AI Is Becoming Indispensable in Dealmaking, But Trust and Governance Will Determine the Winners

A new Datasite/FT Longitude survey of 1,000 senior dealmakers across 27 countries finds AI is now mainstream in M&A: 62% say human-only decision-making is no longer defensible and 71% expect AI-ignoring firms to struggle within five years. Adoption is concentrated in due diligence (50% embed AI regularly, the highest reported ROI) and for risk reduction (66%); however, trust is the key constraint, with accuracy (71%) and security (70%) the top requirements. Roughly half (45%) say signing a deal should always remain a human responsibility, and 27% aren’t using AI for board reporting—implying governance and controls are likely to differentiate winners from laggards.

Analysis

This reads less like a broad AI adoption thesis than a procurement shift toward governed workflow software. In M&A, the value will accrue to vendors that can prove audit trails, permissions, and human override—not to the best model on a demo. That favors incumbents already embedded in transaction pipes and adjacent security stacks, while standalone “AI for finance” startups face commoditization and higher churn risk once buyers standardize around a few trusted platforms.

Near term, the market may overreact to the survey’s headline adoption rate; self-reported usage is usually a lagging indicator of budget, not earnings. The cleaner 1-3 month catalyst is renewal and upsell, not new-logo velocity: if AI is truly embedded in diligence, vendors with data-room, content-management, and compliance modules should see better retention and pricing power. The 6-18 month risk is that security incidents or bad outputs trigger a reversion to heavier human review, slowing automation spend and pushing customers toward closed, enterprise-grade environments.

Contrarian view: consensus is likely missing that “AI in dealmaking” is a governance story, not a model-story. The biggest winner may be the boring middleware provider that makes AI usable inside regulated workflows. The biggest loser may be any point solution selling speed without defensibility, because buyers will pay for trust only after one public failure.

Overall, this is a modest-positive read-through for transaction infrastructure and cybersecurity, but not a standalone bullish signal for the M&A cycle itself. If deal volume weakens, AI tools can still gain share inside the smaller pool of transactions, but that is a revenue-quality story more than a growth-rate story.

Key Decisions for Investors

• Accumulate SSNC and DFIN on weakness over the next 1-3 months; thesis is that governed deal workflows become a sticky premium feature, supporting retention and modest ARPU expansion. Falsifier: no improvement in transaction-related commentary or margin mix by the next two quarters.
• Prefer PANW and CRWD as secondary beneficiaries of the same spend cycle; buy dips rather than chase strength, since security becomes the gating item for AI deployment in sensitive workflows. Risk/reward is better as a quality compounder than as a direct M&A beta trade.
• If you want a relative-value expression, long SSNC/DFIN vs. short IGV over a 3-6 month horizon: the trade is that compliance-heavy workflow vendors monetize AI faster than the broader software basket. Falsifier: IGV outperforms on actual enterprise AI monetization rather than narrative.
• Do not chase pure-play ‘AI for finance’ names on this headline alone; treat them as watchlist candidates until they show audited deployment or revenue conversion. The article is a sentiment signal, not proof of spend.
• Set an alert for any deal-related AI security breach or regulator comment on AI disclosure in diligence/board materials; that would be the fastest way to reverse the thesis and likely benefits PANW/CRWD while hurting point-solution AI vendors.