What We Know Now About Generative Ai For Software Development

Bonisiwe Shabane

-Dec 8, 2025, 4:12 PM

what we know now about generative ai for software development

Last year, I wrote about the 10 ways generative AI would transform software development, including early use cases in code generation, code validation, and other improvements in the software development process. Over the past year, I’ve also covered how genAI impacts low-code development, using genAI for quality assurance in continuous testing, and using AI and machine learning for dataops. Now just over a year into the adoption of AI copilots and other genAI capabilities, it’s a good time to review how developers and devops teams are using generative AI. I will also discuss the impact of genAI-enabled tools and processes and the risks that must be addressed. “GenAI is redefining how developers work by introducing a new era of collaborative programming, acting as a dynamic partner, streamlining workflows, augmenting developer skills, and enhancing creative problem solving,” says Bharat Sandhu, SVP of... “By embedding genAI into development processes, organizations unlock new possibilities and foster a more agile, collaborative approach to building the future.”

Recent data suggests that enterprises seek proprietary genAI capabilities, and most devops teams and developers use it for various tasks in the software development lifecycle. The 2024 AI in software development report found AI application development in high demand, with 79% of enterprises having up to 300 use cases for generative AI in their backlogs. According to the report, genAI’s most popular emerging use cases in software development included devops optimizations, code generation, documentation, and user-interface design. A global survey and expert insights reveal how generative AI has already transformed the software development lifecycle—and what’s coming next. Generative AI’s promises for the software development lifecycle (SDLC)—code that writes itself, fully automated test generation, and developers who spend more time innovating than debugging—are as alluring as they are ambitious. Some bullish industry forecasts project a 30% productivity boost from AI developer tools, which, if realized, could inject more than $1.5 trillion into the global GDP.

But while there’s little doubt that software development is undergoing a profound transformation, separating the hype and speculation from the realities of implementation and ROI is no simple task. As with previous technological revolutions, the dividends won’t be instant. “There’s an equivalency between what’s going on with AI and when digital transformation first happened,” observes Carolina Dolan Chandler, chief digital officer at Globant. “AI is an integral shift. It’s going to affect every single job role in every single way. But it’s going to be a long-term process.”

Where exactly are we on this transformative journey? How are enterprises navigating this new terrain—and what’s still ahead? To investigate how generative AI is impacting the SDLC, MIT Technology Review Insights surveyed more than 300 business leaders about how they’re using the technology in their software and product lifecycles. The findings reveal that generative AI has rich potential to revolutionize software development, but that many enterprises are still in the early stages of realizing its full impact. While adoption is widespread and accelerating, there are significant untapped opportunities. This report explores the projected course of these advancements, as well as how emerging innovations, including agentic AI, might bring about some of the technology’s loftier promises.

Discover how Generative AI is transforming software development from coding to deployment. Faster time-to-market and cut-through velocity are crucial for any software engineering team. Fortunately, generative AI comes with use cases that can help engineering teams improve their workflows, deliver bug-free products in record time, and power up productivity. AI has become an integral part of software engineering, improving repetitive actions across the entire software development lifecycle (SDLC) – from coding to testing, deployment, and documentation. Three-quarters of developers already use or plan to use AI coding assistance, based on a recent StackOverflow community survey. Tech companies too are shifting to generative AI trend with Gartner claiming that two-thirds of the businesses are in the pilot or deployment stages with AI coding tools and predicting that 75% of engineering...

What follows are 11 impactful GenAI use cases that are already transforming the software development process. Additionally, we'll discuss what Generative AI is, the well-known risks, and some tools that can help. Generative AI is designed to learn from the input data, recongize patterns, and generate new content on a large scale without duplicating the original data. This powerful technology can generate content in different formats such as images, video, speech, text, software code, and product designs. Generative AI can accelerate the application creation process and allow coding specialists to focus on higher-level, creative, more complex activities. In March 2023, KPMG conducted a survey of 300 global executives across a wide range of industries, and a follow-up survey three months later.

That 2023 KPMG Generative AI Survey report reveals what executives across industries expect from generative AI and what concerns they have about using the technology. The March survey revealed that 73 percent of the respondents saw AI and machine learning as crucial skill sets for their organizations and are therefore a hiring priority. In June, we found that 19 percent of companies polled already use generative AI for writing code or other development documentation, and that an additional 24 percent are currently researching and piloting ways to... This companion report takes a deeper dive into how this transformative technology can help improve the software (application) development lifecycle (SDLC). It examines potential applications for generative AI across the five elements of a typical SDLC, the variety of ways it can assist developers, the benefits and challenges you can expect along the way, and... Companies that make the transition see enormous advantages

Bob Graham, Chief Market Development Officer, Ness Digital Engineering. Over the years, we have witnessed many advancements in tools and methodologies in software development aimed at enhancing productivity, streamlining processes and accelerating development cycles. Generative AI, which has been made possible by cloud computing’s almost unlimited resources, is becoming a game-changer. GenAI represents a new paradigm in how software is developed, and it's revolutionizing the entire landscape of software engineering. Unlike traditional approaches that rely on human expertise and labor-intensive processes, GenAI empowers developers with intelligent tools capable of generating code, suggesting improvements and even anticipating potential issues—all in real time. Gartner predicts that by 2027, 70% of platform engineering teams will use AI-powered coding tools.

My company is a leading provider of software engineering services. We wanted to move beyond the hype and obtain more empirical evidence of GenAI's impact on our workforce and on the work we do for customers. This way, we can be a better employer and better partner for our clients. To that end, we conducted a recent study with a management consulting firm to analyze the impact of GenAI. To do this, we studied the productivity data of over 100 software engineers over the course of a couple of months. Based on our key findings, I’d like to share some ways we believe GenAI will impact software engineering.

Our research shows that senior engineers saw their productivity rise by 48% when integrating GenAI tools, whilst junior developers saw very few gains as they lacked the experience to leverage the tools effectively. Federico Sendra, CEO and cofounder of SpaceDev, a consultancy and development services company with a focus on blockchain and web3. Not long ago, writing solid code meant sitting with a problem, figuring out the business logic and crafting the best possible solution … line by line. That's still true in many ways, but in the past year, the ubiquitous technology we call AI has become an integral part of the process. If you're not using it already, someone on your team likely is. If you're leading a team, you've probably seen timelines shift, pull requests look slightly different or junior developers become more productive faster than expected.

GenAI is altering how software is made, forcing us to rethink engineering itself. Machines co-writing software isn't a new thing, but the tools have taken evolutionary leaps. GitHub Copilot, launched in 2021, was the first widely adopted assistant of its kind. Trained on billions of lines of public code, it now generates suggestions in real time as developers type. Amazon is building its own internal GenAI coding assistant under the name "CodeWhisperer," and Google has rolled out Duet AI. OpenAI's Codex, which powers parts of Copilot, continues to spread its influence, as do smaller, open-source models that allow for local experimentation and fine-tuning.

Access to this page requires authorization. You can try signing in or changing directories. Access to this page requires authorization. You can try changing directories. Generative AI, powered by large language models (LLMs), brings new opportunities for developers and organizations. Services like Azure OpenAI make AI easy to use with simple APIs.

Developers of all skill levels can add advanced AI features to their apps without needing special knowledge or hardware. As a developer, you might wonder what your role is and where you fit in. Maybe you want to know which part of the "AI stack" to focus on, or what you can build with today's technology. To answer these questions, start by building a mental model that connects new terms and technologies to what you already know. This approach helps you design and add generative AI features to your apps. Think back just a few years.

AI in the workplace wasn’t always that exciting. It was frustrating. Chatbots missed the mark more often than they helped. AI writing assistants sounded robotic, stiff, and very generic. Transcription tools? Maybe 70% accurate on a good day, which made them more hassle than help.

Instead of making work easier, AI often felt like one more thing to manage. It was more novelty than necessity. But things are changing fast. Generative AI has gone from a futuristic buzzword to something much more practical and powerful. It’s now sitting beside us in meetings, drafting our emails, organizing our thoughts, and even helping us solve problems we didn’t know how to articulate. This isn’t just smarter software, it’s a new kind of teammate.

We’re not just using tools. We’re collaborating with them. Whether it’s ChatGPT, Microsoft Copilot, Claude, Perplexity, or internal LLMs customized for specific teams, generative AI isn’t sitting on the bench anymore. It’s helping draft proposals, untangle data, write code, analyze reports, and act as a collaboration partner to spark new ideas. And it's doing all of this without needing a lunch break, vacation time, or sleep. The pace of change is fast with AI.

The impact? Even faster. And we’re only just getting started. This article was featured in the Think newsletter. Get it in your inbox. The idea wasn’t born in a flash.

What We Know Now About Generative Ai For Software Development

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