Accelerate development velocity with AI-assisted code generation tools.

Software delivery teams are under mounting pressure, as traditional development methods struggle to meet rising complexity, talent shortages, and the demand for continuous innovation. While AI-driven development tools promise to accelerate throughput, reduce manual effort, and enhance code quality, many teams still face challenges selecting the right solution for their unique organizational needs. Our Buyers Guide simplifies the decision-making process by helping you identify the tools that best align with your development goals, team capabilities, and existing workflows.

AI-assisted code generation is reshaping software development. However, with a quickly evolving market saturated by vendors, organizations struggle to keep up, leading to missed opportunities, misaligned tool choices, and wasted resources. IT leaders need a strategic approach to simplify decision-making, accelerate time-to-value, and avoid costly missteps.

1. AI isn’t a plug-and-play fix.

AI’s real value lies in its ability to augment human creativity and decision-making. When integrated thoughtfully, it can save developers time when performing repetitive tasks to focus on more creative projects. With human oversight, AI-assisted code generation tools can transform development from a manual craft into a strategic, AI-enabled discipline where maintainable and standardized code are the norm.

1. Don’t fall into the “paralysis by analysis” trap.

With a vast range of AI models available, teams often struggle to select the most suitable one, resulting in delays in implementation. Establishing a framework for evaluation and a centralized AI model library can speed up decision-making and improve model performance. This allows for a better understanding of core features, use cases, and how emerging trends shape strategic decisions.

1. Close the skills gap with AI.

AI-assisted code generation tools accelerate development cycles and automate repetitive tasks. These tools enable developers of varying skill levels to contribute meaningfully and foster greater innovation across the entire team. This AI-enabled talent optimization allows less experienced developers to contribute effectively, reducing dependency on senior engineers and helping teams scale more efficiently.

Use this step-by-step guide to accelerate your AI decisions

Choosing the right AI-assisted code generation tool starts with understanding your primary needs. Whether it’s documentation and explanations or unit tests and quality assurance, there are a broad range of functionalities to consider. Rather than building solutions in-house, this guide helps you evaluate vendor solutions that align with your specific use cases. If you’re unsure where to start, explore our own IT Challenge, which compares nine leading vendors using a SWOT analysis to reveal their relative pros and cons.

Use our two-phase approach, along with a template and tool, to:

• Contextualize the AI code generation market by exploring key trends, future possibilities, a vendor comparison, and SWOT analysis.
• Select an AI code generation tool by constructing a solid business case based on high-value use cases that are aligned to organizational goals.