Dimitar Dimitrov is the founder and Managing Partner at Accedia, a leading European IT services company.
Slow and insufficient definition of requirements, time-consuming coding and error-prone bug detection are just some of the issues that teams face in the software development life cycle (SDLC). Consequently, a paper published by the Harvard Business Review says that only 35% of software development projects are completed successfully. As we navigate through 2024, the potential for AI to improve every phase of the SDLC—from planning and design to coding, testing, deployment and maintenance—has never been more evident. Still, employees tend to resist using AI technologies due to the lack of familiarity. Below, I provide valuable insights from my firsthand experience into how businesses can use their power to increase precision, efficiency, speed and growth.
Revamping The Discovery Process
AI-powered tools like ChatGPT and Google Gemini are often used to help write user stories and requirements. They can improve the discovery process by automating data collection and analysis and identifying user needs through advanced analytics. Documenting user feedback and requirements can be enhanced by leveraging natural language processing to analyze large amounts of data and pinpoint critical patterns and trends.
In our work, for instance, these tools assist us in modeling and simulating scenarios, forecasting potential challenges and refining the requirements and project scope more accurately and timely. Additionally, we use multimodal inputs from speech, images and text, where generative AI can help personalize solutions and better understand the customer profile.
Streamlining The Design Phase
AI has gained momentum in the design process as well. From user research and analysis and flow diagram generation to UX design assistance and usability testing, AI has the power to streamline the design process by recognizing patterns and predicting user behavior. AI-driven tools can enable more precise personalization and automate aspects of the design process. Utilizing ML models can enhance accessibility by scanning and analyzing content and identifying issues in real-time, which allows proactively tackling accessibility barriers in advance, leading to outperforming competitors by 50%, according to Gartner.
Some exciting new tools on the market enable our designers to perform tests and collect data on user interactions and heat maps, pinpointing improvement opportunities and allowing the team to create a more user-friendly and user-centric product. In their everyday work, our team uses Adobe Firefly, Dall-E 3, Uizard and Colormind, which automate and improve the design process from color selection to prototyping, visual generation and personalization—the ultimate goal when it comes to creating a new product.
Eliminating Project Management Bottlenecks
A large number of software development projects need more cross-functional communication or outdated project and team management tools, which leads to technical challenges, poor efficiency, misalignment, scope creep and more. Using legacy tools such as Excel spreadsheets can cause issues with the quality of the product, budget overruns or ineffective resource allocation. On the other hand, the lack of effective communication and collaboration ultimately can create barriers between the research and development phases. Thus, according to Gartner, by 2030, 80% of the Project Management tasks will be performed utilizing AI. Being already on that path, our Project Managers have already noticed having more time to focus on what they do best—lead teams and help them perform to the best of their abilities while leaving tasks such as documentation, reporting and even writing personas and acceptance criteria primarily to tools such as Copilot or ChatGPT.
ML-driven allocation and prioritization, additionally, can lead to eliminating human biases from the decision-making process, bettering talent based on their skill set, assessing risks and allocating and predicting future workforce needs. Automating the collection and analysis of user stories, on the other hand, will eliminate inconsistencies, duplicates and complexities. Furthermore, we can see a significant improvement in risk management where ML and big data allow us to predict risks, continuously monitor project parameters and recommend mitigation strategies when needed. Last but not least, AI tools that monitor the progress of the projects, help us address potential crises and ensure compliance with various policies will become indispensable. Bear in mind these are just a handful of examples of how AI tools such as ChatGPT can streamline project management and execution.
Overcoming Repetitive And Time-Consuming Coding
Coding is the phase in which we can see the most significant changes when adopting AI. New AI-assisted engineering tools are emerging by the day, and as much of a challenge as it is to keep up with the required skills, it is all worth it. It’s no surprise that by 2025, 50% of software engineering leader roles will require oversight of Generative AI. Those are crucial skills that can lead to the reduced time needed to generate and refactor code, as well as to better work experience, flow improvements, and fulfillment, as McKinsey says in а study. At Accedia, we use AI to automate repetitive tasks, improve code quality and accelerate the development process by significantly reducing the time to market. Moreover, we analyze code patterns and detect anomalies to identify and fix bugs and identify potential errors, improving the overall reliability of the solution.
A study found that software engineers using GitHub Copilot complete tasks 55% faster than those who don’t. Used by over 20 million engineers already, the tool utilizes ML models to suggest whole lines or even blocks of code based on context, reducing the time spent writing boilerplate code.
Conclusion
Regardless of the challenges that come with early adoption, the way AI is improving the SDLC is undeniable. Notable, 32% of organizations have reported accelerated product development ideation due to AI, demonstrating its role in conception, prototyping and more. As businesses adopt AI-based tools, they ultimately achieve more successful project completion and a competitive edge in the market thanks to better efficiency, higher speed and precision.
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