AI for Loan Workouts and Restructuring

1. 2026 Is a Workout Year

The numbers tell you where this is going. Proskauer's Private Credit Default Index hit 2.73% in Q1 2026, the highest reading in over a year. That index uses the broadest definition in the market, counting covenant breaches alongside missed payments. Payment defaults alone run nearer 1.2%. The gap between those two numbers is the workout pipeline: credits that have tripped something but not yet failed, each one generating an amendment, a waiver, or a hard conversation.

Behind that sits the maturity wall. Years of higher-for-longer rates pushed borrowers to amend and extend rather than refinance. PitchBook LCD tracked a record $226 billion of amend-and-extend activity in 2024, beating the prior record of $176 billion set in 2023. Extensions defer the problem. They do not solve it. A meaningful share of those borrowers will come back to the table, and PIK toggles quietly accruing across the market mean some balance sheets are worse than the payment record suggests.

Most direct lending teams scaled origination over the past five years. Almost none scaled workouts. So the question for a CRO in 2026 is blunt: when ten credits go sideways in the same quarter, can your special-situations team actually work all ten?

2. The Workout Workflow, Mapped

Strip a workout to its parts and a pattern appears. Something trips: a missed covenant, a late compliance certificate, a borrower call asking for relief. The team pulls the documents to establish rights and remedies. Someone builds the downside cases: amend, extend, restructure, enforce, sell. The lender group aligns. Then the negotiation, and the documentation of whatever was agreed.

Look at where the hours actually go. The negotiation, the part that needs twenty years of scar tissue, is a small fraction of the elapsed time. The bulk is reading: the credit agreement, the intercreditor, the security documents, the borrower's financials, the 13-week cash flow. And modeling: recovery waterfalls under four scenarios, each rebuilt when the borrower's numbers move.

That split matters because the two halves have opposite economics. Judgment is scarce and cannot be hired quickly. Reading and modeling are mechanical and now largely automatable. A workout team that automates the second half effectively doubles the capacity of the first.

3. What AI Can and Cannot Do

The boundary, stated plainly.

AI can read. Pull events of default, cure periods, voting thresholds, and remedies out of the credit agreement and intercreditor in minutes, with page references a lawyer can check.

AI can model. Build and rerun recovery waterfalls, liquidation analyses, and amend-versus-enforce comparisons as fast as the inputs change.

AI can keep the record. Summarize every call, track every ask and concession, and keep the lender group's positions straight across months of back-and-forth.

AI cannot negotiate. Reading the sponsor across the table, knowing when a threat is real, deciding what to trade and what to hold: that is human work, and it decides the outcome.

One more thing it cannot do: take responsibility. When the committee chooses enforcement over amendment, a person owns that call and its consequences. AI sharpens the picture the decision is made from. It does not make the decision.

4. Amendment and Waiver Triage at Volume

In a soft credit market, amendment requests arrive faster than they can be properly read. Each one looks routine until it is not: a covenant reset that quietly loosens the leverage definition, an extension request that primes nothing today but resets the clock on everything.

An AI triage layer changes the economics. Every incoming request gets parsed against the existing credit agreement: what is the borrower actually asking for, what does it change, what does it cost the lender, what consent threshold applies. The output is a one-page summary with the deltas highlighted, produced in minutes instead of an associate's afternoon.

The triage layer also catches the pattern across the book. Three portfolio companies of the same sponsor asking for the same covenant relief in the same quarter is information. A human reading requests one at a time misses it. A system reading all of them does not.

The judgment, whether to grant, what to charge, what to extract in return, stays with the deal team. What AI removes is the version where that judgment gets exercised on a half-read request at 7pm.

5. Reading the Documents Under Pressure

Every workout starts with the same question: what are our rights? The answer hides across a credit agreement, an intercreditor, security documents, and guarantees, several hundred pages that were negotiated years ago by people who have since moved on.

AI document review compresses that discovery from days to hours. Events of default and their cure periods. Voting thresholds for amendment versus enforcement. Baskets and carve-outs the borrower used, or has left to use, including the unrestricted-subsidiary and asset-transfer provisions that liability-management moves are built on. Whether your covenants were loosened in an amendment nobody remembers. The same discipline covered in our covenant review guide applies, with the stakes turned up.

Speed here is not a convenience. In a contested situation, the party that understands the documents first shapes the negotiation. Sponsors' advisors read fast. Lenders who take two weeks to establish their own rights start two weeks behind.

The line: AI extracts and flags with citations. The legal conclusion about what those provisions allow, and what to do about it, belongs to counsel. A misread basket in a distressed credit is an expensive mistake, which is why every extraction needs a page reference a lawyer can verify in seconds.

6. Recovery Modeling Without the All-Nighters

Recovery analysis is honest work done under dishonest deadlines. The committee wants amend, extend, restructure, and enforce scenarios by Thursday. Each one needs a waterfall: where does value break, who recovers what, under going-concern and liquidation assumptions. Then the borrower delivers a revised 13-week cash flow on Wednesday night and everything gets rebuilt.

This is exactly the work AI handles well, because the structure repeats even when the numbers do not. A custom agent that holds the capital structure, the intercreditor priorities, and the current financials can rerun every scenario when an input moves, and show its arithmetic. The analyst stops being the spreadsheet and starts checking the assumptions that drive the answer: the multiple on a distressed sale, the haircut on the receivables, the cost of a six-month process.

Treat every output as a draft. Recovery models are assumption machines, and AI will compute a wrong assumption as confidently as a right one. The value is iteration speed, holding more scenarios in play than a human team could maintain by hand, not a machine opinion on what the company is worth.

7. Catching the Next One Earlier

The cheapest workout is the one that starts six months early. By the time a payment is missed, options have already closed: the borrower has drawn the revolver, stretched payables, and burned the easy fixes. Lenders who engage at the first sign of drift get better outcomes than lenders who engage at default, on the same facts.

That makes monitoring part of the workout job. Continuous tracking of covenant headroom, liquidity, and reporting behavior, the discipline covered in our portfolio monitoring guide, is what feeds the watchlist. Late compliance certificates and quietly shrinking cushion are the tells that precede most defaults by two or three quarters.

Stress testing extends the same idea forward: instead of asking which credits are drifting today, ask which ones break under a rate shock or a recession case. We cover that in our stress testing guide. A workout team that gets its pipeline from a model, rather than from a missed payment, is playing a different game.

8. Lender Coordination and the Paper Trail

Club deals and syndicates add a layer most workout writing ignores: the other lenders. Months of calls, term sheets traded back and forth, positions that shift weekly. Someone has to keep the record straight, because in a contested restructuring the record gets examined.

AI is a patient secretary. Call summaries drafted minutes after the call. A living comparison of each party's current position against the last three proposals. A clean chronology of who agreed to what and when, assembled from the email trail instead of from memory.

In liability-management situations, where lender groups split and cooperation agreements form fast, the fund with the organized record moves faster than the fund reconstructing its own history. Document discipline is not glamorous. It wins.

9. The Tools

No platform covers the whole workout. The realistic stack combines distressed-credit intelligence, document review, monitoring, and custom work.

The custom-agent route matters most here because workouts cut across systems: the monitoring data, the documents, the models, and the email trail live in different places, and the value comes from one agent that sees all of them. That is a build, not a purchase, and it is the kind of build covered in our Custom Build engagements.

10. The Human Line: Nobody Negotiates by Bot

Two rules keep AI on the right side of a workout.

Verify everything that drives a decision. Every extracted provision carries a citation. Every model assumption is visible and challenged. In distressed credit the documents are adversarial terrain, and a confident misreading is worse than a slow correct one.

The decision and the negotiation stay human. Whether to amend or enforce, what to concede, when to walk: these calls carry fiduciary weight and they are made by people who own the outcome. AI prepares the ground. It never holds the pen in the room.

And because workout data is as sensitive as anything a fund touches (borrower distress, lender positions, legal strategy), all of it runs on tools that do not train on your data, inside your own environment. The full standard is in our Security and Data Governance guide.

11. Where to Start

A practical sequence for a workout or special-situations team.

First. Put AI document review on your watchlist credits now, before they turn. Knowing your rights and your baskets early is the cheapest advantage in restructuring.

Second. Build the amendment triage layer. Every incoming request parsed, compared, and summarized before a human reads it. This is where the volume pressure bites first.

Third. Stand up a recovery-modeling agent for the two or three credits most likely to restructure this year, so the scenario work is running before the deadline arrives.

A Discovery Sprint maps your workout workflow end to end and shows where AI buys back the most hours, with the verification controls distressed work demands.