Most AI acquisitions are still capability buys, but platform assembly is the fastest-growing motive. This playbook helps acquirers classify the deal they are actually making, then diligence talent, data rights, model dependency and integration accordingly.
Startup buyers now account for a record share of VC-backed M&A, yet most failed deals trace back to integration. This playbook walks founders and scale-up CEOs through the build-buy-partner call and every stage of a first acquisition, from thesis to value capture.
Most acquisitions do not fail on thesis quality but on translation: only about one in three acquirers fully achieve their deal thesis objectives. This playbook shows how to convert every value lever into evidence, a named owner, a KPI baseline and a Day 100 proof point.
Software targets are diligence-heavy because value sits in revenue quality, not reported EBITDA. This playbook walks buyers through ARR bridges, NRR and GRR cohort discipline, pricing architecture, unit economics, technical debt and AI exposure, ending with an IC-ready evidence checklist.
Tech M&A in 2026 is a conviction market: fewer deals, larger checks, and diligence that must underwrite AI capability, data rights, cyber posture and integration burden before signing. This playbook sets out what to test, the evidence to demand, and the red flags that reprice deals.
AI data centers are not generic real estate: GPUs refresh every four to six years, power is the binding constraint, and a handful of buyers dominate contracted demand. Here is what investors must test on power, compute, contracts, concentration, capex and financing risk before underwriting an AI infrastructure deal.
Software borrowers sit at the centre of the private credit reset: BDCs alone have lent around $115 billion to software firms. This guide sets out how lenders should test ARR quality, retention, AI-driven seat and pricing risk, EBITDA adjustments and covenant headroom before committing.
Private equity firms must evolve from deploying disconnected AI tools to reshaping portfolio company operating models. This comprehensive playbook guides operating partners from Day 1 baselines through the 100-day plan, scaling workflows for a premium exit valuation.
In the AI era, traditional software stickiness is dead. M&A buyers must shift their due diligence to evaluate deep workflow ownership, proprietary data moats, and seat compression risks before paying premium SaaS multiples.
Before approving a deal, private equity investment committees must ask 12 critical AI questions to quantify thesis disruption and margin exposure. While AI accelerates due diligence and surfaces evidence gaps, the final judgement remains human.
Investors assess AI scalability not by counting models, but by evaluating the target's operating model. Discover how to identify pilot theatre, measure workflow integration, and validate P&L impact to ensure a target can scale AI for real returns.
