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How Low-Code and No-Code Platforms Have Democratized The Power of AI

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The use of low-code and no-code platforms to create artificial intelligence (AI) applications is increasing because they enable companies of all sizes - all well as everyday citizens - to capitalize on the power of AI without extensive coding knowledge.

With many companies moving quickly to integrate artificial intelligence (AI) into their workplace, there’s a concurrent surge in the rise of low-code and no-code platforms, aimed at allowing companies of all sizes — as well as individuals — to capitalize on the power of AI without extensive coding knowledge.

Low-code and no-code platforms allow businesses or “citizen developers” to quickly create or optimize applications without needing to acquire programming tools and know-how to get there.

As the names imply, the two platforms are geared to slightly different users, depending on whether they’ve ever touched code before.

“The main difference is that no-code platforms typically have more visual tools and models and are designed for use by business users with little to no coding experience, says Wing To, general manager of the intelligent DevOps business unit at

“Low-code tools, on the other hand, are commonly used by business or technical users with some background/coding skills to create custom applications.”

Why No-Code/Low-Code Platforms Are Increasingly Used To Develop AI Apps

The growing adoption of low-code and no-code technologies in AI app development can be attributed to several factors, says Andrew Manby, vice president of product management at HCL Software.


For one thing, the demand for AI-driven solutions has skyrocketed, prompting organizations to seek efficient ways to capitalize on this trend. And low-code and no-code platforms offer a viable solution by reducing barriers to entry and expediting the development process, Manby says.

However, the ongoing digital transformation across industries has led to a scarcity of skilled developers, making it challenging for organizations to meet their AI development needs.

Manby explains: “Low-code and no-code platforms help bridge this talent gap by enabling citizen developers to contribute to the development process, thus alleviating the pressure on IT departments.”

Additionally, the iterative nature of AI development, which often requires frequent adjustments and fine-tuning, makes low-code and no-code platforms particularly appealing, he notes.

“These platforms facilitate rapid prototyping and deployment, allowing organizations to quickly adapt their AI applications to changing requirements and market conditions,” Manby says.

There’s Revolution Afoot

And then there’s the fact that AI, generative AI, and large language models have created a revolution in the way that individuals work, live, and play.

That’s according to Matthew Scullion, CEO and co-founder at Matillion, a productivity platform for data teams.

As such, Scullion believes that businesses that want to remain relevant must quickly master these technologies to improve their own operations, their own products, and in some cases, to bring to market new products that will become the “picks and shovels” of this new AI gold rush.

And the reality is that companies that fail to do this will wither and die.

The problem is that there is a dearth of the required skills. such as fine-tuning models, designing and populating vector databases, or training algorithms — these all require specialist skills that are in short supply, he notes.

“It’s fine if you’re Netflix or Google because you’ll have the pick of the talent,” he says. “But for the company that will otherwise be disrupted and isn’t a Silicon Valley FANG [Facebook (Meta), Amazon, Netflix, and Google (Alphabet)], how do you compete?”

Low-code and no-code technologies that allow individuals, teams, and organizations to innovate with data with much higher levels of productivity and with different, more commonly available skill-sets are a material part of the answer to this conundrum.

“These technologies allow each player on the pitch to achieve more while putting more players on the pitch (by making the game playable with a more commonly available set of skills),” Scullion says.

Puneet Kohli, president of application modernization at Rocket Software, agrees that low-code and no-code tech is revolutionizing AI app development, enabling rapid creation and innovation across industries.

Real-World Use Cases

Low-code and no-code platforms empower users of varying technical expertise to harness AI’s potential, says Hasit Trivedi, CTO digital technologies and global head-AI at Tech Mahindra, an IT services and consulting company.

Trivedi offers examples of how companies in a variety of industries can use low-code and no-code technology for their AI apps.

A marketing professional can prototype a sentiment analysis tool using a no-code platform, he says, while a healthcare provider can collaborate with developers on a patient diagnosis app via low-code.

The education sector can benefit too, as educators can create AI-driven adaptive learning apps using no-code tools. Or a retail manager can quickly spin up a no-code platform for AI-driven inventory forecasting.

“As these technologies mature, they’re set to integrate advanced AI capabilities, expanding the potential for AI app development,” Trivedi says. “The rise of low-code and no-code platforms democratize AI, fostering innovation in unexpected areas and making AI development accessible and agile.”

Moreover, these platforms enhance scalability and ease of maintenance, while offering access to AI-driven solutions across industries, Trivedi explains.

The democratization of AI development fosters innovation in unconventional sectors as creative minds leverage these tools to solve unique challenges.

“The technology can reduce development cycles, allowing a business analyst to quickly build a customer support chatbot or empower data analysts to develop predictive maintenance solutions,” he adds.

Benefits of Using Low-Code/No-Code Tech To Develop AI Apps

One of the main benefits of using low-code and no-code tech is that businesses can easily solve such problems as automation and better customer/employee experience by enabling citizen developers to leverage these platforms to solve real-world problems without the need for traditional support from IT or technical teams.

Another benefit is that they are the closest to “one-stop shops” for integrating AI solutions into a business or customer workflow, says Kyle Tuberson, CTO of global consultancy ICF.

“Since these platforms often utilize a drag-and-drop editor with little to no coding, developers can easily integrate pre-built and pre-trained AI models into their applications by slotting them into their development flowchart for an application,” Tuberson explains.

An added advantage is that low-code and no-code vendors are building AI capabilities straight into their platforms and resulting applications, so there’s no need for multiple solutions.

Additionally, low-code and no-code platforms can often be tailored to develop workflow solutions, one of the key targets for AI models.

“Overall, these platforms enable the integration of AI applications into existing workflows in a fraction of the time compared to custom solutions,” Tuberson adds.

The Bottom Line

AI is a powerful tool that can optimize and streamline workflows so users can work more efficiently, says Tuberson. As such, one of the goals of implementing AI in the workforce is to augment the decision-making process by automating repetitive or time-consuming tasks.

“This goal goes hand-in-hand with the prime use of low-code and no-code tools — to enable the creation of digital experiences to make work easier,” he says.

“Low-code and no-code platforms and AI applications share this common goal of improving and simplifying workflows, so embedding AI into these platforms and digital experiences is the natural extension to how people are already working.”


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Linda Rosencrance
Technology journalist
Linda Rosencrance
Technology journalist

Linda Rosencrance is a freelance writer and editor based in the Boston area, with expertise ranging from AI and machine learning to cybersecurity and DevOps. She has been covering IT topics since 1999 as an investigative reporter working for several newspapers in the Boston metro area. Before joining Techopedia in 2022, her articles have appeared in TechTarget,, TechBeacon, IoT World Today, Computerworld, CIO magazine, and many other publications. She also writes white papers, case studies, ebooks, and blog posts for many corporate clients, interviewing key players, including CIOs, CISOs, and other C-suite execs.