6.2 The "Mitosis" Strategy: Growing Through Division
When AI portfolios need to grow, the instinct is often to make existing teams bigger. The AI Innovation model takes a different approach inspired by cell biology: successful pods divide into two smaller pods, each carrying the DNA of the parent but focusing on distinct products. This "mitosis" strategy preserves the two-pizza team benefits while enabling exponential growth.
Adding people to a team has rapidly diminishing returns. A 15-person "pod" has lost most benefits of the small team model. Instead of growing teams, we multiply teams. Each division creates two pods that can eventually divide again, enabling exponential scaling while maintaining the intimate team dynamics that make pods effective.
The Biology Analogy
How Cells Divide
In biological mitosis, a single cell divides into two daughter cells, each receiving a complete copy of the genetic material and the ability to function independently. The AI Innovation mitosis mirrors this:
| Biological Mitosis | Pod Mitosis |
|---|---|
| Cell grows beyond optimal size | Pod exceeds two-pizza threshold or scope |
| DNA replicates | Knowledge, culture, and practices transfer to both |
| Cell divides into two | Pod splits into two distinct pods |
| Each daughter cell is complete | Each new pod has full capabilities |
| Daughter cells can grow and divide again | New pods can mature and eventually split |
The Exponential Advantage
Mitosis enables exponential scaling:
Division Triggers
When to Consider Mitosis
Pod division should be triggered by specific conditions, not arbitrary growth targets:
Size Trigger
Pod has grown beyond 10 people and coordination overhead is becoming noticeable.
Signal: Standups take too long, people don't know what others are doing
Scope Trigger
Pod is working on multiple distinct AI products that don't share significant dependencies.
Signal: Subgroups have formed with different focus areas
Domain Trigger
Pod expertise has grown to cover multiple distinct domains that could benefit from specialization.
Signal: Context switching between unrelated problem spaces
Success Trigger
Pod has proven the model and organization is ready to expand AI capabilities.
Signal: Demand for AI products exceeds single-pod capacity
Readiness Assessment
Before dividing, verify that conditions support successful mitosis:
- Current pod is operating effectively (not dividing to escape problems)
- Clear product boundaries exist for the two resulting pods
- Sufficient talent pool to staff both pods adequately
- Leadership candidates identified for the new pod's STO role
- Shared services/platforms ready to support additional pod
- AI Council capacity to oversee expanded portfolio
The Division Process
Phase 1: Planning (4-6 weeks before)
Define the Split
Determine which products/capabilities will go to each pod. Draw clear boundaries to minimize ongoing dependencies.
Identify Leadership
Select the STO for the new pod. Ideally an experienced team member who has been groomed for the role.
Plan Team Composition
Determine which team members go to which pod, ensuring both have critical capabilities covered.
Prepare Charters
Create Model Cards and charters for any new products the new pod will own.
Phase 2: Execution (Division Week)
Formal Announcement
Communicate the division to both pods and broader organization. Celebrate as a milestone of success, not a splitting up.
Physical/Virtual Separation
Establish distinct team spaces, communication channels, and tooling configurations. Make the separation tangible.
Knowledge Transfer
Intensive sessions to transfer any critical knowledge that isn't going with the person. Document thoroughly.
Phase 3: Stabilization (4-8 weeks after)
The weeks after division require careful attention:
- Weekly check-ins between the two STOs to resolve any boundary issues
- Escalation path to AI Council if pods struggle to collaborate
- Permission to ask: new pod members should feel comfortable reaching back
- Success metrics: Track both pods' health during transition
Common Division Patterns
Pattern 1: Product Split
Most common pattern when a pod has grown to support multiple AI products:
Before: Single pod owns fraud detection AND customer churn prediction
After:
- Pod A: Fraud Detection (original STO stays)
- Pod B: Churn Prediction (new STO from senior team member)
Key consideration: Ensure both products have sufficient complexity to justify dedicated pods
Pattern 2: Domain Split
When a successful AI capability needs to expand to new domains:
Before: Pod owns recommendation engine for retail products
After:
- Pod A: Retail Recommendations (established domain)
- Pod B: Financial Products Recommendations (new domain, different regulations)
Key consideration: New domain often requires different expertise and governance approach
Pattern 3: Platform Extraction
When shared capabilities should become a platform serving multiple pods:
Before: Pod builds and operates product AND maintains MLOps infrastructure
After:
- Pod A: Product pod (uses platform services)
- Pod B: MLOps Platform team (serves multiple pods)
Key consideration: Platform team needs different operating model (see Section 6.3)
Pattern 4: Geographic/Market Split
When expansion requires region-specific versions:
Before: Pod owns AI product for US market
After:
- Pod A: US/Americas (original regulatory context)
- Pod B: EU/APAC (GDPR, AI Act compliance, localization)
Key consideration: Significant regulatory differences often justify dedicated pods
Anti-Pattern: Escape Division
Division should never be used to escape problems:
- Don't divide because of personality conflicts (fix the conflict first)
- Don't divide a struggling pod (fix the problems first)
- Don't divide to give someone a promotion (find real product boundaries)
- Don't divide prematurely (ensure real need exists)
The Mitosis Mindset
Successful organizations build mitosis into their AI culture from the beginning. STOs are always developing future STOs. Team members expect that success leads to division. The goal isn't to build one amazing team—it's to build an organism that grows by multiplying its successes while preserving what makes it effective.