How No-Code and Artificial Intelligence Empower Action Across Business Lines

The global no-code AI platform market size is expected to reach $17.5 billion by 2030, and that is no accident: no-code AI represents a transformative development in the landscape of technology, particularly for businesses that don’t operate within the tech sector or areas in which AI is not a part of the core model. 

The benefits of applying no-code AI in a business setting vary across industries and departments, from streamlining processes and workflows to freeing up IT resources and enhancing data analytics.

In this article, we will examine the main ways businesses can leverage this technology in 2024, as well as discuss:

  • What no-code AI means.
  • How no-code AI is different from traditional AI-powered processes.
  • Types of no-code AI tools and technologies.
  • How to use AI for workflow automation.
  • Challenges and limitations of no-code AI.
  • Popular no-code AI tools.
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No-code AI definition

No-code is a feature of what we call citizen development platforms, designed to empower non-technical users to create and manage applications and processes through a visual user interface — think drag-and-drop tools or conversational flows — instead of traditional code writing or editing. Gartner research shows that 61% of corporations have already implemented citizen development projects or plan to do so. 

The range of applications that can be built with no-code platforms is vast. Users can develop everything from simple data collection forms to complex, integrated business applications that handle workflow automation, data analytics and customer relationship management. This flexibility opens up a world of opportunities for businesses to streamline operations, enhance customer engagement and drive digital transformation without the need for extensive technical expertise.

According to IT and business leaders surveyed by Pipefy, the main anticipated benefit of adopting no-code tools is to make better use of IT resources by empowering business lines to build and customize their own workflows and processes. Forty-six percent of executives also believe that conserving costs is one of the main benefits that this type of software can provide.

When this approach that favors agility, cost optimization, and better team integration is applied to artificial intelligence (AI), it becomes even more powerful. 

Although 73% of business leaders interviewed by Pipefy say they already use some AI initiatives in their companies today, the barriers to entry for AI can be daunting, from the investment in infrastructure to the need for a skilled workforce to assemble and manage these systems.

This is where the integration of no-code methodologies with AI technologies offers a groundbreaking solution: visual, often drag-and-drop, no-code AI tools make the technology less intimidating and more accessible to those without technical expertise or those who lack the time or resources to develop such systems from scratch.

Moreover, for those who already possess coding knowledge, such as IT experts, no-code or low-code AI platforms can offer the flexibility to further tweak and fine-tune results. This capability enables the creation of more specialized applications, blending the ease of no-code tools with the precision of traditional coding. 

Traditional AI process vs. no-code AI process

The evolution from traditional AI to no-code AI represents changes in technical requirements, development agility, customization and overall impact on business processes. 

Traditional AI demands a high level of technical expertise, including proficiency in machine learning, data science and programming languages such as Python and R. In contrast, no-code AI platforms democratize the creation and deployment of AI solutions, allowing a broader range of employees to engage in AI projects. This shift not only reduces reliance on highly specialized expertise but also fosters a more diverse and innovative approach to problem-solving.

Cost implications are another critical aspect. Traditional AI involves significant investment not only in human resources but also in computational infrastructure. On the other hand, no-code AI is more cost-effective due to the reduced need for specialized personnel and infrastructure, with many platforms operating on a cloud-based subscription model.

When it comes to development time and agility, traditional AI usually depends on lengthy and resource-intensive cycles, encompassing manual coding, testing and deployment. This often prevents the ability to rapidly respond to changing market demands and can slow down the pace of innovation. No-code AI, however, can offer rapid prototyping and deployment, cutting down development time and enhancing business agility.

In terms of customization and control, traditional AI provides a greater degree of flexibility, allowing for the development of finely tuned, bespoke solutions that meet specific business needs. No-code AI, while offering less in the way of customization, still delivers sufficient functionality for a wide array of standard applications, particularly in areas like business process and workflow automation.

The scalability and maintenance of AI solutions also present a bold contrast. Traditional AI solutions, due to their complexity and custom-built nature, can be challenging to scale and maintain. No-code AI solutions generally offer better scalability and easier maintenance, designed to adapt to growing business needs with less technical overhead.

Integration and compatibility with existing systems is a challenge for traditional AI, often requiring additional customization and problem-solving. No-code AI platforms are typically built with integration in mind, featuring pre-built connectors and APIs for common business tools, which facilitates smoother integration with existing systems and workflows.

Finally, the learning curve and accessibility of these two approaches to AI also differ. Traditional AI has a steep learning curve, requiring ongoing education and a deep understanding of the latest AI technologies and methodologies. No-code AI, in contrast, offers a more accessible entry point, enabling a broader segment of the workforce to participate in AI initiatives, promoting a culture of innovation and digital transformation across the organization.

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The importance of no-code AI for businesses

The importance of no-code AI in the business landscape involves accelerating digital transformation, democratizing AI and enhancing of innovation and competitive advantage. These aspects are particularly relevant in the context of recent findings and trends in the industry.

A study by Microsoft underscores the significant impact of no-code and low-code platforms. According to the survey, 82% of users of these platforms agree that they provide an opportunity to enhance their development knowledge and technical skills. This educational aspect is crucial, as it empowers more individuals to contribute to and understand the technological processes within their organizations. Additionally, the use of no-code or low-code platforms has been shown to have an 83% positive impact on work satisfaction and workload, as well as an 80% positive impact on morale.

Moreover, the impending shortfall of 4 million developers by 2025, as predicted by IDC, places additional emphasis on the value of no-code AI. With this developer deficit, no-code AI platforms emerge as a vital tool to bridge the gap by enabling citizen developers, often employees with non-traditional backgrounds in areas like customer service, human resources and project management, to create and modify business applications without the direct involvement of IT departments. These citizen developers bring fresh perspectives and insights to the tech and service industry, often identifying and addressing process inefficiencies that traditional developers might overlook.

The rise of citizen developers is not just about compensating for the shortage of traditional developers – it’s about leveraging their unique talents for the benefit of the entire team. By reducing the burden on developers and introducing innovative strategies and diverse perspectives, no-code AI enriches the IT industry, which often lacks diversity. The integration of these non-traditional skill sets is key to maximizing the benefits of low-code/no-code platforms. 

By empowering citizen developers and enhancing the capabilities of existing IT professionals, no-code AI platforms represent a fundamental shift in how businesses approach technology, innovation and workforce development.

AI for business process automation 

Business process automation (BPA) refers to the technology-enabled automation of activities or services that accomplish a specific function or workflow in businesses. It’s all about using technology to streamline complex, repetitive tasks, thereby increasing efficiency and reducing the chance of human error. 

The integration of AI in BPA marks a significant leap from traditional automation, making it more adaptive and effective. This integration results in AI-driven innovations such as predictive analytics, which uses machine learning to forecast future trends based on historical data and natural language processing, which automates and enhances customer service interactions. Robotic Process Automation (RPA), powered by AI, further extends the capability of BPA systems to handle tasks that previously required human manual work.

AI-driven BPA is crucial for businesses in 2024 for several reasons. According to Pipefy’s State of Automation report, 79% of C-suite executives expect generative AI to increase the efficiency of their processes by at least 25%. Among other benefits, 52% of IT and business leaders surveyed executives expect AI to help employees make better use of their time, while 48% anticipate that a primary benefit of applying generative AI to process and workflow automation will be “better decision making”.

AI for workflow automation

Workflow automation involves the use of software to complete specific tasks and activities without the need for human input. By automating workflows, companies aim to enhance efficiency, reduce manual labor and improve consistency in their operations, resulting in streamlined processes and better use of employee time.

According to Pipefy’s report, the primary benefit of adopting workflow automation technology is improving data and decision-making, followed by improving agility and saving costs.

For IT and business leaders interviewed, better collaboration within employees and teams, as well as better ability to keep track of KPIs and metrics and more consistent project outcomes are also among the main benefits of adopting workflow automation.

The integration of AI into workflow automation means enhancing these advantages by infusing workflows with greater intelligence and adaptability: according to McKinsey, current generative AI and other technologies have the potential to automate work activities that currently take up 60% to 70% of employees’ time.

For instance, AI capabilities allow these systems to not only operate but build seamless workflows in seconds with a simple user request, as well as learn from data and improve over time. Examples of AI in workflow automation include algorithms for dynamic task allocation, which ensure tasks are assigned to the most suitable resources, and AI-powered chatbots that handle routine customer queries. Additionally, AI enhances decision-making within workflows, providing data and insights that lead to more efficient and effective outcomes.

Challenges and limitations of no-code AI

While no-code AI has emerged as a transformative force in the realm of technology, simplifying the development and deployment of AI applications, it is not without its challenges and limitations. Recognizing and understanding these issues is crucial for businesses to leverage no-code AI effectively and responsibly.

Data security and compliance

The use of cloud-based no-code platforms often raises concerns about data security and compliance, especially when handling sensitive information. Ensuring data protection and adherence to privacy regulations is a significant challenge, and IT and business leaders must look for reputable vendors with relevant certifications, such as ISO 27001, built-in security features, such as SSO, 2FA, and permission management to make sure their data is kept safe and secure. It is also important that platforms are compliant with current data privacy regulations, such as the GDPR.

Overdependence on vendor solutions

Relying heavily on specific vendors for no-code AI platforms can lead to challenges with vendor lock-in, where businesses become dependent on the vendor for updates, support, and service continuity.

Skill and knowledge gaps

There is a misconception that no-code AI eliminates the need for any technical knowledge. However, a basic understanding of AI principles, logical thinking, and data management is still important for effectively using these platforms.

To effectively mitigate these challenges, businesses can adopt several strategic approaches, starting with the careful selection of vendors. Choosing vendors that are known for robust data security, adherence to compliance standards and reliable customer support is essential. This decision should also take into consideration the scalability and integration capabilities of the platforms offered.

Continuous education and training for employees who utilize no-code AI tools are also vital. Gaining a foundational understanding of AI principles, data analysis, and system integration can significantly enhance the effectiveness and appropriateness of no-code solutions. implementing robust data governance policies alongside these is key, especially when using cloud-based no-code platforms.

Overview of popular no-code AI tools


As a no-code BPA platform, Pipefy stands out for its AI capabilities that empower employees to create custom workflows with ease. Additionally, Pipefy AI provides fast access to data and insights that support data-based decision-making, enhancing the strategic capabilities of businesses. Pipefy AI also has a special feature designed for HR service desk through a customizable chatbot that elevates the employee experience by being available 24/7 for answering questions and initiating simple processes. 

Google AutoML

Part of Google Cloud, AutoML allows users to train custom machine learning models with minimal effort and machine learning expertise. It’s particularly known for its capabilities in areas like vision, language, and structured data.

IBM Watson 

IBM Watson offers a suite of AI services that can be used to build models and applications without deep AI expertise. It’s known for its natural language processing and data analysis capabilities.

Obviously AI

Obviously AI is designed specifically for businesses and individuals looking to leverage the data science without requiring any coding or machine learning background. The tool focuses on enabling users to quickly build and deploy machine learning models based on their historical data and allow faster data-based decision-making.

Apple CreateML

Apple’s CreateML is a no-code tool designed for machine learning, offering a user-friendly platform for building and training machine learning models on Mac devices. It simplifies the process of creating custom machine learning models, such as those for image and text recognition, allowing users to train models with their data by simply dragging and dropping the data into the tool. 

Adobe Firefly

Adobe Firefly is a generative AI tool that enables users to create images, text effects, and color palettes using simple text prompts in over 100 languages. It can generate images from text descriptions, modify objects, and apply styles to text. Adobe also integrates generative AI into Adobe Express and Adobe Photoshop, offering AI-powered creative assistance to users.

Leverage AI capabilities in no-code BPA with Pipefy

Ranked the easiest workflow management platform to implement and use by Capterra, G2, Peer Insights, Software Advice, and GetApp, Pipefy is a no-code application that provides AI capabilities to help businesses streamline operations across departments and conserve IT resources.

With Pipefy AI, users can build and optimize their processes even faster by simply telling the chatbot what they need; they receive a ready-to-go workflow in seconds. Pipefy AI also enables business leaders to make data-driven decisions at any given time by providing easy access to analytics and insights about their processes. 

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