LAT
Lifecycle Assessment Tracker — Turning fragmented campus maintenance into a trusted financial decision system
TL;DR
01 — Snapshot
Product
An ML-driven platform helping a Pacific Northwest university manage 60–80+ buildings across three campuses. LAT shipped as a modular API layer alongside their legacy CMMS/ERP, turning fragmented data into financial intelligence without a system replacement.
My role. I owned product vision, workflows, and the design system, aligning field technicians, managers, accountants, and executives — translating institutional needs into requirements and pushing for field research when stakeholders wanted to skip it.
Timeline was 8 months
Discovery to data consolidation to ML framework to pilot to beta to release. The platform continued beyond my tenure.
Team
1 PM, 1 designer (me), 2 external engineers, plus client-side CAPEX, data science, accounting, and property management teams
Impact
Pilot adoption
95% of property managers (vs. 70% benchmark)
Usage tracking during pilot — most rigorously measured
Data accuracy
70% → 95%
Audit of asset records pre/post canonical ID + sync validation
Reporting time per ticket
~20 min → ~8 min
Observed field timing, pre/post offline-first workflow
Budget revisions
↓ 36%
Planning-cycle comparison vs. prior year
Emergency repair incidents
↓ 12%
Work-order classification during pilot period
Unexpected maintenance costs
↓ 25% projected
Lifecycle model forecast validated against pilot repair data
Adoption, data accuracy, and reporting time are the numbers I'm most confident in because they were directly observed. The cost figures came from the platform's own forecasting layer, so I hold them more loosely and say so when asked.
02 — Context & Problem
Fragmented data was forcing humans to do the work a system should have done
Universities manage billions in infrastructure with fragmented tools: a technician underground can't access repair history, a project manager stitches together spreadsheets and invoices before planning meetings, leadership decides on partial data.
Without a unified system, everyone operates in their own flow with no common path:
"I do the physical work in 30 minutes, but reporting takes another 20."
"Each year I'm choosing between urgent-now and smart-long-term with partial data."
Why existing tools failed
CMMS platforms handle tickets and leases but don't model asset lifespan or CapEx tradeoffs. Preventive maintenance ran on time, not risk, causing over-maintenance and surprise failures. Field tools assumed stable connectivity, so slow digital reporting killed adoption. And replacing the legacy stack wasn't viable — it was wired into procurement and budgeting. They didn't need another silo; they needed a layer that worked with what existed.
03 — The Turning Point
Research revealed we were redesigning a system of coordination
We thought we were customizing a product. Research showed we were redesigning a system of coordination.
The breakthrough wasn't the AI — it was recognizing that fragmented data was forcing humans to do system work. Everyone had access to data; what they lacked was context, prioritization, and trust.
That reframed everything. Instead of one dashboard for everyone, we built role-based surfaces over a shared foundation, surfacing only what was actionable.
04 — Constraints & Design Responses
The legacy ecosystem couldn't be disrupted, so LAT shipped as a modular layer alongside it
Shows human-in-the-loop design
Shows connectivity resilience design
05 — Strategy
One north star: reduce unexpected maintenance costs by 25%
Every feature mapped to one of three drivers: cost reduction, planning accuracy, or adoption velocity. If it didn't serve one, it didn't ship.
06 — Solution
Three pillars turned operational signals into financial intelligence
Automatically link Work Orders ↔ Asset DNA ↔ Cost-to-Date. The conversation shifted from "we'll fix it again" to "this unit cost $42K in three years; replacing now saves $18K."
Annotation: "Reporting time: 20min → 8min"
Multiple graphs looked impressive; managers scanned without acting. We replaced it with a ranked priority queue — action first, analysis second.
Right: Clean priority queue with contextual side panels
Annotation: "Decision time: 14min → 4min"
Turn predictive signals into ranked alerts. Early alerts said "Boiler failure risk: 68%" — managers hesitated. We shifted to consequence framing: "High vibration + 9 years in service → delaying replacement may cost $18K." Humans act on consequences, not probabilities.
Right: "High vibration + 9yr service → $18K savings if replaced now" (consequence framing)
Shows: Critical/Monitor/Safe tiers + top 3 drivers
Month 2: the engine flagged a $180K boiler replacement as Critical. Inspection showed duplicated repair entries had inflated the risk. Three guardrails contained it — manager review gate, visible drivers, no auto-procurement. We added a "Needs Verification" state and multi-signal validation. Adoption held at 95%.
"Needs Verification" state badge
Manager override logged in timeline
In-house scenario comparison with side-by-side cost/timeline deltas. Before LAT, feasibility questions meant commissioning external studies; after, teams ran three scenarios instantly and exported board-ready outputs. Decision velocity over spectacle.
Shows: Cost delta, timeline delta, risk comparison
Export button for board-ready CapEx reports
07 — Tradeoffs
We chose human-in-the-loop over speed, accepting slower decisions to build trust
Interventions marked: Boiler incident (Mo 2), Confidence tiers (Mo 3), Consequence framing (Mo 5), Feedback loop (Mo 7)
08 — Impact
The biggest change wasn't cost savings — it was decision confidence
Operational. Linking work orders to lifecycle cost made repair history visible in real time — managers began reviewing repair frequency before approving repeat fixes. Reporting per ticket: 20min → 8min. Emergency incidents: ↓12%. Projected cost reduction: 25%.
Decision-making. Teams stopped entering meetings to reconcile facts and started entering them to decide. Budget revisions: ↓36%. Planning time: ↓60%. Pilot adoption: 95% (vs. 70% benchmark).
Strategic. Scenario simulation moved feasibility analysis in-house (↓4% consultant spend) and drew expansion interest from other universities — a path from consulting project to scalable platform.
09 — Reflection
Clarity drove action more than completeness — users didn't want more data, they wanted less to think about
What I got wrong. I thought predictive accuracy would drive adoption — it didn't. Data integrity and clarity mattered more; strong predictions failed when the underlying data was messy or hard to act on. Next time: audit data before any predictive expansion, and lead with consequence framing from day one.
What mattered more than expected. The lifecycle linkage, not the AI. Once work orders, asset history, and cost-over-time were reliably connected, decisions improved before the predictive layer even matured — users didn't want more data, they wanted less to think about.
What I learned about AI in enterprise. Adoption depends less on model sophistication than on trust architecture — visible reasoning, human control, confidence-aware outputs, failure containment built in from the start. One wrong high-visibility alert can undo months of adoption. Design for the failure, not the demo.