Module 3.4: M&A Technical Assessment
Pre-close assessment, integration cost estimation, and synergy identification. Where technology M&A creates or destroys value.
Lesson 1: Pre-Close Technology Assessment
Technology assessment during M&A has unique constraints: limited access, compressed timelines, and adversarial information dynamics. You're evaluating a company that wants to look its best.
Architecture diagrams, tech stack documentation, open source licenses, security audit reports, incident history. What's NOT in the data room is as important as what's in it.
60-minute technical deep dives with CTO, VP Engineering, and key architects. Ask: "What keeps you up at night?" and "What would you fix if you had unlimited resources?"
How easily could you replace each component? If a system is deeply entangled and irreplaceable: high integration risk. If components are modular: lower risk.
Create a technology data room checklist: 25 documents you'd request during pre-close due diligence. Classify each by risk impact (critical/important/nice-to-have).
Lesson 2: Integration Cost Estimation
Post-merger technology integration is where value is created or destroyed. 60% of technology M&A fails to capture projected synergies because integration costs are underestimated 2-5x.
Two companies = two of everything (two CRMs, two billing systems, two CI pipelines). Consolidation timeline: 12-24 months. Cost: typically 100-200% of annual engineering budget.
Migrating data between different schemas, formats, and quality standards. Every migration has data loss risk, downtime risk, and customer impact risk.
Merging two engineering cultures, processes, and toolchains. Expect 15-25% attrition in the first year post-merger as people self-select out.
For a hypothetical merger of two 50-person engineering orgs: estimate total integration cost, timeline, expected attrition, and retention bonus budget.
Lesson 3: Technology Synergy Identification
Synergies are the value created by combining two companies that neither could achieve alone. Technology synergies include shared infrastructure, combined data assets, and engineering talent leverage.
Shared cloud accounts (volume discounts), consolidated monitoring, unified CI/CD. Typical savings: 15-30% of combined infrastructure spend.
Combined datasets enable better ML models, broader analytics, and cross-product insights. The data may be more valuable than the technology.
Combining specialized teams can create capabilities neither company had. Company A's ML team + Company B's data platform team = full AI stack.
Create a synergy map for two hypothetical technology companies: infrastructure savings, data value creation, and talent capability combinations. Quantify each synergy in dollars.