The UX-AI Hierarchy of Needs: Climbing Toward Trust & Delight

Hey there,

The hierarchy of UX needs is my go-to framework when it comes to designing AI experiences that users not only adopt but rely on. Just like any pyramid, you have to build a strong foundation before you can start stacking the more advanced layers on top.

In AI, especially in healthcare, trust is the key to moving up this ladder. You can’t expect users to love a product that consistently fails even to get its basics right. So, how do we go from basic functionality to creating experiences that users not only trust but actually enjoy?

Let’s break it down…

1. Functionality: Does the AI Do the Basics Right?

Everything starts here. No matter how slick the design is, if the AI can’t do its basic job well, nothing else matters. In healthcare, that baseline is especially crucial.

Think of it this way: if you’re using an AI-powered system to assist in diagnosing patients, the absolute first thing it needs to do is work—no bugs, no hiccups, no guessing. We’re not talking about impressing anyone yet. We’re talking about delivering on the core promise.

But here’s the catch: AI isn’t magic. It’s only as good as its foundation. If it can’t reliably process data or make accurate recommendations, users will abandon it before they even have a chance to appreciate anything else.

Imagine an AI tool in radiology, designed to detect abnormalities in medical scans. If it misses even one significant finding, that’s a failure that no amount of usability or delight can fix. Functionality is the non-negotiable first step.

Quite like Google’s AI-powered spam filter for Gmail. It diligently sorts through hundreds of billions of emails, flagging what’s spam and what’s not. If it fails—letting spam into your inbox or, worse, sending important emails to spam—you’re not going to rely on it. Its basic job is clear-cut: accurately sort your emails - and it gets it done.

In the AI world, this is the foundation of the pyramid—you have to nail it before you can even think about what comes next.

2. Reliability & Transparency: Can Users Count On It—and Understand It?

So, you’ve got an AI that works. Great start! But now users need to trust that it’ll work every single time—without surprises. In healthcare, reliability isn’t just nice to have, it’s critical. If the AI fails mid-task or produces inconsistent results, trust crumbles.

But reliability alone isn’t enough. With AI, it’s not just about delivering results—it’s about explaining how you got there. consistently, but also provides alternative scenarios and explanations to understand how it’s arriving at those translations. It offers transparency into why certain phrases are interpreted in specific ways, leaving it to the user’s discretion to interpret the right results.

You see, transparency is the deal-maker. People don’

t want to feel like they’re taking orders from a black box. They want to know why the AI recommended a particular treatment or flagged an anomaly. Think of it like GPS—you trust the directions more when you can see the route.

Take a compliance officer using AI to scan for regulatory issues. Sure, it’s reliable at flagging possible problems, but if they don’t know how it arrived at those flags, they’ll hesitate to act. Reliability gets the job done, but transparency gives users confidence to act on those results.

Without these two—reliability and transparency—users will constantly second-guess the AI, keeping them stuck in this limbo of partial trust.

Here’s the Chasm

Between reliability and trust, there’s a tricky gap—a chasm of trust. Even if the AI is doing its job, users might hesitate to lean on it. This is especially true in healthcare, where the stakes are high. Here, trust is delicate. AI must not only be reliable but also earn human buy-in. If users don’t understand or feel comfortable with the AI’s role, they’ll hesitate to integrate it into their workflows, and they might revert to manual processes even if the AI is technically superior.

3. Trust: The Hardest Hurdle to Clear

Here’s where things get real. Trust isn’t just earned because your AI works or is reliable—it’s built over time, and it’s the hardest step in this journey. In healthcare, trust can be the difference between full adoption and users abandoning ship.

It’s not enough for the AI to spit out recommendations. Users need to feel confident that the AI is there to support them, not replace them. They want to know that when they rely on the AI’s advice, they still have the final say—and that’s crucial. AI should feel like a trusted partner, not an overzealous assistant taking control.

IBM’s Watson for Oncology nails this factor. It assists doctors by analyzing large volumes of data and recommending treatment options. However, to build trust, it doesn’t just deliver a suggestion—it provides evidence from medical literature, patient data, and studies to back up its recommendations. The AI becomes a partner, not just an automated system.

Consider how care managers function. They need to trust that AI-generated care plans aren’t just suggestions from a machine but well-reasoned decisions that consider patient data thoroughly.

But here's the thing: AI needs to be honest about its limitations. When the system isn’t sure, it should say so—inviting human oversight rather than making assumptions.

This is the point where many AI products hit the chasm—where users stop short of fully trusting the system. They’ll use it, but only as a backup or a check, not the primary tool it could be. Bridging this gap requires transparency, yes, but also communication: the AI needs to make it clear when it’s uncertain and always leave room for human input.

Without trust? Even the best-designed system remains in the background, never fully embraced.

4. Usability: Making AI Work for Everyone

Once trust is established, the next challenge is usability—because even the most trustworthy AI can fall flat if it’s hard to use. In healthcare, where time is scarce and stakes are high, the AI needs to fit seamlessly into the workflow, adapting to the needs of everyone involved.

Here’s the thing: different users have different needs. A doctor may want quick, digestible insights, while a compliance officer needs detailed records. Usability means the AI can’t be a one-size-fits-all solution. Instead, it should adjust based on who’s using it, offering just the right level of depth or simplicity for each role.

For example, if a telemedicine platform’s AI gives a doctor overwhelming amounts of data during a live consultation, it disrupts the flow. On the flip side, if it simplifies too much, advanced users will feel restricted. The key is flexibility—letting the AI scale its complexity to match the user’s expertise and the task at hand.

Microsoft’s AI-powered Excel provides automatic insights and formulas to suggest data trends - features that even the amateur users can make sense of to build their spreadsheets. This is what makes the tool more powerful for everyone, not just data experts.

You see, when usability is spot on, users don’t even notice the AI—they just get things done. And that’s where the magic happens.

5. Proficiency: Empowering Users to Do More

Now that the AI is usable and trusted, it’s time for it to step up. At this stage, the AI isn’t just a tool to assist—it’s there to help users work smarter, faster, and more efficiently. This is where the AI begins to empower its users, letting them do more with less effort.

Grammarly’s AI writing assistant starts by helping you fix typos and grammar mistakes, but as you use it more, it begins to understand your writing style, offering suggestions that improve tone, clarity, and engagement. It doesn’t just correct—it helps you write better.

Proficiency means the AI doesn’t just handle the routine stuff; it anticipates needs, offers meaningful suggestions, and frees up users to focus on the bigger picture. Imagine an AI system that not only summarizes patient records but also flags potential issues before they arise, or suggests next steps in care based on patterns it identifies.

It’s about moving beyond basic support to true augmentation. Care managers, for instance, can spend less time buried in paperwork and more time focusing on patient care because the AI has streamlined the process for them. The goal here is efficiency without sacrifice—users can do their jobs faster, but also better.

When AI hits this level of proficiency, it starts to feel less like a tool and more like an indispensable partner—one that boosts productivity without creating more work or stress. That’s when users really start to see the value.

6. Ethics & Accountability: Security Meets Fairness

At this stage, the AI is doing more than just empowering users—it’s handling sensitive data and making decisions that impact real lives. And that means ethics and accountability can’t be an afterthought.

Apple’s Face ID is a great example of AI that prioritizes security and accountability. It uses biometric data but encrypts it directly on the device, ensuring no sensitive data is ever stored on Apple’s servers.

Particularly in healthcare, this is where things get serious. It’s no longer just about performance—it’s about doing things the right way.

Data privacy, bias, and security are at the forefront here. Every decision the AI makes, every piece of patient data it processes, needs to meet the highest standards of security and fairness. Think about it: if the AI is flagging potential diagnoses or suggesting treatments, those decisions need to be unbiased and rooted in diverse, accurate data. If not, lives could be affected.

Take bias, for example. If an AI system is trained on biased datasets, it could disproportionately misdiagnose or mistreat certain demographic groups. This is why fairness in AI is non-negotiable. And then there’s accountability—users need to know that if something goes wrong, the system has safeguards in place to prevent harm and correct itself.

In healthtech, compliance with regulations like HIPAA or GDPR isn’t just a box to tick—it’s foundational to building trust and ensuring the AI doesn’t just work, but works responsibly. It’s about being transparent with users about how their data is handled and ensuring security is built into every layer.

When ethics and accountability are baked into the design, users feel confident that the AI isn’t just powerful—it’s also safe.

7. Delight: Personalization & Growth

Spotify’s users can vouch that its recommendations are a classic example of delight through personalization. It’s AI-powered recommendations system shuns generic playlists, instead curating them based on the member’s listening habits, moods, and even the time of day. Users don’t just enjoy it; they feel like it knows them intimately.

And that’s how you get to be at the top of the pyramid. Once functionality, trust, usability, and ethics are solid, it’s time for AI to do something truly special—it’s time to delight. This is where AI goes beyond just being useful or efficient, and becomes a tool that users actually enjoy interacting with. It adds that personal touch, offering personalization and helping users feel like the AI is tailored to their specific needs and goals.

In healthcare, delight might come from an AI that remembers how individual doctors work, adapting its suggestions and insights based on past interactions. For care managers, it could mean an AI that predicts their next steps or highlights areas that align with their priorities, without them even needing to ask.

This is where AI begins to anticipate needs. It’s not just responding—it’s proactive, offering users exactly what they need when they need it, without overwhelming them. Imagine an AI that not only assists with patient care but suggests ways for a user to optimize their workflow or reminds them of tasks they might have overlooked. It’s about making users feel understood and supported, not just guided by a machine.

And here’s the thing: delight isn’t about flashy features or gimmicks. It’s about making AI feel effortless, integrated, and even enjoyable—giving users that sense of “Wow, this system really gets me.” When users feel that kind of connection, they don’t just use the AI—they keep coming back to it, because it’s made their job easier, faster, and yes—more enjoyable.

Wrapping It Up

Building great AI products, especially in healthcare, isn’t just about making the technology work—it’s about creating an experience that users trust, understand, and enjoy. Each layer of this hierarchy plays a vital role, from ensuring the AI performs its basic functions to designing systems that feel intuitive and, ultimately, delightful to use.

You can’t skip steps. A solid foundation of functionality and reliability paves the way for trust, and only after trust is earned can you focus on delivering a seamless, even enjoyable, experience. When AI reaches that point—where it’s not just useful but feels like a truly helpful partner—that’s when you know you’ve built something people will not only use but come to rely on every day.

Until next time,

Bansi