Customers’ expectations are sky-high. They’re looking for services that are quick, accessible on their mobile devices, and available before they even realize they need them. This is quite the challenge and true for businesses in any field.

As a leader, recognizing the shift and knowing that traditional methods aren’t enough is a welcome first step. There is more to be done and more to be learned but we are already aware that being agile is key, especially with software development.

The transition from waterfall to agile was a significant step—suddenly, we were moving faster and adapting quicker. Now, Generative AI (GenAI) is here to take us even further. And this time, It’s not just about speed and efficiency. It is about completely transforming our ways of working for dramatic improvements to software development. 30 to 50% productivity gains1 can be achieved through Gen AI driven solutions which could have a direct impact on costs savings.Here’s a look at some of the use cases at each of the Software Development Life Cycle (SDLC) we can implement right away.

Plan: Blueprinting future innovations

In the Discovery Stage, we’re essentially on a mission: gathering intelligence from the market, feedback from customers, and insights from within our own operations. Historically, this has been a painstaking, manual process. But introduce GenAI into the equation, and suddenly, we’re not just assembling the puzzle faster; we’re redefining what the puzzle is.

Imagine an insurance company looking to innovate how claims are managed on their mobile application. We can deploy GenAI to run through data and interpret customer reviews, complaints, market trends, and more, delivering actionable user requirements, backlogs, and user stories. And it does this with astonishing speed and precision, all from a simple prompt.

Design: Creating user-centric experiences

With the development cycle moving into the design phase, GenAI can translate the inputs from -the planning phase into clear, user-centric design prototypes.

Cast a natural language prompt at GenAI—say, crafting a user-friendly claims processing interface that smartly bypasses information already in the system—and it delivers instant results. From generating and refining prototypes to dishing out the actual HTML code, GenAI accelerates the entire prototyping process2. It can also generate a checklist of metrics for validation, ensuring every aspect of the project aligns with defined standards and expectations. It can draft basic architecture diagrams complete with key data entities and user roles, all neatly organized and prepped for building.

This has a massive impact. The teams can go from ideation to development with everything they need at their fingertips and turn ambitious concerts into tangible realities at an unprecedented pace.

Develop: Bringing ideas to code

As we transition from the early planning stages into coding, AI-driven copilots like the GitHub copilot are already boosting developer productivity by auto-generating code. Forrester’s research tells this story, projecting a 20-50% average increase in coder productivity. This spike can even reach 200%3 or more for seasoned engineers who leverage GenAI for unfamiliar languages or libraries.

Take our insurance app example. Imagine instructing GenAI to generate Java code that intelligently queries our system for pre-filled claim form details, asking users only for missing, critical information. This task, complex in nature, becomes straightforward with GenAI, producing accurate and functional code promptly. Developers no longer need to code every single detail. Instead, they issue commands to GenAI, which then delivers code that’s not only functional but optimized for the task at hand.

Test: Ensuring flawless functionality

Automated testing has been a cornerstone of software development for years. So, how exactly does GenAI add value? So far, traditional automated testing can only execute predefined test cases. But GenAI can analyze the application’s code, user stories, and even changes in metadata, GenAI can intuitively create test cases that are more comprehensive and cover scenarios developers might not have anticipated. It creates realistic test data that reflects the diverse range of user inputs, ensuring the app can handle anything thrown its way. It can analyze code to detect vulnerabilities and security issues and improve the quality of testing itself by learning and evolving, all at high speed and quality.

Deploy: Launching with precision

What if we could make the deployment user-centric? Imagine this. GenAI optimizes the release process based on historical data, and it ensures deployments are executed at the most opportune moments, minimizing downtime and maximizing efficiency. It can replace static notes with personalized, interactive release notes. So essentially, it not only makes the deployment smoother and less prone to errors but also enhances communication with end-users and ensures a better alignment of the software with user expectations and business objectives. Just Remember how DevOps and CI/CD revolutionized the way we deliver software? That shift set a new standard. GenAI is on the same path today.

Support: Empowering continuous innovation

When the product hits the market, the features we’ve developed begin to reach our consumers. At this stage, GenAI assesses how these features are being used. This usage data is invaluable, providing real-time feedback to product management and impacting the next iteration of product design. GenAI goes further by generating detailed analytics and pinpointing and addressing any lingering bugs. It enables automated support or self-service options, increasing the productivity of the contact centre upto 50%4. This cycle of feedback and improvement, powered by GenAI, ensures that the product not only meets but anticipates consumer needs.

GenAI’s real value proposition lies in its ability to liberate development teams’ time and allow them to concentrate on strategic tasks. But implementing GenAI in not the same as plugging in a new tool. It needs close consideration of many aspects.

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Overcoming the scale challenge

Experimenting with GenAI in small doses is one thing; scaling it securely is a whole other game. How do we ensure our leap into GenAI is not just innovative but also ethical, responsible, and secure? How do we get everyone from the ground up not just comfortable but excited about GenAI? Here’s our play – kick off with compact teams, say five to seven members, each laser-focused on a slice of the product. Make tasks manageable and show how GenAI can be a game changer. Remember and remind, that GenAI is not replacing us; it is augmenting our capabilities. And for those willing to learn and adapt, it promises to be a valuable ally.

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies