Industry
Headquarters
Baltimore, MD
Founded
2010
Company Size
Key Markets
1M+ users globally
Growth Stage
ARR $8M by 2023
Website
Highlights
Prevented over-automation of core scheduling workflows
Improved clarity in dependency behavior
Reduced support issues tied to timeline edits
Increased user trust in the planning system
Overview
As TeamGantt evolved, automation became a recurring request. Users wanted the system to do more of the scheduling work for them.
On the surface, it made sense: less manual effort, faster planning.
But in practice, it introduced risk.
The goal was not to add automation. It was to protect usability.
Understanding the Problem
Automation was already present through dependencies. Tasks could shift based on relationships. That behavior was powerful, but not always clear.
Increasing requests to automate scheduling further
Existing dependency logic already influencing task behavior
Users starting to lose visibility into what changed and why
Risk of compounding complexity and unpredictability
The system was approaching a tipping point. More automation would reduce control.
Strategic approach
The decision was to optimize for predictability — not automation.
Reinforcing control and clarity
I looked at how users actually worked. They were actively editing schedules, adjusting timelines, and making decisions in context.
They did not want the system to take over. They wanted to understand it.
Instead of adding more automation, the focus shifted to improving visibility and feedback. Dependencies were made clearer. Interactions showed cause and effect. Changes became easier to follow.
Requests for additional automation were pushed back when they compromised clarity.
Principle: Control builds trust. Automation without clarity erodes it.
Key Initiatives
Dependency visibility and feedback
Users did not fully understand how tasks were connected.
What I did
Improved how dependencies were represented in the timeline
Added clearer feedback when actions affected related tasks
Simplified how relationships appeared in the workflow
What changed
Users could see why tasks moved
Behavior became easier to predict
Confidence increased when editing schedules
Product judgment on automation
There was pressure to expand automation capabilities.
What I did
Evaluated requests against impact on user control
Prioritized clarity over feature expansion
Pushed back on automation that introduced ambiguity
What changed
The product remained understandable as it scaled
Fewer unintended side effects in scheduling
A more stable and predictable planning experience
Additional improvements
Reduced confusion around timeline behavior
Lowered support volume tied to scheduling issues
Strengthened mental models for task relationships
Maintained simplicity in a complex system
Cross-Functional Collaboration
Worked closely with product and engineering to align on tradeoffs.
Decisions were grounded in real usage, not feature demand.
The team aligned around long-term usability over short-term expansion.
Financial Impact & Business Enablement
This work protected the integrity of the product. Instead of becoming more powerful but harder to use, it became clearer and more reliable.
Users trusted what they saw. They trusted what would happen next.
Reduced support costs related to scheduling confusion
Improved retention through increased product trust
More efficient product development by avoiding unnecessary features
Stronger long-term usability and adoption
Takeaway
Not building something can be the highest leverage decision.
Clarity is a feature.
Role
Head of Product Design and Design Systems
Led UX strategy and product direction. Evaluated feature requests, guided roadmap decisions, and ensured the core planning experience remained clear, predictable, and scalable.
Related Case Studies
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Keep the product focused on the planning model, not expanding features.
Making product decisions that preserve control and predictability.
Designing scheduling behavior that feels powerful without becoming unpredictable.
Using direct observation to guide product decisions when analytics are incomplete.
Turning design from a service into a system tied to delivery outcomes.
Driving adoption and executive buy-in through phased, measurable impact.
Turning design into a decision driver by making its impact visible.
Turning fragmented research into a system that guides product decisions.