The success of AI implementation depends on what Sequence Holdings calls "organizational physics." Companies with dense, centralized functions (e.g., a bank's underwriting) allow AI tools to be amortized across the entire business, generating higher ROI than in decentralized, fragmented organizations.

Recognizing that enterprises struggle to deploy AI effectively, some PE firms are acquiring traditional businesses. Their strategy is to directly own the change management process, forcing AI implementation to unlock latent value that the original management couldn't capture on their own.

For PE firms buying founder-owned software companies, AI is a game-changer. It dramatically accelerates paying down the technical debt and modernizing the tech stack—often the biggest hurdles to growth post-acquisition. This allows firms to unlock value faster and more efficiently than ever before.

Companies with strong, pre-existing developer platforms, data infrastructure, and analytics layers see the highest returns from AI agents. Foundational investments that made humans efficient provide the necessary leverage for AI to operate effectively and safely at scale.

Enterprises have invested heavily in their existing software (ERPs, CRMs). A successful FDE avoids a "rip and replace" strategy. Instead, they build AI solutions that integrate with and enhance these incumbent systems, respecting the client's prior investments and reducing adoption friction.

Many companies fail at AI by cobbling together disparate tools without a coherent strategy. Successful "pacesetters" adopt a holistic, platform-first mindset, providing structure, expertise, and focusing on high-value projects enterprise-wide, which avoids this pitfall.

KKR leverages its 250+ portfolio companies as a massive R&D grid for AI. By running diagnostics and mandating experiments at each company, they test dozens of vendors and applications simultaneously. This allows them to identify successful combinations of vendor, application, and industry, which are then scaled portfolio-wide.

Legacy companies are siloed, creating IT "spaghetti" that blocks AI progress. In contrast, AI-native organizations structure themselves around a central "AI factory" or unified data platform. Business units function like apps on an iPhone, accessing shared, controlled data to rapidly innovate and deploy new services.

Private equity firms are aggressively implementing AI across thousands of their portfolio companies. This isn't just for efficiency; it's a strategy to boost profitability and make these companies, particularly struggling SaaS businesses, more attractive for exit in a tough market. This creates a massive, real-world testbed for enterprise AI.