Large language models – capable of uncanny cognitive feats – align with agile methods more than we’d expect. Both stand behind automating repetitive tasks, fast iteration and experimentation and even faster decision-making.
What Is Agile?
Agile means being flexible and adapting to change by accelerating the speed of feedback loops.
While people are still very much hung up on generative AI’s coming out party and what that means for the way we work, engineers are seeing a silver bullet for secure, open-source code and the software supply chain.
Based on how development and design teams have been adjusting to LLM tools, it’s a sure bet these tools will make the agile software design lifecycle twice as productive.
AI Irons Out Kinks in the Design Lifecycle
We hope that whatever a development team delivers after the UX design team makes it look pixel-perfect will feel and behave as expected, but 90 percent of the time it doesn’t.
Bottlenecks are part and parcel of the process, leaving development teams to re-allocate their resources in the middle of crucial sprints. But this is where anticipating and mitigating potential challenges before they even occur come in.
By feeding relevant training data into an AI model and tapping into historical software data, user feedback and relevant industry knowledge, teams can use predictive AI solutions to evaluate design ideas, aid in prototyping and automate error-prone repetitive tasks. These forecasts and recommendations serve as the output and should inform how teams prioritize and reprioritize product backlog efficiently.
Incorporating self-learning algorithms into the design process enables systems to analyze and learn from data, improving the system’s performance over time without explicit programming. The game changer is how the system adapts to changing conditions and makes intelligent decisions based on patterns and insights pulled from the data it encounters. And it lends itself to various domains, from recommendation systems and natural language processing to eliminating security blindspots that occur between development and production teams.
AI Shifts the Vibe for Design and Development Teams
Continuous improvement is inseparable from agile methodology. It’s about enhancing team efficiency, productivity and effectiveness with each sprint. When we bring AI into the mix, there’s a significant step up in how this gradual improvement happens.
The key here is to free up time for design and development teams by streamlining how they solve problems with complex data and pattern identification. One way this is done is through rapid prototyping and experimentation using AI so that both teams can iterate quickly and test multiple ideas to deliver higher-quality products.
It could also mean automating tasks like third-party library management, patch management and adherence to best practices. In turn, teams can zone in on more strategic and creative aspects of their work, such as building the next generation of apps that don’t suck for the end user.
The need for a user-centric approach may seem blatantly obvious, but as we’ve seen with addictive platforms like Facebook, Twitter and Instagram, it will take a lot of study and debate to understand and filter out design flaws inherent in them. AI/ML tools are a step in the right direction in the sense that they analyze user behavior and preferences, providing valuable insights on which features to prioritize and how to optimize UX.
However, the possibility of skewed training data is still there, and developers will need to step up to the plate to ensure that the next generation of apps doesn’t reinforce stereotypes or perpetuate inequalities that affect product inclusiveness and fairness.
A good starting point is the shared focus on the user. This strengthens the collaboration between development and design teams which in turn is the backbone of any successful agile team. Cross-disciplinary skill development and fostering a better understanding and appreciation of each team’s work will be non-negotiable as we go down this rabbit hole of AI.
AI Can Squash Bugs and Deliver Faster
How software features work and your team’s ability to detect bugs early on speaks to the quality of your processes to improve overall security posture, and AI provides multiple entry points for this. Developers can achieve faster and more reliable application development and delivery by using AI to generate test cases, perform regression testing, and test coverage. Teams can also apply these tools to continuous integration and deployment (CI/CD) pipelines where code conflicts lie.
Enabling real-time threat intelligence and efficient management of updates makes the road to a more secure and trustworthy software ecosystem not so far off.
Developers can, for example, tap into AI-powered anomaly detection techniques and code repository analysis or receive suggestions to optimize code efficiency or generate code snippets based on desired functionality.
Another plus is how AI-powered natural language processing simplifies documentation processes by extracting relevant information from code comments, forum discussions and documentation repositories.
Over time, the system will get smarter from data inputs and monitoring production environments. Eventually, it will be able to forecast and predict potential bottlenecks, smoothing out application performance overall.
The yellow-brick road to agile software development means that this tech can’t be built in a vacuum and developers will need to have an all-encompassing skillset, with AI algorithms, data analysis and ML being entry level.
With AI systems and the vast amounts of user data that will be filtering through, developers will need to become stronger advocates of privacy and data protection, particularly through transparency and explainability in AI algorithms. Stakeholders will want to see that we’re actively avoiding black-box decision making and ensure that there will be accountability and liability in the event that the AI system fails or causes harm.
After all, people want to be reassured that, especially now, AI will be employed now and in the future in a human-centered manner in product design.
