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Full Description
Rapidly transform your analytics teams to deliver AI-driven insights.
The AI-driven Data Team by Nicholas Kelly is a proven-to-work playbook for data and analytics leaders who want to align analytics capabilities with the demands of an AI-powered business environment. Written for leaders accountable for data, analytics and business intelligence, this book provides tools for diagnosing capability gaps among data analytics teams, modernizing legacy stacks and delivering AI-driven insights to help organizations make better decisions.
You'll learn how to:
- Diagnose skills gaps and map AI-augmented career paths
- Integrate modern AI tools with existing analytics stacks
- Launch six revenue-driving and cost-focused pilots in 90 days
- Embed ISO and NIST-aligned governance without slowing innovation
- Apply ROI calculators, governance checklists and sprint planners
Drawing on expert insights and real-world applications, this book helps you upskill your analysts, strengthen AI governance and provide AI-driven insights that drive real results..
Themes include: AI strategy, data governance, analytics leadership, ROI from AI, organizational transformation, executive decision-making
Contents
Section - ONE: Wake-up call - why you can deliver AI-grade insights in 1 quarter with your existing team;
Chapter - 01: The analyst bottleneck and the AI dividend;
Chapter - 02: Cost-of-hire vs cost-to-upgrade - A five-minute calculator;
Chapter - 03: Skills thermometer - pinpointing gaps, fears & quick wins;
Section - TWO: Charting the new roles - drafting 90-day skills sprints;
Chapter - 04: From dashboards to co-pilots - workflows that change in 12 weeks;
Chapter - 05: Drafting tomorrow's job cards (competencies, pay bands, OKRs);
Chapter - 06: Three team shapes that scale - start-up, mid-market, global hub;
Section - THREE: Equipping your "garage" - tech you can switch on this quarter;
Chapter - 07: Minimal-viable data & AI stack (cloud, on-prem, hybrid);
Chapter - 08: Buy, extend or build? A budget-minded decision grid;
Chapter - 09: Hands-on with low code AutoML, vector search & prompt engines;
Section - FOUR: Momentum in 90 days;
Chapter - 10: Six lighthouse projects for revenue, cost & risk;
Chapter - 11: From business question to work model (regression, forecast, gen-AI);
Chapter - 12: Explaining results non-quants believe - stories, visual, KPIs;
Chapter - 13: Lightweight MLOps - deploy, monitor, iterate (No DevOps army required);
Section - FIVE: Responsible by design - training analysts to think governance;
Chapter - 14: Plain-English governance - ten questions every analysts learns to ask;
Chapter - 15: Bias, fairness and privacy checks anyone can run;
Chapter - 16: Securing generative AI - RAG patterns, PII redaction, policy snippets;
Chapter - 17: Governance simulations & table-top exercises;
Section - SIX: Keeping the fly-wheel turning;
Chapter - 18: Defusing "robot anxiety" and sparking experimentation;
Chapter - 19: Measuring impact - from hours-saved to revenue uplift;
Chapter - 20: Learning loops, communities of practice & continuous upgrade funds;
