# Best AI Code Generation Software - Page 7 *By [Adam Crivello](https://research.g2.com/insights/author/adam-crivello)* AI code generation software uses artificial intelligence and machine learning to automatically produce code from natural-language inputs, helping developers create efficient, functional code that aligns with best practices. These tools streamline repetitive tasks, improve consistency, and integrate either through chat interfaces or directly into IDEs as AI code assistants. ### Core Capabilities of AI Code Generation Software To qualify for inclusion in the AI Code Generation category, a product must: - Be a general-purpose solution that generates code based on natural-language user inputs - Generate code beyond simple autocomplete or inline code completion - Serve as a standalone code generation tool or integrate with developers’ existing environments - Support multiple programming languages - Allow users to customize the AI-generated code ### How AI Code Generation Software Differs from Other Tools AI code generation tools focus on producing new code from natural-language prompts, whereas broader AI developer tools may center on debugging, suggestions, or documentation. These solutions prioritize automated code creation at scale, enabling developers to translate intent into working code across multiple languages. ### Insights from G2 on AI Code Generation Software Based on category trends on G2, increased productivity, reduced time spent on repetitive tasks, and improved code consistency. These platforms often point to strong IDE integrations and customization options stand out as key contributors to a smooth development experience. ## How Many AI Code Generation Software Products Does G2 Track? **Total Products under this Category:** 97 ### Category Stats (May 2026) - **Average Rating**: 4.42/5 (↓0.06 vs Apr 2026) - **New Reviews This Quarter**: 334 - **Buyer Segments**: Small-Business 58% │ Mid-Market 28% │ Enterprise 14% - **Top Trending Product**: Claude (+0.07) *Last updated: May 18, 2026* ## How Does G2 Rank AI Code Generation Software Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 6,000+ Authentic Reviews - 97+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## Top AI Code Generation Software at a Glance | # | Product | Rating | Best For | What Users Say | |---|---------|--------|----------|----------------| | 1 | [ChatGPT](https://www.g2.com/products/chatgpt/reviews) | 4.6/5.0 (2,452 reviews) | — | "[All-Rounder Agent](https://www.g2.com/survey_responses/chatgpt-review-12869865)" | | 2 | [Claude](https://www.g2.com/products/claude-2025-12-11/reviews) | 4.6/5.0 (292 reviews) | — | "[Time-Saving, Versatile, but Memory Needs Improvement](https://www.g2.com/survey_responses/claude-review-12814769)" | | 3 | [GitHub Copilot](https://www.g2.com/products/github-copilot/reviews) | 4.5/5.0 (292 reviews) | — | "[Smooth VS Code Integration and Fast AI Suggestions That Boost Productivity](https://www.g2.com/survey_responses/github-copilot-review-12765647)" | | 4 | [Gemini](https://www.g2.com/products/google-gemini/reviews) | 4.4/5.0 (343 reviews) | — | "[Versatile Tool with Excellent Video Support, Fast and Easy Setup](https://www.g2.com/survey_responses/gemini-review-12866757)" | | 5 | [Replit](https://www.g2.com/products/replit/reviews) | 4.5/5.0 (354 reviews) | — | "[Simple Browser-Based Coding with AI Chat and Stable Small-Scale App Performance](https://www.g2.com/survey_responses/replit-review-12788620)" | | 6 | [Gemini Code Assist](https://www.g2.com/products/gemini-code-assist/reviews) | 4.4/5.0 (65 reviews) | — | "[Fits Naturally in the IDE and Speeds Up Backend Development](https://www.g2.com/survey_responses/gemini-code-assist-review-12753237)" | | 7 | [Cursor](https://www.g2.com/products/cursor/reviews) | 4.5/5.0 (80 reviews) | — | "[Cursor Feels Like a True Developer Tool—Fast, Flexible, and Incredibly Productive](https://www.g2.com/survey_responses/cursor-review-12860991)" | | 8 | [Amazon Q Developer](https://www.g2.com/products/amazon-q-developer/reviews) | 4.6/5.0 (38 reviews) | — | "[Easy-to-Understand Code Guidance That Boosts Confidence](https://www.g2.com/survey_responses/amazon-q-developer-review-12596802)" | | 9 | [IBM watsonx Code Assistant](https://www.g2.com/products/ibm-watsonx-code-assistant/reviews) | 4.2/5.0 (55 reviews) | — | "[Boosts Development Efficiency with Intelligent Code Automation](https://www.g2.com/survey_responses/ibm-watsonx-code-assistant-review-12860775)" | | 10 | [TESS AI](https://www.g2.com/products/tess-ai/reviews) | 4.7/5.0 (384 reviews) | — | "[Magnificent structure to use multiple AIs together](https://www.g2.com/survey_responses/tess-ai-review-12395241)" | ## Which AI Code Generation Software Is Best for Your Use Case? - **Leader:** [ChatGPT](https://www.g2.com/products/chatgpt/reviews) - **Highest Performer:** [TESS AI](https://www.g2.com/products/tess-ai/reviews) - **Easiest to Use:** [ChatGPT](https://www.g2.com/products/chatgpt/reviews) - **Top Trending:** [Replit](https://www.g2.com/products/replit/reviews) - **Best Free Software:** [TESS AI](https://www.g2.com/products/tess-ai/reviews) ## Which Type of AI Code Generation Software Tools Are You Looking For? - [AI Code Generation Software](https://www.g2.com/categories/ai-code-generation) *(current)* - [AI Coding Assistants Software](https://www.g2.com/categories/ai-coding-assistants) --- **Sponsored** ### Checkmarx Checkmarx is a type of application security solution designed to help organizations safeguard their software development processes while enhancing efficiency and reducing costs. The Checkmarx One platform stands out in the realm of enterprise-grade security, offering comprehensive protection that addresses the complexities of modern software development, including legacy systems and AI-generated code. By scanning trillions of lines of code annually, Checkmarx enables companies to significantly lower their vulnerability density, ensuring a robust defense against potential threats. The platform is particularly beneficial for software development teams, security professionals, and organizations that prioritize secure coding practices. With the increasing reliance on AI technologies and the rapid pace of software development, Checkmarx One provides essential tools to mitigate risks associated with both traditional and emerging programming languages. Its innovative architecture, powered by autonomous security agents and AI-native intelligence, allows organizations to integrate security seamlessly into their development workflows, thereby accelerating development velocity without compromising on safety. Key features of Checkmarx One include Triage Assist, which employs an autonomous AI agent to prioritize vulnerabilities based on real-world exploitability and contextual risk. This feature empowers teams to concentrate their efforts on the most critical issues rather than getting bogged down by static severity scores. Additionally, Remediation Assist generates review-ready fixes for validated vulnerabilities prior to code merges, streamlining the secure delivery process and minimizing the manual overhead typically associated with remediation tasks. Developer Assist is another notable feature, acting as a standalone security agent that identifies risks during the coding process. By providing safe, explainable, and verified fixes directly within the integrated development environment (IDE), it supports developers in maintaining a stable and rapid development pace. Furthermore, the platform includes AI Supply Chain Security, which offers centralized governance and visibility for AI components embedded in applications, ensuring that hidden AI assets are discovered and managed effectively. Lastly, Checkmarx One incorporates advanced analysis engines such as AI SAST and DAST for AI, which enhance security measures across various environments. The AI SAST feature expands detection capabilities to cover emerging and unsupported programming languages, while the DAST for AI strengthens runtime protection in continuous integration and deployment (CI/CD) settings. Together, these features position Checkmarx One as a comprehensive solution for organizations looking to fortify their software development lifecycle against evolving threats. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=1005889&secure%5Bdisplayable_resource_id%5D=1005889&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1005889&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=4475&secure%5Bresource_id%5D=1005889&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fai-code-generation%3Fpage%3D3&secure%5Btoken%5D=682b428a114350ef12f9bd3ab7213bee0a47dec96b448c0099e8f6af82a6b5cd&secure%5Burl%5D=https%3A%2F%2Fcheckmarx.com%2Frequest-a-demo%2F&secure%5Burl_type%5D=book_demo) --- ## Buyer Guide: Key Questions for Choosing AI Code Generation Software Software ### What does AI Code Generation software do? I view AI Code Generation software as the development layer that helps teams write, explain, debug, review, and revise code with AI support. Across the G2 feedback I analyzed, users connect this category with inline suggestions, IDE assistants, code completion, bug fixing, boilerplate generation, SQL help, documentation, test creation, codebase context, and repository-aware agents. These tools help developers move from prompt, comment, issue, or existing code to a usable draft more quickly. The category matters most when coding work involves repetitive patterns, unfamiliar syntax, debugging blockers, or context-heavy changes across files. ### Why do businesses use AI Code Generation software? The strongest pattern I saw in G2 reviews was faster development with fewer context switches. Users want help inside the coding workflow, especially when writing boilerplate, fixing bugs, understanding unfamiliar code, or moving through routine implementation work. - **Coding speed:** Reviewers describe faster code writing, function generation, and boilerplate creation as common benefits. - **Debugging support:** Users rely on these tools to explain errors, find bugs, improve SQL queries, and reason through broken code. - **IDE assistance:** Inline suggestions, autocomplete, editor integrations, and codebase context help developers stay inside their usual tools. - **Learning and explanation:** Developers use AI to understand unfamiliar frameworks, syntax, libraries, and existing code. G2 reviewers point to hallucinated answers, wrong code, token limits, pricing, context issues, and the need for human review as areas to check closely. ### Who uses AI Code Generation software primarily? When I reviewed G2 reviewer profiles, I saw AI Code Generation software serving technical users who write, review, test, or maintain software. - **Software engineers:** Generate code, refactor functions, debug issues, and speed up implementation work. - **Developers and programmers:** Use completions, suggestions, and chat-based coding help during daily development. - **QA and test engineers:** Create test cases, automation scripts, and debugging support for testing workflows. - **Data analysts and engineers:** Generate SQL queries, scripts, data transformations, and troubleshooting steps. - **Founders and small technical teams:** Build prototypes, fix issues, and create early product code without a large engineering bench. ### What types of AI Code Generation software should I consider? From the way G2 reviewers describe their workflows, these tools are distinguished by where the coding help happens and how much control the developer needs over the output: - **IDE coding assistants:** Suited to inline completions, code suggestions, refactoring, and editor-based help. - **AI code chat tools:** Built around explaining code, generating snippets, debugging errors, and answering technical questions. - **Agentic coding tools:** Useful when teams want AI to work across files, run tests, propose changes, and prepare pull requests. - **Cloud development platforms with AI:** Designed for browser-based coding, quick prototypes, hosted environments, and collaborative builds. - **Enterprise coding assistants:** A strong match for teams that need SSO, admin controls, codebase context, security settings, and usage visibility. ### What are the core features to look for in AI Code Generation software? When I assessed this category against G2 review themes, the features that make generated code actually useful inside real development work came down to a few core areas: - **Context-aware code suggestions:** The tool should understand open files, repositories, comments, patterns, and existing code before suggesting changes. - **IDE and workflow integration:** Support for VS Code, JetBrains, GitHub, terminals, pull requests, and common developer tools matters. - **Debugging and code explanation:** Error analysis, stack trace help, code walkthroughs, and logic explanations should be clear and practical. - **Testing and review support:** Unit test generation, code review help, linting, refactoring, and PR suggestions should improve code confidence. - **Governance and usage controls:** Admin settings, privacy controls, model choice, token limits, auditability, and security guardrails need close review. ### What trends are shaping AI Code Generation software right now? From the market signals and G2 patterns I reviewed, several shifts are moving this category well past basic autocomplete: - **Agentic coding is moving beyond autocomplete:** Coding tools are starting to take assigned issues, modify files, run tests, and propose pull requests. - **Developer adoption is becoming mainstream:** AI coding tools are now part of regular development work for a large share of software professionals. - **Trust is becoming the buying constraint:** Developers are using AI more often, but accuracy concerns and required review still shape adoption. - **Coding assistants are becoming multi-surface tools:** AI coding support is moving across IDEs, terminals, repositories, cloud agents, Slack, and SDKs. - **Governance is moving into developer AI:** Admin controls, monitoring, analytics, sandboxing, and code security are becoming part of enterprise rollout. ### How should I choose AI Code Generation software? After reviewing G2 feedback, I suggest starting with the coding workflow where your team loses the most time. Engineering teams that write high volumes of application code should prioritize IDE integration, context-aware completions, repository understanding, and test support. Teams using AI for debugging need stronger error explanation, code reasoning, documentation help, and model accuracy. When agents are part of the plan, I advise checking pull request behavior, sandboxing, test execution, permissions, and admin visibility. Pricing, token limits, hallucinated output, security controls, and code review expectations also deserve close review because G2 reviewers often connect those details with long-term trust and adoption. --- ## What Is AI Code Generation Software? [Generative AI Software](https://www.g2.com/categories/generative-ai) ## What Software Categories Are Similar to AI Code Generation Software? - [AI Coding Assistants Software](https://www.g2.com/categories/ai-coding-assistants) --- ## How Do You Choose the Right AI Code Generation Software? ### What You Should Know About AI Code Generators AI code generation software encompasses tools that utilize artificial intelligence (AI) and machine learning (ML) to create and write code based on a user's prompt. This code is written based on the best practices for that particular coding language and a natural language description of the expected code. In short, instead of manually typing out each line of code, the user gives the AI code generation tool a description of what they want the code to do. The software then uses ML and AI to write the code automatically based on user input. Most tools are generalized, but some AI code generation tools are created for specific use cases, like game or web development. Developers use AI code generation software to reduce the time and effort spent on repetitive coding tasks. By incorporating these tools into their workflow, developers at all skill levels can improve code consistency and increase productivity. Additionally, this type of software makes coding more accessible to everyone, regardless of skill level or knowledge of programming languages.  Plus, code generation tools can write code faster and in real time than a human programmer. They’re also more efficient because they can learn any technology or framework represented in their training dataset.  ### What are the common features of AI code generation software? The following are some features of AI code generation software that companies can use to write lines of code on their behalf. Note that specific features may vary between different products. - **Automatic code generation:** This allows the software to generate code snippets automatically, based on various parameters and details, which can reduce repetitive coding tasks and increase developer productivity. - **Machine learning capabilities:** [Machine learning](https://www.g2.com/articles/machine-learning) algorithms are used to improve code suggestions and generate more sophisticated and high-quality code snippets based on the context given by the user. - **Natural language processing (**[NLP](https://www.g2.com/articles/natural-language-processing)**) integration:** This feature allows the software to understand and process human language to generate code based on verbal or written descriptions. - **Support for multiple programming languages:** This enables the software to support various programming languages, such as [Python](https://www.g2.com/glossary/python-definition), Javascript, Ruby, HTML, and [PHP](https://www.g2.com/articles/what-is-php), making it versatile for different development scenarios. - **Advanced debugging tools:** AI-powered code generators often come with advanced debugging tools, which provide detailed insights into the performance of the generated programs. The code editor can also quickly detect errors and vulnerabilities in source code to help programmers identify potential problems before they become significant issues. - **Code suggestions and autocompletion:** This feature offers code completion suggestions to enhance existing code to help developers write more efficiently and learn syntax and functions of languages quickly. ### Types of AI code generation tools There are three types of AI code generation tools a company can choose. - **Rule-based code generators:** These tools generate code based on predefined rules or patterns. These rules can cover syntax, logic, or design patterns.  - **Machine learning-based code generators:** These tools use algorithms and models to generate code. Typically, a user provides the tool with a large dataset of example code snippets or specific programming tasks. From there, the software trains machine learning models, such as neural networks or [decision trees](https://www.g2.com/articles/data-mining-techniques#decision-tree), to learn patterns and relationships between input code and output solutions to generate code. - **NLP-based code generators:** This type of AI code generation software enables developers to program computers by speaking natural language commands instead of manually writing lines of code. This allows non-technical users to communicate directly with computers without knowledge of coding languages. ### What are the benefits of AI code generation software? Whether you’re an experienced developer or a user new to the world of writing code, using AI code generation software has numerous benefits. - **Faster development:** These AI tools can drastically reduce the time and effort needed to write code, complete projects, and work on other repetitive tasks. Since AI coding tools speed up the process of writing code, this also means faster project turnovers.  - **Less task switching:** AI code generators reduce the jumping back and forth between writing lines of code and free developers of the smaller tasks on their plate. - **Boosts productivity:** AI code generators streamline the development process for users, as developers can work faster and more efficiently. This saves time, resources, and money, and projects are completed sooner. - **Frees up developers to work on larger, more complex projects:** Since these coding assistants make the most of algorithms that can write code quickly and accurately, the developer is not required to do as much. This reduces the need for smaller, more minute tasks dedicated to writing code and allows developers to focus on more complex projects. ### Who uses AI code generation tools? Several different types of users and teams within organizations can benefit from using AI code generators.  #### Individuals: - **Software developers:** Software developers use AI code generators to write the code for software and various applications quickly and with minimal effort. - **IT administrators:** An IT administrator uses this software to automate tasks, manage infrastructure, enhance security, develop custom solutions, and optimize performance. This can streamline workflows, strengthen [cybersecurity](https://www.g2.com/glossary/cybersecurity-definition), and enable efficient problem-solving.  - **Data scientists:** Since these AI models can process large amounts of data efficiently and quickly, data scientists use these tools to produce advanced models without having to write lengthy programs every time a new experiment is in the works. #### Teams: - **IT teams** turn to AI code generation tools to assist in troubleshooting efforts and optimizing performance so they can focus on innovation efforts and other strategic initiatives.  - **Software development teams** leverage AI code generation software to automate repetitive tasks, speed up the coding processes, and optimize performance. Additionally, these tools can aid in debugging and generating code for complex algorithms. - **Data science and machine learning teams** use AI code generators to automate tasks like model development, streamlining data processing, and speeding up experimentation.  ### AI code generation software pricing AI code generation software is typically broken into three different pricing models. - **Subscription model:** Users pay a recurring fee to access all features at regular intervals, such as monthly or annually. - **Freemium model:** Offers a free basic version of the software with limited features and functionality. Users can upgrade to a paid version to access additional features when needed. - **Pay-per-use model:** This model allows users to pay for the software based on their usage, data storage, time spent using the tool, seats, or consumption.  Like most software, the price will vary depending on factors such as the complexity of the program and the features it offers. Basic tools can range from free to a few hundred dollars. More robust enterprise-level tools could be upwards of thousands of dollars. Some tools may also have recurring monthly fees and updates to the software with a price attached. Before investing in an AI code generation tool, companies need to list their specific needs and the features on their must-have list for more clarity. ### Alternatives to AI code generation tools Before choosing an AI code generation tool, companies can also consider one of the following alternatives for their needs. - [Low-code development platforms](https://www.g2.com/categories/low-code-development-platforms) **:** Low-code development platforms allow businesses to develop software quickly with minimal coding, reducing the need for extensive coding experience. These tools provide base-level code, scripts, and integrations so users can prototype, build, or scale applications without developing complex infrastructures. Some products are geared more toward developers and require coding for functionality, while others provide element libraries for design.  - [No-code development platforms](https://www.g2.com/categories/no-code-development-platforms): These tools offer drag-and-drop functionality, allowing developers to create applications quickly and efficiently without coding expertise. No-code development solutions offer a range of features, including templates for workflows, libraries of elements, and customizable interfaces. - **Code libraries** : A code library is a collection of reusable code modules, functions, classes, or snippets that companies can use to speed up software development. These repositories have the functionality to address common programming tasks, such as data manipulation, user interface design, or network communication. Developers can incorporate code from libraries into their own projects to save time and limit errors. ### Software and services related to AI code generation software There are certain tools related to AI code generation software that have similar features and functionalities and can be useful depending on a business's needs. Some examples of such tools are: - Low-code development platforms - No-code development platforms - [Integrated development environments (IDEs)](https://www.g2.com/categories/integrated-development-environments-ide) ### Challenges with AI code generation software Despite the several benefits users can experience from AI code generation software, there are some challenges to remember, too. - **Code quality:** One of the main challenges of using AI-generated code editors is the code's quality. Since coding AI assistants use regenerative AI technology and predict how code is written based on specific training material, they may produce low-quality and non-functional source code. It also may not comprehend what it’s writing, leading to more errors or broken code. - **Maintenance:** All code needs to be maintained, but code written using an AI code generation tool may be more complex than necessary, making it harder to maintain over time. This overly complex code can lead to unnecessary abstractions, excessive layers of code, or overly intricate logic, making debugging and collaboration challenging.  - **Less control:** Some users worry that relying too heavily on AI assistants could diminish their coding skills and expertise. Additionally, there’s the potential loss of control over the development process, as users may have less input into the written code. - **Learning curve for developers/organizations:** As with using any new software for the first time, there’s a learning curve for both developers and the organization. However, the curve may be steep and take time to master before achieving maximum productivity. ### Which companies should buy AI code generation software? Any company with a development team could benefit from AI code generation tools, but there are specific types of companies that should consider buying this type of software to add to their tech stack. - **Financial institutions:** Banks and financial institutions can utilize AI code generation to automate regulatory compliance, enhance security measures, and develop custom financial processes for risk analysis and [fraud detection](https://www.g2.com/categories/fraud-detection). - **Healthcare organizations:** Healthcare companies can use AI code generation to develop predictive models, automate medical imaging analysis, and optimize patient care processes. - **Legal firms:** A legal organization can use AI code generation to assist in document automation, legal research and analytics, and compliance monitoring. - **Government agencies:** Government organizations can use these tools to automate administrative tasks, enhance cybersecurity efforts, and develop [predictive models](https://www.g2.com/categories/predictive-analytics) for resource allocation and policy planning. - **Educational companies:** Educational institutions and schools can use AI code generation to develop personalized learning platforms, automate administrative tasks like grading and scheduling, and analyze student performance data for insights into how effective the current [curriculum](https://www.g2.com/categories/curriculum-management) is. - **Retail companies:** Retailers can use this software to optimize pricing strategies, automate customer segmentation for targeted marketing campaigns, and develop recommendations for personalized shopping experiences. ### How to choose the best AI code generation tools? The following explains the step-by-step process buyers can use to find suitable AI code-generation tools for their business.  #### 1. Identify business needs and priorities Before choosing an AI code generation tool, businesses should identify their top priorities in a tool and what exactly they’ll be using it for. Clear goals and requirements can make the selection process easier and more efficient. Companies should consider factors like how quickly they need to generate results, how much automation will be required, and what level of accuracy is needed. #### 2. Choose the necessary technology and features Next, companies should narrow down the features and functionalities they need most. Some essential technology and features a company may be looking for are: - **Auto completion of code** : This provides suggestions and completes code snippets based on context. - **Code refactoring** : With this feature, the tool can restructure existing code to improve performance and readability. - **Syntax checking** : This feature identifies errors and potential bugs in the code. - Code analysis: AI code generation tools must identify bottlenecks, security vulnerabilities, and areas for optimization. - **Machine learning assistants** : These enable AI-powered assistants to help developers write code, suggest improvements, or answer coding-related queries. - **Cross-platform support:** This is required for multiple programming languages and development environments so that users can work across different platforms seamlessly. Once companies short-list services based on their requirements and must-have functionalities, it’s easier to determine which options best suit their needs. #### 3. Review vendor vision, roadmap, viability, and support In this stage, a buyer should start vetting the selected AI code generation software vendors and conduct demos to determine if a product meets their requirements. For the best outcome, a buyer should share detailed requirements in advance so a vendor knows what features and functionalities to showcase during the demo.  Below are some questions buyers should ask vendors during the demo. - How does the software handle different programming languages and frameworks? - What level of customization and flexibility does the software offer for generating code to fit specific project requirements? - How does your software ensure code quality and compliance with coding standards? - What support and training options do you provide for users adopting your software? - What security measures are in place to protect sensitive code and data processed by your software? - Can the tool integrate with existing development tools and workflows, such as IDEs and version control systems? - What level of technical support is available, and how responsive is your support team to customer inquiries and issues? #### 4. Evaluate the deployment and purchasing model Once a buyer has received answers and is ready to move on to the next stage, they will likely loop in key stakeholders within the organization and at least one employee from each department who will be using the software. For example, with AI code generation, it’s best that the buyer loops in the developers who will be using the software to ensure it covers everything they need for maximum productivity. #### 5. Put it all together A buyer makes the final decision after getting buy-in from everyone on the selection committee, including [end users](https://www.g2.com/glossary/end-user-definition). This buy-in is essential for getting everyone on the same page regarding implementation, [onboarding](https://www.g2.com/categories/onboarding), and various use cases.  ### AI code generation software trends Some recent trends that were seen in the field of AI code generation software are: - **Companies integrating and adopting AI code generation tools for the first time:** AI code generation tools are making the most of recent developments in AI and ML for significant improvements regarding their features and functionality. Many companies are learning how to integrate these tools with their current tech stack for the first time. This trend stems from the demand for efficient code, the want to save money, and companies looking for a competitive advantage. - **AI will aid rather than** [replace developers](https://www.itpro.com/technology/artificial-intelligence/can-ai-code-generation-really-replace-human-developers) **:** It’s important that developers see AI and AI code generation tools as an aid they can use to speed up the process of writing code, which gives them the opportunity and time to work on other tasks rather than being a replacement. AI can automate repetitive tasks, provide insights, and enhance productivity. Whereas developers and real users will always lean on critical thinking, creativity, and problem-solving skills that AI currently cannot replicate.  Researched and written by [Adam Crivello](https://research.g2.com/insights/author/adam-crivello) Reviewed and edited by [Supanna Das](https://learn.g2.com/author/supanna-das)