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In this post, Brighton Vino Jegarajan, Aviatrix Head of Design, shares his perspective on UX strategies for incorporating AI into enterprise products.
Enterprise systems are known for their complexity and relatively less intuitive interfaces, leading to subpar user experiences compared to everyday consumer applications. Unfortunately, it often surfaces as a significant disparity between personal and professional everyday technology interactions.
AI advancements present a significant opportunity to revolutionize enterprise products with respect to the user experience. The coming together of AI in enterprise products necessitates more than merely implementing cutting-edge models. It calls for a thoughtful approach to user experience design that fosters trust, maintains user control, and delivers genuine value.
In recent years, we have observed a surge in the incorporation of AI chatbot features into various products. Although these chatbots serve their purpose, and fulfill the common investors’ demands for incorporating AI, this superficial implementation often neglects deeper enhancements in user experience, thereby failing to realize the full potential of AI in truly improving the net user experience.
Hot Take:
AI Chatbots is one of the laziest attempts one could make to bring AI into a product
And here’s why: an AI chatbot interface often carries an undesired resemblance to Clippy from the 90s. It is often implemented as an add-on to an App/Page, appearing in bottom right corner or a ubiquitous button on the screen, that fires up the experience.
Users are typically well-acquainted with the product’s UI, often developing muscle memory for routine tasks. AI Chatbots, however, change this behavior, typically deviating from the standard UX journey in the promise to simplify their task.
One significant challenge that AI Chatbots face in a conversational experience is reliability and accuracy. If your response to address this issue is merely improved prompt engineering and training, it already indicates a failure in delivering good user experience.
Context
Chatbots frequently struggle in comprehending nuanced user queries, which can result in ambiguous response and frustration. They often fail to recognize the prior interactions and path a user has traversed and necessitate users to provide detailed context in order to achieve accurate results. This demand and reliance on the user’s memory and comprehensiveness for accurate results is a tall order.
Impersonal
Users may perceive interactions with chatbots as less personal and engaging compared to human interactions, potentially diminishing overall satisfaction. Consider a recent experience where you desperately sought the option to “speak with a human representative” during a helpline chat or phone call.
Trust Swings
AI can sometimes seem to have a mind of its own, which poses significant challenges-especially in enterprise and critical system products. Losing trust can be an unsurmountable mountain to climb. Even a single unexplained incorrect result can cause users to question every subsequent response, eroding the confidence in the system.
A major issue is the lack of explainability and transparency in AI-generated responses. While newer AI implementations and designs have begun to address these concerns with various countermeasures, the absence of clear context and the inherently impersonal nature of AI often make it difficult for users to relate to the system as they would to a real human.
Enough griping about AI Chatbots, but . . .
What Makes Great AI Experiences?
Explainability and Transparency
Explainability is crucial when integrating AI into enterprise products. Users need to understand how AI generates insights, especially for critical decisions. Explainable AI (XAI) aims to make AI decisions clear and understandable, fostering trust by showing the reasoning behind recommendations or actions, especially ones with significant business impact.
For instance, when an AI system identifies a security threat or suggests a business decision, it should clearly convey:
- The data that informed the decision
- The factors that were most influential
- The system’s confidence in its assessment
- Any limitations or uncertainties in the analysis
Can you spot the AI generated contents and columns on this view?
What elements contribute to explainability and transparency?
Visual Differentiation of AI Content
Users must be able to readily distinguish between content generated by AI and other content on the screen at a glance. This visual differentiation is crucial for maintaining appropriate levels of trust and ensuring that users can apply suitable scrutiny to automated outputs.
Standard Visual Notation for AI throughout the Aviatrix Product
User Holds the Ultimate Control and Feedback
Users should feel in control of the system. Allow them to intervene, change, undo, or dismiss AI actions, and provide feedback. In critical systems, AI should seldom make decisions but make strong recommendations. With users having final decision-making authority. AI is great in integrating multiple contexts for human interpretation, but it’s essential to maintain both “human-in-the-loop” and “human-in-the-end.”
In areas like security, infrastructure management, and compliance, maintaining human oversight is not just a preference, it is the golden rule of thumb.
Context, Context, and More Context
To make AI truly effective, it must be deeply rooted in context, especially in understanding the user’s workflow. AI should not only recognize where the user is within a process but also understand the prior actions, and path to the current point and surrounding ecosystems. The more context an AI system can absorb and utilize, the more relevant and helpful its responses become.
Crucially, users shouldn’t have to repeatedly provide this context. Just as a good friend or partner remembers the details that matter to you, an exceptional AI experience should seamlessly build and maintain context throughout the journey. By anticipating needs and recalling past interactions, AI can deliver responses that feel natural, intuitive, and genuinely supportive and often more human-like.
Organization Practices: The Left Shift
Just as teams “shift security and test left” in the Software Development Lifecycle (SDLC), it is essential to integrate user experience considerations earlier in the AI development process for critical systems. This involves:
- Engaging UX research early to understand the trust and user behavior in the AI model development from the outset
- Establishing a constant feedback mechanism among data scientists, engineers, and UX researchers and designers over the course.
- Accessing robust user behavior metrics and consistently tracking them in aligning with trust, success, and ethical standards.
Product UX Strategy
Building the Foundation of Trust
A successful UX strategy for AI follows a trust-building trajectory by gradually expanding AI capabilities as users get comfortable.
Trust is the true currency of AI adoption. Without it, even the most sophisticated systems will be underutilized or rejected. Building trust requires:
- Start Small: Low-risk, high-value use cases showing clear benefits.
- Deliver Consistency: Reliable and predictable performance.
- Honesty: Be transparent about AI’s limitations.
- Privacy: Clarify data privacy and protection measures.
Expanding AI Capabilities
As trust evolves, enterprises can strategically broaden AI capabilities:
- Gradual Autonomy: Transition from recommendations to constrained autonomous actions with suitable safeguards.
- Personalization Depth: Enhance personalization as the system comprehends user preferences over time.
- Domain Expansion: Broaden AI capabilities to new domains and workflows.
The key is maintaining the appropriate balance between AI assistance and human judgment throughout this expansion. As systems become more capable, maintaining user control becomes more important, not less.
Advanced Human-AI Experience in Aviatrix Products
With these principles, practices, and strategies at Aviatrix we are building responsible AI feature in the product that offer genuine value and uplifts in experience and overall delight. Besides a conversational UX, Human-AI symbiosis can be experienced in two distinct form factors in Aviatrix’s Platform as a Service or PaaS product.
Infused AI: Enhancing Existing User Workflows
Embedding AI into workflows enhances human capabilities with intelligent features. AI is great at processing large context, data volumes and setting relative importance to decision factors better when compared to human brain in limited time,
Key UX considerations include:
- Augmenting contextual suggestions at decision points
- Predictive functionality based on context and history
- Automation of repetitive, low-value tasks with high time-on-task
Example: Workflow Accelerator — Simple Infused AI
Agentic AI: Autonomous Assistants to Humans
Agentic AI in enterprise products can autonomously act, reason, plan, and interact with external tools. While still emerging, it has the potential to transform workflows. While the temptation of autonomy is real, it’s essential to ground deeply with the “human-in-the-loop” and “human-in-the-end” principles.
Key UX Considerations Include:
- Clearly defining boundaries for AI autonomy
- Allowing users to intervene in AI operations
- Providing detailed logs of AI actions
- Gradually increasing autonomous functions as trust grows
Example: The Aviatrix Secure Network Supervisor provides options with increasing autonomy to build trust with agentic AI
The future of enterprise UX with AI isn’t about removing humans from the loop—it’s about creating more effective human-AI partnerships. By applying thoughtful UX principles to AI integration, enterprises can bridge the experience gap between consumer and workplace technologies, creating productive tools that users actually want to use with trust and delight.
Remember that AI in enterprise contexts should augment human capabilities. The goal is to create systems where humans and AI each contribute their unique strengths, resulting in outcomes better than either could achieve alone.
By focusing on the user experience throughout the development process, we can ensure that these powerful technologies serve human needs, enhance productivity, and eventually create a more delightful experience.
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