The ideal industry for an AI roll-up is not one that can be fully automated. If automation exceeds 70-80%, a pure software solution from an incumbent like Microsoft will likely win. The strategy thrives where a human services component remains essential but can be significantly augmented by AI.
Related Insights
Despite hype, true 'autonomous marketing' is not imminent. AI excels at automating the first 80-90% of a workflow, but the final, most complex steps involving anomalies, nuance, and judgment still require a human. This 'last mile' problem ensures AI's role will be augmentation, not replacement.
The integration of AI into human-led services will mirror Tesla's approach to self-driving. Humans will remain the primary interface (the "steering wheel"), while AI progressively automates backend tasks, enhancing capability rather than eliminating the human role entirely in the near term.
Instead of selling software to traditional industries, a more defensible approach is to build vertically integrated companies. This involves acquiring or starting a business in a non-sexy industry (e.g., a law firm, hospital) and rebuilding its entire operational stack with AI at its core, something a pure software vendor cannot do.
GC systematically evaluates industries by mapping their core tasks against four specific buckets where AI is most effective: customer success, data entry, content creation, and basic logic/reasoning. An industry must have significant activity in these areas to be a viable roll-up candidate.
Customers are hesitant to trust a black-box AI with critical operations. The winning business model is to sell a complete outcome or service, using AI internally for a massive efficiency advantage while keeping humans in the loop for quality and trust.
Don't wait for AI to be perfect. The correct strategy is to apply current AI models—which are roughly 60-80% accurate—to business processes where that level of performance is sufficient for a human to then review and bring to 100%. Chasing perfection in-house is a waste of resources given the pace of model improvement.
Despite hype about full automation, AI's real-world application still has an approximate 80% success rate. The remaining 20% requires human intervention, positioning AI as a tool for human augmentation rather than complete job replacement for most business workflows today.
The most effective use of AI isn't full automation, but "hybrid intelligence." This framework ensures humans always remain central to the decision-making process, with AI serving in a complementary, supporting role to augment human intuition and strategy.
Adopt a 'more intelligent, more human' framework. For every process made more intelligent through AI automation, strategically reinvest the freed-up human capacity into higher-touch, more personalized customer activities. This creates a balanced system that enhances both efficiency and relationships.
Traditionally, service businesses lack scalability for VC. But AI startups are adopting a 'manual first, automate later' approach. They deliver high-touch services to gain traction, while simultaneously building AI to automate 90%+ of the work, eventually achieving software-like margins and growth.
