AI in Software Development: 7 Invaluable Tools for Daily Work

Artificial intelligence (AI) is changing the landscape of solutions, offering alternative methods to tackle problems that were once handled by traditional approaches. In the first part of AI in software development series, we discussed that there are situations in which it can help a lot such as bug detection or performance optimisations. However, software developers should be aware of the pitfalls such as AI hallucinations, the high data dependence or the limited transparency. 

In this article, we will explore AI in software development focusing on some popular AI tools, particularly those supporting day-to-day work in software companies, and discuss who they can support and how. I’ve selected AI tools for developers and other business professionals (PMs, BAs, etc.) which can reduce repetitive manual work in their companies and offer enhanced productivity and quality. But before diving into the specific tools, let’s briefly discuss the types of artificial intelligence. 

ANI vs AGI: The two main types of AI (for now)

Before we start with the solutions, it's important to understand that there are different types of AI and how we can appreciate the value of certain tools. Each one of them has its own limitations and availability. There are three main types, but for now, we’ll focus on the first two because Artificial Superintelligence (ASI) is just a concept for now, and there is not publicly known research in its direction.

1. Artificial Narrow Intelligence (ANI) (also known as Weak AI) – when we refer to it, we mean systems that use AI but are trained to perform one specific task. These systems grow over time but within their specific domain. They have limitations that do not allow them to perform tasks they are not trained for. Popular examples of ANI are virtual assistants like Siri and Alexa. Their specific AI-based task is to match voice with command and return the requested result from the voice command.
2. Artificial General Intelligence (AGI) – it is one level above ANI. Its idea is to imitate human behavior and isn’t domain-specific. AGI-based systems are capable of understanding, learning, and applying new knowledge in different tasks just like humans do. However, AGI is still a distant goal in AI research and hasn’t been realised yet. There are however research on the topic and we will later mention some good projects about it.

Simply said, ANI is focused on performing specific tasks, while AGI has to understand and solve a broad range of problems, just like humans.

Top 6 tools to introduce AI in your software development

1. IntelliJ AI Plugin: Your advanced AI-powered ally

ANI or AGI?
The IntelliJ AI Plugin is an example of Artificial Narrow Intelligence (ANI). As a part of IntelliJ software, it is expected to perform specific tasks in the software development domain, such as improving coding speed through smart code suggestions and catching bugs. However, the domain is specifically for software development and writing code and does not have adaptable problem-solving abilities of AGI.

Applicable roles:

• Software developers: The IntelliJ AI plugin helps developers to speed up their coding by suggesting code, detecting bugs, and improving code structure.
• DevOps engineers: DevOps engineers have the responsibility to create scripts and infrastructure. This tool simplifies this by utilising templates that adhere to best practices.

Short description:
The IntelliJ AI Plugin is a plugin for IntelliJ IDEA coming with the new versions. It leverages AI to make the coding process faster and more efficient, offering features like smart code suggestions, improved debugging, and auto generation of documentation. It has a stable version and is regularly updated, adding new features in order to keep up trends and needs.

Most used IntelliJ AI Plugin functionalities:

• Smart code suggestions: It predicts the next line of code you might need, reducing manual input and decreasing errors.
• Code quality insights: The plugin analyses your code and offers suggestions for optimisation, such as streamlining complex logic or using more efficient algorithms. This was conventionally done in IntelliJ already, but the AI tool scales this to a new level.
• Automatic documentation: It generates Javadoc and comments automatically, based on the code you have written, and it explains the purpose of functions and variables.
• Real-time error detection: It identifies potential issues like missing variables or syntax errors as you write, so you can fix them before they become bigger problems.

If you want to boost your efficiency and coding speed, while improving code quality at the same time, the IntelliJ AI Plugin is an excellent choice for both software developers and DevOps teams.

2. Google NotebookLM: Your swift summarisations and note-taking companion

ANI or AGI?
Google NotebookLM is one of the ANI tools provided by Google. It’s designed to help you when dealing with documents, specifically structured data. Google NotebookLM offers summaries of documents, insights and takeaways. However, this AI tool is related to documents and does not have the broader capabilities associated with AGI.

Applicable roles:

• Project managers: Great for summarising project reports and extracting critical information or risks.

• Business analysts: Helps identify trends and patterns in large datasets and documents.

Short description:
Google NotebookLM is an AI-powered platform, which helps PMs and BAs to enhance document and data management. It allows them to work with static documents and transform them into interactive workspaces. The plugin also assists the decision-making process by summarising texts and providing insights through data visualisation. As of January 2025, this AI tool is not fully released and is still in its experimental phase, but it’s evolving and offers updates to improve usability.

Most used Google NotebookLM functionalities:

• Smart summarisation: This helpful AI tool helps developers read data-dense documents, providing key points summaries. For instance, it can look at a long business plan and summarise the key milestones and tasks.
• Custom question answering: You can ask questions about your document, like “What are the top three risks in this report?” and get direct answers to help you with making decisions.
• Relationship mapping: It can analyse two or more documents and link them together showing what they have in common. It is helpful when a lot of documents about a specific domain are provided and it can help the software development team see the big picture.
• Data visualisation: When dealing with numbers or tables, NotebookLM can create visualisations like charts or graphs, making the data easier to digest.

If working with a large datasets and documents, Google NotebookLM can turn those files into actionable insights and improve your work.

3. Tabnine: Your secure and compliant AI code assistant

ANI or AGI?
Tabnine is again an AI tool for software development from the ANI category. It is trained to predict the next line of code, based on the previous lines and method names, understanding what you try to achieve, making coding faster and more efficient. However, like other ANI tools, it again does not have abilities which an AGI tool can offer. It has a stable version and can be integrated in JetBrains products like IntelliJ or WebStorm.

Applicable roles:

• Frontend developers: It helps speed up UI development by suggesting pre-built components and structures.
• Backend developers: Useful for generating API calls and database queries, reducing the need for repetitive code.

Short description of Tabnine:
Tabnine is an AI-driven tool to provide coding productivity enhancement. Its value is that it is integrated with many popular IDEs, and it can offer smart code suggestions based on the work’s context. It can work with different programming languages and can be customised to learn the existing code you write, which makes it a powerful AI companion in software development for a range of day to day tasks related to boilerplate code.

Most Used Functionalities:

• Context-aware code predictions: Tabnine predicts the next line of code based on the previous code, such as suggesting mapper calls or API configurations.
• Team code sharing: The tool can learn from a team’s coding style, ensuring consistency across different projects in different repositories.
• Multi-language support: It works with a large variety of languages like Java, Python, and JavaScript, making it ideal for teams where every project or microservice uses different technology.
• Custom AI models: Teams can train private models using their own code repositories, ensuring that suggestions align with specific needs.

If you want to write efficiently and stay consistent with your style, Tabnine is the software development AI tool you need.

4. Jasper AI: Your go-to tool for creative marketing

ANI or AGI?
Jasper AI is different from the previous tools, because it is not just a summarising tool that takes context-based actions. It is again an ANI tool, designed to generate written content. Unlike some other AI tools, Jasper I has a stable version which can be used.

Applicable roles:

• Marketing specialists: It helps create compelling ads, blog posts, and social media content quickly.
• Technical writers: It assists in drafting user manuals, FAQs, and other types of technical documentation.

Short description:
Jasper AI and the company behind it have the idea of elevating marketing and marketers with the power of AI. This is an advanced tool for written content creation, whether it’s blog posts, marketing copy, or technical documentation. Jasper AI helps with streamlining the content creation by getting topic ideas from marketers and turning them into polished text. It’s a reliable tool, constantly updated to improve its output and features and has a lot of impact based on the number of users using it and the 49 billion+ generated words so far.

Most used Jasper AI functionalities:

• Content drafting: You can enter a very simple prompt, like “Write about AI’s role in business,” and Jasper will generate a whole article or blog post for you.
• SEO optimisation: Jasper helps with the optimisation of writing for search engines by suggesting keywords and it ensures they are naturally integrated into your text.
• Tone and style adjustment: Every article is targeted to a specific audience. Jasper AI can customise the style and tone of the content to match a specific target audience.
• Idea generation: Jasper can help the brainstorming process for content ideas by providing outlines, headlines, or unique topic angles.

If your team needs high-quality content at scale, Jasper AI is perfect for marketers and content creators looking to save time without sacrificing quality.

5. Snyk AI: Your AI-driven guardian against code vulnerabilities

ANI or AGI?

Similar to the other described AI tools for software development, Snyk AI is an ANI tool. Nevertheless, it serves a bit different purpose - to identify and fix security vulnerabilities in code and dependencies. Again no AGI possibilities, it is fixed in the software security domain.

 Applicable roles:

• Security engineers: Detects vulnerabilities in code and offers clear solutions to fix them.
• Software developers: Helps write more secure code by providing real-time feedback on potential risks.

Even though security engineers have the greatest responsibility of maintaining secure code, software developers should also think about code security.

Short description:

Snyk AI is an AI-powered security tool designed to integrate directly into development workflows. It scans code, dependencies, and configurations to identify vulnerabilities and provides suggestions for fixing them. The tool has a stable version and is very widely used in the software development industry for improving applications’ security. Even big companies like Google and Salesforce have already integrated Snyk AI in some of the development teams.

Most used SnykAI functionalities:

• Vulnerability scanning: Snyk scans your codebase and dependencies for known vulnerabilities. For example, if you’re using an outdated library with security risks, it flags the issue and provides safer alternatives.
• Fix suggestions: Along with identifying issues, Snyk suggests specific fixes. For instance, it might recommend upgrading a library version or changing a code line to close a security gap.
• Compliance monitoring: It checks if your application complies with security standards like GDPR or ISO 27001, making it easier to meet regulatory requirements.
• Continuous monitoring: After deployment, Snyk keeps monitoring for new vulnerabilities, alerting you if an issue arises in a live application. 

6. GitHub Copilot: Your intelligent co-developer for effortless coding

ANI or AGI?

GitHub Copilot is an example of Artificial Narrow Intelligence (ANI). It is designed to perform specific tasks within a limited scope, such as providing coding suggestions and autocompletion. Again without AGI capabilities, but efforts in this direction are in progress.

Applicable roles:

• Developers: It assists in writing code faster, reducing boilerplate, and improving code quality by offering suggestions and completions.
• Quality assurance (QA): It can help in writing automated tests and test scripts, improving the efficiency and coverage of testing processes.
• Data scientists: It can aid in writing code for data analysis, model training, and other data-related tasks.

While its primary audience is developers, QA professionals, and data scientists, Copilot's utility is limited in roles like marketing or finance, where the tasks do not involve programming.

Short description:

GitHub Copilot is an AI-powered coding assistant that integrates with popular code editors to help developers write code more efficiently. It is designed to offer real-time code suggestions, autocompletion, and even entire code snippets based on the current coding context. This tool also has a stable version available for use, ensuring reliability for professional development environments.

Most used GitHub Copilot functionalities:

• Smart code suggestions and autocompletion: Copilot provides real-time suggestions as you type, helping to complete lines or blocks of code quickly. The difference from other AI tools for developers is that it can autocomplete statements and functions, reducing the need for repetitive typing which speeds the coding process.
• Code generation: Sometimes implementations are difficult and it can generate whole methods based only on comments for the method.
• Error reduction: By offering contextually relevant code, it helps reduce syntax and logical errors, improving overall code quality.
• Learning and adaptation: Every developer has own coding style and GitHub Copilot learns from it in order to provide more customised solutions.

7. Feedbucket: Your AI-powered Feedback Collection Tool

ANI or AGI?

Feedbucket.app is an example of Artificial Narrow Intelligence (ANI). It specialises in gathering and managing website feedback, allowing teams to collect visual feedback directly on live websites. Unlike Artificial General Intelligence (AGI), which possesses human-like reasoning and adaptability, Feedbucket is built for a specific purpose—streamlining website review processes.

Applicable roles:

• Web developers: Easily collect feedback on websites and resolve issues faster with visual annotations.
• UI/UX designers: Gather design-related feedback in context to improve the user experience.
• Project managers: Oversee website review processes and ensure smooth collaboration between teams.
• QA testers: Report and track bugs with screenshots, screen recordings, and detailed comments.

Short description:

Feedbucket.app is an AI-enhanced feedback collection tool designed for website teams. It enables users to submit visual feedback, complete with screenshots and screen recordings, directly on live sites. This eliminates long email threads and improves communication between designers, developers, and clients. Feedbucket integrates with popular project management tools, making website revisions seamless and efficient.

Most used Feedbucket functionalities:

• Visual feedback collection: Users can leave comments, screenshots, and screen recordings directly on a website.
• Live website annotations: Feedback appears in context, reducing miscommunication and speeding up revisions.
• Seamless integrations: Connects with tools like Trello, Jira, Slack, and ClickUp for efficient task management.
• No browser extensions required: Works directly in the browser without requiring extra plugins.
• Automated screenshots & metadata: Captures essential details (screen size, browser info) to help developers debug faster.

If you're looking for an intuitive way to streamline website feedback, Feedbucket.app is a powerful ANI tool that simplifies collaboration between web teams.

What about AGI tools in software development?

Currently there are no AGI tools that can support the software development process, because the concept is still hypothetical. However, here are some companies with active research on AGI model concept:

• OpenAI: They are trying to integrate AGI concepts into the GPT models in order to understand complex requirements and generate structures.
• DeepMind: AlphaCode is a project that is already solving very complex programming tasks, it is expected their future projects will include AGI.
• Microsoft: Github Copilot is considered as a future AGI in its next releases.
• Anthropic: They have focused on “safer AGI” because they believe we should be careful with the limitations we try to cross with AGI. They are investigating models which understand complex context and make agile decisions.

Almost every software organisation is slowly but steadily integrating AI in their day-to-day work, whether it will be with some GPT wrappers to use the possibilities given by GPT and hide company specific data or some developers tools like Github Copilot. This however does not replace our responsibility as professionals and taking decisions for the right AI tools for our organisation. 

Gartner has observed that around 85% of AI solutions fail, because of poor data and usage without rule of thumb. That’s why we should be careful with the tools we are trying to integrate in on an organisation level. Hopefully, this article helped you find stable tools, which solves some of your company problems and improves the employees’ performance, making their work easier. 

And if you're eager to learn more about the AI mega trend, I invite you to read our newest blog post on Human-in-the-Loop (HITL) in AI software solutions. You'll find real-life use cases from industries like aviation or healthcare and discover two of Dreamix's success projects with AI.

Read the article