the foundation of generative AI platforms will ultimately be able to change the language of low-code. Rather than building an app or website through a visual design format, Rose said, “What you’ll be able to do is query the models themselves and say, for example, ‘I need an easy-to-manage e-commerce shop to sell vintage shoes.’”
Rose agrees that the technology has not quite reached this point, in part because “you have to know how to talk” to generative AI to get what you’re looking for. Kissflow’s Varadharajan says he can see AI taking over task management within a year, and perhaps intersecting with low-code in a more meaningful way not long after.
Like anything involving AI, there are plenty of nuances that business leaders must take into account for successful implementation and iteration of AI-powered low-code.
Don Schuerman, CTO 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 AI is making a particular decision? Without that clarity, he says, companies can end up with a system that fails to serve end users in a fair and responsible way.
This melds with the need for bias testing, he added. “There are latent biases embedded in our society, which means there are latent biases embedded in our data,” he said. “That means AI will pick up those biases unless we are explicitly testing and protecting against them.”
Schuerman is a proponent of “keeping the human in the loop,” not only for checking errors and making changes, but also to consider what machine learning algorithms have not yet mastered: customer empathy. By prioritizing customer empathy, organizations can maintain systems and recommend products and services actually relevant to the end user.
For Varadharajan, the biggest challenge he foresees with the convergence of AI and low-code is change management. Enterprise users, in particular, are used to working in a certain way, he says, which could make them the last segment to adopt the AI-powered low-code shift.
Whatever risks a company is dealing with, maintaining the governance layer is what will help leaders keep up with AI as it evolves. “Even now, 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 the risk.”
While many generative AI platforms stem from open-source models, CircleCI’s Rose says there’s a successor of a different kind to come. “The next wave is closed-loop models that are trained against proprietary data,” he said.
Proprietary data and closed-loop models will still have to reckon with the need for transparency, of course. Yet the ability for organizations to keep data secure in this small-model style could quickly shift the capacities of generative AI across industries.
Generative AI and low-code software puts innovation on a freeway, as long as organizations don’t compromise on the responsibility factor, experts said. In the modern era, innovation speed is a must-have to be competitive. Just look at Bard, the Adobe-Google offering that is set to compete with OpenAI’s ChatGPT in the generative AI space.
According to Scheurman, with AI and low-code, “I’m starting out further down the field than I did before.” By shortening the path between an idea to experimentation and ultimately to a live product, he said AI-powered low-code accelerates the speed of innovation.
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