Common AI Product Mistakes Companies Make Building Their First AI Features

Increasingly, businesses are feeling pressure to add AI to their product. Whether it's from the board, investors, or even internal leaders, there is a fear of "being left behind" in AI. However, AI without a real problem to solve becomes a distraction to your product, causing AI product mistakes and missteps, rather than adding value.

When implemented in line with your customers' needs and a real product strategy, AI can make a significant impact and even feel magical. But too many companies are rushing into AI, and here are the common mistakes.

Chatbots as the Default UI

Companies have been adding chatbots to their products without question, the way some people add hot sauce to everything. Usually, this is done because it's a quick win to say AI is part of your product. The problem: depending on who your customers are, what problem your product is solving, and how customers use the product, chatbots sometimes come across as a more invasive, annoying version of Clippy, Microsoft Office's original assistant. If a chatbot isn't how your users want their questions answered, and it's constantly blocking features hidden behind it, it's probably hurting product engagement more than helping it.

Not Understanding If or Where AI Fits In Your Product

It's entirely possible there are opportunities to solve problems better with AI in your product than how they're currently being addressed. But without auditing your existing features and thinking through the problems they're solving, it's hard to understand if AI will actually impact your users in the way you intend. It's also possible the best way to improve your product with AI is more subtle than a big, in-your-face generative AI feature. No one likes adding new features that never get adoption, or making big changes to existing features without any noticeable benefit for users or the business. A lot of this can be assessed using generative research and prioritizing these features against your overall product strategy.

Assuming AI Features Are Premium

AI is hot, but to many of your customers, certain AI features are becoming expected inclusions rather than premium add-ons. There are enough free AI tools that your B2B SaaS customers may wonder why some of your features are so pricey if they can get a similar outcome from a free product. Of course, we know using AI isn't free, especially if your product is using third-party APIs from OpenAI or Anthropic. If you're adding AI to your product as a premium feature, you need to make sure the solution is actually viewed as premium value to your customers. And if you're adding AI as "expected value," you need to consider how costly it is to add a feature that will increase expenses but your users won't want to pay extra for.

Being Oblivious to Pricing Model Impact

What happens if you build a feature dependent on a third-party AI API and it ends up being wildly successful? Of course, this is a problem not everyone has, but this should be the goal. Your wildly successful feature can become wildly expensive. Make sure you've considered if your current pricing model, or how you plan to charge for the new feature, can cover the cost of maintaining that feature.

Making the AI Product Mistake of Skipping User Testing

These days, it's very easy to build a prototype in a few hours with AI with tools like V0 or Replit, so there is very little excuse to skip user testing before your engineers write a single line of code. AI prototyping tools can even build AI-powered features in the prototype. Jumping into building the feature before testing your user experience and functionality assumptions is a big risk, especially if AI is new to your product. Don't waste design and engineering resources on building something that your users don't understand how to use or find barely usable.

Not Giving Your VIPs VIP Access

Now that you're confident you've built the right thing, and that your customers will love it, you need to get it into customer hands to start getting real-world feedback. Instead of releasing it to all your customers at once, aim to give your best customers early access in exchange for their insights. You can position this as giving these customers a free trial in exchange for their feedback. These should be customers that are willing to share feedback with you and aren't at risk of churn. It gives you time to make any final tweaks before releasing it to the rest of your customers. Make sure your customer base is wowed by the product, and clean up anything that would get in the way of that before the feature is released more broadly.

While it would be foolish to ignore AI and its potential for your product, you should be leveraging it strategically to ensure sustainable success for your business. Your approach needs to be strategic, thoughtful, and assumptions should be derisked before expensive decisions and investments are made.