At a glance
- The assessment runs in the two weeks after close, before any workflow is built, and produces a ranked list an investment committee can act on.
- Expect ERP heterogeneity at a portfolio company: each entity's ledger and access rules can differ, and the assessor works across all of them.
- The deliverable ranks use cases by EBITDA hypothesis, cost, time-to-value, and data readiness, then sorts each into go, defer, or kill.
- Data readiness at close ranges from clean to untangled; the two-week checklist finds out which, before anything is built.
- That four-factor scoring is this assessment's own method, scoped to one portfolio company at a time.
What does an AI readiness assessment for a portfolio company cover?
An AI readiness assessment for a portfolio company starts from the systems and access closing actually produced, not what the deal model assumed. It inventories every system a candidate workflow touches, confirms who can grant access, and rates how ready the data is. Each candidate is scored against that reality, not how the process is supposed to work.
That's different from pre-deal AI diligence, which happens before close and asks whether a target's AI exposure affects the investment case. This assessment happens after close, once the fund has access diligence didn't have. Accordion's PE AI Adoption Benchmark, a May 2026 survey of 150 AI, data and technology operating partners at PE sponsors, found that respondents reported 75% of the portfolio companies they inherit arrive unprepared or only partially ready for AI deployment[1].
The generic version of this self-check — sponsorship, documentation, data access — is covered in our AI readiness assessment: 12 questions piece. This one adds what's portfolio-company-specific: which entity's ERP holds the data, who owns the process, and how findings roll into investment-committee reporting.
- The investment decision itself — whether to buy, at what price — that's pre-deal diligence's job.
- Fund-level or firm-wide AI tooling not specific to one portfolio company's back office.
- Any characterization of what a law or regulator expects; that's a question for compliance or counsel.
The two-week readiness checklist
The checklist below is the assessment, sequenced across two weeks. It assumes a data owner is named and candidate workflows are roughly known; if not, start there.
- Day 1–2: Confirm the systems inventory
List every system touching a candidate workflow at each entity — ERP, billing platform, CRM or deal system, spreadsheets bridging the gaps — and name one data owner per system.
- Day 3–4: Request read-only access
Ask each administrator for a read-only role scoped to the assessment, not a data export; log who granted it and when.
- Day 5: Walk the workflow with its owner
Sit with the person who runs each candidate workflow today and confirm the steps, exceptions, and volume — not the process as documented, the process as run.
- Day 6–7: Pull the baseline
Gather volume, exception rate, and time spent per candidate workflow from the systems reviewed in days 1–4, for a defined period, not one quiet week.
- Day 8: Check data readiness per candidate
Rate each candidate's data clean, partial, or poor, and note what remediation a partial or poor rating would need.
- Day 9: Score against the ranking method
Score each candidate against its EBITDA hypothesis, a cost band, an estimated time-to-value, and the data-readiness rating from day 8.
- Day 10: Sort into go, defer, or kill
Sort the scored list into go, defer, or kill, recording the reason for each defer or kill, not just the verdict.
- Day 10: Package for the operating partner
Assemble the ranked list, the data-readiness gaps, and the access log into the package the operating partner brings to the investment committee.
How long should it take?
Two weeks is the target window for the assessment itself, not the first workflow it leads to: long enough to check data access and walk the workflow, short enough that findings still describe the business the fund bought.
The two weeks compress or stretch based on factors set before the assessment starts: how many entities and ERPs the company runs, how quickly administrators grant access, and how much of the process lives only in someone's head.
What data does the assessor need access to?
Every candidate workflow's assessment depends on reaching the right systems, at the right access level, quickly. Start from the same system list at every portfolio company, even though the products behind it differ:
| System | Why it matters | Access level to request |
|---|---|---|
| ERP or general ledger | Holds the transaction detail this assessment scores | Read-only role, not an export |
| Billing or subscription platform | Where recurring-revenue invoice and dunning data lives | Read-only access to invoices, retries, and disputes |
| CRM or deal system | Shows which workflows touch a customer or vendor relationship | Read-only access to the fields used |
| Close or task-management tool | Confirms whether a close calendar exists anywhere but a spreadsheet | Read-only, or a walk-through if none exists |
| Reporting or BI layer | Shows whether KPI definitions are standardized | Read-only access to existing report definitions |
Accordion's PE AI Adoption Benchmark — a May 2026 survey of 150 AI, data and technology operating partners at PE sponsors — asked how PE firms deploy AI in portfolio finance functions, not which systems one entity runs: 28% of respondents reported activating AI features already embedded in an ERP such as SAP, Oracle, Workday, or NetSuite, and 47% reported a tool-by-tool deployment with no unified architecture[1]. We recommend confirming the actual systems entity by entity before requesting access.
"Read-only" isn't implemented the same way everywhere. Microsoft Dynamics 365 Business Central, for instance, lets an administrator grant only Read Permission on a table, then layer a security filter limiting which records the role can see[3].
Sage Intacct's Company Console API reference separates a user's own permissions from permissions inherited through a role or group, and reserves creating, updating, or deleting users to administrative privilege[4] — not a claim that Foresight has built or certified any integration.
- Which permission model a system uses: one blanket role, or granular settings per table.
- Whether a record-level filter can narrow access to just the entities the assessment needs.
- Who holds administrative privilege to grant, and later revoke, access.
What is the deliverable?
The deliverable is short on purpose: a ranked list of use cases, each scored the same way, sorted into go, defer, or kill, with the gaps a deferred candidate needs fixed before it can move.
How are use cases ranked?
This assessment scores each candidate on four dimensions of its own, in the same order every time, so results are comparable across portfolio companies: its EBITDA hypothesis, a cost band, an estimated time-to-value, and the data-readiness rating from the checklist above.
That discipline matters because it gets checked later: FTI Consulting's 2026 Private Equity AI Radar (May 2026), a survey of 200 fund and operating leaders, found 95% of funds report AI initiatives meeting or exceeding the business case criteria set beforehand[2]. A candidate with no EBITDA hypothesis is hard to hold to that standard.
| Use case | EBITDA hypothesis | Cost | Time-to-value | Data readiness | Go / defer / kill |
|---|---|---|---|---|---|
| Board and lender KPI reporting rollup | Indirect — faster visibility, not a direct lever | $ — mostly integration | 4–6 weeks | Clean: one ERP, consistent chart of accounts | Go |
| Vendor invoice matching (spend leakage) | Direct — recovered spend lowers COGS and opex | $$ — matching plus exception review | 8–10 weeks | Partial: POs captured, receipts manual at two sites | Go |
| B2B collections sequencing | Indirect — pulls in cash sooner; working capital, not EBITDA | $ — rules on existing AR data | 6–8 weeks | Clean: invoice age and history both in the ERP | Defer — behind the Go candidates |
| Multi-entity close task tracking | Indirect — shortens the path to the board package | $$ — one calendar, no shared structure | 10–12 weeks | Poor: no shared close calendar across entities | Defer — fix the calendar first |
| Customer churn prediction | Unclear — no agreed EBITDA link yet | $$$ — new model and pipeline | 4+ months | Poor: churn not labeled in the CRM | Kill — this cycle |
Every band above is invented to show the shape of the output; a real one uses the portfolio company's own numbers. What doesn't change: one row per candidate and a verdict the operating partner can act on directly.
The verdict also depends on where the hold stands: an early Go has time to build and show results; a late Defer may not be worth fixing before exit; a Kill stays out of scope regardless of timing.
What disqualifies a use case at a portco?
A few things sort a candidate into kill or defer regardless of its EBITDA hypothesis:
- Poor data readiness alone sends a candidate to defer. It becomes a kill only when the data gap compounds with no viable EBITDA hypothesis.
- No entity or shared-services team owns the workflow yet, so no one can approve access or review output.
- Volume at this entity is too low to justify a system; a checklist and one person's review cover it for now.
- An ERP module the company already licenses would cover this workflow if turned on.
- The workflow touches data or reporting rules not yet confirmed with compliance or counsel.
None of these are permanent. A defer or kill just changes the order — fix the gap, then re-score before the next update.
Where to start: a first scoped workflow
Start with whichever candidate scored Go and ranked highest, not whichever workflow is loudest. Our AI agents for private equity portfolio companies page maps the recurring patterns this assessment turns into one company's ranked list.
The 360° AI Blueprint is a diagnostic roadmap across your entire organization. It pinpoints where performance leaks, where AI creates the biggest impact, and what each initiative returns — quantified and sequenced from quick wins to long-term moves. It's where we start when the starting point isn't clear; if you already have a specific project in mind, we jump straight in.
- Pinpoint the inefficiencies draining profit — and convert them into growth opportunities.
- The initiatives worth building: AI and automation candidates ranked by ROI, cost, and time-to-value.
- Specific moves for each function, with expected impact on top-line and bottom-line performance.
A 360° AI Blueprint applies that same discipline to one portfolio company's whole back office, not just what this assessment covers; if the workflow is already clear, AI agent implementation starts there directly.
Frequently asked questions
How is this different from pre-deal AI diligence?
Pre-deal AI diligence happens before close and asks whether a target's AI exposure affects the investment case. This assessment happens after close, once the fund has access diligence didn't have, and asks which candidates are actually buildable today, not just plausible.
How do findings map to the hold period?
Early in a hold, a Go has time to build and show results before the next update; a Defer just needs its data gap fixed before the next scoring round. Late in a hold, treat a Defer like a Kill unless the fix lands before exit.
Does the assessment need to happen in the first two weeks after close, or can it wait?
It can run later, but waiting has a cost: the longer it's delayed, the more the portfolio company's systems have already changed under the value-creation plan, and the assessment ends up describing a moving target instead of what closing inherited.
Who from the portfolio company should be involved?
At minimum, one named data owner per system the candidate workflows touch, and the process owner who actually runs each workflow today — not just their manager. Without both, the assessor is reviewing documentation instead of the process as it's really run.
What happens if a candidate's data turns out to be poor quality?
Poor data alone is scored 'defer,' not 'kill' — see the sample table's multi-entity close row, still Poor and Defer, with the fix recorded for the next cycle. It becomes 'kill' only when poor data compounds with no viable EBITDA hypothesis, as in the customer-churn row.
Related reading
- Read what the 360° AI Blueprint delivers, if the whole organization needs ranking, not just this assessment.
- AI readiness assessment: 12 questions covers the generic version of this self-check.
- What is a 360° AI Blueprint? explains how that broader diagnostic works and what it ranks.
- AI agents for private equity portfolio companies maps the patterns this assessment ranks.
- Our AI agent implementation page covers scope and acceptance gates for one workflow.
Sources
- Accordion, "The PE AI Adoption Benchmark" (2026) (accessed 2026-09-30)
- FTI Consulting, "2026 Private Equity AI Radar" (2026) (accessed 2026-09-30)
- Microsoft, "Define granular permissions" — Business Central (accessed 2026-09-30)
- Sage, "Users" — Sage Intacct Company Console API Reference (accessed 2026-09-30)
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