Self-Service Data Prep: a Gartner Guide

Agentic AI promises to unlock massive productivity and efficiency gains for organizations, but those results are not being realized yet. Many see MCP (Model Context Protocol) as the key, but it will only provide the desired business impact if implemented in the right way. This article explores critical areas that will help organizations achieve profound business results through agentic AI and MCP.

Real-time data transforms AI model drift management from periodic model maintenance into continuous system operations, enabling faster detection, smarter adaptation, and safer deployment of AI at scale.

Model-as-a-Service reflects a pragmatic response to the realities of modern AI adoption. MaaS offers a way to balance innovation with control, speed with governance, and scalability with cost discipline.

The AI agent boom of 2025 made automation accessible. The next phase, which involves multi-agent systems, will determine who turns that accessibility into real, sustained value.

Most organizations are attempting to solve a 21st-century problem with a 20th-century approach: manual effort. They are falling into “The Manual Migration Trap,” a predictable and avoidable series of failures rooted in an over-reliance on human intervention to solve a machine-scale problem.

Zero Trust assumes a strong, unified source of identity and policy. Most enterprises have the opposite. Create a cross-functional “Zero Trust Council” with shared KPIs tied to business outcomes to fix the situation.

Organizations that align their data strategies with 2026 cloud evolution trends will be well-positioned for success in the modern AI-dominated business world.

Enterprise leaders call on providers to address the automation gap between enterprise IT systems and service provider networks.