Insights & Ideas

Your enterprise chatbot is only as good as the knowledge behind it

Vodafone moved first-time resolution from 15% to 60%. The enterprise chatbot knowledge base, not the model, decides whether your AI understands your company.

RAG beats fine-tuning for most enterprise use cases

RAG runs in 51% of production enterprise AI deployments against 9% for fine-tuning, and the same retrieval setup swings from 3% to 40% hallucination on corpus quality alone. The corpus is the variable, not the model.

RAG as a service is the rational default for enterprise AI

Enterprises spent $37 billion on generative AI in 2025, but only 17% see real EBIT impact. The variable that decides outcomes is retrieval infrastructure, not the model, and it costs $40,000 to $80,000 to build before the second team to maintain it.

Solving the Enterprise AI Build-Buy Dilemma

Enterprise AI use cases flipped from 53% bought to 76% bought in a single year. Buying clears the deployment hurdle. Only the 6% with mature data foundations earn real returns on either path.

Harness engineering is the missing discipline in enterprise AI adoption

Access to AI is near universal at 88% of organizations, but only 6% reach high performance. The difference is harness engineering, not model choice. BCG puts 70% of AI value in process and pipeline design, 10% in the algorithm itself.

How people use AI matters more than which AI they use

Organizations spending identical amounts on identical AI models see returns ranging from 2% to 300%. The variable is how the product is designed, not which model powers it.