How I pitched and led product development for an AI-powered classification and compensation study platform — moving government agencies from 6–12 month consultant engagements to a self-service, always-on intelligence engine.
$2.4B
US public sector TAM
<4 wks
target study time vs. 4–6 months avg
85%
target AI suggestion acceptance rate
PM Lead
pitched, scoped, led eng team
01 — Problem Framing
Government agencies run classification and compensation studies to answer a deceptively hard question: are our jobs classified correctly, and are we paying people fairly relative to the market?
The current answer takes 6–12 months to arrive. An external consultant reads hundreds of job descriptions, manually matches them to market survey positions, builds a compensation model in Excel, and delivers a static PDF. By the time the report is approved, the market data is outdated.
The deeper problem: there's no unified taxonomy. Every agency calls their jobs something slightly different. "Senior Budget Analyst" in one city doesn't mean the same thing as the same title in another. Before any market comparison can happen, someone has to standardize the job taxonomy — and that work is manual, inconsistent, and never auditable.
The status quo
The opportunity
02 — Market Context
62%
budget forecast accuracy for agencies using traditional study methods
Internal customer data
3,500+
target US public sector agencies in the addressable market
SAM: $850M
50–70%
projected cost savings vs. external consultants for agencies on the platform
Business case model
Strategic timing
Rising pay equity and transparency legislation is forcing agencies to adopt defensible compensation structures. Tightening fiscal budgets require precise cost modeling, not six-figure consultant engagements. The window to own this market is now.
The platform advantage
The platform was already serving 83+ agencies for benchmarking. Connecting classification intelligence to benchmarking and cost modeling on a single data model was a moat no legacy consultant could replicate.
03 — User Personas
Maria
HR Director · Mid-Size City
Owns classification and compensation studies end-to-end. Currently hires external consultants at $40–80K per engagement. Accountable to the city manager for defensible results. Frustrated that the process takes nearly a year and the output is a PDF she can't iterate on.
Pain points
→ Consultant cost and timeline is unsustainable
→ Can't answer "what if" questions mid-study
→ Hard to explain methodology to unions or council
David
Compensation Analyst · County Gov
Runs classification work in-house. Spends the majority of each study on the matching step — reading descriptions, cross-referencing survey databases, making judgment calls. His decisions live in spreadsheets with no audit trail.
Pain points
→ Manual taxonomy matching is slow and inconsistent
→ No way to document or defend matching logic
→ Cost modeling requires starting Excel from scratch
Kevin
City Manager · Budget Approver
Approves study results and presents to city council and unions. Not deep in the methodology — needs results to be defensible, transparent, and fast. Budget forecast accuracy of 62% isn't good enough when the council is asking hard questions.
Pain points
→ Can't model different budget scenarios before approving
→ Results aren't ready until decisions are already made
→ No visibility into how comparators were selected
Key insight from discovery: David (the analyst) was the daily user. But Kevin's approval was the bottleneck. The product had to serve David's workflow speed while generating outputs that could survive Kevin's budget presentation — and the inevitable union challenge.
04 — Options Evaluation
Status quo — consultant-led studies
External firm runs study; delivers static PDF
Keyword / rule-based job matching
Exact/fuzzy match job titles to taxonomy lookup
Digitized point-factor scoring
Structured questionnaire scores job complexity factors
AI taxonomy matching + unified platform
SelectedAI reads job descriptions, matches to internal taxonomy with confidence scores; connected to benchmarking and live cost modeling
The key insight: the value wasn't just in faster job matching — it was in connecting classification to benchmarking to cost modeling in a single data model. The internal taxonomy was already the foundation; the AI layer was the intelligence engine that made it scalable.
05 — Product Vision
Organized around the three phases of every compensation study: classify the jobs, model the cost, position against the market.
CLASSIFY
AI-Powered Job Taxonomy & Matching
MODEL
Real-Time Cost & Financial Modeling
POSITION
Market Intelligence & Benchmarking
06 — North Star & Metrics
North Star Metric
Time-to-complete a classification & comp study
Target: Under 4 weeks vs. industry average of 4–6 months. Every other metric cascades from this one.
Leading Indicators
Lagging Indicators
Counter-Metrics
07 — Wireframes
From data import to final report — an eight-step workflow where AI does the heavy lifting at every stage.
Screen 1 — AI Classification Interface
"Performs entry-level civil engineering work including site inspections, plan review support, CAD drafting, and coordination with contractors on infrastructure projects..."
"Duties align with entry-level civil engineering scope — site inspections, plan review, CAD support. Direct match to regional 'Junior' taxonomy benchmark positions. 14 peer agencies hold this exact classification."
Screen 2 — Cost Modeling & Scenario Analysis
| Grade | Min | Mid | Max | Δ |
| E-3 | $58K | $68K | $78K | +3.5% |
| M-4 | $82K | $97K | $112K | +2.8% |
| P-6 | $96K | $113K | $130K | +4.1% |
Current: $68.4K · Market P50: $77.4K
Supervisor/subordinate pay gap narrowing
Within target range · No changes required
08 — Phased Rollout
Phase 1 — Taxonomy Foundation + Pilot Agencies
PilotFinalize the internal job taxonomy. Build the AI classification engine. Run with 3–5 lighthouse agencies to validate match quality, generate before/after case studies, and calibrate confidence score thresholds. This phase is about earning trust before scaling.
Success criteria: 85%+ analyst acceptance of AI taxonomy suggestions; study completed in under 6 weeks vs. prior 6-month baseline.
Phase 2 — Full Platform Launch (GA)
Q3 2026Launch CLASSIFY + MODEL + POSITION as a unified add-on to existing subscriptions. Connect the full eight-step study workflow. Package with premium Labor Costing bundle for upsell.
Revenue target: 40% attach rate on new deals; 15% ARR uplift for bundled customers.
Phase 3 — Continuous Intelligence
RoadmapMove from one-time studies to always-on market monitoring. Agency jobs are continuously scored against the taxonomy as the market shifts. Budget models update automatically as labor agreements change. The study never ends — it becomes a live system.
09 — Reflection
What worked
What I'd change
The through-line: In regulated, high-stakes environments, explainability is the product. An AI that's 94% accurate and opaque is less valuable than one that's 85% accurate with a clear rationale David can cite in a council presentation and Kevin can stand behind in a union negotiation.