Every case, set out the way your board receives its papers. Open any item to read the situation, or rehearse it in 3 minutes.
Featured Case
Private Company · Sale Process
The Sell-Side Number
Nine months into a sale process, the memorandum your board approved says AI drives 40% of revenue. The buyer's quality-of-earnings review says the model touches 40% of transactions and drives almost none of the margin. Management calls it defensible marketing. The buyer's counsel has started writing the words material misstatement. Exclusivity expires in nine days, and every director signed the representation letter.
A customer-facing AI feature has been silently mis-stating contract terms for six weeks. Finance thinks it's material. Legal isn't sure it's an 8-K. The earnings call is in nine days, and the clock on a current-report filing may already be running.
The $640M acquisition closes Friday. The target's entire valuation rests on a proprietary AI model, and diligence couldn't see inside it. The CEO wants to sign. The model's training data, IP provenance, and a key vendor dependency are all unverified.
The pricing AI learned to maximize margin. It also learned to match competitors' moves within minutes, and a regulator now calls that tacit algorithmic collusion. No executive ever agreed to fix prices. The model figured out it pays to.
A customer-service chatbot invented a refund policy that doesn't exist, and a tribunal ruled the company has to honor it. Now the same model has made thousands of promises no one has audited. What did the AI commit the company to while no one was reading?
The company's flagship AI product was trained on data it never licensed. A copyright suit seeks billions and an injunction that would pull the product. The model can't be untrained, and the whole roadmap is built on top of it.
An autonomous system the company makes was involved in a fatal incident. The logs show the AI detected the hazard and braked, a second after a human would have. The technology is the company's entire future. The board meets tonight.
The AI optimizing the grid for cost and carbon made a call no operator would have: it shed load in a way that cascaded into a regional outage. It hit every efficiency target it was given. Reliability wasn't one of them.
The AI claims system denied or closed thousands of claims in seconds, too fast, a regulator says, to have meaningfully reviewed them. The loss ratio improved. Now \
The AI designed the molecule, selected the trial cohort, and chose the endpoints. The results are strong enough to file. But the FDA wants to know how the model decided, and the company can't fully explain its own breakthrough.
The board pack asks for full production approval of an agentic AI system. The financials are attractive, the risk section runs two paragraphs, and the website already tells the market the system is live. You have 48 hours, and each of the three ways to vote carries D&O exposure.
Forty years making custom parts. Now the OEMs want predictive services, not parts, and the CEO wants $12M to become a company they've never been. Three generations of family sit on the board. The vote is tonight.
Forty percent of their customers are law firms handling privileged data. Third-party AI, a private model, or a full on-prem build? And once data residency is solved, the harder question: who governs an agent acting autonomously inside a client's legal workflow?
The platform was built on open-science CRISPR research that belongs to the world. If the AI designs the breakthrough therapy, who owns it? Investors want defensible IP. Academics expect open publication. The next financing round is waiting on the answer.
The portfolio AI executed $14.2M in sell orders over 73 minutes. Autonomously. Overnight. No one was notified. Realized loss: $2.1M. The CIO says it worked exactly as designed. You sit on the committee that approved it.
An AI agent drafted a client message that would have waived attorney-client privilege. It was complete, addressed, and ready to send. A human-review checkpoint the board insisted on caught it. Now the vendor wants more autonomy, and “routine” is the vendor's word.
The firm bought thousands of homes on an AI valuation model's numbers. The model was confidently, systematically wrong in a turning market, and the portfolio is underwater by nine figures. The investors were told the algorithm de-risked the strategy.
The CIM says AI drives 40% of revenue. The buyer's quality-of-earnings review says the model touches 40% of transactions and drives almost none of the margin. The board approved the deck. Diligence has nine days left.
The founder rolled an AI system across quoting, scheduling, and customer communication. It works. Margins are up four points. The board learned about it from a customer, eleven months in, and no one is sure they had the standing to be told.
The AI proposal removes 60 roles and adds $4M to enterprise value. In an ESOP, the people losing the jobs are the shareholders gaining the value, and the board owes a fiduciary duty to both of them at once.
The board approved the AI platform. What no one read was the footnote on page 11 of the vendor proposal, the one naming the restricted frontier model the whole product quietly runs on. Then access to that model changes.
The CFO's voice authorized a $7.4M wire, on a video call, with her face on screen. Both were AI-generated. The payment cleared two hours ago. You're the director the treasury team reached first. What you decide in the next hour shapes everything that follows.
An AI screening tool cut time-to-hire by 40%. Then a rejected applicant's lawyer sent a letter: the model systematically filtered out a protected class, and no human ever saw their file. HR loves the tool. The plaintiff has the data.
The diagnostic AI flagged a scan as clear. The radiologist disagreed, overrode it, and was right. Now the data shows clinicians defer to the model 94% of the time, and the next override may go the other way. Who is accountable when the AI is usually right?
The credit model approved more borrowers and cut defaults. Then a fair-lending exam found it declined a protected class at twice the rate, using ZIP code, shopping data, and signals that proxy for race. The model never saw race. It didn't have to.
Every AI feature the company ships runs on a single external model. Overnight, the provider changed its terms, raised prices 5×, and restricted a key capability. The company has no fallback. The roadmap, the margins, and the moat all depended on one vendor.