Strategic buyers acquire companies with proven AI ('agentic') capabilities not just for their own value, but to use them as a blueprint to transform their larger organization. This makes AI adoption a key driver of M&A attractiveness and exit value.
Companies risk giving away enterprise value by sending proprietary data to external foundational models. The secure and value-accretive approach is to bring AI models in-house to train on data within a controlled, air-gapped environment, preventing data leakage.
To drive change like AI adoption, top-down mandates are less effective than fostering competition. By sharing anonymized rankings of portfolio companies on key metrics, HG motivates CEOs to improve and proactively seek advice from their top-performing peers.
Advent built an internal AI tool trained on historical deal memos and investment committee (IC) questions. This non-voting 'robot' prompts the IC with questions, highlights changes in a deal's thesis over time, and flags inconsistent assumptions across different deals.
KKR is systematically running AI diagnostics and experiments across its ~250 portfolio companies. Despite this comprehensive approach, the firm's honest assessment is that AI currently provides helpful, incremental gains rather than the massive transformations often hyped in the press.