Every deal thesis now has an AI value creation line. The problem isn't identifying the opportunity — it's executing at the speed a hold period requires. This is the playbook: five business cases, approximated EBITDA impact, and a deployment model that ships working AI in 3–6 weeks.
These are the operational AI workflows that show up in real EBITDA — not vanity metrics. Each page below is a stand-alone business case with the problem, the solution, approximated benefits, and a 90-day rollout plan.
Lead scoring, forecast accuracy, and rep productivity. The single largest revenue lever in most mid-market portfolio companies.
Tier-1 deflection, cost-to-serve reduction, and hybrid CSAT protection. The fastest cost line to move in the first 90 days.
Close cycle compression, variance narrative, and cash forecast accuracy. Puts board-ready numbers 6 days closer to the decision.
Sourcing, screening, scheduling, onboarding. The play that clears a hiring backlog without adding recruiter headcount.
Portfolio KPI standardization, board pack automation, and natural-language BI. The play that gives the fund a live portfolio view.
Deploy the plays above across 5–15 portfolio companies at 40% lower cost. Fund-level dashboard, standardized KPIs, coordinated waves. Our highest-leverage engagement model.
We deploy inside the portfolio company's existing tech stack, target one operational KPI at a time, and measure impact against the pre-deployment baseline. Every engagement is scoped to be visible on the next board deck.
Deep operational audit of a portfolio company. Ranked AI opportunities by EBITDA impact and time-to-value. Delivered before the next partner meeting.
One of the five plays, scoped with defined deliverables and a fixed timeline. Built inside the company's existing CRM, comms, and reporting stack.
30-day post-deployment measurement against the pre-build baseline. Results documented for the board, the LP report, and the buyer at exit.
The same play, rolled out to 4–14 more portfolio companies with shared benchmarks and 40% cost savings vs. one-offs. Fund gets a portfolio dashboard.
Portfolio companies that deploy AI across two to four operational workflows typically see 200–400 basis points of EBITDA improvement within 12 months. Documented case studies show 280 bps of operational EBITDA lift in year one, driven primarily by cost-to-serve reduction, forecast accuracy improvement, and reallocation of manual labor. The largest single-workflow gains usually come from customer support automation and finance close acceleration.
Five operational AI use cases have the fastest payback: Sales/RevOps, Customer Support, Finance/FP&A, Recruiting/HR Ops, and Data/Reporting Infrastructure. Each is covered as a stand-alone business case above. Customer Support and Finance/FP&A generally show the shortest payback (under 2 quarters) because they compress existing cost lines directly. Sales/RevOps and Data/Reporting produce the largest 12-month EBITDA impact because they compound across the whole company.
A scoped operational AI deployment runs 3–6 weeks from first conversation to a live workflow. The model is: 30-minute diagnostic call, 48-hour AI opportunity assessment with prioritized workflows, then a fixed-scope build inside the company's existing tools. No new platforms, no dedicated IT hires, no 18-month implementation cycle.
Yes. Everything is deployed inside the portfolio company's existing stack — their CRM, their comms, their reporting layer. The company owns all workflows, prompts, and integrations. No ongoing platform fees. No subscription that follows the company post-exit. This is a hard requirement operators flag when evaluating AI vendors during hold periods, and it's how we're built.
Yes, and it's significantly more efficient than one-offs. Portfolio-wide programs realize ~40% cost savings versus independent implementations and compress timelines by half. We support portfolio programs where one playbook (Sales AI, Support AI, Finance AI) rolls out sequentially across 5–15 portfolio companies with shared benchmarks and standardized fund-level KPI reporting.
Two mechanisms. First, direct EBITDA expansion — 200–400 bps of margin improvement compounds through the trailing twelve-month calculation at exit. Second, multiple expansion — buyers increasingly diligence portfolio companies on AI maturity and pay premiums for companies that show automated, repeatable workflows rather than headcount-dependent operations. Documenting AI-driven KPI lift during the hold period materially improves the story at exit.
48 hours from kickoff. Ranked opportunities by EBITDA impact. Delivered before the next partner meeting.
Book an assessment →Response within 1 business day. Fixed-scope, fixed-fee engagements.