A six-dimension framework for gauging a company's exposure to AI disruption. Drawing on insights from 50+ AI experts and search fund operators, each dimension is scored on a 1–5 scale to benchmark relative exposure, surface vulnerabilities, and prioritize strategic responses.
Each dimension is assessed on a 1–5 scale, where 1 indicates minimal exposure to AI disruption and 5 indicates maximum exposure. The total score (range: 6–30) determines a company's overall disruption category.
Core product or service is at risk of displacement. Survival requires rapid adaptation or reinvention.
Core offering remains viable, but AI can materially enhance efficiency, customer experience, and growth. Companies that invest early can capture significant upside.
Structural factors (regulation, human judgment, relationship-driven sales) delay major AI impact for 3+ years. Adoption of AI can still strengthen long-term positioning.
Automation potential of internal processes
Tasks involve high variability, unstructured inputs, real-time problem-solving, or physical world complexity (e.g., disaster mitigation services)
Tasks are repeatable, predictable, digital, and rules-based with clear steps and minimal edge cases (e.g., call center technical support)
Risk of AI displacing the core value of the product/service
Product depends on high stakes, nuanced, and individually customized decisions where human judgement is core (e.g., emergency medical services)
Core value is standardized, low-stakes, and easily replicable by AI with minimal human input (e.g., simple tax returns)
Access to data that can provide a long-term sustaining advantage
Exclusive rights to large volumes of high-quality, structured proprietary data that competitors cannot access (e.g., Nielsen data)
Reliance on public or 3rd party data sources that competitors can also access; contracts prohibit data re-use
Complexity and replaceability of customer relationship
High $ value, multi-year contracts with complex customer needs requiring deep human engagement (e.g., defense cloud computing)
Low $ value, short-term, transactional purchases with little customer loyalty (e.g., one-click online shopping)
Speed and likelihood of AI disruption across the industry
Highly regulated, asset-intensive industries with slow adoption cycles (e.g., utilities)
Fast, digital-first industries with low physical overhead and low regulatory oversight (e.g., SaaS)
Intensity and nature of AI-driven competitive threats
Niche market with limited TAM, strong structural moats, and slow-moving incumbents (e.g., niche assisted living software)
Large, high-growth market where Big Tech/AI-native entrants are already shipping AI products (e.g., legal tech)
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