Low-code and no-code platforms are used to build applications, websites, mobile apps, forms, dashboards, data pipelines, and integrations. No-code platforms help business users, sometimes termed citizen developers, to migrate from spreadsheets, extend beyond email collaborations, and transition from manual task execution to using tools and automations across departments. Low-code platforms are usually for technologists and provide ways to deliver and support software with little or no coding.
“You have to remember low code is just a fancy term for abstraction. We are abstracting away non-essential elements in order to simplify the user experience,” says Gordon Allott, President and CEO of K3.
Low- and no-code platform providers have continued to invest beyond applications and automation into other emerging areas. Last year, I wrote about low-code enabling machine learning. Since then, a growing number of tools and platforms are enabling AI capabilities.
The lines are blurring between business workflow platforms, AI tools, and low- and no-code platforms. More low- and-no code platforms are interfacing with AI and machine learning (ML) capabilities, while some AI tools are building in no-code capabilities.
How product companies and analysts categorize technologies is far less important than what people can do with them. I searched for examples of businesses using low- and no-code technologies to deploy ML and AI capabilities. I also asked tech executives how they use low-code or no-code AI tools.
AI capabilities in SaaS tools
If you want to use AI to help generate or review editorial content, the list of tools includes ChatGPT, Jasper.ai, and many other AI content tools. There are AI video creation tools, image recognition software, and many platforms to build chatbots.
Rishi Bhargava, CEO and founder of Descope, shares one example using Descript, an AI tool for videos. “Descript has used AI to change the entire video editing paradigm. By targeting people who are comfortable working on online documents, which is pretty much all of us, Descript has opened the door for someone who has never edited videos before to pick it up quickly.”
A growing number of SaaS tools have AI capabilities, but most aren’t no-code. While they perform an AI-enabled business function or workflow, tools targeting business users without a programmatic capability don’t classify as no-code.
AI in no-code workflow tools
One area where there’s a history of AI and no-code usage is extracting information from documents. This procedure often requires a mix of data processing, machine learning, and workflow automation.
NLP Lab, a no-code text annotation tool by John Snow Labs, is used in healthcare to embed the knowledge of clinical specialists into machine learning models. In one example, doctors worked to create clinically accurate definitions of tumor characteristics trained from pathology reports.
David Talby, CTO of John Snow Labs, told me about the underlying technology. “The enterprise-grade natural language processing (NLP) tools are no-code, meaning users can assemble high-quality training data, train models, and deploy them in production without writing a single line of code. The end-to-end platform can be used by domain experts—nurses, doctors, lawyers, accountants, investors, and more—to extract meaningful facts from documents or images automatically, further democratizing AI for all.”
Low-code integrations with ML
As AI tools are plugging in no-code capabilities, the no-code and low-code platforms are doing the reverse: adding easy ways to experiment with AI and bring machine learning models to production use cases.
Microsoft’s AI Builder service, part of the Power Platform, has more than 10 prebuilt AI models, including text recognition, entity extractions, and sentiment analysis. Microsoft recently announced a GitHub copilot integration that should help low-code developers request, review, and integrate AI-generated coding examples.
Kin Lane, chief evangelist at Postman, comments, “Products such as the Microsoft Power Platform have coined the concept of “fusion development,” allowing low-code and code-first developers and IT pros to collaborate on enterprise-wide applications. Fusion development teams are the solution to scaling out the low-code tech solutions via ML experts and AI models for form detections, invoice data extraction, and object detection.”
One example is AI at the Zoo, a fun application built with Microsoft PowerApp and Lobe to detect animals such as tigers, zebras, and pandas at the zoo. Unfortunately, this example showcases one of the issues with low-code integrations, as Microsoft recently announced the deprecation of the AI Builder image classification model by Lobe.
A commercial example is Ardent Mills using AI Builder in its baking lab to detect bread or grains that need flagging for further evaluation. Another example is the international energy company Equinor using AI Builder to increase the efficiency and automation of counting tubular goods.
Microsoft Power Platform isn’t the only low-code platform with AI capabilities and commercial use cases. Ricoh used OutSystems to build an intelligent process automation service for claims management. Zurich UK used Mendix to develop FaceQuote, an application that calculates a monthly premium for prospective life insurance customers by soliciting a selfie.
Low-code AI search and IoT
Businesses are also finding opportunities to build AI and ML capabilities in SaaS, business, and technology platforms with low- and no-code capabilities.
For example, low-code AI search can help developers integrate data sources, build customer and employee-facing search apps, and leverage AI and machine learning capabilities. Even tech companies are using low-code platforms to accelerate AI use cases. For example, using AI search, Salesforce achieved a 90% self-help success rate, and Dell achieved a three-times improvement in its employee satisfaction score.
Arturo Garcia, CEO of DNAMIC, shared a different example of Twitter using Google’s AutoML. He said, “We think about AI and automation as massive blocks of technology that enable companies to build faster and more assertively. It caught our attention that the Twitter team leveraged Google’s low-code machine learning capabilities to suggest Twitter Spaces for users to discuss topics they were interested in.”
Marty Sprinzen, co-founder and CEO of Vantiq, says, “Low code is enabling the use of AI technologies in areas that may not have technical expertise. Smart farms, for example, use AI to monitor animal welfare and ensure they operate within regulatory boundaries.”
Connecting thousands of sensors with IoT platforms and machine learning capabilities used to be a complex engineering project. Low-code platforms are helping more businesses to use IoT and AI, including smart building, manufacturing, and agriculture use cases. In one example, a low-code IoT and real-time data processing platform connected with edge devices helps improve worker safety and food quality assurance.
IoT and search are two examples of SaaS with low-code or no-code development options and AI capabilities. Many CRM, CMS, e-commerce, and other SaaS platforms have AI and low coding options.
Conclusion
As more businesses look to experiment with AI, they will seek development options that accelerate delivery and reduce the required expertise. Tools that integrate AI capabilities with no-code and low-code development options will be a desirable approach, especially for businesses that don’t have expert data scientists and software developers on staff.