Tag: best practices

AI pricing is increasing, but the real problem is token waste. Enterprises are overspending because poor data architecture forces models to process too much irrelevant context. Better retrieval, semantic enrichment, rules, and governance reduce cost, improve accuracy, and make AI more scalable.

Trust is now the differentiator: AI capability is rising fast, but enterprise adoption depends on governance, explainability and control. User-first beats tool-first: The winning model is bringing AI into the flow of work, not forcing people to learn complex tooling. Boring is what scales: Predictable, policy-aligned and auditable AI is what turns pilots into production outcomes.

AI fines are no longer theoretical; regulators are now enforcing control requirements as AI moves from pilots to production in financial services. The post explains why contracts alone won’t satisfy supervisors, what evidence regulators will expect to see in production and how organizations can operationalize governed, defensible AI with runtime guardrails, provenance, lineage and auditable controls—setting the agenda for the RegTech Conference in London on March 26.

In this two-part series, this blog dives deep into best practices and shares some predictions for the remaining months of the year.

This blog explores how RAG enables more efficient, governable AI, and why it’s now a critical requirement for scaling enterprise AI cost-effectively.