/helm

Disruption Detection Reference — Helm

Purpose: Use this file when Helm must evaluate disruption risk, position a business on the industry lifecycle, or assess technology adoption trajectories.

Contents

• Christensen disruption theory
• Industry lifecycle stages
• S-curve and technology adoption
• Disruption risk scoring
• Detection signals
• Strategic response patterns
• Templates

Christensen Disruption Theory

Core Model

Disruptive innovation enters from below: initially inferior on mainstream metrics but superior on a different value dimension (price, simplicity, accessibility). It improves over time until it satisfies mainstream needs — at which point incumbents lose.

Two Types of Disruption

Disruption Vulnerability Assessment

## Disruption Vulnerability: [Business/Product]

### Overshoot Analysis
- Are we improving faster than customers need? [Yes/No]
- Evidence of feature fatigue in customer feedback? [Yes/No]
- % of features used by average customer: [estimate]
- Price sensitivity increasing in bottom segment? [Yes/No]

### Low-End Threat Scan
| Potential disruptor | What they offer | Where they're inferior | Where they're superior | Trajectory |
|---|---|---|---|---|
| [Company A] | [product] | [our advantage] | [their advantage] | ↑ improving / → stable / ↓ fading |

### New-Market Threat Scan
| Potential disruptor | Non-consumer segment served | Value proposition | Could it migrate to our market? |
|---|---|---|---|
| [Company A] | [segment] | [proposition] | [Yes — timeline / No — why] |

### Vulnerability Score
| Factor | Score (1-5) | Weight |
|---|---|---|
| Overshoot risk | | 25% |
| Low-end threat presence | | 25% |
| New-market threat presence | | 20% |
| Business model rigidity | | 15% |
| Organizational inertia | | 15% |
| **Disruption vulnerability** | | **[X/5]** |

Interpretation:
- ≥ 4.0: HIGH — active disruption defense required
- 3.0-3.9: MODERATE — monitor and prepare contingencies
- 2.0-2.9: LOW — maintain awareness
- < 2.0: MINIMAL — focus on execution

Industry Lifecycle Stages

Stage Definitions

Stage Identification Protocol

## Industry Lifecycle Position: [Industry/Category]

### Evidence Assessment
| Indicator | Current state | Points to stage |
|---|---|---|
| Market growth rate | [X]% | [stage] |
| Number of active competitors | [trend] | [stage] |
| Customer acquisition cost trend | [rising/stable/falling] | [stage] |
| Product differentiation | [high/moderate/commoditizing] | [stage] |
| M&A activity | [low/moderate/high] | [stage] |
| Regulatory attention | [low/moderate/high] | [stage] |
| Venture funding pace | [accelerating/stable/declining] | [stage] |

### Lifecycle Position: [STAGE]
### Confidence: [H/M/L]

### Strategic Implications
- At this stage, winning strategies typically involve: [...]
- Losing strategies at this stage include: [...]
- Expected transition to next stage: [timeline estimate]

S-Curve and Technology Adoption

S-Curve Model

Performance
    │         ╭────── Maturity (diminishing returns)
    │        ╱
    │      ╱    ← Rapid improvement phase
    │    ╱
    │  ╱
    │╱──────── Introduction (slow initial progress)
    └────────────────── Time

Technology Adoption Lifecycle (Rogers)

Adoption Stage Assessment

## Technology Adoption Assessment: [Technology/Product Category]

### Current Adoption Stage
- Estimated adopter segment: [Innovators / Early Adopters / Early Majority / Late Majority / Laggards]
- Evidence: [adoption metrics, market penetration data]
- Chasm status: [pre-chasm / crossing / post-chasm]

### S-Curve Position
- Current phase: [Introduction / Rapid improvement / Maturity / Decline]
- Performance trajectory: [accelerating / linear / decelerating / flat]
- Emerging S-curve (replacement technology): [identified / not yet / unknown]

### Strategic Implications
| If at this stage... | Then prioritize... |
|---|---|
| Pre-chasm | Product-market fit, reference customers, use cases |
| Crossing chasm | Whole product, pragmatist messaging, vertical focus |
| Post-chasm growth | Scaling, partnerships, ecosystem |
| Maturity | Efficiency, bundling, next S-curve exploration |
| New S-curve emerging | Evaluate jump timing, dual-track investment |

Disruption Risk Scoring

Composite Disruption Risk Score

## Disruption Risk Assessment: [Business/Industry]

| Risk dimension | Score (1-5) | Evidence | Weight |
|---|---|---|---|
| Technology S-curve maturity | | [where on curve] | 20% |
| Low-end disruptor activity | | [specific threats] | 20% |
| New-market disruption signals | | [non-consumer innovation] | 15% |
| Customer satisfaction with "good enough" | | [overserving evidence] | 15% |
| Industry lifecycle stage | | [stage and trajectory] | 15% |
| Adjacent market convergence | | [ecosystem threats] | 15% |
| **Composite disruption risk** | | | **[X/5]** |

### Risk Level
- ≥ 4.0: CRITICAL — disruption likely within 2-3 years
- 3.0-3.9: HIGH — disruption possible within 3-5 years
- 2.0-2.9: MODERATE — disruption unlikely in near term
- < 2.0: LOW — stable position, monitor periodically

Detection Signals

Early Warning Indicators

Strategic Response Patterns

Response Selection Rules

• If disruption vulnerability ≥ 4.0 and internal innovation capability is high → self-disrupt
• If disruption vulnerability ≥ 4.0 and internal innovation capability is low → acquire or partner
• If disruption risk is MODERATE → monitor + prepare contingency, do not overreact
• Never ignore disruption signals because current performance is strong — this is the innovator's dilemma

Integration with Helm Workflow

Disruption detection integrates into the SURVEY phase:

• Run during external environment scan alongside PESTLE/Porter
• Feed disruption risk score into scenario simulation (especially pessimistic scenarios)
• Include in LONG horizon analysis as a standard check
• Cross-reference with Compete ecosystem mapping for convergence threats
• Escalate to Magi (HELM_TO_MAGI) if disruption risk ≥ 4.0 for Go/No-Go evaluation

AI as Disruption: The 2025-2026 Christensen Institute Position

After Clayton Christensen's death on 2020-01-23, the Christensen Institute (christenseninstitute.org) has continued to publish on how disruption theory applies to GenAI. The institute's standing position as of 2025-2026:

• AI is not inherently disruptive or sustaining — what matters is the business model in which it is deployed. The dominant tech firms (Amazon, Apple, Google, Meta, Microsoft) are largely using AI as a sustaining innovation, layering it onto existing offerings to keep current customers happy (christenseninstitute.org "AI and Disruptive Innovation").
• DeepSeek (Jan 2025) is the most-cited current candidate for a textbook low-end / new-market disruptor: open-weight, dramatically lower training cost, initially "good enough" performance, attacking non-consumers of frontier-model APIs (CAI-Jun, Medium Feb 2025).
• AI in education is now the institute's clearest disruption case — entrant schools use adaptive AI as the primary instruction channel while incumbents bolt AI onto teacher-directed models (sustaining only).

"Disruptor Detective" — AI-Assisted Disruption Scoring

Imerman & Fabozzi (2025) — Quantifying disruption in the age of AI: An AI-based approach to evaluating startup innovation and investment potential (ScienceDirect S1544612325007500) — propose a GPT-powered tool that scores companies on seven refined Christensen criteria. Treat its output as input, not verdict; calibrate against the standard 5-dimension vulnerability scorecard above and require the human assessor to attach evidence per dimension.

Updated Disruption Classification Cheat Sheet (2026)