AI code generation uses artificial intelligence and machine learning to automatically produce computer code based on prompts or descriptions. This technology facilitates coding by adhering to best practices and governance standards—accelerating productivity among skilled coders.
Programming languages are the backbone of software development, serving as the medium through which humans communicate tasks and processes to digital systems. Every application—from simple calculators to complex operating systems—relies on code to understand how to function in any given circumstance. Unfortunately, even for skilled developers, working within these programming languages can be a slow, time-consuming and often-inefficient process.
The surge in demand for application development across industries and the ever-widening software developer skills gap has led to an increased need for citizen developers—individuals who can create applications without traditional programming skills. No-code and low-code development platforms have helped bridge this gap, and AI is further optimising what can be accomplished without extensive programming experience. More importantly, advances in artificial intelligence are enhancing the capabilities of professional programmers, allowing them to accomplish more while also improving the accuracy and effectiveness of their work. At the heart of this transformation is AI code generation.
AI code generation (sometimes also called AI-assisted programming or GenAi code assistance) leverages artificial intelligence technologies to translate human-language commands into functional code, making software development more accessible to everyone. Low-code development platforms reduce the complexity of coding through visual interfaces and prebuilt components; AI code generation creates code from scratch based on the user's descriptions, completes code fragments and recommends code-based on user input. Each approach helps simplify the development process and boost the capabilities of those involved. When used together, these solutions can greatly enhance an organisation's ability to create effective applications quickly.
Low-code development platforms are designed to speed up the application development process by allowing users to assemble apps using a graphical user interface that requires minimal coding experience. These platforms are ideal for business professionals and non-developers who wish to create functional applications without delving into the more esoteric aspects of programming. However, they rely heavily on predefined segments of working code. In essence, they provide pre-made building blocks from which a user can construct new software.
By interpreting plain language inputs, AI code generation creates original code snippets that are tailored to the specific requirements of the user. This method not only facilitates more rapid development; it also enhances flexibility, allowing developers to innovate without the constraints imposed by the limitations of existing components and templates. As a result, AI code generation is an invaluable tool for trained software developers to create better code at scale, while also broadening the scope of who can develop software, how they can do it and what that software may be capable of.
Again, it is important to recognise that these are not either/or technologies. Low-code and AI code generation complement each other extremely well and can be used in tandem to create a more well-rounded and efficient development programme.
AI code generation has the potential to significantly simplify the coding process. Unfortunately, it also introduces several challenges that developers and organisations will need to consider before they can get the most out of it. These challenges require businesses to take steps to ensure the technology enhances—rather than complicates—their software development efforts:
AI models generally focus on producing syntactically correct code that may not be optimised for quality, efficiency or maintainability, increasing the burden of technical debt over time. Skilled developers are typically capable of distinguishing good AI-generated code from bad, which is one reason this technology is more effective at assisting trained programmers than as a low-code or no-code solution for citizen developers. To further mitigate this risk, teams should implement standards and review processes that assess the maintainability and scalability of AI-generated code before it is integrated into larger projects.
Tools may generate overly complex code to address specific functionalities or execute a task in the most comprehensive way possible. This complexity can lead to code that is difficult or time consuming to debug, update or even understand, thus complicating future maintenance efforts. Organisations can address this issue by setting clear guidelines for the simplicity and clarity of code and by regularly reviewing AI-generated code for unnecessary complexities.
AI code generation is perhaps the fastest path to creating working code available today, but the output may not always be reliable or secure. This can lead to increased potential for bugs or vulnerabilities. As previously addressed, these issues are easier for trained developers to recognise and repair—citizen developers may not have the experience needed to properly evaluate the quality of AI generated code. Ensuring that all AI-generated code undergoes rigorous testing, companies are encouraged to establish comprehensive review frameworks that specifically assess the functionality, security and performance of AI-generated software.
Today, AI coding tools function as valuable support, but as this technology becomes more prevalent, it might lead to a loss in coding skills among developers. This, in turn, may result in a lack of oversight, as businesses have AI to create their code but no skilled human employees able to review or assess it. To counter this, it is essential that organisations make a point to view AI code generation as supplemental to human expertise rather than as a replacement for it. Providing ongoing training and encouraging developers to engage critically with AI suggestions can help maintain a high level of skill and control.
Despite the challenges associated with AI code generation, the benefits it brings to software development tend to far outweigh any drawbacks. The following are among the most significant advantages:
Speed, accuracy, quality—all these benefits come together in AI code generation to produce a solution capable of enhancing developer productivity in ways that was never before possible. By automating the routine and mundane aspects of coding, developers can focus on more complex and creative tasks. This shift speeds up the development cycle and allows for quicker turnaround times for project completion. And because the code is built on a comprehensive dataset of working solutions, it requires only a basic quality review before developers can release it into the market and begin their next projects.
Rather than doing all the work, AI code generation tools act as advanced assistants. They offer code suggestions and alternatives that might not be immediately obvious even to seasoned programmers. This enhances a developer's work by providing options that optimise and improve code efficiency, leading to better overall software performance.
Traditional coding is mentally exhausting and can easily lead to burnout. AI code generation's ability to handle a portion of the cognitive load during software development counters this mental strain. For example, developers can offload the burden of remembering syntax and boilerplate code, allowing them to concentrate on solving higher-level problems. Debugging is likewise simplified, replacing a tedious, mentally intensive task with something far less draining.
The dangers of context switching—where developers shift their attention between multiple tasks—are often overlooked. Still, they have been shown to have a negative impact on a programmer's time, energy and productivity. AI tools can generate code across different layers of the stack, which minimises the need for developers to switch between different coding environments and mindsets. Instead, they can remain within their integrated development environment (IDE) without the need to search online for solutions, refer to documentation or get sidetracked working with various external applications.
As with other areas under the growing umbrella of AI application, AI code generation is seeing extensive use within various software development processes. More to the point, as this technology evolves, its role in software development is set to expand, promising even greater efficiencies and innovations down the road. The following demonstrate the increasingly central role of AI code generation:
The initial stages of software development often involve gathering and documenting requirements—a process prone to inaccuracies and ambiguities. AI helps clarify, validate and track these requirements, ensuring they are complete and unambiguous from the start. By improving the quality of these documents, developers can set a solid foundation for their projects, reducing the likelihood of misunderstandings and the need for reworks.
AI algorithms, trained on vast datasets of existing code, can analyse new code for patterns that have been shown to lead to bugs. By identifying potential issues early in the development process, these tools help maintain high standards of code quality and security. This proactive approach to code review minimises downstream errors while enhancing the overall reliability of the software.
Whether suggesting lines of code or entire functions, AI code generation tools help streamline the development process. These tools adapt to the developer's preferences and can incorporate user feedback for continual improvement.
AI-driven tools can implement tests automatically as new code is integrated into the existing base. This ensures that the software performs as expected, using consistent and reliable testing that easily outperforms manual methods.
Capable of correcting spelling and syntax errors in real time, AI code generation eliminates the need for manual debugging and correction tasks. Additionally, translating code from one programming language to another becomes a non-issue, facilitating smoother transitions and broader accessibility across various technology stacks.
Specific use cases for AI code generation include:
IT service management (ITSM)
Automated script generation for incident management, change management and problem resolution leads to improved service quality and faster resolution times.
Human resources
Streamlined employee onboarding and offboarding playbooks reduce manual effort and ensure compliance with organisational policies and regulations.Customer service
Automated chatbot script generation for 24/7 customer support improves response times and overall customer experience.Security operations
Generation of scripts for security incident response, threat detection and vulnerability management enhances an organisation's overall security posture.
Business process automation
Creation of custom workflows and applications tailored to specific business needs drives efficiency and productivity across various departments and functions.
As a good example of code generation in action, ServiceNow's AI code generation tools enable developers to quickly create and deploy custom applications, generate scripts, flows, playbooks and service catalogue items tailored to their organisation's specific needs, thereby accelerating the implementation and maintenance of out-of-the-box workflows. For instance, Accenture experienced a 40-60% increase in coding productivity using ServiceNow's Now Assist for Creator, a developer-focused GenAI solution.
The decision to integrate AI code generation into a business is an important one. It should be guided by a thorough assessment of various critical factors that align with the organisation's specific needs and regulatory environment. In other words, every organisation should start by taking a detailed look at its own goals, capabilities and circumstances before finalising their decision.
The following are among the most essential considerations when selecting an AI code generation tool:
With global regulations like GDPR and CCPA shaping data handling practices, enterprises should review the AI tool's policies for managing user data to ensure it does not pose a potential security weakness. Features such as data anonymisation techniques are valuable for enhancing privacy protection.
Compliance goes hand in hand with privacy. Given the potential penalties associated with various data protection acts, ensuring that the AI tool adheres to all relevant regulations and standards should be a top priority. This includes checking if the tool has received compliance certifications and if it meets regulatory requirements specific to the industry.
Enterprises should examine whether the AI tool provides comprehensive encryption for data transmission and storage. Additionally, it is important to assess the strength of access controls and authentication measures to prevent unauthorised access. Even once the tool is implemented, security must remain a top-consideration—security audits and the tool's compliance with established security standards are critical factors that need thorough evaluation.
Make sure that the tool can manage access based on user roles and responsibilities. Tools that offer granular permissions, enabling them to control access to different features and functionalities, decrease the risk of data leakage. Opt-in and opt-out mechanisms should also be available, providing users with the flexibility to manage the use of their data according to their preferences.
Be wary of choosing an AI code generation solution based on price alone. A low-priced tool may not provide the necessary functionality or reliability, while a higher-priced option may quickly pay for itself through increased ROI and scalability. Solutions that feature transparent pricing models make it easier to assess the financial viability of integrating the AI tool.
Increased productivity, enhanced code quality, reduced manual coding efforts—the benefits are hard to ignore. With the integration of AI code generation technologies, businesses can navigate the complexities of their software needs more efficiently and with greater agility. ServiceNow, a leader in enterprise AI solutions, is at the forefront of this transformation.
The ServiceNow Now Platform® provides a comprehensive framework for harnessing the power of AI to optimise business operations and software development. Utilising generative AI (GenAI), machine learning (ML), natural language processing (NLP), process mining and more, this fully centralised platform boosts efficiency and improves decision-making across every relevant business process. App Engine Takes things further regarding AI-enhanced coding, allowing organisations to employ AI code generation to build custom applications that are effective and directly tailored to their specific business needs. From low-code solutions to optimised support for experienced programmers, ServiceNow provides the tools for optimising your organisation's coding capabilities.
Demo ServiceNow App Engine today, and see how to carry your business into the future of application development.