Operational Stabilization & AI-Enabled Recovery in a Post-Disruption Territory
Following the conclusion of a federal consulting engagement impacted by DOGE-related cost reduction initiatives, I was brought into a global equipment manufacturing organization to stabilize a high-risk territory devastated by hurricane damage. Over a defined five-month scope, I retained 27 of 30 at-risk client accounts, reduced ticket resolution time by 67%, cut field survey time by 77%, and introduced AI-assisted workflows across a team with no prior exposure to digital productivity tools.
This engagement was not a standard account management role. Following the conclusion of a federal consulting contract impacted by government cost-reduction initiatives under the Department of Government Efficiency (DOGE), I transitioned into a global equipment manufacturing organization to support a high-risk territory experiencing severe operational disruption after hurricane-related damage impacted service continuity, client responsiveness, and field execution.
The five-month engagement had a defined stabilization mandate: reduce service backlog, improve execution efficiency, protect client retention, and introduce operational structure to a team that had been operating reactively since the disruption. Approximately 30 client accounts were identified as at risk due to ongoing service failures and operational instability. Although the industry differed significantly from my prior background in consulting, technology, and analytics-driven environments, I applied transferable consulting frameworks, operational problem-solving methodologies, and AI-enabled productivity strategies to accelerate impact within a compressed recovery timeline.
Stabilize field operations, reduce open service backlog, protect at-risk client accounts, and introduce scalable AI-assisted workflows to a team with no prior exposure to digital productivity tools · all within a five-month engagement scope.
Three core operational bottlenecks were identified in the first two weeks of the engagement:
ServiceNow was the platform. Email was the process. When a ticket was created, the only notification account managers and managers received was a system-generated email that quickly got buried in inboxes. There was no centralized view of what was open, what was overdue, or who owned what. Tickets sat for days without follow-up not because people didn't care, but because nothing made them visible.
I built a ServiceNow ticket tracker in Excel with dropdown menus for team, category, priority, and status · pulling the same data that lived in the platform into a format the team could actually use in a meeting. Then I designed a structured weekly cadence to run alongside it: a Monday morning 10-minute triage to review all open tickets, a Wednesday check-in to track progress and follow up with IT support, and a Friday close-out meeting to confirm resolved tickets and flag anything still open. Three touchpoints, 10 minutes each, zero additional headcount.
Below is the ServiceNow Open Tickets tracker built in Excel · category dropdowns, priority levels, SLA status, resolution times, and real-time age tracking across all open tickets. Alongside it, an IT Operations Executive Summary dashboard showing SLA compliance, open ticket counts, and team-level breakdown · built so any manager could see the full picture in under 30 seconds.
Since I came from a consulting background, not the elevator industry, I started where any good analyst starts: research. I used AI to run deep market research on the most common upgrade opportunities and lead signals in commercial elevator maintenance, including obsolete door edge types, aging controllers, overheating machine rooms, and cab interior wear patterns. That research became the backbone of the form.
I combined that research with my consulting skills and Generative AI to design a structured digital survey in Excel. Every inspection category became a dropdown menu with standardized condition ratings. The form guided account managers through each section in a fixed sequence, eliminated the "what do I look at next" hesitation, and auto-flagged upgrade priorities by category. Paper and pen were gone. The mental load of running the survey dropped to near zero.
Below is a screenshot of the Executive Survey Form (v2) built in Excel. Each section · Cab Interior, Door System, Machine Room · uses dropdown menus with condition-specific options. Account managers move through the form in sequence, select from standardized choices, and the form flags upgrade opportunities automatically.
Cut diagnostic survey time to under 30 minutes with no loss in quality, and make the branch manager's proposal style transferable to every account manager without requiring his direct involvement in every revision.
System One: The Diagnostic Form
- Free-text entries for each component
- No standardized condition ratings
- No automatic upgrade flagging
- Average completion: 90 minutes
- Dropdown menus for all component conditions
- Standardized ratings: Good / Monitor / Upgrade Required
- Auto-flagged upgrade priorities by category
- Average completion: 27 minutes
System Two: The Proposal Prompt Library
Generic prompts produced generic proposals. The few-shot approach gave Claude a concrete style target. After version 3, proposals matched the manager's tone closely enough that he stopped requesting revisions. The writing style became a team asset, not a single person's skill.
1. The form flags upgrade priorities but never makes the final recommendation. The account manager reviews every flag before it appears client-facing.
2. Machine room and controller assessments require physical inspection confirmation. The form cannot override on-site judgment for safety-adjacent components.
3. The output is an internal working document. It feeds the proposal but is never shared directly with the client.
1. Every AI-generated proposal is reviewed by the account manager before submission.
2. Pricing, contract terms, and technical specifications are always entered manually. The AI handles tone and structure only.
3. If the client has prior relationship history, that context is added manually before the prompt runs.
| Test Scenario | Expected Behavior | Actual Result | Status |
|---|---|---|---|
| Diagnostic form on building with 3 flagged components | Flags all 3, no missed items | All 3 flagged correctly, priority order accurate | PASS |
| Form used by colleague unfamiliar with original checklist | Completed without guidance | Completed in 31 min, 1 clarification on machine room section | PASS |
| Proposal prompt on new client type not in examples | Maintain tone, adjust context | Tone held, but opening led with product specs · caught in review, constraint added | FLAGGED → FIXED |
| Proposal prompt on client with prior service history | Neutral draft, manager adds context manually | Correctly produced neutral draft; context added manually | PASS |
| Route planning with AI on 5-visit day | Optimized by traffic and geography | Reduced estimated drive time by 34 minutes | PASS |
The proposal prompt test revealed one failure mode: without an explicit instruction to lead with client risk, Claude defaulted to product feature language · exactly what the manager's style avoided. One constraint added to the system prompt fixed it. That catch happened in testing, not in front of a client.
At 5 account managers saving 93 minutes per site visit and an average of 3 visits per day, the diagnostic form alone frees roughly 23 hours of field time per week. Applied to a regional team of 20 account managers, that represents over 90 hours of recovered selling time per week · without adding headcount.
