To drive portfolio-wide AI adoption, THL facilitates cross-pollination of ideas between companies in different verticals (e.g., healthcare and tech). It also frames initiatives as gamified 'challenges' rather than top-down directives to foster innovation, secure buy-in, and better navigate change management.

The most successful companies deploying AI use a "leadership lab and crowd" model. Leadership provides clear direction, while the entire organization is given access to tools to experiment and discover novel use cases. An internal team then harvests these grassroots ideas for strategic implementation.

Instead of a top-down AI strategy, Brookfield encourages its 500 portfolio companies to experiment independently. The key is a structured process for sharing all outcomes. A successful application in one business can be rapidly deployed elsewhere, while failures prevent 499 other companies from making the same mistake.

To manage the risk and opportunity of AI, LeadEdge ranks all its portfolio companies on their readiness. The score considers data structure, new AI product releases, and AI-driven revenue, facilitating knowledge sharing between high- and low-scoring companies.

Rather than allowing siloed AI experiments, Boehringer Ingelheim uses a centralized "AI innovation team." This overarching function supports the entire enterprise, pilots ideas to "fail fast or scale up," ensures compliance, and builds economies of scale.

Small firms can outmaneuver large corporations in the AI era by embracing rapid, low-cost experimentation. While enterprises spend millions on specialized PhDs for single use cases, agile companies constantly test new models, learn from failures, and deploy what works to dominate their market.

Apollo uses its diverse portfolio companies as a 'test kitchen' to experiment with new technologies and processes like AI-driven customer service. Best practices are then identified, shared across the portfolio, and ultimately integrated back into Apollo's own internal operations for investing and management.

OpenAI runs numerous parallel research projects (expansion), knowing most will fail. When a few show promise, it consolidates talent and resources onto those winners (contraction) to scale them up, before spreading out again to explore the next frontier. This cycle is applied to product as well.

While many firms are just now reacting to AI's impact, major credit investors like KKR have been actively underwriting AI-driven business model risk for nearly six years. This proactive, long-term approach to assessing technological disruption is a core part of their due diligence process, not a recent development.