Grab Cuts Mechanical Analytics Work From 44% to 30% with AI Agents

Grab is using AI agents to automate analytics workflows, reducing the share of routine work handled by analysts and shortening the time required to answer business questions. The company reports that the share of mechanical tickets handled by analysts fell from 44% in February to 30% in June, covering tasks such as data preparation, alerting, and reporting.

The approach is based on a five-level autonomy model that defines how much of an analytics workflow an agent can own while retaining human oversight. At Level 3, humans frame the question and review the result while agents discover data, write and execute queries, validate results, and draft analysis. At Level 4, agents can plan and orchestrate workflows while humans review defined gates. Level 5 represents end-to-end autonomy, with humans setting objectives, quality thresholds, and escalation rules. Grab retains human accountability for metric definitions, causal interpretation, business assumptions, and final decisions.

Maanas Prabhakar, who leads analytics at Grab, highlighted the changing role of analysts in a LinkedIn post:

The harder question is what an analyst does when an agent handles the data preparation, the analysis, and everything else.