Generative AI in IT industry

Posted by: Prof. D. Suseela

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Generative AI in IT industry

Developer productivity has been a big topic in the software engineering world. Generative AI is one emerging technology that can take this to the next level and completely disrupt how we build software.


Evolution of productivity improvement

Engineering teams have always sought ways to improve productivity through efficient, agile processes, organizing teams as PODs, measuring productivity KPIs, etc. The next level of productivity improvement came through more automation from DevOps practices, leveraging tools for test automation, Infrastructure as Code, and use of Low code/No code platforms where applicable. In short, we have used all available methods thus far to deliver high-quality products efficiently.

Generative AI has taken productivity improvement to the next level. Code generation tools such as GitHub Copilot and AWS CodeWhisperer translate the natural language into code. They can be useful to automate repetitive tasks traditionally done by developers. It can help improve the productivity and quality of the product, as outlined in the next section. But remember, like agile has not worked for all scenarios and automation doesn’t work in every situation, Generative AI will not write code for everything.

Generative AI in the software engineering lifecycle

Generative AI-based code generation tools will change the actual engineering process, estimation techniques, the way we write code, testing of software, code deployment, tools used, etc.


It can enable productivity improvement for various roles involved in the software engineering process.

It can help Architects

It can help Product Owners and Business Analysts

It can help UI/UX designers

It can help developers

It can help QA Testers


The impact of generative AI tools on productivity varies across roles and the product development lifecycle – but we see a high impact during development and testing and a low impact when the product is in maintenance mode.

For the last few years, we have said, “Software is eating the world, but AI is eating software.” Today, Generative AI has given a whole new meaning to this line!


Continuous productivity improvement

Recent studies have touted productivity improvements from 25% to 50%. While these numbers can vary based on the domain, complexity, size, team maturity, etc., we know that AI-based code assistant tools will bring productivity improvement is never seen before.


These tools can learn from past projects to continuously improve the quality of the generated code. As these tools improve over time, and we also know how to use them effectively; we can expect continuous productivity improvement in our engineering teams in the coming years.


Looking ahead:

On the one hand, Generative AI-based tools can bring significant productivity improvements resulting in a fear that there will be less demand for software engineers and other roles across the development lifecycle. But on the other hand, it could mean that we can develop software much faster and accelerate product roadmaps. Engineering teams will be able to focus on more complex tasks and the quality of the software. In addition, productivity gains will allow them to focus on tech debt that keeps getting pushed out.


With the help of Generative AI, we will write new software to solve a new class of problems previously considered very complex and expensive. There will also be a big demand to build new software (or rewrite existing software) that uses Generative AI at the core to offer everyone unique experiences and productivity tools. E.g., Reimagining enterprise knowledge management tools can transform them into ChatGPT-type interfaces, enabling access to all enterprise content such as finance, HR, legal, contracts, marketing, etc. This innovation will save significant time for all employees.


Engineering will never be same again

The adoption of generative AI is a game-changer for software engineering. The benefits of this technology are clear, as it brings about productivity gains and introduces new ways of working that will change the face of software engineering forever, despite the need to address certain concerns and challenges. Engineering teams must embrace this change, adapt to new ways of working, and stay ahead of the curve in this rapidly evolving field.



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