AI for your role

AI for Analytics & Data Leaders

Let AI handle the synthesis and monitoring so you can lead on strategy, trust, and ROI.

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The shift

How AI is changing the Head of Analytics / CDO role

In 2026, AI absorbs the synthesis, reporting, and monitoring that fills a data leader's calendar, but it barely touches the core of the job. Leadership attention shifts to the hard parts: setting a data strategy tied to business outcomes, building organizational trust in data and models, governing responsible AI use, and turning a fragmented data estate into durable competitive advantage.

What AI can take off your plate

  • Synthesizing team output into board-ready narratives
  • Monitoring KPI movements and flagging anomalies
  • Drafting data-strategy and governance documentation
  • Producing first-draft vendor and tooling evaluations
  • Benchmarking the org's data maturity

What stays distinctly human

  • Setting a data strategy tied to business outcomes
  • Building organizational trust in data and models
  • Governing data, privacy, and responsible AI use
  • Winning executive investment for platform and talent
  • Deciding build vs buy as the stack consolidates
Tools

Five AI tools for Analytics & Data Leaders

Claude
Turns a quarter of team output into a board narrative, drafts a data-strategy memo, and pressure-tests a governance policy.
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ChatGPT
Models ROI scenarios for a platform or hire, drafts vendor evaluations, and reframes technical work in executive language.
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Microsoft Copilot
Pulls context from your docs and decks to draft strategy updates, board materials, and investment cases.
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dbt
A governed semantic layer that makes the whole org's numbers consistent — the foundation trust and AI depend on.
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Monte Carlo
AI-driven data observability that quantifies quality and reliability risk across your estate for you and the board.
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Prompts

Five prompts to try today

Paste these into Claude or ChatGPT and replace the bracketed parts with your own details.

1. Model the ROI of an investment
We're considering [platform/hire/tool] costing [amount]. Build 3 ROI scenarios (conservative, base, upside) with the assumptions and the decision it would inform, in business terms.
2. Draft a data strategy on a page
For a [company type] at [stage], draft a one-page data strategy: the outcomes it drives, the 3-4 priorities, and what we will explicitly not do this year.
3. Write an AI governance policy
Draft an enterprise AI-usage and data-governance policy covering allowed tools, data handling, logging, and review — practical enough that teams will actually follow it.
4. Make the case for data quality
Turn this data-quality and lineage risk [paste] into an executive argument that frames fixing it as the enabler of every AI initiative, with the cost of inaction.
5. Assess data maturity
Here's how we work with data today: [describe]. Benchmark our maturity across strategy, quality, governance, and talent, and name the highest-leverage next move.
The playbook

Every AI play for Analytics & Data Leaders

Your full AI playbook for your role — updated every week. Tap any card for a step-by-step walkthrough and examples.

✦  New AI plays are added every week — and go straight to subscribers in their morning brief. Skip the scrolling and get yours delivered free. Get my free brief →
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A day in your inbox

This is the kind of brief a Head of Analytics / CDO gets, every weekday morning.
Monday morning
✦ Personalized for: Head of Analytics / CDO
Data PlaybookWriting and debugging SQL
Fix the query that returns nothing

A query runs clean but returns zero rows. The bug is in your join or filter, not your syntax.

Claude  FREE  reads your SQL and spots the logic error

The old way
You re-read the same 40 lines six times and start commenting out WHERE clauses at random.
The AI way
You paste the query, the schema, and what you expected. You get the likely cause in one read.
This [Postgres/MySQL/BigQuery] query returns 0 rows but should return data. Schema: [paste CREATE TABLE or column list]. Here is the query: [paste SQL]. I expected [what you expected]. Find the bug. Check join type, filter order, NULL handling, and date ranges. Explain what is wrong and give the fixed query.

Why it works: Most zero-row bugs are an inner join that should be left, or a filter that drops NULLs. A second reader catches those fast. You keep control of the fix.

Your role, all in one place
  
Tools, prompts & tricks
Your full library, one tap away.
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Your playbook
Every entry, building each week.
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How AI is changing your role
Where your work is heading.
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