How AI is transforming analytics at Grab

Summary

Grab describes analytics as central to business decisions and presents AI agents as a way to democratise access to trustworthy answers. Over two years, increased model capability has enabled agents to prepare data, write and run queries, analyse opportunities, design experiments, interpret results, and draft commentary in minutes rather than a week. The proposed ladder ranges from AI-assisted work through human-planned and agent-planned workflows to end-to-end autonomy, while people retain accountability for framing, definitions, causal interpretation, decisions, and stakeholder relationships. Grab describes Spartan, which routes natural-language Slack requests across more than 50 skills and 120 analysis frameworks, plus Scarlet for pipeline failure triage and recovery. The programme also maintains agent context through ContextIQ and reports lower analyst intervention, a roughly 33% reduction in cycle times, and greater self-serve resolution in its support channel.

Takeaways

1. 1

   Spartan routes natural-language Slack requests through more than 50 skills and 120 analysis frameworks, using certified metrics and domain-specific guidance to choose diagnostic or experiment-analysis paths.

2. 2

   The autonomy ladder assigns humans responsibility for problem framing, metric definitions, causal stories, business-case assumptions, decisions, and stakeholder relationships, even as agents take over more workflow steps.

3. 3

   Grab reports that cycle times fell by roughly 33%; in its Slack channel, no-human-involvement responses increased for metric questions, data pulls, and SQL requests between March and May.