AI product management · Industry brief

This content is a Q2 2026 digital M&A market report from Acquiry that comprehensively covers M&A activity, trends and strategic themes relevant to AI product management. Key regulatory and M&A developments for AI product management: Regulatory consolidation is occurring around AI infrastructure and digital assets. Fintech and crypto M&A shows financial institutions moving beyond pilot programs to acquiring regulated digital-asset capability, with regulatory licences, custody infrastructure and compliance systems becoming primary value drivers. Bullish's US$4.2 billion acquisition of Equiniti demonstrates the strategic importance of regulated transfer-agent infrastructure for tokenised markets. Standard Chartered's acquisition of Zodia Custody marks the first major systemically important bank to acquire a crypto-native business. Strategic M&A in AI is shifting from generic "AI-enabled" valuations to demonstrable defensibility through proprietary data, regulated access, and infrastructure control. ServiceNow's US$7.75 billion Armis acquisition and Accenture's US$4.175 billion stack of Dragos, runZero and NetRise reflect buyers prioritising cyber-physical visibility, OT security, and integrated threat intelligence. AI companies without proprietary datasets or embedded workflow ownership face more conservative underwriting. The broader M2A trend shows capital concentrating in seven control layers: physical infrastructure (power, data centres, fibre), regulated infrastructure (licences, custody, compliance), proprietary data, mission-critical workflows, security (identity, asset visibility), distribution (audiences, relationships), and IP. Replaceable point solutions and generic horizontal tools lost valuation; mission-critical workflow ownership with high net retention attracted premiums. Infrastructure M&A expanded beyond data centres into power generation, grid access, and permitted development, with MARA's US$1.5 billion Long Ridge acquisition exemplifying convergence of energy ownership and AI compute demand. Digital infrastructure assets with contracted demand and power certainty commanded premium valuations.

# Unmasking AI Washing in Digital Health M&A: Key Due Diligence Considerations The article addresses a critical M&A challenge in digital health: "AI washing," where target companies disguise basic analytics as proprietary AI to inflate valuations. Buyers face unique risks including opaque model architectures, unverifiable training datasets, and unquantified intellectual property exposure from copyright issues, open-source licenses, and data provenance problems. Traditional software due diligence cannot audit AI line-by-line since capabilities emerge from numerical parameters trained on data of varying legality. The key distinction is whether the technology represents a proprietary foundation model or merely a "wrapper" on third-party APIs—the latter offers minimal defensibility and significant platform dependency risks. Since complete AI training data audits are infeasible within normal transaction timelines, buyers should shift from issue elimination to risk quantification, allocating risks through tailored representations, warranties, and indemnities. Remediation often remains technically feasible: models trained on problematic data can be retrained on clean datasets, algorithmic bias can be mitigated through fine-tuning, and open-source issues may be isolable and replaceable. Post-closing structures like ring-fencing and retraining covenants can preserve deal viability while protecting acquirers. Beyond M&A guidance, the content includes extensive global AI regulation updates relevant to product management: the EU AI Act's high-risk classification framework now applies to health systems inferring health status from biometric signals, assessing healthcare access, pricing insurance, or triaging emergency care; the EU's Digital Omnibus delayed high-risk AI rules to December 2027 for standalone systems and August 2028 for embedded systems; and the UK's MHRA National Commission is developing a new regulatory framework for AI in healthcare, exploring "earned autonomy" models for developers with strong post-market surveillance records. The FDA issued updated guidance on clinical decision support software removing automatic exemptions for time-critical use, and announced real-time clinical trial initiatives with AI-enabled proof-of-concept trials and a summer 2026 pilot program. These regulatory developments directly shape AI product governance and compliance obligations across major markets.