Requirements Elicitation in Systems Engineering: Techniques, Challenges, and How AI Is Changing the Game

Requirements elicitation is arguably the most critical phase in any systems engineering project. Get it right, and the rest of the development lifecycle flows from a solid foundation. Get it wrong, and even the most talented engineering teams will build the wrong thing.

Yet despite its importance, requirements elicitation remains one of the hardest activities to do well. It is time-consuming, difficult to scale, and heavily dependent on the availability and communication skills of stakeholders.

In this article, we explore what requirements elicitation is, the most common techniques, the persistent challenges engineers face, and how a new generation of AI-powered tools is making the process faster, deeper, and more scalable.

What Is Requirements Elicitation?

Requirements elicitation is the process of discovering, extracting, and documenting the needs, expectations, and constraints of stakeholders for a system under development. It sits at the very beginning of the requirements engineering lifecycle and feeds directly into requirements analysis, specification, and validation.

Unlike requirements gathering — a term that implies stakeholders already know what they want and simply need to hand it over — elicitation acknowledges that requirements often need to be drawn out through conversation, observation, and exploration. Stakeholders may not be aware of their own assumptions, may struggle to articulate tacit knowledge, or may hold conflicting views that only surface during dialogue.

The output of elicitation typically includes:

Functional requirements — what the system should do
Non-functional requirements — performance, security, usability, reliability constraints
Business rules and constraints — regulatory, organizational, or operational boundaries
Stakeholder priorities — which needs are essential versus nice-to-have

Traditional Requirements Elicitation Techniques

Systems engineers have long relied on a toolkit of elicitation methods, each suited to different contexts:

1. Interviews

One-on-one or small-group interviews remain the gold standard for deep, qualitative elicitation. A skilled interviewer can probe vague answers, follow unexpected threads, and build rapport that encourages stakeholders to share what they really think.

Strengths: Depth of insight, ability to adapt in real-time, captures tacit knowledge.

Weaknesses: Extremely time-consuming, difficult to schedule, does not scale beyond a handful of stakeholders.

2. Workshops and Focus Groups

Facilitated sessions bring multiple stakeholders together to discuss requirements collaboratively. Techniques like JAD (Joint Application Development) sessions encourage group consensus.

Strengths: Surfaces conflicts early, builds shared understanding.

Weaknesses: Dominant voices can suppress input, requires significant coordination, expensive to organize.

3. Surveys and Questionnaires

Structured forms distributed to a broad stakeholder base can collect requirements at scale.

Strengths: Scalable, consistent, easy to analyze quantitatively.

Weaknesses: Static questions cannot adapt to responses. Stakeholders often give shallow, incomplete answers. Abandonment rates can exceed 60%.

4. Observation and Contextual Inquiry

Watching stakeholders in their work environment reveals unarticulated needs and workarounds.

Strengths: Captures real behavior rather than self-reported behavior.

Weaknesses: Labor-intensive, hard to scale, observer effect.

5. Document Analysis

Reviewing existing documentation, process descriptions, legacy system specifications, and regulatory texts to extract implicit requirements.

Strengths: Leverages existing material, good for compliance-driven domains.

Weaknesses: Documents may be outdated or incomplete. Cannot capture emerging needs.

The Persistent Challenges of Requirements Elicitation

Despite decades of practice and research, several challenges continue to plague elicitation efforts:

Stakeholder availability. The people who hold the most critical knowledge are often the busiest. Scheduling interviews and workshops becomes a bottleneck.

Scalability. Traditional interviews produce deep insights but cannot realistically be conducted with dozens or hundreds of stakeholders.

Incomplete and vague responses. Stakeholders often answer at a surface level, especially in written surveys. Without follow-up, critical detail is lost.

Bias and groupthink. In workshops, louder voices dominate. Junior stakeholders may defer to senior ones, and real concerns go unspoken.

Geographic and timezone distribution. Modern systems engineering projects often involve distributed teams, making synchronous elicitation sessions a scheduling nightmare.

Cost. Professional qualitative research — the kind that produces actionable, deep requirements — typically costs $150 or more per interview when accounting for preparation, execution, and analysis.

The core tension is clear: interviews give you depth but not scale; surveys give you scale but not depth. Most teams are forced to compromise, settling for shallow data from many stakeholders or deep data from too few.

How AI Is Reshaping Requirements Elicitation

Advances in large language models and conversational AI have opened the door to a fundamentally new approach: adaptive, asynchronous interviews conducted by AI agents.

The idea is simple but powerful. Instead of a human interviewer sitting across from a stakeholder, an AI agent conducts the interview via a chat-like interface. The agent is briefed by the systems engineer with the goals, context, and specific areas to explore. It then interviews each stakeholder individually, asking follow-up questions when answers are vague, probing deeper when important themes emerge, and pivoting when unexpected insights surface.

This approach combines the depth of a traditional interview with the scalability of a survey — and adds the flexibility of fully asynchronous participation.

The Advantages for Systems Engineering Teams

Asynchronous by design. Stakeholders participate on their own schedule, from any device. No calendar coordination needed.
Scales to hundreds of participants. Each stakeholder gets a personalized, adaptive interview — not the same static set of questions.
Consistent depth. The AI never gets tired, never forgets to ask a follow-up, and never lets a vague answer slide.
Reduced bias. Without a human interviewer or group setting, stakeholders are free from social pressure and groupthink.
Structured outputs. Responses are automatically summarized into actionable, structured insights — ready for the requirements analysis phase.
Dramatically lower cost and time investment. What would take weeks of scheduling and hours of transcription can be completed in days.

Eliciteer: AI-Powered Requirement Elicitation at Scale

One tool that brings this vision to life is Eliciteer. Designed specifically for conducting AI-powered interviews, Eliciteer lets systems engineers brief an AI agent with their elicitation goals, share a unique interview link with stakeholders, and receive structured, summarized insights — all without scheduling a single meeting.

Here is how it works in a requirements elicitation context:

Brief the AI. The systems engineer creates a campaign describing the system under development, the information needed, and the specific requirements areas to explore (e.g., performance constraints, workflow expectations, integration needs).

Share the link. Each stakeholder receives a unique interview link. They can respond at any time — during a commute, between meetings, or at the end of the day.

AI conducts the interview. The AI adapts in real-time. If a stakeholder gives a vague answer about performance expectations, the AI probes: "Can you give a specific example of when latency was a problem?" If an unexpected constraint surfaces, it explores further.

Get structured insights. Results are automatically collected and summarized. The engineering team receives organized, actionable data ready for requirements analysis and specification.

Eliciteer also supports webhook integrations, making it easy to pipe results directly into tools like Jira, Confluence, or any requirements management platform.

For distributed engineering teams, or any project where stakeholder input needs to be gathered broadly and deeply, this approach can dramatically compress the elicitation timeline while improving the quality and completeness of the requirements captured.

When to Use AI-Powered Elicitation

AI-powered interviews are not a replacement for every elicitation technique. Observation, document analysis, and certain high-stakes face-to-face workshops will always have their place. But for the interview and survey portions of your elicitation strategy — which typically account for the largest time and cost investment — an adaptive AI approach offers a compelling alternative.

Consider AI-powered elicitation when:

You have more than 10 stakeholders to interview
Stakeholders are distributed across locations or timezones
Scheduling synchronous sessions is a bottleneck
You need both depth and breadth in your requirements data
Budget or timeline constraints make traditional interviews impractical

Conclusion

Requirements elicitation remains the foundation of successful systems engineering. The techniques have been well-understood for decades, but the challenges — scalability, stakeholder availability, depth versus breadth — have persisted just as long.

AI-powered tools like Eliciteer represent a genuine step forward: the depth of a skilled interviewer, the scale of a survey, and the convenience of fully asynchronous participation. For systems engineering teams looking to gather better requirements from more stakeholders in less time, this is an approach worth exploring.

Want to try AI-powered requirements elicitation for your next project? Visit eliciteer.ai to get started for free.