How AI-based prototyping is changing the validation phase in requirements engi-neering

Requirements engineering (RE) is essential to any successful software project. It ensures that the end product meets the actual needs of the stakeholders concerned. And since this discipline is so fundamental to a project, a high-quality RE process can really pay dividends. Requirements engineers have a crucial role to play, because even imperfect RE provides a better framework than none at all. Investing in thorough and precise RE is therefore vital for exploiting the full potential of a software project and avoiding risks. These figures show what is at stake here: A study by the Project Management Institute revealed that unclear or poorly communicated requirements are responsible for around 37 percent of all project errors (1). The cost of resolving these errors uses up 25–40 percent of the original project budget (2).

The main challenge in RE relates to human communication. A requirement formulated by a stakeholder can be interpreted completely differently by the requirements engineer and the development team. These different perceptions inevitably lead to functions being developed incorrectly, or not developed in line with requirements. To prevent this from happening, requirements have to be validated to ensure that everyone involved has the same understanding of what is needed.

But this is precisely where the problem lies.
 

Traditionally, validation has been based on tools and techniques that often raise more questions than they answer:

• Static mockups and wireframes: These show the “what”, but not the “how”. The interaction aspect—the “feel” of an application—is still basically left up to the imagination.
• Lengthy requirements documentation: These text-heavy documents, which also include complex process diagrams and sometimes incomplete drawings done in tools such as Miro, often present an obstacle. They offer broad scope for interpre-tation and are rarely scrutinized in detail by busy stakeholders.

The validation process is often slow, laborious, and prone to errors. Since feedback cycles take days or weeks, there is often still some lingering uncertainty at the end. As a result, errors are not detected until later on—sometimes not even until the product has gone live. By this time, the cost of fixing the errors will have increased exponentially. This is demonstrated by the wellknown “1-10-100 Rule” in software development, as explained by McConnell (3). This rule states that fixing an error at the earliest possible stage (e.g., during data entry or during the design phase of a project) will cost around $1 to prevent or immediately correct the error. This rises to $10 to correct the error if it is discovered later on (e.g., in the system or during development), and $100 if the error is not fixed at all and results in failure (e.g., due to unclean data causing poor business decisions or customer dissatisfaction). Basically, the sooner an error is prevented through proper validation, the more follow-up costs can be saved. This is why validating requirements is so important for every project.
 

Imagine what it would be like if you could not only describe a requirement, but also make it instantly perceptible to the stakeholder so that it can be validated more accurately. This is where AI-based prototyping comes in. AI-based prototyping is an approach based on rapid prototyping and interactive simulations. Instead of formulating a requirement in text form, it is translated directly into a clickable, tangible prototype. This is not a fully functional piece of software, but a lightweight simulation that focuses on the user experience and the core logic of a function. This turns validation from a passive reading process into an active, collaborative workshop-style approach.

Instant, tangible feedback
Stakeholders no longer have to imagine how a function is meant to work; they can try it out directly—“Ah, that’s what the process feels like!” or “Wait a minute, clicking on this should actually take me there.” This immediate feedback is invaluable and reveals any misunderstandings in minutes rather than weeks.

Overcoming the communication gap
Prototypes are a universal language. They bridge the gap between jargon and technical language. Specialist literature has confirmed that interactive models help users to articulate and refine their own needs more effectively, as they often only realize what they really want through interaction.

Spotting errors early based on the “Fail fast, fail cheap” principle
An error discovered through AI-based prototyping costs virtually nothing. That’s because it takes very little time or effort to change a line of code in the prototype. Correcting the same logic in a system architecture that has already been implemented and tested, however, is extremely time-consuming, and therefore costly.

Iterative and agile refinement
The prototype becomes a “living document”. Each round of feedback is immediately incorporated into an adapted version. This approach develops the perfect requirement—which has not only been read, but also understood and approved by all those involved—in an iterative process that progresses rapidly. Our own experience has shown that this is an effective method for validating requirements.

Our practical experience in various projects also underlines the effectiveness of this approach. AI-based prototyping not only reduces the number of reworking steps required, but also shortens validation cycles. This translates directly into significantly lower project costs and a faster time to market.

The use of AI-based prototyping is not an example of adopting new technology just for the sake of it. In actual fact, it is an economic decision. By drastically speeding up the validation phase and massively enhancing the quality of requirements, we can directly address the main cause of budget overruns and project delays.

If we reduce requirement-related errors by even a fraction, we can not only save time and effort spent on development, but also—and most importantly—make sure that we are building the right thing, right from the start. In a world where time to market is everything, we can no longer afford to build on foundations weakened by slow processes and misunderstandings. It is time to ramp up the speed, right where it is needed.

If you would like to find out more about the use of AI-based prototyping in requirements engineering or are considering how you can use this approach in your project, please contact me directly. I would love to hear your thoughts and discuss this fascinating topic with you!
 

More information on requirements engineering at ti&m is available here: