“Software defects found in production cost four times as much to fix as defects found during system testing, and two hundred times as much to fix as defects identified during requirements definition” (Gartner).
AI: Requirements form the foundation of the software development process, poorly written requirements statements collapse the whole software project.
Typically, a project begins, after consultation with business users and determining their business needs, with the creation of a business requirements document. The document expresses both explicit & implicit requirements, what the business needs and expects. The client or business group reviews the document and, if in agreement with its specifications, signs it. This signoff process is intended to ensure that client representatives or business users have agreed – in writing – on the specifics involved. However, once the design and development process is underway, clients think of changes which may impact either the core systems or the interface systems (upstream or downstream) and superfluities they would like, a phenomenon known as requirements creep (or scope creep) that, if not managed appropriately, can result in lost time and money.
An important part of project management, requirements management has become more challenging with the faster pace of technology advances and the marketplace influence requirements.
With the remarkable speed at which projects are being developed thanks to the agile world, with sky-high expectations and ever-increasing exposure and risk, adoption of AI in managing project requirements effectively and efficiently is key to delivering a successful product.
The rise of AI in requirements management is a consequence of large-scale AI adoption across industries.
Companies increasingly have an escalating volume of data to crunch and analyze. IoT devices with many form factors and functions have increased the need for high quality requirements management and requirements engineering.
AI significantly impacts requirement management by automating tasks, enhancing accuracy, improving collaboration, and providing predictive insights, ultimately leading to faster development cycles and improved product quality.
AI-powered tools can analyze data, identify patterns, and generate documentation, freeing up business users for more strategic work.
AI-driven requirements management is the practice of using artificial intelligence tools to enhance the efficiency and quality of processes like elicitation, analysis, documentation, and writing.
By leveraging AI-driven requirements management, organizations can achieve greater accuracy, efficiency, collaboration, regulatory compliance and risk reduction.
This will result in improved project outcomes, reduced costs, faster time-to-market and enhanced customer satisfaction.
By
Rajesh Shankar
AVP Banking
