I find the EBITDA — and I can build what captures it.
Ex-Bain Senior Manager — commercial due diligence, PE operations, and performance improvement. I evaluate AI and operational opportunity through an investment-thesis lens, then lead the initiatives that realize it from diligence through the ownership lifecycle — on both sides of the P&L, because revenue acceleration, not just cost, is where funds now put AI to work.
The AI Operating Partner seat asks for two skills that rarely co-occur.
Recent hands-on machine learning, and boardroom-level translation — in the same person. That pairing is the whole job, and it's why the seat is hard to fill. My version of it: I sized value at Bain in deal-team language (EBITDA, IRR, working capital), and I architect the production systems that deliver it post-close. Bring a portfolio company; I'll bring a specific view on where AI moves its P&L — in the first conversation, not after a diagnostic.
Talent is the primary constraint to scaling AI adoption across PE portfolios — cited by 35% of fund and portfolio-company leaders.
— FTI Consulting, 2026 Private Equity AI Radar (n=200)Six engagements, told straight.
Three kinds of proof, in descending order of certainty: money booked, prizes sized and sequenced, and capabilities that outlived the engagement.
$20M realized · embedded operations turnaround
The situation. A manufacturer's cost structure had drifted and plant performance was eroding faster than the monthly reporting showed. What I did. Deployed as half of a two-person embedded team — plant-floor diagnostics, a rebuilt operating cadence, weekly working sessions with the CEO. The result. $20M in realized savings; the CEO retained the team six months to run weekly diagnostics, train the operating team, and embed the cadence permanently.
~$10M margin delivered in-year · supply-chain reinvention
The situation. Nearly 2,000 SKUs across two categories, statistical forecast error near 47%, and packaging lines running at roughly half their usable capacity — margin was leaking through complexity. What I did. Owned the analytics across three consecutive monthly steering committees: built the margin-per-labor-hour scheduling model, rebuilt the statistical forecast, and ran the complexity-to-efficiency regression that priced what each SKU was really costing. The result. ~$10M of margin delivered in-year (realized); forecast error cut to 39%; portfolio-simplification recommendations worth $29–70M in gross profit and a ~$40M working-capital path, handed to a client execution team with the playbook to run them.
Up to $210M gross-margin prize · materials management
The situation. Post-COVID materials chaos at the flagship plant: ~130,000 production hours lost to shortages, one in ten supplier orders arriving on time, $45M of open order exceptions, inventory up 2.5x in two years. What I did. Led the materials-management workstream end to end — analytic safety-stock targets that buffer up to 70% of shortages, an exception-management playbook with escalation gates, and the design of a central materials organization with a dedicated recovery team. The result. A prize of up to $210M in gross margin (~60% throughput recovery) with $90M actionable in year one and a 35–45% working-capital reduction path — playbooks validated by nineteen client operators across five plants before handover.
$150–200M automation ambition · five C-suite programs
The situation. Years of automation activity had produced less than $1M in savings — effort without an operating thesis. What I did. Sized the enterprise ambition across HR, finance, sales, and order-to-cash; wrote the five opportunity charters; secured a named executive sponsor for each — including the CFO; and sequenced a $40–55M first wave that funded the rest. The result. A board-level program with a $150–200M two-year target against a ~$400M full potential, built to pay for itself from the first wave.
The enterprise automation operating model · still in use
The situation. Automation demand across the enterprise had outrun governance — no common intake, no scoring, no way to tell a good opportunity from a loud one. What I did. Authored the operating model their automation office runs on: an eight-stage intake-to-value lifecycle, decision-rights gates at every stage, three-level opportunity scoring, and ~30 standardized artifacts, with named client owners for each. The result. A single workshop wave surfaced 27+ opportunities worth $11–14M in net benefit against $0.6M of investment; across the three engagements, process cycle time fell 75%. The playbook was handed to client owners — the definition of a capability, not a project.
Portfolio finance playbook · eight CFOs
The situation. A fund wanted finance-function value creation it could repeat across portfolio companies rather than reinvent per deal. What I did. Designed the six-resource playbook suite: a strategy field guide, a balanced scorecard with quartile benchmarks, maturity checklists, process playbooks to the third level of detail, an org-and-talent model, and a systems-and-data roadmap. The result. Deployed with eight portfolio-company CFOs, with targets like total cost of finance from 1.0% to 0.7% of revenue and a sub-7-day close — a repeatable asset, not a report.
All nineteen, one line each.
The six above are the deep-dives. This is the full Bain caseload behind the $500M+ figure — blinded by scale and sector. Ask me about any of them.
$4B agricultural & industrial-equipment manufacturer
Leading OTC consumer-health manufacturer
Multi-billion-dollar investment fund
Global commercial-aerospace OEM
Fortune-50 aviation manufacturer
Enterprise-technology OEM
International healthcare provider
Building-products distributor
Top-5 U.S. health insurer · three engagements
National office-products retailer
Global technology OEM
Regional health payer
Specialty P&C insurer
B2B business-services provider
Impact-investing nonprofit
Diligence to value capture. End to end.
In diligence
Partner with deal teams to assess data and AI readiness, identify value-creation opportunities, and quantify AI-driven upside — translated into investment-relevant terms (revenue, cost, resilience) the IC will act on.
In the operating plan
Design and execute AI-enabled value-creation initiatives across portfolio companies — from first pilot to scaled production — partnering directly with CEO, COO, and CIO to embed AI into core workflows and decision-making.
In governance & reporting
Stand up initiative tracking, value-capture and benefits-realization reporting, and board-ready scorecards. Run the operating reviews and steering committees that hold initiative owners accountable to the number.
As a firmwide capability
Build the repeatable playbooks, frameworks, and tooling that turn one win into a firmwide value-creation engine — and bring an external read on how leading funds and consultancies are using AI for advantage.
Measured in the only units that matter.
Figures from Fortune 100 engagements, anonymized by scale and sector per confidentiality.
Selected efficiency gains from Fortune 100 engagements, anonymized by scale and sector.
The first 90 days, in writing.
Every figure on this page started as someone's ambiguous problem. This is the sequence I run to turn a new one into a number — committed here so you can hold me to it.
Find the number
Diligence sprint across the portfolio (or the P&L): data readiness, two to three quantified opportunities, each sized in margin or revenue terms an IC would underwrite. Not a maturity assessment — a target list with dollar figures.
Put the first initiative in motion
Highest-confidence opportunity moves first — owner named, baseline measured, system in build. I write the operating model and the governance while the build runs; I've authored one an enterprise still runs on.
First system in production, value tracking live
Something running — evaluated, observable, owned by the team that keeps it. A value-capture scorecard the CFO signs off on, and a sequenced roadmap for the next two quarters. In-year impact is the standard I've hit before: ~$10M delivered inside a fiscal year.