PAYCOM
IWant: Transforming fragmented workflow into a centralized, AI-powered experience
IWant is the industry's first command-driven AI engine, centralizing scattered user workflow into a single point of action. As one of two lead designers on this project, I partnered closely with executives, product managers, engineering leadership, and design team leads to define the product vision, interaction model, and cross-modular scalability.
Timeline
2024 Q4 - 2025 Q2
My role
Product Designer
Team
Principal Product Manager,
Engineering Team
Skills
Product Design, Strategic Vision, Stakeholder Management, Interactive Prototyping
Challenge
Paycom's platform is built on a single employee database, allowing organizations to manage the entire employee lifecycle in a secure and reliable manner. As the platform matured, however, the growing number of products, workflows, and data points made information increasingly difficult to access. While the data existed in one place, the user experience accessing it remained fragmented across the platform.
Recognizing this gap, we saw an opportunity to transform how users interact with our software through an AI experience.
Before IWant, users navigated through multiple entry points to access workflows spread across the system.
Objective
We set out to establish IWant as the primary entry point into Paycom's ecosystem, enabling users to easily navigate through multiple products. Success would be reflected through increased product adoption, reduced navigation effort, and stronger user trust. These goals aligned with Paycom's latest investment in AI infrastructure and platform-wide modernization.
Research
To better understand the user expectations on AI experience within an HR system, we conducted competitive analysis among consumer and enterprise AI products. After analyzing emerging interaction patterns and usability standards, our research boiled down to following common themes.
Build trust through consistency
Trust is essential in AI-guided experiences. Successful AI experiences establish confidence through consistent and predictable interactions.
Reduce cognitive load
Users value clear priorities, relevant information, and direct access to actionable next steps, without visual and cognitive buzz.
Design for diverse user needs
AI interactions vary significantly depending on user goals, roles, and technical expertise. The experience needs to support such variety.
Conceptual development
Based on our research findings, we explored two primary interaction models: an omni-search experience and an interactive conversational assistant. We created low-fidelity concepts for both approaches, analyzing how each model supported different user needs, information retrieval patterns, and levels of task complexity.
1. Omni-search
While omni-search provides a familiar and efficient way to quickly locate specific information, it relied heavily on users knowing what to search for and how to phrase their requests. This approach worked well for simple lookups but became limiting when users needed guidance, context, or assistance completing multi-step workflows.
2. Conversational model
The conversational model allowed users to express intent naturally, ask follow-up questions, and receive contextual information and actions in a single experience. Given Paycom's complex HR ecosystem—with thousands of data points, workflows, and varying user expertise—we determined that a conversational approach better aligned with our goal of making workforce information accessible to every user.
Prototyping
Interactive prototypes became one of the team's primary communication tools. I took charge of creating high-fidelity mobile and desktop prototypes to evaluate interaction flows and demonstrate new concepts to executives. This facilitated detailed UI discussions with product managers and engineering teams before implementation began.
Because the product was highly visible across the organization, these prototypes served not only as design validation but also as a shared artifact that aligned technical feasibility, business priorities, and user experience.
Final Outcome
IWant established a single conversational entry point into Paycom's ecosystem, allowing users to retrieve workforce information and complete actions without navigating multiple products. The experience was made available across desktop and mobile and leveraged Paycom's single database to deliver accurate, permission-aware responses.
Beyond the MVP, the project introduced a scalable interaction framework that allowed future modules to integrate into the experience while maintaining consistency across the platform.
Key Takeaways
Leading IWant reinforced that designing AI products is fundamentally an exercise in designing trust.
Success depended less on conversational technology itself and more on creating a clear interaction model, aligning cross-functional teams around shared principles, and building infrastructure capable of supporting long-term product evolution.
It also strengthened my ability to lead without formal authority, communicate across disciplines, and translate ambiguous business goals into actionable design direction.
Impact
The impact extended beyond a single feature release.
Within the design organization, the interaction framework and shared design resources became the foundation for consistent IWant integration across multiple product modules. The design systems team leveraged these patterns to establish reusable guidance for future implementations.
I designed documents and recorded demo videos to guide designers into the IW design standards.
At the product level, IWant fundamentally shifted Paycom from a navigation-first experience toward a conversation-first experience. In some manager-focused workflows, IWant became the primary entry point into the application, reducing reliance on traditional multi-step navigation while strengthening users' relationship with the platform's centralized employee data.
431%
3-Year ROI
600
Manager
hours saved
3,600
Employee
hours saved
IWant became one of Paycom's most significant product launches, receiving company-wide support and public recognition.