Enterprise AI shifts focus toward business context and model evaluation

Enterprise AI implementation is shifting focus from raw model capability toward business context and rigorous evaluation frameworks. Experts suggest that model performance in production is often limited by a lack of access to reliable, authoritative business data and clear definitions rather than the intelligence of the model itself.

To address this, a five-layer trust stack is proposed, encompassing data ingestion, semantic definitions, ontology, context engineering, and self-service layers, all underpinned by governance. This approach aims to make business knowledge understandable and trustworthy for AI agents.

Simultaneously, technology leaders from companies including Swiggy, ShareChat, and Razorpay emphasize the necessity of systematic evaluations, or ‘evals’. As model providers face capacity constraints or performance shifts, companies are moving toward treating models as replaceable components. By investing in workflows and rigorous testing, organizations can ensure that model changes improve quality, latency, or cost without disrupting the customer experience.