What AI and low-code/no-code do and don’t do

Low-code and no-code are designed to simplify the creation of new applications and services, so that even non-programmers (i.e., the knowledge workers who actually use these applications) can create the tools needed to complete their respective tasks. Basically, they work by creating modular, interoperable functionality that can be mixed and matched to meet various needs. If this technology can be combined with AI to help guide development efforts, the productivity of the enterprise workforce could be greatly improved in just a few years.

INTELLIGENT PROGRAMMING

Venture capital has begun to flow in this direction. A startup called Sway AI recently launched a drag-and-drop platform that uses open source AI models to enable low-code and no-code development for novice, intermediate and expert users. The company claims this will enable organizations to deploy new tools, including smart tools, into production more quickly while promoting greater collaboration among users to efficiently scale and integrate these emerging data capabilities. The company has customized its general platform for specialized use cases in healthcare, supply chain management and other fields.

Jason Wong of Gartner said that the contribution of AI in this field is basically the same as in other fields, that is, processing monotonous repetitive tasks, and the development process includes tasks such as performance testing, quality assurance and data analysis. Wong specifically pointed out that although the application of AI in no-code and low-code development is still in its early stages, large companies such as Microsoft have strong interest in applying it to areas such as platform analysis, data anonymization and UI development, while This will go a long way to alleviating the skills shortages that currently prevent many projects from reaching production-ready status.

According to developer Anouk Dutrée, there are several practical issues that need to be solved before we can start dreaming of an optimized, AI-powered development chain. For example, abstracting code into composable modules introduces a huge overhead, which introduces delays into the process. AI is increasingly skewed towards mobile and web applications, where even a 100 millisecond delay can drive away users. This shouldn't be a big deal for background applications that tend to run quietly for hours, but this is unlikely to be a mature area for low-code or no-code development.

Constrained by AI

Since most low-code platforms basically deal with predefined modules, they are not very flexible. However, AI use cases are often highly specific and depend on the data available and the way it is stored, adapted and processed. Therefore, you will most likely need custom code to make the AI ​​model work with other elements in the low-code/no-code template, which may end up costing more than the platform itself. This will also affect aspects such as training and maintenance. The flexibility of AI will be challenged by the relative rigidity of low-code/no-code.

However, adding a bit of machine learning to low-code and no-code platforms can help add flexibility and add much-needed ethical behavior. Dattaraj Rao of Persistent Systems recently highlighted how machine learning allows users to run pre-programmed patterns for processes such as feature engineering, data cleaning, model development, and statistical comparisons, all of which should help create transparent, explainable, and accountable processes. Predictive model.

There are good reasons to expect that AI and no-code/low-code can complement each other and reduce their respective disadvantages in many key application areas. As businesses become increasingly reliant on the development of new products and services, both technologies could remove many of the barriers that currently impede the process—and that may be the case whether they work collaboratively or independently.

Original title: ​​AI and low/no code: What they can and can't do together​​ ​, Author: Arthur Cole​

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