Productivity blocked by manual workflows
Teams were drowning in time-consuming, repetitive tasks, leading to delays, rising costs, and missed revenue opportunities. Scaling these inefficient workflows would only make things worse.
Product Strategy, Vision, Research, IXD, Visual Design
3 months
1 PM, 4 Engs, 2 PDs
Teams were drowning in time-consuming, repetitive tasks, leading to delays, rising costs, and missed revenue opportunities. Scaling these inefficient workflows would only make things worse.
The challenge is that users didn’t trust AI to handle critical tasks and feared losing control over decision-making.
Through user research, iterative design, and cross-functional collaboration, I transformed AI from an abstract concept into a trusted, time-saving assistant, driving a 75% increase in efficiency and enabling teams to focus on revenue-generating work.
By designing AI as a supportive tool rather than a disruptive force, we transformed skepticism into efficiency—proving that when done right, AI is not just a productivity booster, but a key driver of business success.
Adoption
Manual overhead
Task completion time
I led the design of an AI-driven task management platform that automated manual workflows, enabling teams to focus on high-value work instead of administrative overhead.
Design enhancements
• Automated task assignment – AI intelligently assigned tasks to the best-suited team members.
• Real-time progress tracking – A dashboard provided instant visibility into workload distribution.
• Smart prioritization – AI dynamically adjusted priorities based on urgency and deadlines.
• Predictive insights – Actionable analytics helped teams optimize workflow efficiency.
Real-time task progression
I led stakeholder interviews, user research, and process audits to pinpoint key challenges:
• Users feared losing control – They wanted visibility into AI-driven task decisions.
• Confusion around AI’s role – They weren’t sure where automation would help versus hinder.
• Resistance to workflow changes – Existing habits made AI adoption feel like a disruption.
• Need for clear ROI – Teams needed proof that AI would actually save them time and drive measurable business value.
User journey
I focused on designing an AI experience that enhanced, rather than replaced, human decision-making:
• Transparent AI Decisions – Clear justifications for why AI recommended or automated certain tasks.
• User Override & Control – Users could modify, reprioritize, or reject AI-suggested tasks.
• Seamless Workflow Integration – AI worked within existing task management tools, rather than requiring users to adapt to a completely new system.
• Small Wins First – AI started with low-risk automations (e.g., task prioritization), then expanded based on user trust and adoption.
This initiative reinforced a key lesson:
AI adoption isn’t just about automation—it’s about trust, control, and measurable business impact.
• Users need to feel in control – Transparency and flexibility drive AI adoption.
• Seamless integration matters – AI should enhance workflows, not disrupt them.
• Efficiency unlocks business growth – Freeing teams from repetitive tasks allows them to focus on higher-value initiatives.