As any engineering team knows, requirements management is critical to the success of any project and knows that poor requirements definition results in project delays, cost overruns, and poor product quality. However still today poor requirements are the cause of failed project and huge cost overruns. In today’s age of growing engineering complexity this is becoming ever more critical, as engineering teams are facing even more pressure to quickly adapt to the pace of change, with ever more data to leverage. In this whitepaper we analyse the status quo of why we need to improve requirements engineering and why Artificial intelligence (AI) has the potential to be an important leverage and work hand in hand with the requirements engineer to improve requirements quality early on in the project when the impact is high and the cost is still low.

Opportunities of using AI for Requirements Engineering:

Reduce errors

The requirements analysis phase takes up only about 2 percent of total design time. However, poor requirements account for more than half of all engineering errors.

Reduce Costs

The cost of correcting errors increases exponentially as a project progresses. Decrease product development costs and delays by catching errors early and reducing rework.

Strengthen Requirements

Isolate requirement issues before sending them for manual human review. Receive suggestions for improvement based upon a score provided by AI.

Streamline Use of scarce Expertise

Additionally, engineering teams are faced with managing expertise and the the ‘gray washing’ of their industries as senior employees reach retirement. The next generation of requirements authors can only benefit from their predecessors’ expertise.  Teams can disseminate engineering expertise to junior engineers who are less experienced in writing requirements. This mitigates the risk of losing valuable existing knowledge from employee turnover and a retiring workforce.