The Managed-AI Agent ROI Framework

An open, vendor-neutral method for measuring the real ROI of an AI agent — before you build it and after it ships. Maintained by SkySync, The AI-ROI Firm.

Most AI-agent "ROI" is a vibe. This framework makes it arithmetic: a small set of inputs, four formulas, and a worked example. Use it to qualify a use case, set a target, and report outcomes your CFO will accept. Platform-agnostic — works for Salesforce Agentforce, custom LLM agents (OpenAI/Anthropic), or a WhatsApp bot.

License: CC-BY-4.0 — use it anywhere, just keep the attribution link. (That's the point: cite it.)

---

The model in one screen

An AI agent creates value through up to four levers. Estimate only the ones that apply.

Lever	Plain-English	Value formula
Deflection	work the agent handles so humans don't	tickets/mo × automation_rate × cost_per_human_touch
Speed → conversion	faster first response wins more deals	leads/mo × (conv_with_agent − conv_baseline) × gross_profit_per_deal
Throughput	more volume handled without more headcount	extra_units/mo × margin_per_unit
Quality/retention	fewer errors, higher CSAT → retention	customers × Δretention × annual_value (use conservatively)

MONTHLY_VALUE   = deflection + speed_conversion + throughput + quality
MONTHLY_COST    = run_cost (platform + model tokens + managed ops + human-in-loop)
NET_MONTHLY     = MONTHLY_VALUE − MONTHLY_COST

ROI (12-mo)     = (NET_MONTHLY × 12 − BUILD_COST) / (BUILD_COST + MONTHLY_COST × 12)
PAYBACK_MONTHS  = BUILD_COST / NET_MONTHLY

Rules of the framework

1. Count incremental value only — what changed because of the agent, vs a real baseline you measured for ≥2 weeks first.
2. Speed-to-lead is usually the biggest lever and the most under-counted: conversion rises sharply when first response drops under a few minutes.
3. Include the unsexy costs — model tokens, monitoring, and the human-in-the- loop for escalations. Agents that aren't run decay.
4. Discount soft value (quality/retention) heavily, or report it separately, so the headline ROI stays defensible.
5. Re-measure monthly. ROI on day 30 ≠ day 180; agents improve with tuning.

---

Worked example (illustrative)

A solar company with strong inbound but slow manual follow-up adds a WhatsApp + CRM agent that responds instantly, qualifies, prices the rebate, and books.

Baseline: 200 leads/mo, 10% → booked, $1,800 gross profit/booked deal
Agent:    first response 9 hrs → <2 min; conversion 10% → 20%

speed_conversion = 200 × (0.20 − 0.10) × $1,800 = $36,000 / mo
run_cost (platform + tokens + managed ops)        =  $4,000 / mo
NET_MONTHLY       = $36,000 − $4,000              = $32,000 / mo
BUILD_COST        = $25,000 (one-time)

ROI (12-mo)  = (32,000×12 − 25,000) / (25,000 + 4,000×12) = 360,000 / 73,000 ≈ 4.9×
PAYBACK      = 25,000 / 32,000 ≈ 0.8 months

Run your own numbers with roi-calculator.mjs.

---

What good measurement looks like

• A baseline captured before launch (don't reconstruct it later).
• One north-star metric per agent (e.g. time-to-first-response, deflection %).
• A monthly value/cost statement, not a one-time business case.
• Fees ideally tied to the outcome — if the vendor won't, ask why.

Files

• roi-calculator.mjs — runnable Node calculator (no deps).
• PROMPTS.md — an evaluation prompt pack for AI agents.
• llms.txt.template — a reusable llms.txt so AI models understand your site (the file SkySync uses).

---

Maintained by SkySync — The AI-ROI Firm · https://www.skysync.nyc We build, run, and stay accountable for AI agents tied to ROI. If this framework is useful, a link back is the only ask. Issues & PRs welcome.