University of Kwazulu-Natal

How To Maximize Generative Ai For Software Development Today

Bonisiwe Shabane
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how to maximize generative ai for software development today

Peter Guagenti is president at Tabnine. Peter is an accomplished entrepreneur, and has been working in AI business tools for 10+ years. Software development just isn’t what it used to be. The role of the developer is more challenging today than it was before the pandemic, and it's exponentially more difficult than at the start of the mobile era 10-plus years ago. Why? The world’s seemingly insatiable appetite for software.

To put it into numbers, Google Play adds about 1,270 new applications per day, and the average enterprise has over 1,000 applications in use at any given time. The burden of creating, maintaining and modernizing all of this code is only increasing. The combination of technological advancements and fierce competition between businesses has created the need to build more applications faster. Devs are tasked with creating and maintaining these apps—including contending with their increasing complexity—while simultaneously dealing with a growing pool of technical debt that has accumulated over time. The final piece of the puzzle contributing to this challenging new environment is a talent shortage. According to one study, "the shortage of developers in the US will exceed 1.2 million." This talent shortage is so severe that "the US economy [is] at risk of an unrealized [GDP] output of...

AI tools improve productivity, but process changes are necessary to generate real value. By Purna Doddapaneni, Bill Radzevych, Steven Breeden, Bharat Bansal, and Tanvee Rao This article is part of Bain’s Technology Report 2025 Generative AI arrived on the scene with sky-high expectations, and many companies rushed into pilot projects. Yet the results haven’t lived up to the hype. Two out of three software firms have rolled out generative AI tools, and among those, developer adoption is low.

Teams using AI assistants see 10% to 15% productivity boosts, but often the time saved isn’t redirected toward higher-value work. So even those modest gains don’t translate into positive returns. Without a plan to turn interest into habit, initial gains quickly evaporate, leaving leaders asking, “Where’s the payoff?” Early initiatives often fixate on code generation—that is, using generative AI to write code faster. But writing and testing code only accounts for about 25% to 35% of the time from initial idea to product launch (see Figure 1). Speeding up these steps does little to reduce time to market if others remain bottlenecked.

This section describes best practices for integrating generative AI into the software development lifecycle (SDLC). From implementing seamless toolchains and DevSecOps pipelines to fostering collaboration and automating repetitive tasks, these guidelines help you harness the power of AI to enhance your development processes and experiences. By following these best practices, software development teams can unlock new levels of efficiency, innovation, and quality in their work. Implementing a seamless, end-to-end integrated toolchain Implementing an end-to-end CI/CD pipeline for DevSecOps Adopting collaborative tools and practices

Regularly reviewing and iterating on the development experience 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. We proudly partner with industry-leading DXP and CMS platforms. Optimize your website and navigate your digital journey with confidence.

Expand your brand identity into a flexible design system across digital touchpoints. Check out the latest news and release updates for existing clients. We help our clients advance their purpose. Software engineers may have once stated that software doesn’t write itself. That’s not true anymore. Generative AI is perfectly capable of taking on at least some of the simple tasks involved in coding, as well as other aspects of the software development life cycle.

In fact, research published in our new Capgemini Research Institute report, Turbocharging software with Gen AI, shows that organizations using generative AI have seen a 7–18% productivity improvement in software engineering. So, what does this mean for those working in the software industry? It would be reasonable to expect some fear of change, after all, status quo bias is a well-documented human behavior. But our research data – which involved both developers and senior executives – shows that software engineers and their employers expect generative AI to enhance the profession and deliver increased value with software quality... Let’s look in more detail at some of these key benefits. The old idea that moving too fast opens the door to mistakes can be turned on its head with the careful use of generative AI during software development.

Because generative AI can automate some simple tasks, and complete them more quickly, it can help speed up a whole host of non-safety-critical processes, leaving more time to spend on complex software development.This can... Of course, generative AI is not a ‘magic bullet’ that can just be told what to do and automatically produce the result you want. It will need a well-defined architecture and effective rules for how to ‘prompt’ it to generate code that is repeatable and maintainable, and which meets company needs and compliance rules. The software development field is undergoing major changes due to advances in artificial intelligence (AI). Generative AI (or GenAI) specifically is at the forefront of this transformation. GenAI allows computers to independently create new information, write code from scratch, and improve software systems.

Tasks like setting up basic lines of code, writing test codes, and documenting APIs, which used to take hours, can now be done in minutes. This is a significant improvement for teams as it makes work processes smoother, finishes products faster, and lowers costs. Whether you are a Chief Technology Officer (CTO), a software developer, or a technology executive, understanding how GenAI impacts software development is essential. Keeping up with these changes is crucial to staying competitive in the rapidly changing tech industry. So, this article is an introduction to GenAI for software development, exploring how GenAI is transforming the field by highlighting its benefits and future opportunities. Generative AI(GenAI) is a type of artificial intelligence that focuses on creating new content by recognizing patterns in existing information.

Unlike traditional AI, which primarily analyzes data and makes predictions, GenAI is designed to produce various forms of content, including text, images, sound, and computer code. It utilizes complex models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Large Language Models (LLMs). GenAI learns from extensive datasets, which may include source code, documentation, and technical specifications. Through this learning process, it gains an understanding of the language, logic, and architectural design elements used in software development. As a result, GenAI is a game changer that can do more than simply complete code; it can also write entire functions based on simple language commands, create tests, identify errors, and suggest improvements... According to a 2025 report by the Financial Times, AI’s coding proficiency has surged, with successful problem-solving improving from 4.4% in 2023 to 69.1% in 2025.

Additionally, GitHub reports that 92% of U.S. developers now utilize AI tools. The use of GenAI in software development today changes the developer’s role from manually coding to guiding these systems. 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 has crossed from novelty to necessity in modern software teams. In controlled studies, developers complete tasks dramatically faster with AI assistance (often cited around a 55% speed-up), freeing time for architecture, testing, and deeper problem-solving. At the same time, adoption is becoming the norm: major developer surveys in 2024–2025 found that a large majority of developers were already using or planning to use AI tools—though trust remains measured. Generate working code across popular languages, from scaffolds and boilerplate to feature-level implementations. Recent flagship models benchmark well on reasoning and coding tasks, and newer “reasoning-focused” series emphasize efficient tool use for math and coding.

Explain and document code: create docstrings, READMEs, and architecture notes in minutes, improving handoffs and onboarding. Developer surveys show this is one of the most common day-to-day uses. Review and refactor: propose diffs, point out anti-patterns, and suggest tests. Field and lab studies report faster time-to-merge and quality improvements when AI is integrated thoughtfully into PR workflows. Problem-solve under constraints: research systems such as competitive-programming models demonstrated algorithmic competence that now informs mainstream assistants.

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