# How AI is transforming analytics at Grab

DevFeed: [How AI is transforming analytics at Grab](<https://devfeed.tech/articles/how-ai-is-transforming-analytics-at-grab-1249.md>)

Original publisher: [Read original article](<https://engineering.grab.com/how-ai-is-transforming-analytics>)

Author: Maanas Prabhakar

Published: 2026-08-01T00:23:00Z

Content type: article

Language: en

Sources: [Grab Tech](<https://devfeed.tech/sources/grab-tech.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [data](<https://devfeed.tech/topics/data.md>), [Code](<https://devfeed.tech/topics/code.md>), [coding](<https://devfeed.tech/topics/coding.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [analytics](<https://devfeed.tech/tags/analytics.md>), [data](<https://devfeed.tech/tags/data.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

## AI overview

Grab describes how increasingly capable AI agents are transforming analytics by automating data preparation, query writing, analysis, experiment design, result interpretation, and commentary. The article presents an autonomy ladder in which agents take ownership of more of the analytics loop while humans retain accountability for problem framing, metric definitions, causal reasoning, business assumptions, decisions, and stakeholder relationships.

## Source excerpt

Introduction At Grab, analytics sits close to almost every decision that matters. Our north star is the democratisation of intelligence, ensuring that anyone making a business call has immediate access to trustworthy answers. Over the last two years, model capability has crossed a threshold enabling this shift. Agents now do in minutes what used to take a week: preparing the data, writing queries, running deep analysis and developing insights for business opportunities, designing experiments and interpreting the results, drafting the commentary that follows, and more. Our throughput is no longer rate-limited by how fast an individual can write code, build a deck, or run a deep-dive. It is rate-limited by how fast we can frame the right problem, judge the right answer, and influence the right decision. As autonomy climbs, an analyst's impact moves from producing the artefact to owning the question and the call behind it, and the role evolves to become part builder, part advisor, part strategist, owning the loop rather than running it. That unlocks two things at once: work we already do, faster and at lower marginal cost, and work we could never staff before, sitting beside every product manager, business owner, and operator at the moment they decide. The ladder We were heavily inspired by Dan Shapiro's framing of five levels for AI coding. We use a similar ladder that defines how much of the loop an agent should own and where human judgement stays for every analytics loop. One distinction runs across every level: who owns the loop, and where human judgement is required. Level What Human role Agent role L2 AI-Assisted Owns and executes every step; uses AI to draft, suggest, summarise Drafts SQL, suggests a visualisation L3 Human plans, agent owns steps, human reviews Frames the question, picks the metric, the segment, and the comparison frame, reviews evidence, owns the recommendation Discovers data, writes and runs the query, sanity checks, drafts the write-up, flags