Stop asking what AI can do. Instead, ask your users what problems they’re facing and how you can help solve them.
Most companies building AI products start by focusing on what AI can do, but this approach is wrong. The technology is widely accessible now, making it easy for anyone to plug in an API or train a model. However, understanding user needs deeply enough to know what problems are being solved is the real challenge.
The key to success lies in starting with users, not technology. When you ask users about their pain points, you rarely hear complaints about tools themselves but rather more fundamental issues that AI can help solve. For example, customer service teams struggle to answer tickets quickly, sales teams need personalized outreach at scale, and creators want their content to get discovered.
The pattern is the same across industries: no one wants AI to replace them; they want it to handle repetitive work so they can focus on what matters. To address this, you need to build an AI that understands users’ individual needs and preferences.
For creators, for instance, 54% cite “making sure my content gets found” as their top challenge. They didn’t need more content generation but rather an AI built around what they do, one that can handle mundane tasks so they can focus on their most important work.
Building transparency into your AI infrastructure from day one is crucial to earn user trust. Transparency isn’t a feature; it’s infrastructure. If you don’t control how the AI works, you can’t explain its functionality or data sources. This means building it into the foundation of your architecture.
When deciding whether to build or buy AI tools, consider three questions: Do you need per-user customization? Can you explain how your AI works? And do you control the data’s safety and privacy?
If you answer yes to all three, it’s likely that buying off-the-shelf tools won’t deliver what you need. Building gives you control over customization, transparency, and data protection.
Ultimately, building AI with users in mind is about starting with their needs, designing transparency from day one, and giving them the technology they actually need.
Source: https://www.unite.ai/user-centric-ai-development-transparency-trust