AI for Officer Efficiency
Turning a workflow pain point into an AI opportunity
Renee Romero • AI Systems Designer • 2025
A case study in specification precision, evaluation design, trust & security, and context architecture.
Project overview
My role:
AI Systems Designer, Renee Romero
The product:
A letter editor used by government officers to create and send official correspondence to applicants and petitioners.
Responsibilities:
Evaluation design, user flow analysis, stakeholder synthesis, cost & token economics, specification precision for AI concept, roadmap influence
Project duration:
8 weeks (September – October 2025)
Project overview
The problem:
Officers were spending more time fixing formatting than writing official letters. During discovery, I noticed officers constantly leaving the system to copy legal citations from external sites. When pasted back, formatting broke—costing 10-15 minutes per letter.
The goal:
Identify the root cause of this inefficiency and define precise specifications for a solution that could be added to the product roadmap. Quantify the cost impact to build a business case for AI-powered assistance within a trust-critical government letter editor.
Understanding
the system
Evaluation design
Conducted a systematic evaluation across 3 key pages using Nielsen's 10 Usability Heuristics to identify failure patterns and design gaps
Dashboard Page
Draft Page
Letter Page
The insight
A two-fold pain point revealed a deeper design gap.
1. Context switching
Officers leave the letter editor to find legal citations from external sites, interrupting workflow.
2. Manual reformatting
Pasted citations break formatting, requiring manual fixes—wasting time and creating inconsistency.
→
The context architecture gap
Officers lacked the right context surfaced at the right time to find and apply citations efficiently within the editor.
This exposed a decision-support gap—the system had no context architecture to help officers complete their core task without leaving the application.
Quantifying
the impact
Cost analysis
10 min
Time Saved
×
$35/hr
Avg Rate
×
91,256
Letters
=
$532K+
Annual Savings
10 minutes
Avg time officers spend per letter leaving the system to find citations and manually fixing formatting after pasting.
$35 per hour
Avg hourly rate across GS-5 to GS-13 pay grades, calculated from OPM federal salary tables.
91,256 letters
Total correspondence volume from 2023 fiscal year data provided by stakeholders.
Note: With 15 min savings at higher GS levels, annual impact reaches $1.5M+. This cost modeling approach reflects cost & token economics thinking--quantifying ROI before building.
The
opportunity
Current state: The letter editor
Officers use this interface to compose official correspondence
Current pain points:
What if the system surfaced the right citations at the right time?
Proposed solution: AI citation assistant
A precisely specified inline assistant that surfaces relevant legal citations within the editor
Suggested citations for this letter:
8 CFR § 214.2(h)(4)(ii)
INA § 101(a)(15)(H)
Click to insert with formatting
Key benefits:
"AI isn't magic. It's specification precision that anticipates what users need next."
Stakeholder alignment
From pain point to roadmap—building the case for AI exploration
1. Discovery
Synthesized insights from officer interviews and stakeholder research
2. Evaluation design
Connected pain points to measurable time-on-task inefficiencies through systematic evaluation
3. Cost & token economics
Translated time savings into dollar impact to justify building before committing resources
4. Roadmap
Influenced PM to add AI exploration to the product roadmap
Key outcome
AI-powered citation assistance was added to the product roadmap for future exploration, with analytics tracking set up via Matomo for ongoing impact measurement.
✓
Roadmap Adopted
Going
forward
Impact
$1M+
Potential annual savings
10-15 min
Saved per letter
91K+
Letters impacted yearly
Key achievements
Takeaways
AI is a specification precision problem, not just a tech solution
The opportunity was not about adding AI--it was about defining precisely what officers needed and removing friction. AI happened to be the right tool.
Evaluation design speaks louder than opinions
Quantifying the cost impact through systematic evaluation transformed a "nice to have" into a business priority.
Small observations reveal big context architecture gaps
Noticing officers leaving the system to copy citations revealed a $1M+ context architecture problem--the system was not surfacing the right information at the right time.
"AI isn't magic. It's specification precision that anticipates what users need next."
— Project reflection
Let's connect!
✉
If you're interested in further discussions or collaboration, I'm Renee Romero, AI Systems Designer, and I warmly welcome the opportunity to connect. Thank you for exploring this case study!
Email: reneeromero326@gmail.com
LinkedIn: linkedin.com/in/renee-romero
Portfolio: muralderomero.com