B2B buyers have developed a highly sensitive BS meter. And right now, it’s pointed directly at your AI features.
Everyone claims to have "game-changing AI." But few can actually prove it.
When a buyer sees an AI feature, they often default to skepticism.They assume it’s a wrapper, a hard-coded script, or a future-state wireframe.
If you show them a slide deck or a click-through video, you’re just proving them right.
If you're truing to win deals on your AI features, you need to show the AI actually working.
But for most GTM teams the thought of demoing AI features live is terrifying.
So teams panic. And in their panic, they focus on fixing the outputs, instead of tackling the root problem: the inputs.
The "Input" Solution: Fixing the Root Cause
Why do so many AI demos fail?
Most of the time, it's not the product - it's actually the data in the demo environment.
AI is a pattern-matching engine. It can’t find insights in an empty or poorly seeded environment.
- A "Summarize Account" feature can't work if the account history is empty.
- A "Predict Revenue" feature looks broken if there are only three closed deals in the system.
We’ve found that the key to demoing AI effectively is to flood the backend with massive, realistic, high-fidelity data. In other words: Fix the input.
The "Data Quality" Trap: Why Internal Builds Fail
This is where we see most internal attempts fall flat.
Solutions teams know they need data, so they try to build it themselves. They write scripts to generate thousands of records.
They solve for volume. But they fail on nuance.
You can’t shortcut your way to good data. If you feed your AI thousands of flat, disconnected records, your AI will give flat, disconnected answers.
To demo AI effectively, your data needs to reflect real-world usage. It needs to tell a specific story that supports your sales narrative.
- It’s not just 100 emails. It’s a thread about a pricing negotiation that stalls and then recovers.
- It’s not just 50 support tickets. It’s a specific technical issue that triggers a high-priority escalation workflow.
This is the hardest part of demo management.
Generating data at scale is easy. Generating realistic data that tells a coherent story at scale is incredibly difficult. That is the specific problem TestBox was built to solve. We ensure the data doesn't just fill space — it actually gives your AI something meaningful to analyze.
How Gong Fixed Their AI Demos
This is how teams like Gong demo their AI features with confidence. Their product relies entirely on analyzing conversation intelligence.
Their platform relies on conversation intelligence, which requires analyzing thousands of data points within recorded virtual calls.
We didn't just fill their environment with generic calls. We filled it with calls that had different sentiments, varying talk-to-listen ratios, and complex deal progression stories.
We gave the AI a massive amount of nuanced ammo to pull from.
The result? They threw out the script. (Literally, prior to using TestBox they had been hard-coding answers for their AI.)
Now they have real data density in their demo environments, which means reps can show their AI working and use it just like a real customer would. They can:
- Invite the Curveball: Ask your prospect, "What question do you want to ask?"
- Show the Work: Let them see the AI analyze the data in real-time.
- Prove the Value: When the AI gives a smart answer based on the data, the buyer knows it will give a smart answer based on their data.
When the "Safe Option" is Actually a Risky Choice
If you lock your reps into a "safe mode" with your AI, you are telling the buyer that your product is fragile.
Real confidence comes from letting the product do the work. Give your AI the data it needs, and show buyers exactly what it can do.
Struggling to demo your AI features live? Chat with our team. We can help you get the right data in place so you can show buyers exactly what your product does.
