Analysts created a method to evaluate corporate AI adoption across six key areas: personalization, customer acquisition, product innovation, labor productivity, supply chain, and inventory management. Companies are then ranked on the breadth, depth, and proprietary nature of their AI initiatives.

Past tech solutions for fragmented industries like logistics often failed because they required universal adoption of a new platform. AI can succeed by meeting users in their existing, messy channels—email, texts, calls. It automates work within current workflows rather than forcing a difficult behavioral change, lowering adoption barriers.

For fragmented, tech-averse industries, GC funds startups to first build an AI automation platform. Then, instead of a difficult sales process, the startup acquires traditional service businesses, implementing its own AI to dramatically boost their margins, providing immediate distribution and data.

Most successful SaaS companies weren't built on new core tech, but by packaging existing tech (like databases or CRMs) into solutions for specific industries. AI is no different. The opportunity lies in unbundling a general tool like ChatGPT and rebundling its capabilities into vertical-specific products.

Traditional software automated standardized processes but struggled with complex human interactions like call center support. Generative AI's ability to understand natural language allows software to automate these nuanced tasks, dramatically expanding the total addressable market by tackling problems that were previously impossible to solve with code.

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.

To maximize AI's impact, don't just find isolated use cases for content or demand gen teams. Instead, map a core process like a campaign workflow and apply AI to augment each stage, from strategy and creation to localization and measurement. AI is workflow-native, not function-native.

For companies wondering where to start with AI, target the most labor-intensive, process-driven functions. Customer support is an ideal starting point, as AI can handle repetitive tasks, leading to lower costs, faster response times, and an improved customer experience while freeing up human agents for more complex issues.

Don't underestimate the size of AI opportunities. Verticals like "AI for code" or "AI for legal" are not niche markets that will be dominated by a few players. They are entire new industries that will support dozens of large, successful companies, much like the broader software industry.