What messaging techniques are effective to build greater trust in AI/ML features of products and solutions?

Building trust in AI products starts with the product and company itself—is there a dedicated AI safety team? Are the right AI safety checkpoints in place to safeguard from abuse? Is the company taking a principled approach to rollout? If the foundations of AI safety are there, then the messaging becomes much easier. I approach messaging with a few things in mind: Being transparent about the intended use of the technology and the risks, including how the company is mitigating those risks through ...Read More

AI/ML means a lot of things to a lot of businesses. Some companies have high data maturity where AI/ML tools are a vehicle for data science teams to create proprietary value for the business. Others are fairly low on their data maturity (which is fine!) and they want out-of-the-box AI/ML for fairly simple use cases, usually around personalization.The best messaging technique here is clarity. Do the work for the prospect. Is this product for advanced or basic users? Technical users? Does it requi ...Read More

It’s true that customers are both skeptical of AI outputs and excited about AI’s potential. At Zendesk, we’ve definitely seen this in the customer service space, since this is one of the most obvious areas that AI is poised to disrupt. Here are some tactics we’ve found useful: Show value: Focus on demonstrating tangible value through real-world examples and clear use cases that customers can latch on to. Showcase specific customer success stories that highlight ROI and impact, making the benefit ...Read More

To build greater trust in AI/ML features of a product, I would focus on a couple of different aspects. The first is the quality and quantity of data being used to train your AI/ML models. People like to know that the data is ethically sourced and accurate. Usually quantity of data also helps to validate how well your AI/ML models work and how well it scales. Second, I would focus on expertise - this could be in terms of years developing and training the AI/ML or in terms of data scientists who a ...Read More

With any emerging technology, I've found the best approach is to compare to the previous way of doing things. People digest a new way of doing something by comparing it to how they do it today. Think about a lot of the technologies we use today: Email = electronic mail, Software as a Service = Software that you don't run locally on your comuter, TV (tele-vision) = vision at a distance, etc. There's some real fear and trust issues with AI/ML. But there's also a lot of just fear of the unknown. Th ...Read More

One of the biggest mistakes I see marketers make when talking about Artificial Intelligence (AI) or Machine Learning (ML) is they just throw the terms around. Like their employer can just sprinkle some AI salt on something to give it extra marketing zest. Think like a journalist for a small-town news site, instead, and try to explain why AI/ML is important and how it works for the use case at hand. Interviewing a software developer working on the AI/ML aspect of your product is one of my favorit ...Read More

While I often bristle when people call out that technical audiences don't like being marketed to (the implication being other personas like fluff marketing?!), I absolutely believe that core differences in how personas evaluate solutions matter. To build trust in the value of AI/ML features, here are a few ideas that come to mind: Given the amount of hype in the AI/ML space, customer proof becomes even more powerful. Showcasing concretely how users have used your solution, their goals, and the i ...Read More