UX in B2B Products – How to Measure Its Impact on Sales and Retention

Do your users trust your AI?
In a world filled with chatbots, predictive engines, and recommendation systems, simply having AI is no longer enough. What truly matters now is trust—not blind trust, but responsible trust built on clarity, transparency, and control.

For Product Owners, Project Managers, CTOs, and team leaders, this means one thing: UX must align with AI ethics. Why? Because poor design choices in AI experiences don’t just harm usability—they erode brand trust and can directly impact user retention and conversion.

Responsible Trust – The New UX Imperative

AI increasingly operates behind the scenes—suggesting, sorting, interpreting, and nudging. But users often don’t understand how or why these systems make decisions. That’s where responsible trust comes in.

According to the Nielsen Norman Group, responsible trust means designing systems so users clearly understand their purpose and limitations—enabling confidence without false certainty.

In short, it’s about building ethical, transparent AI that earns—not demands—user trust.

The Ethical Pitfalls of AI Interfaces

Many AI-powered interfaces unintentionally fall into problematic patterns that damage trust:

• The “black box” problem – Users can’t see how AI reaches decisions.
• Over-anthropomorphizing AI – Interfaces make it seem like AI “understands” or “feels.”
• Hidden automation – No clear indication when AI is acting on behalf of the user.
• Data bias – Algorithmic decisions based on skewed or non-representative training data.

These issues may seem subtle, but they deeply affect how much control and confidence users feel when interacting with your product.

6 Principles for Designing Ethical AI Interfaces

To foster responsible trust, teams must intentionally embed ethics into UX decisions. Here are six key principles:

1. Explainability – Help users understand why AI made a recommendation. Tools like IBM’s AI Explainability 360 support this.
2. Transparency – Clearly indicate when AI is in use and how it influences outcomes.
3. Consent and control – Give users options to disable or adjust AI functionality.
4. Disclose limitations – Be upfront about what AI cannot do or might get wrong.
5. Avoid manipulation – Don’t use dark patterns that nudge users toward AI-driven decisions.
6. Contextual sensitivity – Match AI guidance to the level of risk and user expectations (e.g., banking vs. entertainment apps).

Applying these principles shifts the AI-user relationship from passive dependency to informed cooperation.

Case Studies in Responsible UX for AI

Ethical AI design isn’t just theory—some companies are already doing it well.

Google has adopted a set of AI Principles that guide their product and UX decisions, emphasizing transparency and fairness .

Duolingo provides a great example of ethical AI in action. Its chatbot tutor clearly discloses it’s an AI and transparently explains how it generates suggestions—building trust without overpromising.

In contrast, when ChatGPT was initially used for health-related queries without clear disclaimers, it led to serious misunderstandings. OpenAI’s subsequent updates added explicit warnings, underlining the need for transparency in sensitive use cases.

Ethical UX = Smart Business

Responsible AI isn’t just a moral obligation—it’s a strategic advantage.

A 2023 McKinsey report found that organizations applying ethical AI practices see higher user satisfaction and loyalty, especially when users feel in control and informed .

For small and medium agencies, product teams, and tech leaders, investing in ethical UX is an opportunity to stand out—by creating AI that users genuinely trust.

UX and AI Collaboration: A Shared Responsibility

Embedding ethics into AI UX isn’t the job of designers alone. It requires alignment across:

• UX teams, who identify interaction risks and trust gaps.
• Data and engineering teams, who expose model limitations and explainability.
• Product Owners and CTOs, who enforce responsible standards in delivery.

To measure trust effectively, consider tracking UX KPIs like perceived control, user comprehension, and clarity of AI outputs.

What You Can Do Today

Ready to build more trustworthy AI experiences? Start with these five practical steps:

1. Audit your AI touchpoints – Where is AI active in your product? Do users know?
2. Identify trust risks – Any hidden automation, unexplained decisions, or black-box moments?
3. Add layers of explainability and transparency – via tooltips, visual cues, or summary screens.
4. Train your team – Make sure designers, PMs, and devs understand AI ethics fundamentals.
5. Test for trust – In usability testing, ask: “Do users feel informed? In control? Safe?”

Conclusion

Designing AI interfaces is not just about function—it’s about responsibility.
Trust doesn't emerge on its own—it must be intentionally designed.

If your goal is to create products that are future-proof, user-friendly, and competitive, then embedding ethical UX practices into your AI features isn’t optional—it’s essential.

At UX GIRL, we help organizations design AI experiences that are not only smart—but trustworthy, explainable, and transparent.
Want an AI UX audit for your product? Let’s talk

In the high-speed world of Agile, where user expectations evolve faster than sprint cycles, clear communication is not just a nice-to-have — it’s critical. Yet even in well-functioning teams, UX designers and developers often struggle to stay perfectly aligned. The result? Beautifully crafted prototypes that don’t quite behave as intended once implemented, or ambiguous flows that leave testers guessing.

Enter Gherkin — a simple, structured language that can help bridge the gap between design, development, and product.

What is Gherkin (and why should UX designers care)?

Gherkin is a structured, plain-language format used to write behavior-driven development (BDD) scenarios, typically used by QA and dev teams to write automated tests. But its value goes far beyond testing.

Its real strength lies in its simplicity — it describes user behavior in a "Given–When–Then" format, making it the perfect candidate for aligning cross-functional teams around how a feature should behave.

Example:

This isn’t code — it’s user intent, written in plain English. And that makes it a powerful communication tool for designers.

How UX Designers Can Use Gherkin to Document Intent

Designers don’t need to become developers to leverage Gherkin. Instead, they can use it to clearly define interaction logic — supplementing wireframes, prototypes, and user flows with behavior-driven context.

By embedding Gherkin-style scenarios into design documentation or user stories, designers ensure that the team understands not just what the interface looks like, but how it should behave.

Here’s a UX-specific example:

This format reduces ambiguity and ensures that design intentions translate into correct implementations.

What’s in it for your team (and your bottom line)?

Introducing Gherkin into the UX process may feel like an extra step, but it pays off. Studies show that reducing ambiguity in handoffs and requirements can lead to major efficiency gains.

• Fewer misunderstandings: A McKinsey report found that companies improving requirement clarity saw up to a 40% increase in team productivity (Source: McKinsey & Company).
  
  
• Faster onboarding: Gherkin scenarios give new team members immediate context for how the product should behave.
  
  
• Better alignment with business goals: Stakeholders can validate behavioral flows early — even before development starts.
  
  
• Improved testability: QA teams can use Gherkin to write automated or manual tests directly aligned with design.
  
  

Gherkin becomes a shared language between design, product, dev, and QA — cutting down feedback loops and minimizing rework.

How to Start Using Gherkin in Your Design Process

You don’t need to overhaul your workflow overnight. Start small:

1. Pick one key user flow — such as login, checkout, or onboarding.
   
   
2. Write 1–3 scenarios in Given–When–Then format.
   
   
3. Share them during refinement or planning with developers and testers.
   
   
4. Attach the scenarios to your design files or link them in your backlog.
   
   

This lightweight addition can dramatically improve alignment — even in teams that already communicate well.

Design Is Behavior — Not Just Visuals

UX is about more than how things look — it's about how they work. While wireframes and prototypes show structure and visuals, they often leave room for interpretation when it comes to logic, rules, and edge cases.

Gherkin helps designers express interaction logic in a way that’s unambiguous and testable. And in an Agile team, that means fewer assumptions, faster delivery, and better user outcomes.

At UX GIRL, we encourage product teams to experiment with Gherkin as a way to reduce misalignment and build stronger bridges between design and development. You don’t need to be technical — you just need to care about clarity.

What’s Next?

Ready to give your design handoffs a boost? Start with a single Gherkin scenario for your next feature. Use it to open a conversation between design, dev, and QA. You might be surprised how quickly your team aligns when you’re finally speaking the same language.

Need help integrating UX practices like Gherkin into your Agile process? Reach out to UX GIRL — we help teams turn design decisions into product clarity.

‍

Conversational AI has changed the rules of user experience. With OpenAI Apps SDK, teams can now create embedded applications that live directly inside ChatGPT — offering users seamless, intelligent, and contextual support.

But building these apps isn't just about writing smart code. It's about designing meaningful, intuitive interactions. That’s why OpenAI published official design guidelines — and why UX GIRL is here to help you translate them into real results.

What Are ChatGPT Apps and the Apps SDK?

ChatGPT apps are mini-tools that users can access directly in the ChatGPT interface. They allow users to perform tasks, analyze data, create documents, fetch information from external sources, and more — all within the flow of conversation.

The Apps SDK lets developers define these app interactions using JavaScript while maintaining full compatibility with the ChatGPT interface. But to deliver real value, apps need to feel intuitive — and that’s where UX comes in.

The Core Design Principles from OpenAI

OpenAI’s UX guidelines are built on six core principles. Here’s what they mean in practice, with insights from the UX GIRL team:

Clarity is key

Your app’s interface must clearly communicate what it does, how it works, and what users can expect. Avoid vague labels or overloaded screens. Guide users with simple language and clean layout.

Respect the user’s intent

Let users take the lead. Your app should support user goals, not hijack the conversation. Avoid aggressive prompts or forced flows.

Make progress visible

Users need feedback. Loading indicators, success confirmations, and microinteractions help users trust the process — especially in a conversational UI.

Minimize user effort

Reduce friction wherever possible. Use smart defaults, context-aware suggestions, and auto-filled values to streamline user input.

Be consistent

ChatGPT has a defined look and tone — follow it. Use system UI components and maintain consistency in voice, spacing, and layout.

Fail gracefully

Errors are inevitable. Design them to be informative and friendly. Offer users clear explanations and next steps without making them feel lost.

How Product Teams Can Apply These Guidelines

Following these principles doesn’t require a full UX overhaul — but it does require strategic thinking. Here are two practical ways your team can implement them:

1. UX-aligned development workflow:

• Define realistic user conversations and app responses early.
• Prototype conversations using mock UIs or prompt flows.
• Test early and often — even with basic, Wizard-of-Oz style setups.
• Build in real-time feedback elements (confirmation messages, visual states).

2. UX checklist for Product Owners:

• Does the user always know what they can do next?
• Are all actions and outcomes clearly explained?
• Is app progress or system state visible?
• Is tone and layout consistent with ChatGPT?
• Do error messages guide users constructively?

The Unique UX Challenges of Designing Inside a Chat Interface

Unlike traditional apps, ChatGPT apps don't rely on menus, tabs, or visual hierarchies. Users interact through text — with fluid, nonlinear intent. This makes context one of the biggest UX challenges.

Small design gaps (e.g., unclear responses or missing context) can lead to confusion. That’s why good conversational design includes scenario testing, intelligent defaults, and visible state changes — even without a traditional UI.

Final Takeaways

Designing inside ChatGPT isn’t just about building functionality — it’s about earning user trust through clarity, empathy, and consistency.

At UX GIRL, we recommend:

• Start with a small MVP to test a focused user goal.
• Use OpenAI’s design principles as a design audit tool.
• Involve UX early — especially for dialogue design and testing.
• Don’t rely on AI to do everything. Guide the user intentionally.

Building with Apps SDK? Let UX GIRL help you design AI-powered experiences that convert, engage, and delight.