Sam Cusano

Role

Product Designer, UX Researcher

Timeline

3 months

Responsibilities

Product Strategy, Vision, Research, IXD, Visual Design

Collaborators

1 PM, 4 Engs, 2 PDs

Context: 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.

The solution is an AI-powered task platform to automate routine work, prioritize tasks, and free up time for high-value initiatives.

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.

Impact

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

100%

Manual cut

50%

Task completion time

20%

Solution: Design AI to feel like an assistant, not a replacement

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.

View prototype

Real-time task progression

Research takeaways

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

Iterative approach

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.

Iteration 1–low fidelity

Iteration 3–MVP

Retrospective

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.

Optimizing workflow: how AI increased efficiency by 75%

Preqin