Unlocking enterprise AI: why orchestration Is the next frontier (www.techradar.com)

Enterprises are rapidly expanding AI investments—LLMs, chatbots and automation—but widespread benefits are being undermined by fragmented toolsets, siloed data and inconsistent outputs. Real-world examples include finance teams swamped by false-positive fraud alerts, retail engines missing customer nuance, and inconsistent diagnostic recommendations in healthcare. Without a unified approach, disparate models and ad hoc deployments create security blind spots, governance gaps and poor user trust, limiting ROI and scalable impact across organizations. AI orchestration is presented as the unifying layer to coordinate models, data sources and interfaces: techniques include model routing to select the best model per query, contextual awareness to infer user intent and entitlements, and a single assistant that aggregates public and proprietary data rather than forcing users to juggle multiple tools. But orchestration brings integration, lifecycle management and standardization challenges when stitching proprietary, open-source and third‑party components together. The path forward is building flexible, secure internal frameworks that prioritize governance and interoperability now, with a shift expected toward industry-specific platforms and integration tools—analysts predict ~70% of multi-LLM deployments will use integration platforms by 2028—to enable scalable, auditable and trustworthy enterprise AI.