For a V1, I led product design that helps Schedule Managers move from repetitive manual planning toward automated schedule creation - while balancing customer needs, technical contraints, and an evolving design system.
Phase 1 established the implementation and administrative foundation for automated scheduling. In Phase 2, the focus shifted to designing Oracle’s Advanced Scheduling solution: an automated experience that helps Schedule Managers create schedules more efficiently. To better understand customer needs and inform product direction, I initiated a partnership with our internal research team. The research uncovered a key insight that influenced how scheduling information should be organized and presented.
As the project evolved, technical limitations constrained what we could deliver in the initial release. Anticipating these challenges, I explored alternative design approaches to ensure we could still deliver a valuable V1 experience within the available constraints.
This release represents V1 of a multi-year initiative, establishing a foundation for future enhancements to the advanced scheduling experience. I designed and delivered an automated scheduling experience that enables Schedule Managers to generate schedules and automatically assign shifts based on existing work patterns, shifts, and scheduling rules — reducing manual effort and providing a more efficient path to schedule creation.
At the time, Oracle did have a smart scheduling solution, when Schedule Managers want to create a new schedule for an upcoming scheduling period, they had to resort to excel sheets in order to create and plan their schedules.
Schedule Managers must continuously evaluate staffing needs, like contracted hours, workers preferred availability, scheduling rules, and labor regulations. They need to ensure the right people are assigned to the right shifts while meeting operational and workforce requirements.
The original scope focused on the balancing phase: adding or removing shifts based on staffing needs, reallocating resources through transfers or float pools, and considering external agencies before schedules were published.
The initial strategy was to build on Oracle’s Redwood framework and introduce AI where it could provide meaningful value. Questions remained around how customers would use AI credits, how the experience would be priced, and how much customers would be willing to pay.
The scope expanded to schedule creation, balancing, publishing, and ongoing management with exemptions. The strategy shifted away from a hybrid approach toward an AI-first, agentic experience through Oracle’s Agentic Application.
Because the product and its capabilities were evolving alongside the project, I had to adapt the design approach while staying focused on users’ needs.
I initiated a partnership with our internal research team to better understand how Schedule Managers identify staffing gaps and make decisions about reallocating workers.
One key question was how much of the schedule they needed to see at once. Would a daily or weekly view be enough, or did they need visibility across the full scheduling period and beyond?
Enables schedule managers to proactively protect staffing coverage across departments. It brings the highest-priority staffing risks into one place, explains what is driving each risk, identifies compliant options to resolve it, and helps managers make and authorize timely decisions—before gaps affect patient care or require costly overtime or agency coverage.
Schedule Managers who schedule and manage full-life cycle of staffing across multiple department locations.
Schedule Managers are tired of balancing, they want Agents to handle the repetative task and use smart reasoning.
I led three research sessions through an internal Design Critique group. Participants were Oracle employees with inpatient hospital experience or profiles similar to our primary users.
Validate concepts based on customer-requested needs and existing Redwood design patterns.
Explore an agentic scheduling experience that customers had not explicitly asked for, but that could address deeper pain points.
Participants responded strongly to a cross-department schedule view they called a “House View.” It helped reveal where resources could be reallocated.
Design implication
Showing open shifts alone was not enough. Managers also needed shift, job role, competencies, float usage, acceptance percentage, call-outs, and other staffing indicators.
We expected some resistance to a new AI-driven workflow. Instead, participants were receptive—and often excited—about the possibility of reducing repetitive scheduling work.
Design implication
The opportunity was not just to make balancing easier, but to reduce how much manual balancing managers needed to do.
Schedule Managers already understood a centralized hub for shift coverage, labor allocation, open shifts, and float availability.
Design implication
Position AI as an intelligent layer in a familiar, centralized experience instead of introducing an entirely unfamiliar way of working.
I explored three possible directions to gain alignment with the Product team and presented my design explorations to our VP of Product that helped me understand what is the long-term product strategy.
Improve the existing Redwood experience instead of fundamentally changing the workflow.
Preserve visibility and control while reducing the need to jump between schedules and streamlining existing tasks.
Research feedback was positive, but the full experience could require more than two years of development and risk falling behind an increasingly AI-driven market.
Combine familiar Redwood workflows with AI recommendations at moments where they can provide the most value.
Keep managers involved in decisions while helping them identify potential assignments faster.
A lower-risk transition to AI, but it would require maintaining traditional experiences alongside the agentic model. This direction was not directly tested with customers.
Automate repetitive scheduling work and surface the issues that need human attention.
Shift from managing every task to overseeing exceptions and making important decisions, with human judgment kept in the loop.
V1 also needed to preserve critical familiar workflows, including assigning shifts and responding to Open, Claim, Drop, and Swap requests.
| Direction | Customer control | Automation | Strategic alignment |
|---|---|---|---|
| Buildable + Enhancements | High | Low | Low |
| Hybrid Experience | High | Medium | Medium |
| Agentic Application | High, human-in-the-loop | High | High |
Research suggested customers were open to AI taking on more scheduling work. Combined with the evolving product strategy and competitive landscape, this supported moving toward the Agentic Application direction.
Once product requirements were updated, I organized required metrics into common themes to define what information agents should provide and how it could be made digestible.
Gather the required metrics and group them into themes to establish the content needed for the experience.
Use rapid prototypes to explore ways to present a large volume of information clearly and actionably.
Refine the approach as scope, requirements, product strategy, technical possibilities, and the design system changed.
I used ChatGPT with a skill that turned my product manager’s Confluence requirements into a working prototype. With so many metrics to consider, this helped me explore more digestible ways to present information and test concepts against the Agentic design system.
It was not always accurate—some components did not match the design system—but it was useful for generating inspiration and communicating an idea early.
Designing an agentic scheduling experience meant building new expertise while keeping the team aligned through a rapidly evolving product direction.
Agentic AI was new territory for me. Alongside my design work, I needed to quickly understand what an agent is, what it can do, and how it could create meaningful value for our customers.
How I approached it
I enrolled in classes and online training through our internal learning resources, building the knowledge I needed to make more informed design decisions as the project progressed.
Project scope, requirements, product strategy, and even the design system continued to evolve. This made it challenging to maintain momentum while designing toward a shared direction.
How I approached it
When repeated scope changes began affecting planning, I involved my manager to help coordinate a meeting with the product team and realign on scope and timeline. I also partnered closely with the Agentic Application designers to validate the end-to-end flow and apply the right interaction patterns.
The experience is designed to help managers see the issues that matter, understand the context, and take action with AI-supported recommendations.
Agents can take on repetitive scheduling work and provide best-fit, availability-aware recommendations. Schedule Managers retain oversight and step in when human judgment is needed.
The broader vision also connects workforce, talent, skills, payroll, and costing insights so managers can adapt to changes and fill open shifts with more context.
AI was changing both how I worked as a designer and how customers could work. The opportunity was to rethink the workflow itself—not simply add AI to an existing process.
The project’s stated outcome frames work that traditionally took weeks of planning and 10+ hours of balancing and rebalancing as a short daily workflow.
Help managers identify suitable, available workers to cover shifts, reducing the burden of finding the right assignment.
Help managers respond to change with connected HCM, talent, skill, payroll, and costing insights, guided by an intelligent assistant.
Before publishing, confirm whether the 10–30 minute figure is a measured result, target, or intended product outcome and label it accordingly.
AI did more than change the tools I used as a designer. It challenged me to rethink how our customers could work—and where human attention creates the most value.
When strategy and technology shift quickly, research helps separate a changing solution from the underlying problem worth solving.
Rapid prototyping helped make complex requirements tangible. The designer’s role remained to assess the output, align it with the design system, and make intentional decisions.