AI for Developers
Embed AI across the software development lifecycle, from AI powered coding assistants to production-grade agentic systems that automate and orchestrate complex developer workflows.
AI for Developers Overview
AI is a core part of modern software engineering
From AI coding assistants embedded in IDEs to emerging agentic tools that automate multistep development tasks, AI is reshaping how software is designed, built and delivered across the SDLC.
For enterprise development teams, the opportunity isn't whether to use AI—but how to adopt it safely, integrate it into existing workflows and turn productivity gains into consistent delivery outcomes.
WWT helps organizations adopt AI for developers in a secure, governed way so teams can move faster, reduce risk and deliver higher quality software—driving competitive advantage, fostering innovation and accelerating business outcomes.
Why AI for Developers now
Tooling in AI software development is the most widely adopted enterprise use case for generative AI—and the great AI unlock for modern organizations.
Improving how teams build and modernize software increases capacity and velocity for the full set of digital transformation and AI initiatives businesses want to deliver, because software is the lifeblood of modern organizations.
As teams embed AI into everyday development work, organizations are seeing clear, quantifiable gains in efficiency and productivity, creating opportunities not only to scale adoption responsibly but also to move faster on initiatives tied to revenue growth and competitive advantage.
90%
Enterprise adoption of AI coding assistants projected by 2028
30 - 40%
Efficiency gains from reduced hands on coding time using AI coding assistants
2-3x
Faster revenue growth for companies that excel at digital execution and agile delivery
Enterprises are prioritizing AI for developers to:
Speed up development cycles—from prototyping to production
Improve code quality, consistency and maintainability
Modernize legacy code and reduce technical debt
Retain and upskill developer talent
What AI for Developers really means
AI is reshaping how developers build, test and modernize software.
As teams move from no code to pro code, AI coding assistants evolve from helping individuals prototype faster to empowering engineering teams at scale across the software development lifecycle.
App Builders
Tools that empower non-developers to create applications through natural language prompts and intuitive, visual interfaces. This enables rapid prototyping and iteration, democratizing software development.
Coding assistants
AI-powered coding assistants that integrate directly into your development environment (IDE) or editor and provide real-time code suggestions, refactoring, error detection and documentation.
Autonomous coding agents
Advanced AI agents capable of executing multi step tasks such as generating code, running tests and remediating issues with minimal intervention, allowing engineers to focus on intent, review and higher value problem solving.
Workflow tools
Tools that manage how AI agents work across orchestration, code review, and agent memory. Engineering teams oversee multi-agent workflows, leverage persistent memory across sessions, and automate code review processes.
Trending in AI for Developers
Dive deeper into content about AI coding assistants
Compressing Idea to Outcome (I2O): WWT Co-Founder and CEO Jim Kavanaugh on Why AI Coding Assistants Could Reshape the Enterprise
Choosing the Right AI Coding Assistant: What Every Enterprise Leader Must Know
Inside the AI Coding Revolution: Tools, Tradeoffs and Transformation
Unlocking the Power of AI Coding Assistants
WWT Research for AI for Developers
Access WWT Research related to AI software development and engineering
AI Coding Assistants: Enterprise Market Landscape and Tools Evaluation
AI Coding Assistants: Seizing the Once-in-a-Generation Opportunity for Your Organization
How to Securely Implement AI Coding Assistants Across the Enterprise
Secure Development Lifecycle (SDLC): Ensuring Integrated NVIDIA AI Security at Scale
Our approach to AI software development
A practical approach that helps organizations adopt AI for developers at any stage.
Organizations adopt AI for developers in different ways, depending on their tooling maturity, risk tolerance and scale. We meet teams where they are, helping them select, validate, enable and scale AI coding tools as part of their software development lifecycle.
Evaluate the right AI coding tools
We guide teams through a rapidly evolving market of AI coding assistants and agentic tools to identify fit-for-purpose solutions aligned with their development environments, workflows and risk profile. Leveraging WWT's technology partnerships, organizations can assess capabilities, integration requirements and build vs buy tradeoffs, and deploy the right tools across on premises, cloud and hybrid environments.
Design and validate development ecosystems
Before broad rollout, teams need confidence that AI tools work as expected in their real environments. We help organizations evaluate how coding assistants and agents interact with existing IDEs, repositories, CI/CD pipelines and security controls, reducing integration risk and clarifying where automation adds the most value.
Deploy and enable developers
Rolling out AI coding tools is as much a people challenge as a technical one. We support secure deployment alongside developer training, enablement and change management so teams understand how to use AI tools effectively, responsibly and consistently in day to day development work.
Scale adoption and engineering impact
As organizations move from proven integration to readiness for scale, we enable organizations to operationalize AI for developers across teams by standardizing guardrails, measuring impact and extending AI into higher‑value engineering activities such as modernization, testing, refactoring and workflow automation.
Capabilities in AI and software development
Explore AI for software development
Go deeper into WWT's capabilities and perspectives that support AI for developers across AI-native engineering.
Our work in AI coding enablement
2026 Special Olympics USA Games
WWT supported the adoption of Windsurf to improve development efficiency. The team saw a 30%+ increase in productivity, with development tasks that previously took days completed in hours—a pace that would have been impossible without Windsurf.
Windsurf Developer Enablement
WWT delivered a strategic and tactical plan for developer enablement and change management at a global technology company. Our Windsurf rollout enabled the company reduce cycle time by 30-50% for repetitive work and double-digit improvements in complex feature delivery in software products.
Experts in AI software development
A team that combines expertise, strategy and hands-on experience to mature your SDLC.
AI for Developers FAQs
What is AI for Developers?
AI for Developers refers to the use of AI tools—such as coding assistants and agentic tools—across the software development lifecycle to improve productivity, quality and delivery speed.
Explore common questions about this topic.
AI coding assistants help developers write, review, refactor and test code more efficiently by automating repetitive tasks and improving consistency across software projects. Modern tools integrate directly into IDEs and support a wide range of languages and frameworks, acting as AI‑powered pair programmers.
There is no one‑size‑fits‑all answer. The best AI coding tools for enterprise teams depend on development maturity, security and governance requirements, and how well the tools integrate with existing IDEs, CI/CD pipelines and platforms
Successful adoption requires more than selecting tools. Enterprises need governance, security controls, integration planning and developer enablement to ensure AI is used safely, consistently and at scale across the SDLC.
AI coding tools can be used securely when implemented with the right architectures, data protections, access controls and governance policies. Enterprise‑ready adoption focuses on minimizing data exposure, preventing shadow AI and aligning usage with compliance requirements.
Agentic tools add the most value when applied to repetitive or multi‑step development tasks such as automated testing, refactoring, remediation and workflow orchestration. These tools help reduce manual effort while allowing developers to focus on higher‑value engineering work.
AI-native engineering embeds AI as a collaborative partner across the full lifecycle from strategy to operations. At WWT, our AI-native engineering envisions positioning AI as a co-strategist, co-builder, and co-validator with teams, moving beyond point solutions. By embedding artificial intelligence into every phase of the engineering process, organizations unlock the speed, scale, and adaptability needed to accelerate broader digital transformation initiatives, enabling organizations to modernize legacy systems, drive innovation, and deliver measurable business value faster.
The value of AI coding assistants and autonomous coding agents for developer teams is typically measured through improvements in development velocity, code quality, cycle time, test coverage and reliability, along with developer satisfaction. The digital products these engineering teams create deliver on broader business outcomes like faster time to market, customer loyalty, competitive advantage, market share gains, revenue growth, risk reduction and operational efficiency.
Partners
The power of partnerships
WWT's deep expertise and long-standing partnership with this ecosystem of partners enables us to design and deploy AI native engineering successfully at enterprise scale. We help organizations identify the best fit AI coding assistants and agentic platforms for their coding teams and ensure it integrates with cloud and GenAI solutions from these companies.