6 B2B Growth Lessons: Scaling an AI Product to Acquisition

TL;DR: B2B marketers need to leverage the novelty effect for top-of-funnel awareness, but quickly pivot to gated pricing to validate true intent. The conversation highlights why gamified waitlists beat standard signups, why niche positioning beats broad appeal, and how long-game SEO creates a defensive moat against future hype cycles.

The era of “build it and they will come” is effectively dead. 

We are operating in what Tony Beltramelli calls an attention deficit economy, where customers are overwhelmed with content, skeptical of sales pitches, and harder to reach than ever before.

In this environment, traditional demand generation often falls flat. Breaking through requires leveraging psychological triggers like novelty and scarcity, and having the discipline to ignore vanity metrics in favor of friction-tested data.

In a recent episode of the Attributed podcast, we sat down with Tony, co-founder of Uizard and now Head of Product, AI at Miro, to deconstruct the growth engine that powered his company’s massive acquisition. 

The conversation reveals that while the technology changes, the fundamentals of capturing attention and validating markets remain constant. 

Keep reading for a look at the specific go-to-market levers that actually drive revenue today, or listen to the entire conversation here. 

1. The Novelty Effect is Your Best Top-of-Funnel Asset

Long before they had a polished product, Uizard had a video.

In 2018, the concept of turning a hand-drawn sketch into a functional app interface was unheard of. Uizard released simple YouTube demos showing this magic in action. The videos didn't have high production value, but they had a high wow factor, and they performed well. 

So the novelty, the wow factor, is what drove that amount of engagement I would say and I guess it provoked an emotion in a lot of people into ‘wow technology can do this’.

In the early stages, you don’t need a complex brand manifesto. You need visual proof. If your product solves a problem in a way that looks like magic, show it raw.

2. Gamify the Waitlist (And Do It Shamelessly)

With viral videos driving traffic, Uizard next engineered demand. 

They directed traffic to a landing page with a waitlist. But there was a catch: you could skip the line.

By gamifying the process, inviting friends and colleagues to move up the queue, Uizard turned passive interest into active recruitment. This simple mechanic generated 10,000 signups before the product was even public. 

Years later, they ran the exact same play for their AutoDesigner feature and gathered 60,000 signups in two weeks.

Scarcity works, but only if there is a reward for action.

3. The Pricing Trap: Why "Free" Can Be Expensive

For a long time, Uizard was free. Tony admits this was a mistake.

You will always find millions of people that want to try your product for free... But that's not how you're going to build a business... Once we introduced pricing, we started to get strong product market fit signals. Before that, it was just weak product market fit signals.

Pricing introduced friction, and that friction filtered out the casual observers from the power users. Retention data became real, and they could now see who actually stuck around.

Monetization is a validation tool. If you are struggling to find product-market fit, it may be time to introduce a paywall. 

4. Stop Being a Tool For Everyone

At one point, Uizard’s marketing claimed it was a design tool for everyone. But in B2B marketing, everyone usually means no one.

They were attracting designers, engineers, students, and marketers. But by digging into the data of their paying users, they found their true champions: Product Managers.

Once they realized this, they overhauled everything. The landing page copy, the testimonials, and the blog posts were rewritten to speak directly to Product Managers who needed to prototype fast. This pivot in positioning unlocked their next stage of scale.

You can expand your audience later, but you need to dominate one specific persona first.

5. The ChatGPT Break That Wasn't Luck

When ChatGPT launched in 2023, search volumes for "AI design" and "AI prototyping" skyrocketed.

Uizard was there to catch them.

Because the team had been writing content about AI for creativity since 2019, their domain authority was already established. They weren't scrambling to write trend-jacking articles; they were the incumbents. Meaning the success of their SEO traffic was actually five years in the making.

Tony says the fundamentals of SEO still apply in the age of LLMs, just under the guise of AI Engine Optimization. If you consistently produce high-quality content on a niche topic, when that topic eventually hits the mainstream, the algorithms (and the AI overviews) will cite you as the source.

6. The Attention Deficit Reality

Reflecting on what he would do differently today, Tony was blunt: Distribution cannot wait until the product is finished.

Distribution is the mechanism you use to actually get your product in front of users: whether that is through SEO, viral loops, influencer partnerships, or cold sales.

We are living in what Tony calls an attention deficit economy. Because the internet is so noisy and users are so distracted, simply having a great product isn't enough to break through.

If you wait until the code is finished to figure out where your customers hang out, you are too late; you must validate your channel at the same time you validate your code.

Conclusion

Uizard’s story is a reminder that while technology changes, human psychology doesn't. We love novelty, we value what we pay for, and we trust what we see our peers using.

No matter who you are selling to, the lesson is clear: Build a great product, yes. But build a story that makes people want to tell their friends.

About the Speaker

Tony Beltramelli is the Head of Product, AI at Miro. He was previously the co-founder and CEO of Uizard, an AI-powered UI design tool that democratized product prototyping. An AI engineer by training, Tony specializes in bridging the gap between machine learning research and usable, scalable commercial products.