Unlocking the potential of generative AI in the software development life cycle

Digital Transformation is critical to modern enterprises, yet creating it remains inefficient. Nearly half of C-suite respondents report that over 30% of tech projects are late or over budget, with one in five dissatisfied with most outcomes. Generative AI is poised to redefine software creation and digital transformation.

The traditional software development life cycle (SDLC) is fraught with challenges, particularly requirement gathering, contributing to 40-50% of project failures. A 2024 study found that three-quarters of product features are rarely used, underscoring the need for precision.

And the challenges don’t end there. The testing phase, particularly user acceptance testing (UAT), can become a labor-intensive bottleneck — and a budget breaker. According to a 2023 Capgemini report, companies spend about 35% of their IT budget on testing — a figure that has remained stubbornly high despite advancements in automation.

These challenges persist because companies still rely on traditional SDLC management methods, which can result in slow, error-prone processes. It’s time we demand a shift in our approach to the SDLC.

How generative AI transforms the SDLC

GenAI has emerged as a transformative solution to address these challenges head-on. By integrating GenAI into various phases of the SDLC, organizations—including EXL’s customers—have significantly enhanced efficiency and effectiveness. Based on data from EXL’s Business Analyst Center of Excellence, here’s how GenAI has delivered measurable benefits:

• Comprehensive requirement gathering: GenAI analyzes vast datasets across multiple systems – from user feedback, emails, chats, and meeting transcripts and to pre-trained Domain and Tech Stack-specific documents  – to generate comprehensive requirement docs. This AI-augmented approach ensures that no critical feature falls through the cracks and that accurate requirements documents reduce the likelihood of defects.
  • Result: 80% less rework.
• Proactive defect reduction: GenAI creates comprehensive test cases — even edge cases, analyzes requirements to predict potential issues or failure points, and generates clear, specific acceptance criteria for each user story. This proactive approach dramatically reduces the burden during UAT.
  • Result: 40%-50% fewer UAT issues
• Streamlining workflows: GenAI analyzes post-deployment metrics to optimize SDLC workflows for faster, more reliable development.
  • Result: 70% more efficient.

Best practices for implementing generative AI in SDLC

The potential of generative AI in SDLC is immense, but its implementation requires a strategic approach. Here are some best practices to consider:

1. Start with a clear strategy: Whether it’s reducing development time or enhancing quality, specific objectives guide successful GenAI integration, as seen with EXL’s BA CoPilot.
2. Invest in data quality: GenAI models are only as good as the data they’re trained on -with GenAI, mistakes can be amplified at speed. EXL’s BA CoPilot, for instance, leverages clean, comprehensive datasets across all aspects of the SDLC, ensuring accuracy in requirement gathering and defect prediction.
3. Upskill your team: Human-AI collaboration is key. A recent McKinsey report found that, although up to 30% of Americans’ work could be automated by 2030, GenAI will be an enhancement to humans, not a replacement. EXL’s BA CoPilot has been designed with a user-friendly design to ensure that business analysts can easily collaborate with AI, maximizing the benefits of this technology.
4. Implement responsible safeguards: As AI becomes more integral to the development process, it’s crucial to have checks and balances that ensure ethical, fair, explainable, and transparent use and avoid biased outputs or any hallucinations. Document your organization’s guidelines for using output (i.e., text, images, videos, code, etc.) for commercial purposes (i.e., advertising, marketing, or software development). Copyright is still being researched and argued, so users should be instructed to check the documentation regularly.
5. Monitor and adjust: Start with smaller projects and gradually scale up. This allows you to refine your processes and build confidence in the GenAI-augmented SDLC.

The road ahead: A new era of software development

GenAI in the SDLC unlocks efficiency and innovation, automating tasks and freeing developers to solve higher-order problems. Solutions like EXL’s BA CoPilot enhance accuracy, reduce defects, and streamline workflows, speeding up development and improving quality. As we enter this new era, the question isn’t whether to adopt GenAI but how quickly. Those who do will gain a lasting competitive edge.

The future of software development is here, and generative AI powers it. Are you ready to lead the charge?

Unlock the full potential of digital transformation for your business, visit us here.  

Sumit Taneja, senior vice president, global lead of intelligent transformation services and Manbir Singh, senior assistant vice president, practice lead and business analyst center of excellence at EXL, a leading data analytics and digital operations and solutions company.