How Generative AI can drive engineering productivity

Generative AI, or GenAI, is transforming business operations, from automating processes to creating content, and organizations are exploring high-value use cases while minimizing risk. In the software development landscape, it helps improve time-to-market, employee productivity, and customer experience. While technology leaders are innovating to optimize the true potential of GenAI use cases, it is crucial to understand that just tools are not enough. The limitations must be considered to make the best use of human validation. Ness Digital Engineering and Zinnov, a global management and strategy consulting firm, conducted a comprehensive study on maximizing GenAI’s impact on engineering productivity through a comprehensive approach that considers distributed development environment, engineering processes, methods, and tools.

Utilizing Ness’s proprietary platform Matrix to collect data, the study engaged 100+ software engineers across use cases and development settings, offering an in-depth analysis of engineers’ experience with GenAI in live engineering environments. The research aims to empower CXOs of large enterprises, global software engineering capability centers, and software product companies to transform engineering productivity.

Why AI Software Development Is Becoming the Enterprise Standard?

• Faster release cycles 
• Lower engineering cost 
• Improved product quality 
• Better scalability across teams 

Impact of Gen AI

Our study reveals that GenAI significantly impacts repeatable sustenance activities, reducing knowledge barriers and increasing the potential for globalizing a higher number of products. GenAI tools accelerated the completion of reviewing, fixing, and modifying existing code, improving engineering engagement, and expediting tasks through collaborative team dynamics, collective problem-solving, right upskilling, tooling, and processes. These accelerated workflows help reduce task completion time by 70%.

In AI software development environments, testing is increasingly automated using AI tools. GenAI tools assist in various activities, such as creating test cases, automating testing, generating mock data, improving code coverage, and reducing human effort.

GenAI enables engineers to navigate complex coding scenarios more efficiently, contributing to faster and more accurate resolutions. In fact, engineers witnessed a ~10% reduction in high code complexity tasks.

GenAI streamlines development processes and expands the scope for globalizing products by facilitating effective collaboration within distributed product development teams.

In AI software development, generative AI impacts multiple areas, including:

• Provides code recommendations aligned with the existing codebase, significantly reducing onboarding time for new engineers and ensuring seamless integration.
• Adheres to coding standards, promoting consistency across distributed teams, fostering uniformity in coding practices, and simplifying cross-team collaboration.
• Generates high-quality documentation, enhancing understanding of code functionality among dispersed teams and mitigating communication gaps.

Implications of GenAI on Engineering Productivity and Distributed Product Development

Implementation of GenAI has far-reaching implications, encompassing automation, collaboration, data-driven decision-making, and continuous learning. Embracing these changes can lead to a transformative and more productive landscape in engineering.

In addition, GenAI tools contribute significantly to increased job satisfaction and lower attrition rates within engineering teams by minimizing mundane activities, enhancing learning opportunities, and promoting a collaborative coding environment. With GenAI-powered educational platforms, engineers can continuously upskill and adapt to evolving technologies.

By simplifying tasks and fostering a more collaborative and dynamic work environment, GenAI plays a pivotal role in creating a positive and fulfilling professional experience, improving engagement by 70%.
To leverage GenAI for accelerating productivity, two other levers are needed to maximize the impact.

• At Ness, we drive productivity to the next level by implementing DevOps practices, including test automation, Infrastructure as Code, and Low code/No code platforms where applicable to deliver high-quality products efficiently. 
• Ness’s Intelligent Engineering Maturity Model assesses the entire engineering life cycle to benchmark and better understand the engineering team’s maturity and identify improvement areas. 

This collaborative study offers insights to organizations leveraging GenAI to revolutionize global product development. Ness’s Engineering productivity studios, Intelligent Engineering centers, and hands-on implementation enable organizations to navigate the evolving landscape to accelerate innovation, optimize costs, and streamline global expansion.

Learn how you can navigate the evolving product development landscape with GenAI –
Harnessing the power of GenAI in Transforming Software Engineering Productivity.