“The IT department of every company is going to be the HR department of AI agents in the future,” says Jensen Huang, the CEO of NVIDIA. Is this a proactive statement by one of the industry leaders, or is it a profound insight into where software development is headed? 

A big question lies on a broad spectrum of software development: Why do we need Gen AI? 

The why of this question has been answered by McKinsey in one of its reports in 2023, where it stated, “Generative AI has the potential to automate tasks that currently consume 60 to 70 percent of employees’ work hours”. 

Now, if we are interested in knowing the “how” part, here’s a straightforward answer: Building apps with generative AI for the right scenarios can lead to substantial business benefits through enhanced efficiency, innovation, and engagement. 

Do you know? In the latest survey, 88 percent of respondents say their organizations use AI in at least one business function, up from 78 percent a year earlier and 72 percent in early 2024. 

-Mckinsey

Code generation, upgrading creativity, and reducing ready-to-market time are just some of the ways developers are using Gen AI today in the software development industry. 

For example: 

• ToolJet reduces more than 67% of the development cost of an internal tool. Moreover, it saves more than 800k developer hours. 
• Walmart introduced the GenAI Playground for employee experimentation, followed by My Assistant, which aids associates in drafting and summarizing documents. 
• Procter & Gamble: Developed an internal generative AI platform using Imagen to accelerate the creation of photo-realistic images and creative assets, allowing marketing teams to focus on high-level planning by leveraging images generated by AI.
• Goldman Sachs: Created a coding tool and a documentation automation platform using generative AI to streamline internal workflows and improve business intelligence. 
• PwC: Launched ChatPwC, a conversational generative AI tool for internal employees, built with OpenAI’s technology.

These examples strongly prove that AI-powered apps are the future of software development, promising multiple benefits and use cases. Therefore, let’s break it down: What do we need to build an AI-powered app?

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If you want to build an app, we need more than an idea. The most common method for creating an AI-powered app includes programming languages, frameworks, data science, and more. 

Why is it a common approach? Many people do not know that all of this can be done with the help of low-code development. 

Low-code is a software development approach that uses visual interfaces and drag-and-drop tools to build applications with minimal hand-coding. This method accelerates development, allowing professional and citizen developers to create application AI quickly and efficiently. 

If you want to build Gen AI applications traditionally, you need to be a skilled AI talent who is the Jack of all trades.

Languages you need to learn for traditional AI-powered app development

1. Python: Python is the most popular language for AI development due to its simplicity, extensive libraries (TensorFlow, Keras, PyTorch), and large community support. It’s ideal for machine learning, deep learning, and natural language processing tasks.
2. JavaScript: JavaScript, particularly with TensorFlow.js, allows for AI model deployment in web applications, making it crucial for client-side AI tasks.
3. R: R is a powerful tool for statistical computing and data visualization, making it ideal for AI tasks involving heavy data analysis and statistical modeling.

Some of the skills you need to be well-versed with

AI Frameworks:

• LangChain:

An open-source framework for building dynamic applications using large language models (LLMs). It offers flexibility, scalability, and integration with various data sources.

• Haystack:

An open-source framework for building and deploying search and question-answering systems powered by LLMs. It integrates with multiple platforms and databases.

• Hugging Face:

A popular platform providing pre-trained models and a wide range of tools for NLP tasks. It simplifies the deployment of AI models in applications.

• LlamaIndex:

Specialized for search and retrieval tasks, connecting LLMs to various data sources. It’s ideal for creating knowledgeable agents and chatbots.

Deep Learning Frameworks:

• TensorFlow:

An open-source framework for machine learning and deep learning. It’s widely used for building and training neural networks.

• PyTorch:

It’s popular among researchers and developers for building deep learning models and ai agents because of its ease of use and rapid prototyping capabilities.

• Natural Language Processing Techniques:
  1. Tokenization: Breaking down text into smaller units for analysis.
  2. Part-of-Speech Tagging: Identifying word types (noun, verb, adjective).
  3. Named Entity Recognition (NER): Identifying key entities in a text (names, locations).
  4. Sentiment Analysis: Determining the emotional tone of a text.
  5. Stemming and Lemmatization: Reducing words to their base form for normalization.
  6. Language Models: Predicting word sequences for tasks like text generation.
  7. Word Embeddings: Representing words as vectors to capture semantic relationships

Is knowing all this enough? Well, apparently, no. It’s not enough. According to the IBM engineering community, the usage of Java for generative AI applications has been growing recently. Today, it’s Java; tomorrow, another framework will be used. So, the question is, what’s the best way to build generative AI apps? Let’s find out in the next section of our blog. 

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Forrester projects a 7.8% growth in global technology spending for 2026, driven by investments in generative AI, cybersecurity, and cloud solutions. This growth reflects a significant acceleration from the 5.6% increase in 2025.

Generative AI with low-code platforms is revolutionizing industries for good reasons, and Bosch is leading the charge. We’re exploring numerous opportunities to boost productivity and drive innovation across our global operations. 

–Elton Escaleira, Bosh, Product & Service Owner

We highlighted low-code as the best approach for developing GenAI apps, addressing this integration’s ‘why’ and ‘how’ aspects. Perspectives from industry leaders further validated our insights, providing a comprehensive understanding of this innovative development strategy.

Now, we will address the third important pillar of our blog: how to build Gen AI apps with ToolJet’s low code platform. 

ToolJet is a low-code platform that enables developers to build and deploy custom internal tools.  

1. Visual app builder​

Enables the creation of visually appealing front-ends with a drag-and-drop interface and pre-built components.

2. Integrations​

Offers seamless integration with a wide range of data sources, including over 80+ applications, databases, and APIs.

3. ToolJet Database​

A robust, scalable database solution built atop PostgreSQL. It allows no-code database management, enabling users to build, manage, and scale databases effortlessly.

4. Workflow automation​

Simplifies complex manual business process automation, reducing the required engineering effort.

5. Enterprise-grade security​

Designed with advanced security features and a scalable infrastructure to meet the needs of enterprise teams.

6. SSO Support​

Single Sign-On (SSO) capabilities support various providers, including Okta, Google, Azure AD, and OpenID Connect.

7. Multiple environments​

Multiple environments can be created and managed for efficient application lifecycle management, allowing different stages like development, testing, and production to be handled seamlessly.

8. Multiplayer editing​

Multiple users can collaboratively work on app development in real time. Simultaneous edits and contributions from different team members optimize the development process and foster a more dynamic and interactive workspace. 

With ToolJet, you can effortlessly build business applications using natural language. Whether you’re starting from scratch or refining an existing app, its intelligence simplifies the process.

Additionally, it comes with an AI-powered documentation assistant ready to answer any questions about ToolJet’s features, components, and integrations, helping you build faster.

Follow these step-by-step instructions to create an inventory management application:

1. Describe your application: Provide a prompt detailing the business application you want to create. (Example: “Inventory management system for a manufacturing company.”)

2. Refine the requirements: Review and accept or modify the application requirements suggested.

3. Customize your application: Use AI to customize the generated application to your specific needs, adjusting components and styles and performing data source queries.

• Generated application

• Customizing application

Refer to Generate Applications and AI Docs Assistant documentation to learn more.

As we conclude this hands-on guide to building generative AI-powered apps, it’s clear that the application development landscape is rapidly evolving. With ToolJet, developers can harness the power of AI to optimize their workflows, reduce development time, and create robust applications by simply describing their needs in plain language. 

This transformative approach democratizes app development for technical and citizen developers alike and assists teams in innovating faster. Grab this exciting opportunity to use generative AI with low-code flexibility in your projects and watch your ideas come to life with unprecedented ease and efficiency. 

Start building with ToolJet today and redefine what’s possible in app development!