Practical work on trusted analytics, AI-native data teams, and executive decision systems.
I built this repo for Chief Data Officers, heads of data, analytics leaders, and builders who want AI to improve real decisions without weakening metric trust or skipping human judgment.
Models are getting easier to access. The harder work is giving them reliable context, setting decision rights, and building operating habits people can trust.
The publications/2026.md archive is current through August 3, 2026. It records 34 verified channel publications across LinkedIn and Insight Extractor:
12long-form essays, session archives, and event notes22original LinkedIn posts or article releases
The archive includes public URLs and exact publication dates. It excludes reposts, private messages, drafts, scheduled work, and personal material outside AI+Data.
| Artifact | What it demonstrates |
|---|---|
projects/agentic-analytics-in-production.md |
A four-week path from one recurring analytics question to a narrow production pilot |
docs/cdo-operating-system.md |
A 90-day roadmap, KPI tree, governance model, and executive cadence for AI-native data leadership |
projects/ten-metric-trust-layer-pilot.md |
A bounded pilot for making recurring metric questions safer and faster |
playbooks/trusted-answer-lifecycle.md |
The full route from a scoped business question to a reviewed, reusable answer |
examples/synthetic-funnel/README.md |
A reproducible analysis-to-decision workflow on synthetic data |
docs/ai-adoption-board-brief.md |
A concise executive format for adoption, trust, risk, and decisions |
For a 30-minute evaluation path, use docs/executive-reading-path.md.
World-Class Agentic Analytics in Production is the guided, hands-on route through the repo's core ideas. It includes a public-safe practice lab, runnable prompts, proof and review exercises, a feedback-to-regression loop, and a four-week launch plan.
Use the workshop when you want to build end to end. Use projects/agentic-analytics-in-production.md when you want the matching AI+Data playbooks, templates, examples, and launch checks in one place.
- What data science professionals need to do now to stay relevant
- AI can write the SQL. Companies are still screening for five things it can't do.
- When DoorDash released a CLI, I had to try it.
- 3 things data professionals shouldn't outsource to AI
- What OpenAI, Anthropic, and Meta learned after putting data agents to work
Browse the complete current record in publications/.
AI-native data leadership depends on four connected layers:
- Trusted definitions: metrics, source paths, owners, caveats, and permissions.
- Reliable workflows: scoped questions, validation, refusal rules, and review loops.
- Decision cadence: pre-reads, explicit decisions, owners, dates, and follow-through.
- Learning systems: misses improve the definitions, tests, and operating process.
The playbooks explain how these layers work. The toolkits turn them into something a team can use. The examples test them on synthetic data. The evidence files spell out what the repo demonstrates, what public sources support, and what I have intentionally left out.
| Path | Use it for |
|---|---|
publications/ |
Verified public writing and appearances, organized by year and channel |
projects/ |
Bounded operating initiatives with a concrete outcome |
playbooks/ |
Reusable patterns for trusted AI-assisted analytics |
toolkits/ |
Scorecards, checklists, templates, and evaluation tools |
examples/ |
Synthetic, inspectable walkthroughs |
docs/ |
Executive artifacts, evidence, research, and publication standards |
areas/ |
Ongoing leadership responsibilities |
archives/ |
Retired or historical material |
This repo separates four kinds of proof:
| Layer | What it supports |
|---|---|
| Direct repo artifacts | The operating models, methods, and tools published here |
| Verified public writing | The ideas Paras has published and taught in public |
| External public sources | Attributable role, recognition, and outcome evidence with caveats |
| Illustrative scenarios | Transferable examples using synthetic measures, not claims of a private deployment |
Start with docs/evidence-and-scope.md, docs/external-evidence-of-impact.md, and docs/evidence-ledger.md.
Everything here is either a generalized method, a public link, or a synthetic example. Private strategy, customer or employee data, internal SQL or schemas, dashboards, credentials, bot history, and unpublished drafts stay out.
See docs/repo-privacy-policy.md and docs/publication-safety-checklist.md.
I'm a data and AI executive and former Amazon leader. I write Insight Extractor, teach data professionals, and build practical systems for trusted analytics and AI adoption.
The strongest public evidence outside this repo is cataloged in docs/external-evidence-of-impact.md. The editorial lens and authorship boundary are in docs/founder.md.