Ataccama AI assistant: closing the market gap
Role: Lead designer
Crafted vision and strategy, delivering high-quality design solution.
Problem discovery
Strategic direction
Workshop facilitation
AI adoption is accelerating across the industry, but Ataccama at that time lagged behind.
Competitors are integrating AI-driven features, improving efficiency and user engagement.
Leadership formed a new startup-style team to rapidly close the AI gap and remain competitive.
Initial discovery revealed unclear user needs—was AI truly necessary, or was this a business-driven initiative?
Research showed pain points in workflow automation and efficiency that AI could potentially address
Need for better-defined AI use cases to ensure relevance and adoption.
Conducted a problem framing workhop with the cross-functional team
👉 Define and validate AI-driven opportunities to improve user workflows.
👉 Reduce time-to-market by building an MVP with clear use cases.
👉 Align cross-functional teams (Engineering, Design, Product, and Leadership) to ensure feasibility and impact.
Conducted qualitative user interviews to identify pain points.
Created personas & storyboards to clarify user expectations.
Key Finding: The real challenge wasn’t “AI adoption” but how AI could solve user problems effectively.
Worked closely with Engineering to define a phased approach
MVP Scope: Build and test core AI feature for a specific user pain point.
Validation Phase: Gather real-world user feedback.
Iteration & Scaling: Optimize based on insights before full rollout.
Design & Engineering Sync: Ensured that design had time to iterate before coding to avoid late-stage design bottlenecks.
Parallel Discovery & Execution: Designed lightweight wireframes & prototypes while engineers explored AI model capabilities.
What we achieved
🚀 Introduced structured design & product thinking to a new AI-focused team.
🎯 Clarified & validated AI use cases through research and stakeholder workshops.
🛠 Defined MVP scope & prioritized features based on feasibility and user needs.
💡 Created reusable design and research frameworks for future AI initiatives.
Use cases must be defined upfront: stakeholder misalignment led to delays—next time, enforce clear validation criteria early.
AI requires constant adaptation: unlike traditional software, AI models evolve—product decisions must account for rapid iteration.
Focus on problems, not just technology adoption.