How low code and artificial intelligence help businesses become more efficient

A low code AI platform expedites the ‘data-to-insights’ journey, enabling organization-wide efficient decision making

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Digitization has given rise to tremendous amount of data. The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 64.2 zettabytes in 2020. Over the next five years up to 2025, global data creation is projected to grow to more than 180 zettabytes. That’s a torrent of information created by the combined forces of social, mobile, and cloud technologies. This data contains a lot of hidden information. Business leaders must use this data to make informed decisions, which can be broadly categorized into two types:

  • High volume, low risk decisions, where AI can help in analyzing large data sets and enabling error-free decision making.
  • High value, high risk decisions, where AI/data science models assist humans in extracting intelligent insights and enabling intelligent decisions in real-time.

Low Code-based AI Platform is What Every Enterprise Needs

A low code AI platform expedites the ‘data-to-insights’ journey, enabling organization-wide efficient decision making. Business leaders can develop, deploy, monitor, and retrain machine learning and AI models that uses the enterprise data and conveniently provide detailed insights via an intuitive drag ‘n’ drop interface. Furthermore, the platform is equipped with the ability to integrate data, and deploy and monitor these models.


Let’s understand how a low code AI platform helps businesses to be efficient

  • Faster time-to-market: Low code AI platform democratizes AI in an organization and empowers various stakeholders, including business analysts, domain experts, IT engineers, and citizen data scientists to use and process high volumes of data, build ML models, and make intelligent decisions. The platform helps create and deploy proof of concepts (POCs) at speed, thereby ensuring faster time to market and reducing the turnaround times. In addition, since majority of tasks can be done using the intuitive drag-and-drop interface, the turnaround time for various activities, including preparing data and creating pipeline for developing, experimenting, and monitoring model can be done in a hassle-free manner.
  • Reusability and Collaboration: Assets, once created, such as models, data pipelines, and documentation can be shared across an organization and reused by other teams working on similar solutions, reducing the time lost on similar projects being done across different departments. Furthermore, there’s improved collaboration among citizen data scientists, tech teams, and data teams.
  • Scalability: “75% of business leaders feel they will be out of business in five years if they cannot figure out how to scale AI; 76% also admit they do not know how to do it.” Scalability is a significant area that requires to be considered to scale offerings. And research suggests that companies strategically scaling their AI projects generate 5x returns compared to those who are unable to scale. Low code AI platform solves a variety of business problems. Organizations start by using it to automate internal processes, boost productivity, and increase employee and customer engagement. As they progress, the goal shifts towards strategic gains.
  • Cost Reduction: As per Statista, 51% of respondents said that service operations functions in their organizations witnessed cost decreases greater than twenty percent. With an ideal low code AI platform, it becomes easy to upskill business users to be citizen data scientists. While your citizen data scientists’ contribution is paramount to your organization's success, you must also leverage the skills of your business users to bring speed and agility into your application development environment.
  • Improved Business Outcomes: Like humans, AI is a continuously evolving and learning system. It helps you make the most of the increasing volume of data by building, training, and monitoring the models constantly, while retaining previously acquired knowledge. Monitoring the results in depth helps you further retrain and re-deploy the model, without need to develop a new model from scratch.

To Conclude


In today's corporate landscape, there's a constant pressure on businesses to optimize costs and stay competitive. The right low code AI platform offers a perfect balance between ease of use and in-depth control over the data science lifecycle. Furthermore, the platform enforces a uniform data science process across user groups and communities, thereby leading to an AI adoption at scale.

The article has been written by Rajan Nagina, Head of AI Practice, Newgen Software