LBO with AI Uplift

Model how AI-driven EBITDA growth and cost savings change the entry multiple, IRR, and MOIC of a leveraged buyout. This is the showpiece for the AI-in-PE case: a small operational uplift, compounded over the hold period, can materially move the return. Adjust the assumptions below; the math updates live.

Deal inputs

Entry EV/EBITDA multiple

Year-1 EBITDA ($M)

Debt / total capital (%)

Hold period (years)

Exit EV/EBITDA multiple base case, before AI

AI uplift assumptions

Annual AI-driven EBITDA uplift (%) extra EBITDA growth from AI, on top of base

One-time AI implementation cost ($M)

Annual AI run cost ($M)

Entry enterprise value: $0M

Year-1 EBITDA: $0M

Exit EBITDA (AI case): $0M

Exit EV (AI case): $0M

Equity check (at entry): $0M

Exit equity value: $0M

AI-case IRR: n/a | AI-case MOIC: n/a

Base-case IRR (no AI): n/a | Base MOIC: n/a

Assumptions: entry EV = entry multiple x Year-1 EBITDA. Debt = debt% of entry EV. Equity check = entry EV minus debt. Base case: EBITDA flat at Year-1, exit EV = exit multiple x Year-1 EBITDA, exit equity = exit EV minus debt. AI case: EBITDA grows at the annual AI uplift each year (compounded), exit EV = exit multiple x exit-year EBITDA, exit equity = exit EV minus debt minus AI implementation cost. IRR and MOIC are computed on the equity check over the hold period. This is a planning tool, not a financial model; real deals have debt amortization, fees, taxes, and operating detail. Adjust inputs above.

How to read this

The spread between the AI-case IRR and the base-case IRR is the value of AI in this deal. Note the base case here assumes flat EBITDA (no organic growth) to isolate the AI effect: with the default 8% annual AI uplift, AI adds roughly 12 points to IRR and lifts MOIC from 1.0x to about 1.8x, because the exit multiple is applied to a larger EBITDA number. That is the core argument: AI is not just a cost line, it is a multiple and growth driver. Watch the break-even: if the AI run cost eats more than the growth it creates, the AI case will fall below base, which is the sign that the use case is not worth it yet. Use this to size the investment case for deploying AI in a portfolio company.

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