Enter a handful of numbers from the client’s business to estimate the impact of a data & analytics foundation — cash released, recurring EBITDA improvement, faster month-end close, and the enterprise value it creates. Every assumption is editable and cited, so the output stays defensible in the room.
The reframe
Priced like infrastructure — pays back like four things
A data foundation gets priced as one number on one date. Its return is spread across four places nobody adds up in the same sentence — the CFO’s working capital, the ops team’s recovered hours, the board’s confidence in the numbers, and next year’s AI spend. Because no single P&L line says “data foundation savings,” the whole thing defaults to the cost column.
1
The money already leaking
A cost they pay today, blind. The build redirects money that already bleeds out — an invisible cost, not a new one.
2
The direct operating return
Working capital released, labor recovered, close accelerated. The cleanest, most defensible math.
3
The amplifier
Durable EBITDA improvements are worth the exit multiple, not their face value. The PE lever.
4
The option value
Whether AI spend returns depends on the data beneath it — most enterprise pilots have yet to show measurable return. The foundation changes the odds.
Client inputs
Ask the client for these seven. Rough figures are fine.
$
days
days
hrs/wk
$/hr
×
$
▸ Assumptions (editable & cited)
Conservative defaults grounded in the published benchmarks cited below. Tune per client; every figure drives the estimate on the right.
DSO reductionAR automation/integration commonly cuts DSO; 15% is conservative. Hackett 2025 Working Capital Survey (18-day top-quartile-vs-median DSO gap); Billtrust/Wakefield 2025 (n=500, >$250M, vendor-commissioned): of AI-in-AR users, 99% cut DSO.
%
Reporting hours automatedReporting/reconciliation automation often removes a large share; 60% is conservative. AFP/APQC FP&A survey: ~42% of finance time is spent gathering data, 25% analyzing; Nucleus Research: $3.03 per $1 on cloud data integration.
%
Close-cycle reductionClose cycle: 4.8 calendar days at the 25th pct, 6.4 median, 10+ at the 75th; floored at 5 days. APQC (n≈2,300).
%
Margin / efficiency gainDurable EBITDA from true cost & margin visibility, as % of revenue — set from a named initiative, not a generic %. McKinsey (2024); user-defined scenario.
%
Revenue protectedBase on identified leakage (billing errors, missed fees, write-offs), not a generic %; 0.5% is a conservative placeholder. Redman, MIT Sloan Mgmt Review (2017), estimate; Gartner (2020): ≥$12.9M avg cost of poor data quality.
%
Illustrative potential enterprise-value impact
$0
Annual EBITDA improvement multiplied by the exit multiple — the amplifier on every recurring gain.
Example scenario — edit the inputs to model a specific business.
$0
One-time cash released — working capital freed from lower DSO
(Revenue ÷ 365) × DSO days cut · standard working-capital math
AI readiness — the multiplier on spend you’re already committing
You’ll spend on AI regardless. Whether that spend returns depends heavily on the data beneath it — most enterprise AI pilots have yet to show a measurable return. This is option value on money already committed, so it sits apart from the year-one total above.
$0
Your planned AI spend committed / planned, annual
$0
Exposed to abandonment risk without an AI-ready foundation — Gartner predicts 60% of such projects are abandoned through 2026
3.7× → 10.3×
Reported AI return per $1 average vs. the most AI-mature cohort — by maturity, an association, not a promised result (IDC, 2024)
The exact math behind each output above, with the benchmark each figure leans on. Illustrative inputs show only the shape of the return — replace with the prospect’s real numbers in discovery.
Every benchmark behind this estimator, with its origin. Cite the source when you use a number, flag enterprise-scale and practitioner figures as directional, and never attach a benchmark to a named client as its actual result.
Gartner (2020) — poor data quality costs organizations at least $12.9M a year on average.
Thomas Redman, MIT Sloan Management Review (2017) — estimates bad data costs most companies 15–25% of revenue.
Nucleus Research (2023) — $3.03 returned per $1 on cloud data integration, ~5-month payback.
APQC (n≈2,300) — monthly close 4.8 calendar days (25th pct), 6.4 median, 10+ (75th pct).
The Hackett Group — 2025 U.S. Working Capital Survey — 18-day DSO gap between top-quartile and median performers.
Billtrust / Wakefield (2025, n=500, companies >$250M, vendor-commissioned) — of enterprises already using AI in AR, 99% report lower DSO; 75% cut ≥6 days.
Gartner (Feb 2025) — predicts 60% of AI projects unsupported by AI-ready data will be abandoned through 2026.
IDC (2024, Microsoft-sponsored, self-reported) — avg $3.7 return per $1 of GenAI; most AI-mature cohort reports 10.3×.
MIT Media Lab / Project NANDA (2025, preliminary, non-peer-reviewed) — 95% of custom enterprise GenAI pilots show no measurable P&L return yet.
McKinsey / KPMG — operations-focused value creation and analytics driving EBITDA and exit valuation in PE portfolios.
Attribution discipline. Don’t quote $12.9M at a $40M operator — it breaks credibility. Use enterprise-scale and practitioner figures as directional color, then let discovery quantify the client’s own version. Every number here is a benchmark, not a promise.
Estimate only. Figures are modeled from the inputs and editable assumptions above, grounded in the published benchmarks cited (Gartner, APQC, Nucleus Research, MIT, Billtrust, and standard working-capital math). They are directional and intended to frame a conversation — not a guarantee, forecast, or a specific client’s actual result. Replace assumptions with the client’s real numbers before presenting as a projection. Enterprise value created assumes the annual EBITDA improvement is durable and valued at the entered multiple.