When Jeff Totten founded Evergreen, AI was not on the roadmap. The model drew on companies like Berkshire Hathaway, which is not exactly known for being technology forward. But AI has become one of the most interesting transformations happening across the group, and not because it is a topic companies feel obligated to discuss. It is reshaping how the operating businesses work, and it turns out to be threaded into Evergreen’s decentralized structure in a way that was not obvious at the outset.
The companies Evergreen looks up to are known for acquiring businesses and helping them operate more efficiently, not for innovation. What Evergreen is finding is that a decentralized structure may actually be better suited to innovation than a centralized one. Rather than a single R&D function, the group has more than 100 separate environments to test ideas in, and roughly 6,000 employees who can each surface something that changes how the wider business operates. Big companies are often poor at innovation. The breakthroughs tend to come from small teams working with few constraints, and while Evergreen’s companies are not startups in a garage, they are far closer to that than to a large enterprise.
The other advantage is speed from idea to implementation. Employees experimenting with AI tools are close to the customer and close to the decision maker, often sitting in the same office as their company’s CEO. An idea can move from a side project to an actual business process without clearing multiple layers of approval. Once a CEO identifies something that works, knowledge sharing across the group allows it to scale to other companies. That combination supports broad-based experimentation while also making it possible to go deep with one or two companies and rebuild a business around an AI-first mentality, because a bet at that scale is not a risk to the group as a whole.
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