AI coding assistants are reshaping how software is developed across enterprises. From integrated development environment (IDE) plugins to autonomous agents, these tools are accelerating delivery, improving code quality and increasing productivity throughout the development lifecycle.

This research report explores the evolving market for AI coding assistants and offers a practical guide for technical leaders evaluating tools, managing risks and scaling adoption. It highlights the strengths and limitations of leading solutions, including Claude Code, Cursor, Windsurf, GitHub Copilot and Gemini Code Assist. Readers will also gain insight into agentic capabilities, enterprise-grade controls and how these assistants fit into real-world workflows.

How WWT engineers are reducing hands-on coding time through bug fixing, rapid prototyping and data reconciliation

Inside the full report:

A radar-style comparison of top tools and their agentic capabilities
Real-world examples from enterprise, government and education
Key considerations for secure and scalable deployment
Strategies to overcome limitations like short-term thinking and team misalignment
Insights into the future of vibe coding, hybrid teams and democratized development

The report also explores how AI coding assistants support developers with visual impairments, accelerate onboarding and help modernize legacy systems. It covers technical capabilities such as code generation, debugging, documentation and autonomous workflows, and explains how different interface types—from IDE plugins to low-code platforms—impact adoption and productivity.

Whether your team is just starting to explore AI coding tools or refining an enterprise-wide strategy, this guide provides actionable insights to help you make informed decisions and prepare for what's next in software development.

Geoff Armstrong

John Sextro

Vijay Ganesh