Designing an AI Digital Experience Platform (AI DXP) starts with understanding how people engage with technology in their everyday workflows. It requires clear, functional solutions that integrate AI without complicating the experience.
This blog shares the journey of creating a sector-agnostic AI DXP focused on delivering intuitive, AI-powered solutions. At its core, the platform emphasizes user-centric design—making sure it works seamlessly across industries while adapting to specific needs.
The process was all about answering practical questions — How can AI simplify tasks without taking control away from users? What design choices make the platform feel approachable and effective?
The goal was to create something that feels natural to use while making the most of the tech to improve productivity and decision-making. By focusing on user journeys, we ensured the platform prioritizes usability, making the technology work for people in a way that’s simple, practical, and meaningful.
Building the foundation
To lay the groundwork, we organized a couple of workshops involving stakeholders, project managers, engineers, and business leaders. These sessions helped align user priorities and product objectives, ensuring the platform addressed meaningful challenges effectively.
The design team started by identifying three key use cases and mapping user journeys to establish the foundation of the Minimum Viable Product (MVP). Tasks were approached collaboratively, with regular discussions to integrate ideas. Daily task-level meetings and weekly strategy sessions kept the development dynamic and adaptable, ensuring the platform evolved in line with user and business goals.
This collaborative process made sure that every aspect of the platform was built to serve real-world needs while remaining flexible for future iterations.
A snapshot of the AI DXP Workshop framework, showcasing a collaborative approach to defining project goals, ideating solutions, and prioritizing tasks.
Mapping the user journey — a step-by-step approach
Cross-Functional team collaboration on user journey mapping, This visual captures the detailed user journey map, broken into key stages: Onboarding, Integration, Configuring, Stimulation/Testing, and Analysis. Each column outlines user steps, actions, goals, experiences, feelings, pain points, and opportunities
We identified five key steps that define the user journey for AI DXP.
1. Onboarding
Users begin by creating an account and exploring the platform’s features. This phase includes interactive tutorials and resources that guide users through uploading and integrating their data. By the end of onboarding, users gain a clear understanding of the platform’s capabilities.
2. Integration
Users connect their existing databases, CMS, or external systems to the platform. This step enables the AI to process datasets, providing the foundation for delivering relevant results and actionable insights. Compatibility with popular platforms and APIs ensures smooth integration.
3. Configuration
This phase involves tailoring the platform to specific business needs. Users select the AI features they want to activate, Customised search parameters & some user experience tweaks. These adjustments ensure the AI aligns with their goals.
4. Testing
Users conduct tests to see how the system performs in real scenarios. This includes running sample queries, analyzing how results are displayed, and fine-tuning configurations to improve precision and usability before full deployment.
5. Analysis and Reporting
Dashboards provide insights into key metrics such as search accuracy, user engagement, and content performance. Users can review analytics like common queries, areas of high engagement, and system-generated suggestions. These reports help track performance and highlight areas for optimization.
Each step was mapped to specific user interactions, emotions, potential friction points, and opportunities for improvement. This granular approach ensured the platform delivered a streamlined experience while addressing real challenges users face in managing and interpreting their data.
User flow and workflows
Based on the user journey, we identified three essential workflows to focus on for the Minimum Viable Product (MVP).
Prioritized user flow defined after team alignment for MVP, A user flow diagram illustrating the step-by-step journey users take through the product. This visual maps out interactions, decisions, and transitions, providing clarity on pathways and potential bottlenecks.
Onboarding
Getting users started and comfortable with the platform.
Onboarding was all about making the first steps clear and approachable. We worked on simplifying account setup and guiding users through their initial interactions with the platform. A lot of attention went into answering questions like:
- How can users see the platform’s potential right away?
- What’s the simplest way to help them get started?
- How do we make this first step feel smooth and purposeful?
The result was an onboarding experience that was straightforward and practical, helping users feel confident as they began exploring the platform.
Configuration
Letting users set up the platform to match their needs.
This workflow focused on creating a simple, clear process for users to adjust the platform’s settings and features based on their goals. Whether it involved customizing preferences or adjusting workflows, the emphasis was on ensuring flexibility without overwhelming complexity.
Testing
Helping users fine-tune the platform with hands-on feedback.
Testing allowed users to explore how the platform worked in their context. Through small iterations and adjustments, they could refine its functionality to fit their specific needs. By testing features in real time, users were able to see what worked and adjust anything that didn’t.
These workflows formed the foundation of the MVP, ensuring the platform was functional, easy to adopt, and adaptable to different use cases without over-complicating the experience.
Building the core
At the centre of this product is an AI-powered content management system (CMS) designed to simplify workflows and automate complex processes. AI capabilities were integrated to handle tasks like dynamic content generation, data management, and personalized content delivery, reducing the need for manual intervention.
The platform includes AI-driven features for content tagging, automatic metadata generation, and contextual suggestions to improve usability and save time. These tools enable the CMS to adapt to user behaviors, making it intuitive and practical for day-to-day operations.
The focus remained on ensuring the system delivers value to users by automating repetitive tasks and enabling smoother interactions with content and the workflows they want to set up.
Guiding principles for good UX
When designing the platform, we focused on two key principles to make the experience intuitive and effective.
1.User control
We wanted users to feel confident and in charge of how they use the platform. Whether it’s deciding when to activate AI integrations or how they fit into their workflows, the goal was to give them clear control without unnecessary complexity.
2.Error prevention and iteration
We built the platform to allow users to test things as they go. They can tweak configurations, see immediate results, and make adjustments in real-time. This process helps them refine how AI fits into their needs without worrying about getting things perfect the first time.
These principles guided every design decision, ensuring the platform feels approachable and adaptable. By keeping users at the center, we created something that works across different industries while still feeling personal and practical.
Lessons learned
Designing AI products is a constant exercise in balancing complexity, uncertainty and simplicity. There’s always a tension between pushing the limits of what technology can do and making sure it still feels approachable for the people using it. Along the way, we learned that the best solutions come from listening—whether it’s through workshops, team collaboration, or iterating based on feedback.
One of the biggest takeaways was the importance of focusing on what truly matters: flexibility, reducing friction, and giving users the control they need to trust the platform. It wasn’t about cramming in features but about creating something that fits naturally into their workflows and makes their lives easier.
Looking ahead, these lessons will guide how we approach every new challenge. Whether we’re rethinking a small detail or introducing entirely new features, the goal will always be the same: to build AI products that feel intuitive, adapt to real-world needs, and make a tangible difference for the people using them. This journey with AI DXP has set the tone, and we’re excited to see where it takes us next.