Tracks/Track 10 — AI Due Diligence/N10-9
Track 10 — AI Due Diligence

N10-9: Post-Acquisition AI Integration

The playbook for integrating an acquired AI company without destroying its value.

3 Lessons~45 min

🎯 What You'll Learn

  • Plan AI team integration
  • Merge ML infrastructure
  • Retain key talent
  • Realize synergies without disruption
Free Preview — Lesson 1
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.

Protection Phase

First 30 days: zero changes to the AI team's workflow, tools, or reporting structure.

Change causes attrition. Attrition destroys the acquisition value.
Mapping Phase

Days 31-60: where are the integration synergies between AI teams?

Map shared infrastructure, data assets, and model capabilities
Integration Phase

Days 61-100: begin merging infrastructure and workflows incrementally.

Each integration step must be voluntary and reversible
📝 Exercise

Design a Day 1-100 integration playbook for an AI acquisition. Specify the exact actions and non-actions for each phase.

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.

Platform Selection

Choose the better ML platform, regardless of which company built it.

Forcing the acquired team onto an inferior platform causes attrition
Training First

Migrate training workloads before inference. Training is offline and lower risk.

Validates the merged platform before touching production
Independent Evaluation

Maintain separate model evaluation benchmarks for 6+ months.

Ensures the merger doesn't silently degrade model quality
📝 Exercise

Plan the ML infrastructure consolidation: which platform wins, migration order, and quality safeguards.

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).

Retention Bonus

6-12 months of salary, vesting over 24 months, triggering at acquisition close.

The cost of the bonus is a fraction of the replacement cost
Scope Protection

Acquired AI engineers keep working on the AI product, not integration work.

Integration work = mundane = attrition
Career Commitment

Within 30 days, each key AI person should have a personal career discussion with their new VP+.

People stay for growth opportunities, not just money
📝 Exercise

Design a retention plan for the 5 most critical engineers in an acquired AI team. Calculate the cost vs the replacement risk.

Unlock Full Access

Continue Learning: Track 10 — AI Due Diligence

2 more lessons with actionable playbooks, executive dashboards, and engineering architecture.

Most Popular
$149
This Track · Lifetime
$799
All 23 Tracks · Lifetime
Secure Stripe Checkout·Lifetime Access·Instant Delivery
End of Free Sequence

Unlock Execution Fidelity.

You've seen the theory. The Vault contains the exact board-ready financial models, autonomous AI orchestration codes, and executive action playbooks that drive 8-figure valuation impacts.

Executive Dashboards

Generate deterministic, board-ready financial artifacts to justify CAPEX workflows immediately to your CFO.

Defensible Economics

Replace heuristic guesswork with hard mathematical frameworks for build-vs-buy and SLA penalty negotiations.

3-Step Playbooks

Actionable remediation templates attached to every module to neutralize friction and drive instant deployment velocity.

Highly Classified Assets

Engineering Intelligence Awaiting Extraction

No generic advice. No filler. Just uncompromising architectural truths and unit economic calculators.

Vault Terminal Locked

Awaiting authorization clearance. Unlock the module to decrypt architectural playbooks, P&L models, and deterministic diagnostic utilities.

Telemetry Stream
Inference Architecture
01import { orchestrator } from '@exogram/core';
02
03const router = new AgentRouter({);
04strategy: 'COST_EFFICIENT_SLM',
05fallback: 'FRONTIER_MODEL'
06});
07
08await router.guardrail(payload);
+ 340%

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.

15 MIN

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.

20 MIN

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).

25 MIN
Encrypted Vault Asset