The gap between what AI can do and what people actually feel when they use it keeps getting wider. The models get smarter. The interfaces stay confusing, unpredictable, or worse, untrustworthy.
As someone who has spent two decades designing digital products, I’ve watched teams race to integrate AI without stopping to ask why they’re doing it or what tradeoffs they’re making. The result? We end up with products that feel like prototypes disguised as finished work, or worse, in some cases, tools that quietly create harm.
Before you add AI to your product, stop and answer these three questions honestly.
3 Questions to Ask Before Adding AI to Your Product
- Are you designing for unpredictability or pretending it doesn’t exist?
- What are you asking users to trust you with, and what are they getting back?
- What are you actually making, and for whom?
1. Are You Designing for Unpredictability or Pretending It Doesn’t Exist?
The biggest mistake teams make is expecting AI to behave like traditional software. It won’t. When you’re working with language models, you can’t guarantee standardized outputs. Right now, hallucinations and errors aren’t edge cases. They’re built into how these systems work, and you can expect that to be the norm until it’s not. Being able to separate fact from fiction will only continue to get harder and harder.
This means you need to design for failure and build trust from the start. What happens when the AI gives users results they didn’t want or didn’t expect? How do they recover? How do they get to their actual goal when the system misunderstands them?
Most teams skip this step entirely. They design the happy path and ship it, assuming the model will just get better over time. But users don’t care about your technical roadmap or your founder’s vision. They care whether the thing in front of them works today, not whether it will someday.
2. What Are You Asking Users to Trust You With, and What Are They Getting Back?
Trust in AI products is a value exchange. Users give you their data, time and attention. You give them intelligence that should feel worthy of that exchange.
This dynamic isn’t new to software, but the stakes are higher now. The more data you feed into an AI experience, the more relevant and useful it becomes. But users need to believe that exchange is worth it.
If your data layer is thin, the AI won’t have enough historical memory to know users well. The relevance of its output then drops. If you’re asking users to hand over sensitive information, the consequences of getting this wrong multiply.
The equation has another side too: user skill. Even with perfect data and a trustworthy system, if people don’t know how to prompt effectively or use the tool well, they won’t get value from it. Are you designing interfaces that help users learn the system, or are you assuming everyone arrives as a power user?
3. What Are You Actually Making, and For Whom?
Just because you can build something with AI doesn’t mean you should. The technology is neutral. It reflects the choices designers and builders make about what to prioritize and who to serve.
High-risk domains like healthcare and finance require extra scrutiny. These aren’t spaces where you can afford to design and hope for the best. The data is sensitive, the consequences of errors are serious and users need guarantees that traditional AI interfaces can’t provide yet.
Making the Decision
AI will continue advancing regardless of whether we make thoughtful choices or not. But the gap between capability and experience will only close when designers stop treating AI as magic and start treating it as a tool that requires the same rigor, user research and ethical consideration as any other technology.
Products that earn trust will come from teams willing to slow down long enough to ask the uncomfortable questions. Teams must remember that AI isn’t the hero of the story. The human using it is.
And if you can’t answer these questions clearly, you’re not ready to ship.
