ai-readiness

What this skill does

# 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:

| Rank | Company | Opportunity | Est. EBITDA ($) | Months to Value | Gate | First Step |
|---|---|---|---|---|---|---|
| 1 | | | | | Go | |
| 2 | | | | | Go | |
| 3 | | | | | Wait — [blocker] | |

### 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.