Explore a general overview of my experience in B2B, B2C and B2B2C.
Executive Summary
Integrating AI into chatbots has introduced a transformative era for UX/UI design within SaaS, particularly in data and dashboard design. This project phase focused on defining the guiding principles for AI-driven chat assistance.
At the heart of the inquiry lay whether AI could augment human capabilities in data modeling without the informal knowledge held by professionals. The tension between providing intelligent Guidance and maintaining quality assurance became a core challenge, prompting a deep exploration into personalized AI assistance.
Through an innovative and user-centric design approach, the project employed A/B testing to compare two courses for AI-enhanced chat: one that offered personalized recommendations with a human tone and another that used a tabbed chat wizard to guide users through data modeling according to best practices.
While the outcome and impact of this phase are still a work in progress, the exploration has already illuminated pathways to redefine roles, enhance collaboration, and uphold quality in the tasks performed. It marks an exciting step toward developing a roadmap that leverages the transformative potential of AI to guide data and dashboard designers.
In conclusion, this A/B testing phase sets a foundation for understanding how AI can be strategically and thoughtfully integrated into chat systems, potentially reshaping the future of data modeling in SaaS. It promises to unlock new AI-driven UX/UI design frontiers by setting the goal of balancing newly acquired capabilities and quality assurance.
Introduction:
A New Frontier in UX/UI Design
Integrating AI into chatbots is transforming UI/UX design in SaaS. This section explores the UX/UI processes in developing an AI-guided chat system to empower data & dashboard designers, ensure quality, and provide a personalized user experience.
Overview
The project explores whether AI can enhance human capabilities without the informal knowledge professionals possess. The tension between AI assistance and quality assurance raises the challenge of harmonizing quality and ability with a focus on personalized, tailor-made AI assistance.
UX Challenge and Vision
The challenge was discovering the best way to create an AI-enabled chat system to simplify data modeling in BI SaaS. The goal was to define the building blocks for a seamless blend of quality assurance and capability extension, demanding innovative, user-centric solutions.
Design Approach
Investigating Feasibility and Quality Assurance: AI leveraged to balance quality with personalized experiences.
User-Centric Design: discovering user tendencies toward open-ended assistance or contextual chat.
Intelligent Guidance: finding user tendencies toward flexible or tight Guidance.
Key Features and Design Decisions
The MVP project used A/B testing to compare two approaches: one employing a tabbed chat wizard, guiding data modeling step by step according to best practices. Automation was introduced in the data modeling process, starting with defining the business goal for tailored support. Another one offers personalized recommendations with a human tone.
Outcome and Impact - work in progress
An A/B test contains two approaches to validate which AI-empowered assistance will improve quality for users who perform tasks beyond their daily capabilities.
Conclusion
This task illustrates innovative ways AI can redefine roles, enhance collaboration, and uphold quality through UX/UI design for SaaS. The insights will offer a roadmap for integrating AI into chat systems, demonstrating the transformative potential of AI in guiding data and dashboard designers.
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