Demoing AI Software is Different: Why Control is Key

Have you ever asked ChatGPT the same question twice, and received two different answers? Then you understand why demoing AI software is different from demoing a typical SaaS app — predictability.

The ability for AI to surprise you is part of what makes it so compelling (and magical, even). But unpredictability is the enemy in a live demo with a prospect. You want your demos to flow seamlessly, and with no surprises.

AI can give different responses to the same question. The answers can be downright wrong, or even surface personally identifiable information (PII) or customer data. Sellers can have typos in their prompts, or the AI can be slow to load. In other words, there’s a lot of potential for your demo to go off the rails.Let’s explore some of the unique risks for demoing AI software. We’ll cover some of the best ways to show off the magic in your AI application, while mitigating these risks.

AI demoing: training dataset challenges

Above all else, your demo should prove the value of your software, regardless of the industry you’re in. With AI, the industry promises are often lofty, so there’s a lot to live up to in terms of customer expectations vs. the reality of what you can achieve. Proving ROI within the span of a short live demo can be a major challenge.

Since AI relies on training data for its outputs, there are a few other key considerations and risks to take into account before unleashing a demo of your production application on a prospect.

• Sensitive demo data: Educate your sellers about the datasets they’re using with prospects during demos. For example, training data in your live production environment may contain PII or other sensitive customer data a prospect shouldn’t see. A better alternative is to inject a synthetic dataset made up of realistic data into your demo environment. Data injection makes the demo hyper-relevant to your prospect, while keeping sensitive data under wraps.
• Data processing speeds: Many AI applications process vast amounts of data before you receive an output. For a sales rep, demonstrating a data-heavy AI app can be extremely stressful and unpredictable. Lag time between query and response can make a prospect impatient and potentially decrease their confidence in your application’s performance. To avoid these types of issues, it may be best to create a demo on guardrails to ensure that the prospect can see exactly how your software works, without the risk of slow processing speeds.
• Surprising answers: Sellers should know exactly what they’re going to see in their demo. Every time. But when you’re demoing live AI, it can give back wildly different responses. These unpredictable responses can catch even the most seasoned sales rep off guard. Or, the answer can be wrong and the prospect immediately loses confidence. As we’ll describe more below, a self-contained demo environment can keep responses predictable.

In addition to these challenges, many enterprise prospects are sophisticated and aware of general AI risks, including regulatory compliance, security, and algorithmic bias. As a result, it’s important to provide reps with the proper training to handle objections or questions about your application’s use of data, and to know when to redirect prospects to a solutions engineer (SE) for more technical detail.

Creating a predictable and reliable demo environment for AI

As we’ve touched on briefly, the right demo approach can help you mitigate some of the risks around AI datasets and the predictability of your software’s outputs. Many teams may find demoing a live production application too risky for a number of reasons — including processing speed or the variability of outputs.

That’s where a self-contained demo environment can be particularly beneficial for AI companies. Instead of demoing a live app, a self-contained demo environment creates an editable copy of your application in which you can control the inputs and outputs of any AI functionality.

You can inject custom datasets into a self-contained demo environment to customize it to specific prospects, creating multiple datasets in advance to accelerate your time to demo. In addition, you can keep certain features on guardrails with prebuilt demo flows, to ensure that your prospect sees the optimal processing time for your app. Incorporating features like autotyping into your presentation speeds up the demo even further, making it easier for reps to remain focused on your product’s benefits and ROI potential.

If demoing AI in your production environment feels too risky — or if your demo environment isn’t able to make realistic-looking AI with predictable responses — it might be time to explore the advantages of a self-contained demo environment.

Read more: Demoing from a production environment: Is it worth the risk?