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The AI Initiative Playbook

Principles and Moves for Problem-Led Outcome-Driven Work

The AI Initiative Playbook
This book is 100% completeLast updated on 2026-10-19

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About

About

About the Book

Enterprises are adopting AI quickly. Ask which business outcome actually changed, and the room often goes quiet.

This book is about why.

It is for anyone trying to get real outcomes from AI inside an organisation, especially banks and other regulated enterprises. CIOs and transformation leaders who decide where AI goes. Enterprise architects who design around it. Scrum Masters and Agile coaches who see where work actually gets stuck. And the engineers building AI tools, who want what they build to change something that matters.

It gives you a way of thinking about AI decisions that outlasts whichever model or vendor you are using next year.

Some of what is in here:

  • AI amplifies the system you already have. Good systems get better. Broken systems get more visibly broken, faster.
  • Capacity freed becomes a saving by default. It becomes customer value only when someone deliberately redirects it.
  • Let code do the calculating and AI do the explaining. Keep the two apart and both stay checkable.
  • Governance intensity should match consequence. Treating it as all-or-nothing is how agent deployments end up shut down.
  • Every task AI takes over was also somebody’s practice. The reviewer who stops drafting stops noticing what they used to catch.

The core arguments in this book came from building something and watching what happened. Six projects sit behind it, including a governed AI delivery pipeline for regulated environments and an agentic system with explicit governance boundaries. Where the evidence stops and the reasoning begins, the book says so.

You will also find the failures. A governance gate built wrong, and caught by a question the tests had missed. An experiment where AI made the writing faster and worse at the same time. They are in here because they taught more than the wins did.

ShriKant Vashishtha helps organisations turn AI from faster steps into faster outcomes. He has 25+ years in enterprise delivery and transformation, most of it in banking and financial services (HSBC, JPMorgan Chase, ANZ, Bankwest), and today architects AI agents for a global semiconductor manufacturer.

With forewords by Rishi Khullar, President, Unusual Insights Inc., and M K Vaijayanth, VP Product Management, Salesforce.

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Author

About the Author

ShriKant Vashishtha

ShriKant Vashishtha is an experienced software executive, adept at transforming teams and organizations through Agile methodologies. With a robust technical foundation, he excels in implementing Agile frameworks, fostering collaborative environments, and driving continuous improvement.

He is an active contributor to the Agile community, publishing blogs on Agile Buddha, recognized as one of the top 30 Agile blogs globally.

To get in touch, write to svashishtha@malonus.in or visit https://malonus.in

Contents

Table of Contents

Dedication

Foreword by Rishi Khullar

Forward by M K Vaijayanth

Introduction

Chapter 1 — What AI Actually Does to the System You Already Have

Chapter 2 — The Amplification Effect: Why AI Returns More to Some Than Others

Chapter 3 — Seeing Where Flow Actually Breaks

Chapter 4 — Why Most AI Programmes Fail Before the Model Is Even Chosen

Chapter 5 — Before You Say Yes

Chapter 6 — What the Demo Does Not Show

Chapter 7 — The Mistakes Enterprises Make When They Think They’re Measuring Outcomes

Chapter 8 — Moving the Needle: From Capacity Freed to Customer Value Created

Chapter 9 — The Context Problem: Why AI Output Is Only as Good as What You Put In

Chapter 10 — The Invisible Risk: Confidently Wrong

Chapter 11 — Designing and Reviewing One Honest Experiment

Chapter 12 — The Regulator in the Room: What Governed Industries Need to Ask Before They Scale

Chapter 13 — The Boundary Question: Where Autonomous Action Ends and Human Judgement Begins

Chapter 14 — The Skills the AI Is Quietly Replacing

Chapter 15 — From One Builder to Hundreds of Teams

Chapter 16 — Where to Start

Chapter 17 — The First Ninety Days

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