AI Governance & Regulation

We have mapped 1,200+ enterprise AI companies across 47 categories. The fastest-growing segments are: AI agents for operations (340% YoY growth in funding), code generation platforms (280% YoY), and AI governance tools (250% YoY). Notably, the "AI wrapper" category that critics dismissed is now producing companies with $50M+ ARR.

Our annual survey of 1,800 enterprises shows that 72% now have at least one AI application in production, up from 55% a year ago. However, only 18% report having AI embedded across multiple business functions. The primary barriers remain: data quality (cited by 64%), talent shortages (58%), and unclear governance frameworks (52%).

The organisations succeeding with AI share a common pattern: they treat AI as an operating model change, not a technology project. This means restructuring decision rights, creating AI product management roles, establishing federated governance, and measuring outcomes rather than deployments. The technology is 20% of the challenge; the organisation is 80%.

The EU AI Act's first enforcement provisions take effect this quarter. High-risk AI systems -- including those used in employment, credit scoring, and critical infrastructure -- now require conformity assessments, technical documentation, and human oversight mechanisms. The fines for non-compliance can reach 7% of global revenue.

We are advising all our portfolio companies to establish AI red teams. The threat surface of LLM-powered applications is fundamentally different from traditional software. Prompt injection, data poisoning, model theft, and adversarial inputs require specialised security expertise that most organisations lack.