N10-9: Post-Acquisition AI Integration
The playbook for integrating an acquired AI company without destroying its value.
🎯 What You'll Learn
- ✓ Plan AI team integration
- ✓ Merge ML infrastructure
- ✓ Retain key talent
- ✓ Realize synergies without disruption
Lesson 1: Day 1 Through Day 100 Playbook
AI acquisitions fail most often in the first 100 days. The playbook: Days 1-30 (protect the AI team — no org changes, no tool changes, no process changes), Days 31-60 (map integration synergies and dependencies), Days 61-100 (begin incremental integration with the AI team's buy-in). The single most important rule: keep the AI team intact and productive.
First 30 days: zero changes to the AI team's workflow, tools, or reporting structure.
Days 31-60: where are the integration synergies between AI teams?
Days 61-100: begin merging infrastructure and workflows incrementally.
Design a Day 1-100 integration playbook for an AI acquisition. Specify the exact actions and non-actions for each phase.
Lesson 2: ML Infrastructure Consolidation
Merging two ML stacks is treacherous. The framework: standardize on the better platform (not the acquirer's), migrate training pipelines before inference pipelines (lower risk), and maintain independent model evaluation until confident in the merged system.
Choose the better ML platform, regardless of which company built it.
Migrate training workloads before inference. Training is offline and lower risk.
Maintain separate model evaluation benchmarks for 6+ months.
Plan the ML infrastructure consolidation: which platform wins, migration order, and quality safeguards.
Lesson 3: Talent Retention Strategy
The acquired AI team is the asset you bought. Lose them and you paid millions for a codebase that will be obsolete in 18 months. Retention strategy: immediate retention bonuses (6-12 months of salary vesting over 2 years), title and scope preservation (no demotions), and intellectual autonomy (keep them working on interesting problems, not integration tickets).
6-12 months of salary, vesting over 24 months, triggering at acquisition close.
Acquired AI engineers keep working on the AI product, not integration work.
Within 30 days, each key AI person should have a personal career discussion with their new VP+.
Design a retention plan for the 5 most critical engineers in an acquired AI team. Calculate the cost vs the replacement risk.
Continue Learning: Track 10 — AI Due Diligence
2 more lessons with actionable playbooks, executive dashboards, and engineering architecture.
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Module Syllabus
Lesson 1: Lesson 1: Day 1 Through Day 100 Playbook
AI acquisitions fail most often in the first 100 days. The playbook: Days 1-30 (protect the AI team — no org changes, no tool changes, no process changes), Days 31-60 (map integration synergies and dependencies), Days 61-100 (begin incremental integration with the AI team's buy-in). The single most important rule: keep the AI team intact and productive.
Lesson 2: Lesson 2: ML Infrastructure Consolidation
Merging two ML stacks is treacherous. The framework: standardize on the better platform (not the acquirer's), migrate training pipelines before inference pipelines (lower risk), and maintain independent model evaluation until confident in the merged system.
Lesson 3: Lesson 3: Talent Retention Strategy
The acquired AI team is the asset you bought. Lose them and you paid millions for a codebase that will be obsolete in 18 months. Retention strategy: immediate retention bonuses (6-12 months of salary vesting over 2 years), title and scope preservation (no demotions), and intellectual autonomy (keep them working on interesting problems, not integration tickets).