Operationalizing AI Workflows
Improved reliability, consistency, and usability of AI-assisted production workflows by identifying system gaps, refining prompts, and strengthening operational guidance.
Revel (Now Tenari)
At Revel (now Tenari), I worked on improving the reliability and consistency of AI-assisted production workflows within Gradial. While the platform was actively being adopted, teams experienced inconsistent outputs and unclear usage patterns. I focused on diagnosing workflow friction points and refining how AI inputs, prompts, and outputs were structured in real production environments.
Focus Areas
- AI workflow optimization
- Prompt structure refinement
- Operational clarity in emerging systems
- Workflow gap analysis and iteration
Systems I Shaped
1. AI Workflow Diagnosis & Optimization
- Analyzed how teams were using AI tools in real production workflows
- Identified breakdowns in consistency and output reliability
- Surfaced gaps between intended vs actual platform usage
- Translated findings into actionable workflow improvements
2. Prompt Refinement & Standardization
- Refined prompt structures to improve clarity and predictability
- Reduced ambiguity in AI inputs across use cases
- Introduced clearer guidance for expected outputs
- Improved consistency in generated content across teams
3. Workflow Clarity Improvements
- Simplified unclear or overly complex AI-assisted steps
- Helped standardize how teams approached AI tasks end-to-end
- Reduced variation in execution across different users and teams
- Improved usability of Gradial in production environments
4. Cross-Team Feedback Loop
- Gathered feedback from users across design and production teams
- Translated real-world usage challenges into system improvements
- Collaborated with stakeholders to iterate on workflow design
- Strengthened alignment between tool capability and user behavior
Results
- Improved consistency of AI-generated outputs across teams
- Increased clarity in how workflows should be executed
- Reduced friction in day-to-day AI-assisted production
- Strengthened reliability of Gradial as a production tool
- Improved alignment between tool design and real-world usage
Outcome
By refining AI workflows and improving prompt structure, I helped stabilize early-stage adoption of Gradial in production environments. This work ensured the platform was not only usable, but reliable—bridging the gap between experimental AI tools and scalable operational systems.