Sponsor built rival prototype in diligence

A private-equity buyer’s diligence team built a working rival to an artificial-intelligence healthcare target and the buyer walked away from the deal. (bain.com) Bain & Company published the example in an April 2026 brief on merger diligence, saying its team built and tested the prototype “outside in” while evaluating an “AI-native healthcare company.” Bain said the exercise showed the target’s technology could be challenged more easily by incumbents or new entrants. (bain.com) The firm did not name the sponsor, the target, the product category, or the test results. A later legal commentary said an earlier version of Bain’s description circulated with a more specific claim that the prototype was built in roughly two weeks and rivaled the target’s functionality, but Bain’s current public version is narrower. (lucialaw.com) In healthcare software, buyers are already running structured head-to-head clinician evaluations before signing large contracts. Cleveland Clinic said it piloted five artificial-intelligence scribe systems across more than 80 specialties and subspecialties in 2024, using documentation quality, provider satisfaction, implementation, and return on investment as scoring criteria. (aha.org) That testing culture has moved into acquisitions because buyers now treat artificial intelligence as a core diligence issue, not a side question. Bain said “most acquirers” tell the firm that artificial-intelligence diligence has convinced them to abandon at least one deal. (bain.com) The issue is sharper in healthcare because the product is often software that touches clinical workflows, patient data, or both. Sheppard Mullin wrote in October 2025 that buyers need to assess Health Insurance Portability and Accountability Act exposure, data ownership, vendor terms, security, indemnities, and state disclosure rules when a target uses artificial intelligence in care delivery or operations. (sheppard.com) Lawyers have been warning that diligence on artificial-intelligence targets can slip from evaluation into replication. The American Bar Association’s Business Law Today said buyers should examine how a target’s models are trained, whether it owns the needed rights to inputs and outputs, and what safeguards protect proprietary technology. (americanbar.org) Lucia Law argued that the Bain example raises a separate contract question: whether a nondisclosure agreement that allows information sharing for a possible transaction also allows a bidder to use that information to recreate a competing product. The firm said that question turns on the agreement’s “permitted purpose” language and on what the diligence team actually did with the target’s materials. (lucialaw.com) Bain’s broader point was that artificial intelligence can either erode a target’s moat or strengthen it, depending on switching costs, customer loyalty, proprietary data, and workflow lock-in. In the same article, Bain said it reached the opposite conclusion on a separate specialty-workflow software company and found artificial intelligence was more opportunity than threat. (bain.com) The unresolved part of the healthcare anecdote is the one buyers and founders now have to price in: a bidder may no longer need months to decide whether a product is defensible. Bain’s account says one prototype test was enough to end the transaction. (bain.com)