Self-Service Data Prep: a Gartner Guide

Shadow AI isn’t going away. Enterprise that get out in front of it can convert the shadow AI problem into a competitive advantage with demonstrable ROI.

The latest MLPerf benchmark results paint a clear picture of an AI infrastructure ecosystem in rapid transition.

To successfully scale agentic AI, enterprises need to approach it as a systems engineering problem, not just a model deployment exercise.

Accepting that AI is being designed for machines rather than humans forces a shift in responsibility. The burden moves away from making systems intuitively understandable and toward making them structurally accountable.

In this week’s real-time analytics news: Qlik introduced a sovereign AI initiative and an expansion of its trust and governance capabilities for AI.

Digitizing Leadership Standard Work (LSW) is not the same as deploying a new tool and expecting behavior to change. Organizations that get real value from this approach are the ones that treat digital LSW as a management system, not a software rollout.

Building AI operations is a journey. The companies that succeed treat it as an ongoing operational capability, not a one-time technology project.

The next real shift with agentic AI will come from changing how work is done. Humans will move away from clicking through systems and toward commanding outcomes, with agentic layers coordinating execution across tools on behalf of the user.