How AI can support developer productivity

How does AI increase developer productivity?

As AI transforms build processes and design responsibilities, it offers unique opportunities while also requiring individual developers to adapt and revise their standard engineering approaches. At the current stage of AI integration, human oversight remains crucial to development success. But it's also clear that organizations are accelerating project completion times and reducing operational costs. According to StackOverflow research, 69% of developers who rely on AI agents report increased productivity.

AI is also affecting application architecture as team leads and engineers redesign their development workflows to reach targeted deployment numbers. The GitHub platform recorded significant increases in AI infrastructure projects, from 68,000 in January 2024 to 200,000 by August 2024, demonstrating adoption momentum and AI's growing influence on production environments. For a high percentage of developers who regularly rely on AI-driven tools, the majority are adopting AI for a variety of menial processes. These include boilerplate code generation, language conversion, code documentation and change summaries.

However, facility in coding and a firm understanding of software architecture remain crucial skills to have in place. Considering the spectrum of development tasks, from basic shell scripts and API endpoint creation to SQL queries that represent the most advanced transactions, autonomous AI software development remains well-positioned for the future. While the environment is changing fast, success depends on selecting straightforward tasks and providing clear contextual cues. In fact, one study found that in early 2025, AI tools increased task complexity and actually slowed down experienced developers.

Anecdotal evidence also reveals developer frustration with the steep learning curves of implementation and the time-consuming nature of AI integrations. However, for those programmers who upskill in relevant areas and focus on high-level problem-solving, the advantages will likely accrue over time.

7 essentials for facilitating AI-assisted development

In the short term, the following steps will help facilitate smooth integration into software development workflows as adoption expands from generative to agentic AI use cases.

1. Define project goals in advance

The benefits of defining goals in preparation for AI adoption extend to several key areas. To achieve strategic gains, such as faster build times, code generation and testing, identify the specific development and workflow problems that AI integration can address. Align AI tools with your organization's coding standards and style guidelines to provide important guardrails. Taking a methodical AI adoption approach will help avoid future headaches, wasted resources and unnecessary complexity in the building process.

2. Adopt an incremental workflow approach

Instead of generating an entire module, developers should break down larger build tasks and implement manageable subtasks. Start with individual functions or classes and add complexity step-by-step. For example, when building modules or individual functions, developers can deploy AI to specifically create unit tests using clear inputs and outputs based on well-defined parameters. One advantage is that by designing specific prompts for each subtask, developers can guide AI to produce code outputs that will be easier to review, validate and refine later on.       

3. Track every change

In the current web and mobile development era, research shows that nearly half of surveyed organizations use AI to handle document generation. Developers generally deploy AI to write documentation and ensure long-term code stability. While the technology excels at summarizing and cataloguing recent changes, developers should annotate AI-assisted changes to ensure long-term code stability. Moreover, documenting commits and pull requests can provide essential context for team members and simplify future maintenance.

4. Improve through repetition

Improvements in AI iterations depend on regular evaluations of AI-generated outputs as well as continually refining prompts and incorporating feedback to enhance accuracy and effectiveness. Well-crafted prompts provide clear model instructions, leading to better results, while continuous testing can improve configurations. Moreover, by implementing structured feedback loops, developers can enhance results, enable self-correction and ensure that AI systems evolve over time.

5. Undertake a multi-API strategy

Distribute workloads across different AI providers and tools, such as Microsoft Copilot, Meta Llama, OpenAI and Gemini, to provide increased deployment flexibility and use specialized provider strengths to extract maximum AI deployment value. Having more than one AI provider to meet targeted requirements also helps prevent operational downtime due to a service malfunction or underperformance in a particular platform.

6. Secure workflow processes and data

Implementing external security controls is essential for protecting development processes. These include role-based access control and zero trust architecture for continuous verification to ensure workflow integrity, block data leaks and prevent unauthorized changes. Team leads should also ensure that data is encrypted at rest and in transit while conducting regular audits of access controls and data protection.

7. Test rigorously and often

Through regular testing, team leaders and individual developers can ensure code meets all functional and security standards as well as performs reliably in real-world applications. Again, human interventions can make a crucial difference since relying on AI to independently check code can lead to missed errors and vulnerabilities. Both test scaffolding and iterative testing can provide a foundation for collecting early performance results and ensuring a continuous cycle of feedback loops to improve quality over time.

Kerry Doyle writes about technology for a variety of publications and platforms. His current focus is on issues relevant to IT and enterprise leaders across a range of topics, from nanotech and cloud to distributed services and AI.