Artificial intelligence is often framed as a purely technological endeavor — a race to build the most powerful models and the fastest, smartest algorithms. But for AI to be truly transformative, it must be built with people, not just data, at its center.
AI isn’t valuable just because it’s advanced; it’s valuable because it meets actual needs. Users must shape your approach from the start. So, with that in mind, let me share some lessons from building a solution specifically for students and researchers navigating the plethora of information available online — some reliable, some not.
3 Ways to Make AI Work for Customers
- Target your actual users and find out what they want.
- Cultivate trust through transparency.
- Make sure it augments human users.
Building AI for Real Users, Not Hypotheticals
One of the biggest mistakes AI developers make is assuming they understand their users’ needs without actively engaging with them or connecting with them. Rather than designing a solution in isolation, we integrated ourselves into the audiences we aim to serve.
User-centric AI development goes beyond surveys and focus groups — it requires continuous iteration based on real-world interactions. Our campus ambassador program, for example, gives us direct access to students at universities, allowing their feedback to shape how we refine our AI. Building this program meant being part of a continuous feedback loop with our primary users, understanding their needs better and ensuring that we were able to provide them with updates that suited their needs. Current college students are invested in their own learning, as well as their own tools. They want to be part of the build, understand what’s next and get hands-on experience with AI, specifically when it directly impacts them.
For instance, when master’s and doctoral students told us they wanted help conducting literature reviews, we created Scholar Mode, which sources information exclusively from more than 200 million academic journals and peer-reviewed articles, minimizing the noise often found in broader search tools. Likewise, when some of our highly engaged users said they need support on writing assignments because of the challenge of finding relevant sources, we built Essay Mode, to make reference-backed essay writing easier for students.
Growing Transparency and Trust in AI
Another key lesson we’ve learned is that users don’t just want their AI tools to be accurate. They also want their search tools to be transparent, and they really value clarity. Students and educators are rightfully skeptical of black-box algorithms determining what information they see. They like to understand why they see certain search results first.
For us, this meant not just prioritizing accuracy but also proactively showing users how our AI works. Why does a certain article appear first? How are sources ranked? What signals drive recommendations? Building trust in AI is just as important as building the AI itself. In the world of AI search, that means visibility into where the results come from, how they’ve been gathered, and why they’re trustworthy.
AI Is an Assistant, Not a Replacement
AI should empower people, not replace them. In education, this means AI should assist users in finding and interpreting information rather than dictating what’s important, which is why we provide users with a framework to help them navigate complex research landscapes more efficiently.
This approach applies beyond academia. Whether AI is used in customer service, healthcare or finance, it should serve as an extension of human expertise, not a substitute. The most successful AI programs are those that respect and enhance human judgment rather than attempting to override it.
AI development should not be a one-time deployment, but rather an ongoing conversation with users. It should evolve as their needs change, incorporating real-world feedback rather than rigid assumptions. This means investing in long-term relationships with users, not just data pipelines.
The future of AI is not about the technology itself, but about how effectively it serves people. If we want AI to be truly useful, we need to move beyond engineering marvels and start with something more fundamental: listening.
