Boosting productivity by up to 40% with AI augmented development

Generative AI (Gen AI) is transforming workflows across industries, including software development. Gartner forecasts that by 2028, up to 75% of enterprise software engineers might rely on AI and ML coding assistants, a sharp climb from less than 10% in early 2023. The explanation is straightforward: AI augmented development automates repetitive tasks and enhances capabilities in areas where machines excel, like data processing, freeing developers to focus on more complex processes and tasks. 

N-iX has embraced this trend by integrating AI augmented development into our operations, resulting in a productivity boost of up to 40%. This means that a software engineering task previously completed in a week can now be completed much faster. This shift has streamlined our processes and helped automate time-consuming tasks. Our journey also involved overcoming significant challenges, leading us to develop best practices for successful AI adoption. Let’s explore what we learned from integrating AI into our workflows and tackling the accompanying challenges.

How N-iX integrated AI into our software development process

AI augmented development is not just about boosting efficiency but also about empowering tech experts to focus on tech challenges rather than getting bogged down by routine tasks. Here are the main use cases of AI in software development we found most impactful:

Code generation and completion

AI-powered code completion tools, like GitHub Copilot, predict and suggest code snippets in real-time as developers type. They understand the context of the codebase, function names, and libraries being used, allowing them to offer highly relevant suggestions. Such suggestions can significantly speed up development by reducing the need to type out boilerplate code or search through documentation.

AI can also translate natural language into entire blocks of code. This is particularly useful for generating repetitive code structure prototypes or implementing standard algorithms. AI can further speed up the writing process by quickly identifying simple bugs and suggesting fixes based on vast knowledge from training data. It can analyze codebases, recognize patterns, and diagnose issues that may not be immediately obvious to developers. This accelerates the overall development process and allows developers to focus on higher-level problem-solving.

Automated testing

Gen AI enhances automated testing by automatically generating tests, completing test suites through code analysis, and understanding natural language inputs, which improves testing coverage. This capability allows AI to generate unit tests and detect potential bugs early. However, while AI-driven tests can enhance efficiency, they still require human oversight to ensure the generated tests align with real-world scenarios and business requirements.

Refactoring and technical debt management

AI augmented software development simplifies code refactoring and managing technical debt. It can identify redundancies and inefficiencies, allowing for automated optimization. It can then offer suggestions for modernizing the code. 

DevOps optimization

DevOps processes also see significant benefits from AI usage:

• Automated shell script generation: AI creates efficient shell scripts for environment configuration and application deployment, reducing manual effort and minimizing errors.
• Optimized infrastructure as code (IaC): Creating and managing configuration files like Terraform and AWS CloudFormation can be made easier. AI analyzes existing setups, detects inconsistencies, and suggests resource optimization.
• Improved configuration management: AI can generate playbooks, detect configuration drift, and recommend optimizations.
• Proactive monitoring and issue detection: AI-driven tools monitor system performance in real-time, detect anomalies, diagnose issues before they escalate, and provide proactive alerts and solutions based on historical data analysis.

Code review 

AI in code review can help automatically find bugs and potential performance problems. This reduces human workload and speeds up development.

Expanded web search

LLM assistants can save developers significant time by finding relevant documentation or specific solutions. These tools can quickly retrieve code snippets, Stack Overflow answers, or documentation links. This eliminates the need to open a browser and manually sift through search results, streamlining the development process.

Our Gen AI implementation strategy for overcoming key challenges

Adopting AI wasn't just about adding new tools; it was about adjusting and rethinking how we work, balancing innovation and automation with compliance. There are several reasons why our software engineers cannot just type their query into Chat GPT: 

• Data security and privacy: Our clients' code, both existing and new, cannot be shared with third parties. Additional requirements for data protection are set by regulations such as GDPR and various industry-specific acts.

1. Intellectual property: The way LLMs train themselves poses risks to intellectual property, as generated code could inadvertently incorporate elements from its training data, potentially leading to claims of code plagiarism or intellectual property infringement.
2. Accountability: Engineers need to maintain accountability for their work, and using an AI like ChatGPT complicates attribution and responsibility for the code's outcomes, including errors or failures, which must be clearly owned by human developers.

Here are the steps we took in our implementation to make sure these main concerns are properly addressed:

Choosing the most fitting model

We began by looking for a model that fit our company's needs for code generation, bug detection, and automated testing. We tried GPT, GitHub Copilot, Claude 3.5 Sonnet, Gemini 1.5 Pro, and Perplexity AI. The main challenge was balancing capability with data security—since we're handling sensitive client data, using public models wasn't an option. In the end, after narrowing down the list, our engineers chose models based on each project's unique needs and what the team preferred.

Tool selection with data privacy in mind

With the model in place, the next challenge was choosing the tools it offers and integrating them into the development workflow. When picking tools for AI augmented development, the main things to consider are:

• Data security: We avoided using tools that require sending the client’s code or data to external servers owned by third parties. All functionality had to be accessible via our private cloud to minimize the risk of data leaks or unauthorized access.
• Capabilities: To truly boost productivity, the tools needed to perform their tasks precisely. If not, engineers would spend their time correcting AI errors instead of focusing on improvements. Throughout development, the performance of each model fluctuated. Some models proved exceptionally good at certain tasks but fell short in others, with their strengths and capabilities shifting as they evolved.
• Fit with existing workflow: The tools had to integrate smoothly or be offered with the software and platforms we already use. Embedded tools enhance our coding, testing, and collaboration processes without transitioning to different environments.
• Team preferences: Our engineers' comfort and familiarity with the tool matter. After all, they would be the ones using it.

Updating our data governance framework

Introducing AI-augmented development required us to update our data governance framework to uphold compliance and data security standards. To start, we established a mandatory review and approval process for any new AI components before they could be integrated into a project. Building on that, we deployed software that scans codebases to identify copied code or any content that might infringe on licenses or copyrights and scheduled periodic audits to review AI-generated code for compliance with licensing and copyright regulations.

In the final stage, we formalized the process for human review of all AI-generated content. Specific team members were assigned to conduct these reviews, ensuring clear accountability and consistency. This added layer of oversight allows us to catch any issues early, reducing the risk of releasing flawed software or spreading inaccurate information. This safeguards our quality standards but is also required under GDPR and the upcoming EU Artificial Intelligence Act.

These practices aim to reduce legal and ethical risks while fostering a culture of responsibility and transparency throughout our AI augmented software development processes.

Forming a Gen AI productivity toolset

To help our developers embrace AI for increased productivity, we organized two rounds of workshops. The first round focused on the general principles of AI—how to use it safely, efficiently, and effectively. This provided a solid foundation for understanding the capabilities and limitations of AI tools. In the second round, we tailored the sessions to specific roles, diving into practical applications and best practices for integrating AI into day-to-day tasks. These sessions also encouraged knowledge sharing among colleagues, fostering a collaborative environment where developers could learn from each other's experiences.

AI augmented development boosted our teams’ productivity by up to 40%

After implementing AI-augmented development practices, our team's productivity increased by 10% to 40%. This boost was largely due to AI's ability to streamline routine activities, which accelerated troubleshooting and facilitated the rapid adoption of new technologies and frameworks. AI freed engineers to focus on more high-level tasks, significantly shortening the overall development cycle. This shift enhanced our developers' ability to tackle complex challenges more efficiently, keeping project timelines on track. Additionally, AI provided contextual guidance and best-practice recommendations, smoothing the learning curve associated with the adoption of new technologies and frameworks.

Ultimately, the integration of AI has significantly optimized both the productivity and agility of our development processes, allowing us to operate more efficiently and effectively.