AI Readiness

Portfolio AI Readiness

Workflow

Step 1: Connect to Portfolio Data

First, ask the user where the portfolio materials live. Don't assume — offer the options:

• MCP servers — data room, SharePoint, Google Drive, or a portfolio-ops database if one is connected
• Local files — a folder path on disk with quarterly decks, financials, board packs
• File uploads — drag PDFs, PowerPoint, or Excel directly into the conversation

Once connected, pull quarterly updates, board decks, and financials for the portfolio (or a subset). For each company, extract: sector, revenue, headcount by function, tech stack mentioned, and any AI/automation initiatives already in flight.

If the user provides a single company, still run the scan but skip the cross-portfolio ranking.

Ask up front if not obvious from materials:

• Hold period remaining per company (AI payback matters less 12 months from exit)
• Whether any portco has already deployed something that worked

Step 2: Per-Company Scan

For each company, answer three gate questions. All three yes → Go. Any no → Wait with a note on what unblocks it.

1. Is the data there? Can they produce a clean input for the use case — customer list, invoice feed, contract repository — without a 6-month data project first?
2. Is there an owner? Someone on the management team who will drive this, not a sponsor who will "support" it.
3. Can we pilot in 30 days? One team, one workflow, off-the-shelf tooling. If the answer starts with "first we'd need to...", it's not a quick win.

Then identify the top 2-3 leverage points. Look for these patterns in the cost structure and operations:

Back Office (usually fastest to pilot)

• Invoice processing, AP/AR matching, expense categorization
• Contract abstraction — vendor agreements, leases, customer MSAs
• Month-end close: reconciliations, flux commentary, lender reporting first drafts

Revenue / Front Office

• RFP and proposal first drafts — big lever if revenue is project-based
• Sales call summaries and CRM hygiene
• Customer support ticket triage and first-response drafting
• Quoting for configured / complex products

Operations (sector-dependent)

• SOP and quality documentation generation
• Scheduling and dispatch (field services, logistics)
• Code generation and review (software portcos)

For each leverage point, capture in one line: what it replaces, FTE-hours/week saved (assume 30-50%, not 100%), and whether it's buy-off-the-shelf or needs a light build.

Step 3: Rank Across the Portfolio

Stack every leverage point from every company into one list. Rank by:

1. Dollar impact — annualized EBITDA contribution (cost out + revenue lift, net of tool cost)
2. Speed to value — months to first measurable result
3. Probability — discount for data quality, change management risk, management team capability

Tiebreaker: favor opportunities with <18 months of hold period remaining — those need to move now or not at all.

Output the stack:

Step 4: Find the Replays

The highest-leverage move in a portfolio is running one successful play at multiple companies. Scan for:

• Same sector, same function — two healthcare services portcos with manual prior-auth? One implementation, two deployments.
• Same tool, different company — if one portco already has a working invoice-processing setup, flag every other portco with >$Xm in AP volume as a fast follower.
• Shared vendor leverage — three portcos buying the same tool is a pricing conversation.

List each replay with the lead company (who proves it) and follower companies (who copy it).

Step 5: Output

One page for the operating partner, structured for a portfolio review:

1. Top 5 across the portfolio — the ranked table from Step 3, with owner and 30-day first step
2. Replays — 2-3 playbooks that hit multiple companies at once
3. Go / Wait by company — one line each; for Waits, what unblocks them
4. What we're NOT doing — the opportunities that looked good on paper but failed a gate; saves the operating partner from relitigating them every quarter
5. Aggregate EBITDA contribution — total portfolio-wide AI opportunity, split Year 1 quick wins vs. Years 2-3 scale

Important Notes

• Rank by dollars, not excitement. A boring AP automation that saves $400k at a $40m revenue company beats a flashy customer-facing chatbot every time.
• The binding constraint is almost always data, not models. If a company can't produce a clean customer list, AI isn't the first project — a data cleanup is. Say so plainly.
• Off-the-shelf first. Custom builds are slow, expensive, and fragile for companies without engineering depth. Favor tools they can buy and deploy.
• Ownership is the real gate. A quick win with no internal owner dies in 90 days. If no one on the management team wants it, mark it Wait regardless of the dollar size.
• Hold period drives urgency. A company 3 years from exit can afford a foundational data project. A company 12 months out needs something that shows up in the LTM EBITDA for the CIM — or skip it.
• Failed pilots are signal. If management already tried something and it didn't stick, find out why before proposing the same thing again.