The Private Credit AI Readiness Benchmark, 2027 Edition
Twenty-five operators at $500M to $10B managers, asked how last month’s work actually got done.
Findings publish in aggregate in Q4 2026. No firm is named.
The Private Credit AI Readiness Benchmark measures whether a manager’s core work, from close to borrowing base to LP reporting, keeps running when the single colleague who knows where every file lives is out. The 2027 edition interviews 25 operators.
Last updated: September 29, 2026
Most AI readiness studies poll executives about plans. Budgets, pilots, the vendor shortlist. This one asks whoever actually runs the work what happened in the last completed month, and it writes down a number of days, a count of systems or a name. Opinions don’t get coded.
The study belongs to Khurram Tehseen, the KORE1 consultant behind our private credit data operations work, who has built the data and AI function inside regulated firms five times. It’s in field now. Until the findings are out, this page shows exactly what gets measured, so you can ask your own team the same questions first. When a firm decides to fix what those questions turn up, the seats come through KORE1’s accounting and finance staffing desk, usually on contract first, then permanent once the seat has shown what it’s really for. The investor side of that work, the quarterly pack and the DDQ, has a page on LP reporting data.
Who Sits for a Private Credit AI Readiness Interview
We ask for operators. The COO, the CFO, the head of portfolio operations or the head of data, whichever of them could name last month’s close date without looking it up or calling the one analyst who keeps the calendar. Whoever owns the AI budget is welcome to sit in, and usually learns something.
Every manager in the sample manages somewhere in the $500 million to $10 billion range. That band is the point. A firm that size has usually bought at least one serious platform, and the work that matters is still done by hand around it. Often by one analyst who has quietly become the database. Putting a number on it starts with counting one person’s last twenty working days.
No firm is named in the results. Not in a footnote, not in a logo strip, not in a quote someone could trace back.
What’s Already Fixed About the 2027 Edition
A filled mark on this page means a fact that won’t change. Four things are settled. Everything else stays an open question until the interviews close.
- 25 operator interviews One per firm, answered from last month.
- $500M–$10B manager size band Assets under management, private credit.
- Q4 2026 findings publish As ranges across all 25 firms.
- 0 firms named Aggregate only, every measure.
Eight Measures of Private Credit AI Readiness, in Interview Order
Each answer is recorded in a unit. Days, a count, a yes or no, a name. No self-ratings. Nothing gets scored from one to five, and the interviewer codes each answer into the bands shown. The empty marks fill in when the findings publish.
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M1
How many business days passed between period end and the pack going out last month?
Unit: business days- 5 or fewer
- 6–10
- 11–15
- 16–20
- 21 or more
-
M2
Your largest borrower. In how many systems does it appear, under how many different spellings?
Unit: spellings- 1
- 2–3
- 4–5
- 6 or more
-
M3
Which of these still get typed in by hand: credit agreements, borrowing-base certificates, compliance certificates, borrower financials?
Unit: document types, of four- 0
- 1
- 2
- 3
- 4
-
M4
Of last month’s borrowing bases, how many were checked line by line, and how many were sampled?
Unit: share checked in full- None
- Some
- Most
- All
-
M5
If one named person had been out for the whole close, would it have finished on time?
Unit: yes, no, or a name- Yes
- No
- Depends who
-
M6
The monitoring or reporting platform you already bought. Who opened it last week?
Unit: people- Nobody
- 1
- 2–4
- 5 or more
-
M7
How many AI pilots are running, and how many of them have a named financial owner?
Unit: pilots with an owner- No pilots
- All owned
- Some owned
- None owned
-
M8
When an LP, a lender or the board asks about AI, whose job is the answer?
Unit: a seat- A named seat
- Whoever is asked
- Nobody yet
open until publication on the record
M1 has its own tool. The close replay diagnostic sorts those days by what held each one up. M3 and M4 are the ground our borrowing base verification page covers, certificate by certificate. For the credit agreements in M3, Khurram has written up the test a machine should pass before anyone trusts what it extracts.
Why the Benchmark Uses Interviews, Not a Survey
A survey gets filled in by whoever the email reached, from memory, in the ten minutes before a meeting. Ask a CFO to rate the firm’s data quality and you get a mood. Moods don’t chart. Ask the head of operations how many days the August pack took and you get a date. A date can be checked.
The paper load keeps climbing, too. Volume is outrunning the book. Secured finance volume hit $6.5 trillion at year-end 2024, a 34.5% rise on 2022, according to the Secured Finance Network’s sizing work, while balances outstanding rose only 4.8% across the same two years, to roughly $12.1 trillion. More money turning over on nearly the same balances means more certificates, notices and statements for somebody to read every month.
We interview that person.
Four Ways to Check Your Own Firm This Quarter
-
M1
Replay last month’s close
Put last month’s close into the month-end close tool stage by stage and see which days were hand work.
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M3 · M4
Test one borrowing base
Pull one certificate and check it against the fields machines misread, then count what nobody verified.
-
M5
Find the one-name dependency
Walk one decision through the AI readiness scorecard and see which link stops at a person.
-
M7
Put an owner on every pilot
Run each live pilot through the AI pilot to production checklist and stop the ones nobody will sign for.
Common Questions
What does AI readiness mean for a private credit manager?
AI readiness is whether the work that matters survives a week without whoever knows where the data sits. It’s a property of how the firm operates, not of the software it owns, which is why the benchmark counts days, systems and names instead of licenses.
When will the 2027 benchmark results come out?
Q4 2026. Not before. Findings go out as ranges across the 25 interviews, with no firm, fund or person identified. Until then, the eight measures on this page are everything that’s public, and nothing here should be read as a result.
Can our firm take part in the interviews?
If you run operations at a private credit manager with $500 million to $10 billion under management, ask. Message Khurram Tehseen on LinkedIn with your role and the firm’s size band. Each interview works through the eight measures above, in order, and every answer comes from last month’s actual files and dates rather than from a policy document or a vendor deck somebody kept.
Where does a firm our size usually land?
Honestly, nobody knows yet. Nobody has the data. That’s the reason for the study. Most peer comparisons in this market come from vendors describing their own customers. The 2027 edition gives a range for each measure across managers from $500 million to $10 billion, so your own answers have something real to sit against.
How is this different from an AI readiness assessment?
An assessment scores one firm, while a benchmark compares many. KORE1’s AI readiness scorecard walks a single decision at your company link by link. The benchmark asks 25 firms the same eight questions, so each answer has a range around it. Use the scorecard now and the benchmark once it’s out. Different jobs.
Why study private credit on its own?
$1.7 trillion, give or take. That’s roughly where a February 2024 Federal Reserve note sized private credit, and the same note called the sector relatively opaque. Most of that opacity lives in documents, borrower files, certificates and notices that were never built to be data, and that’s where readiness shows up first, long before anyone at the firm gets to ask which model to buy.
What if our answers show the firm isn’t ready?
Usually a seat gets filled before any software gets bought. Seats first. The gaps that repeat are a data engineer who owns the pipelines, plus a credit analyst to clear the exceptions they throw up, sometimes with a fractional chief data officer setting the order of work. KORE1 staffs all three, on contract or as direct hires.
Bring Us the Question You Couldn’t Answer
Most operators hit one of the eight they can’t answer without asking someone else. Almost everyone does. That gap is usually where the work starts, and often the hire. Tell us which one it was and we’ll say plainly whether it needs a project, a person or nothing yet. For what it’s worth, a year out, 92% of KORE1 placements haven’t left.
Talk Through Your Answers →Want your firm to be one of the 25? Message Khurram Tehseen on LinkedIn. The seats behind most fixes are data engineers, credit analysts and fractional chief data officers.
