Why Discovery Should Be Voice-First in AI Projects

In complex AI projects, success often comes down to what happens before a single line of code is written: discovery. The discovery process is where teams align on priorities, uncover hidden challenges, and clarify opportunities. Traditionally, discovery relies on forms, surveys, and stakeholder interviews—but these methods have limitations.

Recently, while onboarding a $20M+ ARR business for an AI education initiative, we tested a different approach. Instead of relying only on forms, we introduced a voice agent built on ElevenLabs, trained on our standard discovery questions. The results were striking:

• We saved more than five hours of manual effort.

• We gathered richer, more authentic context than any form could have provided.

• We started the project with a sharper, more complete understanding of their challenges.

The experience confirmed what we’ve been leaning toward for some time: the future of discovery should be voice-first.

The Limitations of Traditional Forms

Forms and surveys have long been the backbone of project discovery. They’re efficient for collecting structured data, but they rarely capture depth. Stakeholders often provide quick, surface-level responses because typing out detailed answers is time-consuming. Important nuance—the “why” behind decisions or the frustration in a workflow—gets lost.

Interviews improve on this, but they’re resource-intensive. Coordinating schedules, running multiple conversations, and manually synthesizing notes can slow down momentum before a project even begins.

Why Voice-First Discovery Changes the Game

Voice-first discovery combines the structure of a survey with the richness of a conversation. Here’s why it’s a game-changer:

1. Natural Expression
   People speak more freely than they type. They share details, context, and stories that often never make it into a form field. This creates a far richer dataset for understanding needs.

2. Efficiency at Scale
   A voice agent can run discovery with multiple stakeholders simultaneously. This saves hours of coordination and synthesis, while still giving each participant a personalized, conversational experience.

3. Deeper Context
   Beyond the words themselves, tone, pacing, and phrasing reveal priorities and pain points that are otherwise invisible in written surveys. Voice captures how something is said, not just what is said.

4. Consistency
   A trained voice agent asks the same questions every time, ensuring no gaps in the discovery process. It’s structured enough for data, but flexible enough for nuance.

How It Worked in Practice

For our AI education client, the voice agent was preloaded with:

• Questions from our standard discovery form.

• Additional prompts we normally ask during stakeholder interviews.

Stakeholders engaged with the agent asynchronously—on their own time—answering in a conversational way. The result was:

• Richer detail: responses included specific anecdotes and workflows, rather than one-line answers.

• Broader participation: more stakeholders engaged, since scheduling wasn’t a blocker.

• Faster synthesis: transcripts and audio provided a full record for analysis, making patterns easier to spot.

By the time the project kicked off, we weren’t just checking boxes—we had a clear, nuanced picture of the organization’s AI maturity, pain points, and priorities.

The Case for Voice-First Surveys

Discovery sets the tone for any project. The more authentic the inputs, the better the outputs. Voice-first methods flip the dynamic from “fill out this form” to “let’s have a conversation.”

And while this started with AI projects, the principle applies broadly:

• Creators could use voice-first surveys to engage their communities and uncover what products or features fans want most.

• Tech companies could use voice-first feedback loops to capture richer product insights without slowing down their teams.

The future of discovery is not static—it’s conversational.

Closing Thought

From now on, surveys should be voice-first. By capturing depth, nuance, and efficiency all at once, voice agents unlock a level of context that forms simply can’t provide. For organizations investing in AI—or any innovation—starting with richer discovery means ending with better products, stronger adoption, and more meaningful results.