AI is no longer a differentiator, it’s assumed. The real question isn’t if you use AI, but why it matters to your users. In this new landscape, differentiation comes from how well your product solves real problems, leverages proprietary data, and delivers an experience users actually trust and want to use. These advantages aren’t isolated moats, they work together, reinforcing one another, to create a stronger, more resilient product that’s not just smart, but truly valuable.
At DesignMap, we're seeing a major shift in the product teams we work with across industries – from scrappy AI-first startups to established enterprise platforms. What started as a race to get something AI-powered into the roadmap has evolved. Product leaders are now asking deeper, more strategic questions:
- Where can AI create real value for our users?
- What does it take to build a purpose-specific AI tool that’s more compelling to users than the general tools they already rely on?
- How do we build trust in an experience that’s constantly learning?
We believe that understanding the last question there is central to the success of any tool incorporating AI. As we work more and more with AI, we often return to a trust model we built years ago, grounded in academic research. Trust starts with calculus-based trust (understanding how something works and why it can be trusted) and matures into experience-based trust over time.
For AI-powered tools, those early moments matter. If users don’t feel in control or can’t see how the AI arrived at a suggestion, they won’t stick around long enough to build confidence.
This piece shares some of what we’ve learned partnering with founders and product leaders on AI-centric projects. Specifically: how to identify the right problems to solve, lightweight methods for surfacing user insights, and early signals of trust and adoption.
Making AI’s Value Apparent to Users
With new AI tools and capabilities emerging every day, it’s easy to feel pressure to bolt on a feature just to keep pace. But the speed of AI development doesn’t erase the need for thoughtful design. In fact it raises the stakes.
We’re in unfamiliar territory. Many users are unsure how AI fits into their workflow. There’s little standardization in how AI shows up in UIs. And when people don’t understand what AI is doing or how it’s helping, trust erodes quickly.
That’s why, before we dive into designing screens, we help teams ask and answer these core questions:
- Where are users spending time today and where do they want to be?
- Which tasks deserve to be automated, and which benefit from human oversight? (Ye olde “human in the loop” question.)
- What does “helpful” look like when the system is doing more of the thinking?
Three Modes of Discovery
To answer questions like these, we’ve been applying different modes of discovery depending on how much clarity a team already has on the problem:
Three modes we’ll cover here are:
- Rapid Understanding
- Concept Validation
- Rapid Refinement
These lightweight, targeted approaches are designed to quickly surface insight while reducing risk of investing time and resources towards building the wrong thing.
Rapid Understanding
When teams are still figuring out where AI fits, we run accelerated research sprints to explore how AI might show up in ways that matter. Even simple tools, like sketching rough maps of users' focus areas, can uncover where people want help from AI, what they might be ready to automate, and what they still probably want control over. These sessions shape hypotheses about AI’s role before anyone writes a line of code.
Concept Validation
Once we’ve defined a direction, we shift into early concept testing — not with polished screens, but with just enough visual fidelity to communicate the core ideas and value behind them. These early concepts are tested with real users, ideally a mix of current customers and others who fit the ideal customer profile (ICP), to explore how well they understand what the AI is doing, how it integrates into their workflows, and whether they trust its outputs. The goal isn’t to test UI polish, but to validate users’ mental models and expectations. We want to ensure we’re building an AI-powered experience that aligns with how people think and work rather than bolting on AI features that feel like accessories.
Rapid Refinement
For teams optimizing existing experiences, we focus on refining the relationship between user and AI. We’ve seen teams start with “wow” moments, flashy interfaces or chat UIs, to showcase AI’s potential. But over time, we find those teams pivot to simpler more familiar patterns that users actually trust and understand.
In a recent project, we sought to illustrate a smart proactive system that dynamically served everything up to the user in the right place and at the right moment. In this vision, navigation was minimal since all the information displayed was highly contextual.
However, we learned through testing that users still want some level of control. They didn’t trust that the system always knew what they needed to do next and had an agenda of their own that they wanted to pursue. They still wanted autonomy on where to navigate depending on their goals in any given moment. This led to re-incorporating more familiar patterns (list, navigation bar, dashboard elements). We learned that effectively incorporating AI doesn’t necessarily mean stripping your UI down to the bare minimum and that some more traditional navigation patterns are still necessary.
Across all phases, the goal is the same: design experiences that address core user needs, feel intuitive, earn trust, and actually make people better at what they’re trying to do.
In our next post, we’ll expand upon how the relationship between users and tools is fundamentally changing and how we’ve approached building trust in this new paradigm.
