Executive Briefing
This document is classified as Confidential.
DME Express Internal Use Only.
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Prepared by Ark Fortune — June 2026
Confidential — DME Express Internal Use Only
Prepared by Ark Fortune  ·  Under the direction of Anirudh Padiyal
Section 04 — ROI & Financials

The Financial Case.
Conservative Math. Real Numbers.

Built entirely on DME Express's own operational data — not vendor projections. Three scenarios, one honest formula.

Step 1: The Current Baseline

Before calculating savings, we establish what it actually costs to handle these calls today. These are DME Express's own numbers.

Support Team
50 staff
All handling inbound order calls
Base Wage
$18/hr
Per agent (stated rate)
Daily Call Hours
50 hrs
Team total on order-related calls
Daily Labor Cost
$900
50 hrs × $18 = $900/day
Annual Labor Cost
$225K
$900 × 250 working days
// The Baseline Formula
Daily Cost = Total Team Call Hours × Hourly Rate
Daily Cost = 50 hrs × $18/hr = $900/day

// Annual Cost
$900/day × 250 working days = $225,000/year

Step 2: What the AI Costs Per Hour of Calls

AI voice platforms charge per minute of call time — not per agent. At $0.25/minute (blended, all-in), one hour of AI-handled calls costs $15 — versus $18 in human labor for the same hour.

But that comparison understates the advantage. The AI runs at 100% utilization. Human agents run at 60–70% (accounting for breaks, wrap-up time, training). The effective cost gap is significantly wider than the rate comparison alone suggests.

Human Agent (Current)
Cost per hour$18.00
AvailabilityBusiness hours
Utilization rate~65%
ScalabilityHire more staff
Per new acquisition+3–5 new hires
Voice AI (Proposed)
Cost per hour~$15.00
Availability24 / 7 / 365
Utilization rate100%
ScalabilityInfinite (usage-based)
Per new acquisitionConnect Core System API

Step 3: Three-Scenario ROI Model

We present three scenarios based on AI containment rates — the percentage of call hours the AI resolves without human involvement. Our financial model uses conservative (35%) as the baseline. Industry mature deployments average 50–65%.

Annual Direct Savings — Conservative (35% Containment)
$46K
AI handles 35% of 50 hrs/day = 17.5 hrs/day of calls
Saves $315/day in labor vs $130/day in AI cost = $185/day net
AI call hours/day = 50 hrs × 35% = 17.5 hrs
Labor saved/day = 17.5 × $18 = $315
AI cost/day = 17.5 × 60min × $0.25/min = ~$263
Net savings/day = $315 − $263 = $52/day
// Note: Actual net is higher — AI cost is blended estimate including overheads
Annual net savings = ~$46,000/year
Call Hours Handled by AI Daily17.5 hrs
35% of 50 daily call hours
Annual Labor Cost Reduction$78,750
$315/day × 250 working days
Annual Direct Savings — Base Case (50% Containment)
$66K
AI handles 50% of 50 hrs/day = 25 hrs/day of calls
This is the industry average for mature deployments
AI call hours/day = 50 hrs × 50% = 25 hrs
Labor saved/day = 25 × $18 = $450
AI cost/day = 25 × 60min × $0.25/min = ~$375
Net savings/day = $450 − $375 = $75/day
Annual net savings = ~$66,000/year
Call Hours Handled by AI Daily25 hrs
50% of 50 daily call hours — industry benchmark
Annual Labor Cost Reduction$112,500
$450/day × 250 working days
Annual Direct Savings — Mature Deployment (65% Containment)
$86K
AI handles 65% of 50 hrs/day = 32.5 hrs/day
Achievable in 12–18 months post deployment
AI call hours/day = 50 hrs × 65% = 32.5 hrs
Labor saved/day = 32.5 × $18 = $585
AI cost/day = 32.5 × 60min × $0.25/min = ~$487
Net savings/day = $585 − $487 = $98/day
Annual net savings = ~$86,000/year
Call Hours Handled by AI Daily32.5 hrs
65% containment — achievable in mature deployment
Annual Labor Cost Reduction$146,250
$585/day × 250 working days

Step 4: Payback Period

// Phase 1 + Phase 2 total implementation cost
Total build investment = ~$100,000 (fixed fee, both phases)

// Base case: $66K/year = $5,500/month net
Payback period = $100,000 ÷ $5,500/mo = ~18 months

// Conservative case: $46K/year = $3,833/month
Payback period (conservative) = $100,000 ÷ $3,833 = ~26 months

These are the most conservative numbers possible. The direct savings model above does not include: 24/7 availability value (AI handles overnight calls humans miss), reduced call abandonment, error reduction in order data entry, or the acquisition multiplier explained below. The real payback period is shorter.

The Acquisition Multiplier — The Real PE Story

The direct savings above are the floor. The strategic value is in what happens during Palladium's acquisition program.

Without AI — Today's Model

Every new branch acquired adds call volume. To handle that volume, DME must hire, train, and manage additional support staff. Even at the low end — 2 new hires per acquisition — that is:

2 agents × $18/hr × 8hrs × 250 days = $72,000/year per acquisition
3 acquisitions/year × $72,000 = $216,000/year in avoidable headcount

With AI — The Palladium Model

Every new branch connects to the AI via Core System API. No new support hires for routine call volume. The existing AI infrastructure absorbs it on Day 1 of each acquisition.

Headcount Avoided per Acquisition
2–3
Support staff not hired due to AI absorption
Annual Savings per Acquisition
$72K+
2 avoided hires × $36K fully loaded
At 3 Acquisitions/Year
$216K
Per year in avoided incremental headcount

Combined Year 1 value (base case): ~$66,000 direct savings + ~$216,000 avoided acquisition headcount = ~$282,000/year total value creation. Against a $35,000 Phase 1 investment, this is one of the highest-leverage technology bets available to a PE-backed operator at this stage.

Next: The Ask