Seeing the Invisible
By Douglas Ferguson, Founder & CEO of Voltage Control
Originally inspired by “A Lantern in the Fog” on Voltage Control, where teams learn to elevate their ways of working through facilitation mastery and AI-enabled collaboration.
Innovation isn’t just about generating ideas — it’s about testing assumptions before they quietly derail your progress. The faster a team can get something tangible in front of real eyes and minds, the faster they can learn what works, what doesn’t, and why.
Yet many teams stay stuck in abstraction for too long. They debate concepts before they draft them, reason about hypotheses before they visualize them, and lose energy to endless interpretation loops. That’s where AI, when applied strategically, becomes a powerful ally in human-centered innovation — not as a shortcut, but as a clarifier.
At Voltage Control, we’ve been experimenting with a practice we call AI Teaming — bringing AI into the collaborative process as a visible, participatory teammate. Using new features in Miro, like AI Flows and Sidekicks, we’re able to layer prompts in sequence so that teams move from research to prototypes in minutes. We call this approach Instant Prototyping — because the prototype isn’t the end goal. It’s the beginning of the real conversation.
Tangibility Fuels Alignment
In human-centered design, the first artifact is often the first alignment. When a team sees a draft — even one that’s flawed — it changes how they think and talk. Suddenly, discussions move from “what if” to “what now.” That’s the tangible magic: the moment ambiguity becomes visible enough to react to.
AI can now accelerate that moment. With one-click flows in Miro, facilitators can generate structured artifacts — such as user flows, screen requirements, or product briefs — based on real research inputs. The output isn’t meant to be perfect; it’s meant to be provocative. A flawed draft surfaces hidden assumptions faster than another round of theorizing ever could.
Each iteration reveals new learning: the missing user story, the poorly defined need, the contradiction in the strategy. These insights aren’t AI’s achievement — they’re the team’s. The AI simply provides a lantern, lighting up the fog so humans can decide where to go next.
Layering Prompts for Better Hypothesis Testing
One of the most powerful aspects of Miro’s new AI Flows is the ability to layer prompts in connected sequences. Instead of a single one-off query, you create a chain of generative steps that build on each other. For example:
- Synthesize research into user insights.
- Translate insights into “How Might We” statements.
- Generate user flows based on selected opportunities.
- Draft prototype screens or feature lists.
Each layer of the flow uses the prior outputs as inputs — so when you adjust one, the rest evolves. Change a research insight or tweak your “How Might We” framing, and within seconds, your entire prototype ecosystem updates. It’s an elegant way to make hypothesis testing iterative, dynamic, and evidence-driven.
In traditional innovation cycles, these transitions can take weeks of hand-offs. With AI flows, they happen in minutes — creating immediate feedback loops that invite teams to think in public and react in real time.
(You can see this process in action in the video embedded below — where we walk through how small prompt adjustments yield dramatically different outputs.)
The Human Element: Facilitating Sensemaking
The irony of AI-assisted innovation is that the faster machines generate, the more valuable human facilitation becomes. Instant prototypes don’t replace discussion — they accelerate it. They make reflection, critique, and sensemaking more productive because there’s something concrete to reference.
Facilitators play a critical role here. Their job is to:
- Name the decision up front: “By the end of this session, we’ll have a directionally correct concept we’re ready to test.”
- Guide feedback: Ask, “What’s useful? What’s missing? What will we try next?”
- Anchor evidence: Trace changes to specific research insights so teams stay grounded.
- Enable iteration: Encourage re-running the flow after prompt updates to test the effect of new assumptions.
Through this rhythm of generation, reflection, and adjustment, AI becomes a conversation catalyst — not a black box. And the process stays deeply human-centered because it focuses on learning through doing.
Case in Point: Building “Breakout Buddy”
We recently used this exact approach to prototype a new tool called Breakout Buddy — a Zoom app designed to make virtual breakout rooms easier for facilitators. The problem was well-known in our community: facilitators love the connection of small-group moments but dread the logistics. No drag-and-drop, no dynamic reassignment, no simple timers.
Using our Instant Prototyping flow, we gathered real facilitator pain points, synthesized insights, and created an initial app concept in under two hours. The first draft had errors — it misunderstood terms like “preformatted” and missed saving room configurations — but that’s precisely what made it valuable. Those gaps surfaced the assumptions we hadn’t yet defined.
After two quick iterations, we had a working prototype detailed enough for a designer to polish. Within days, we had a testable artifact, a story grounded in user evidence, and a clear set of next steps. The magic wasn’t in the speed — it was in how visible our thinking became.
Designing for Evidence, Not Perfection
If innovation is about learning, then prototypes are your hypotheses made tangible. AI just helps you create more of them — faster — so you can test, compare, and evolve. But the real discipline lies in how you use them.
- Don’t rush past the drafts. Study what’s wrong and why.
- Don’t hide your versions. Keep early artifacts visible to trace the evolution.
- Don’t over-polish. Each iteration should teach, not impress.
When teams treat AI outputs as living evidence rather than final answers, they stay in the human-centered loop — grounded in empathy, focused on context, and oriented toward shared understanding.
A Lantern in the Fog
At Voltage Control, we see AI not as a replacement for creative process, but as a lantern in the fog — illuminating just enough of the path for teams to take their next confident step. Whether you’re redesigning a product, reimagining a service, or exploring cultural transformation, the goal isn’t to hand creativity over to AI. It’s to use AI to make your learning visible faster.
Because once the team can see it, they can improve it. And that’s where innovation truly begins.
🎥 Watch the Demo: How layered AI prompts accelerate hypothesis testing in Miro
Join the waitlist to get your hands on the Instant Prototyping template
Image Credit: Douglas Ferguson, Unsplash
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