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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Unlock predictable bottom line growth through tailored data and AI strategies.
In The Data & AI Imperative: Designing Strategies for Exponential Growth, celebrated data-driven growth leader, Lillian Pierson, delivers a masterclass in developing custom strategies to harness the full potential of data and AI within your organization. This book offers a clear, actionable roadmap for leveraging your company's data and technology assets to drive significant, reliable growth.
With over two decades of experience, Pierson unveils her proprietary STAR framework through which you'll learn to survey, take stock of, and assess your company's current state. Finally, you'll be guided on how to recommend strategies that drive growth via the execution of optimally positioned data- and AI- intensive projects or products that directly improve your business bottom line. From conception to execution, learn to:
- Identify high-impact opportunities for data or AI interventions within your business.
- Assess your organization's readiness and data literacy to ensure successful outcomes.
- Implement practical, effective tactics for overseeing your data-intensive projects, from strategic plans to profitable realities.
- Develop and deploy AI and data strategies that exceed your business goals.
While ideal for executives, managers, and other leaders of data- or AI-intensive companies, The Data & AI Imperative is also invaluable to data and technical professionals who aspire to elevate their impact by turning technical expertise into strategic leadership success.
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Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Praise for Data & AI Imperative
Title Page
Copyright
Dedication
Acknowledgments
About the Author
Introduction
Part I The Data & AI Advantage in Modern Business
Chapter 1 Leveling the Playing Field with Data and AI
Evolving Business at Breakneck Speed
How to Use This Book
How This Book Benefits You
To the Business Leaders and Executives
To the Product and Program Managers
To the Data and Technology Professionals
Notes
Chapter 2 Introduction to Data Strategy
Introduction to the STAR Framework™
Quick Win Use Cases vs. Strategic Win Use Cases
A Brief Introduction to Generative AI and Foundation Models
Introducing Fine-tuning of LLMs, RAG, and AI Agents
Crafting an Effective Plan: Data Strategy vs. AI Strategy
Data Product vs. Data Project vs. Data Initiative
Note
Recommended Reading
Chapter 3 Types of Data-Intensive Use Cases Based on Business Objectives
Operational Improvements Use Cases
Product-Led Growth Improvements Use Cases
Growth Marketing Improvements Use Cases
Decision-Support Improvements Use Cases
Financial Improvements Use Cases
Data Monetization Use Cases
Notes
Recommended Reading
Chapter 4 Data- and AI-Driven Product-Led Growth
Harnessing Data for PLG
The Role of Analytics and ML in PLG
Driving PLG with Foundation Models
Notes
Recommended Reading
Chapter 5 Amplifying Growth Marketing Outcomes with Data and AI
AI- and ML-Driven Marketing Strategy Support
Segmentation and Campaign-Level Optimizations
Driving Efficiency Across Ongoing Marketing Activities
Note
Chapter 6 Validating Product-Market Fit for Commercial Data and AI Products and Services
Identifying Reasons Why Most Tech Start-Ups Fail
Achieving Product-Market Fit
Executing the Fundamental Tenets of PMF
Exploring Powerful Practices within PMF Strategy Development
Notes
Part II The Data & AI Trifecta: Ethical Considerations, Deployment Tactics, and Competitive Analysis
Chapter 7 Complying with Regulatory and Ethical Standards
The Standards
Early Regulatory Compliance Considerations
Ethical AI Should Be Top of Mind
Implications When Building with Generative Models
Notes
Recommended Reading
Chapter 8 Practical Tactics for Successful AI Deployments
Setting the Stage for AI Deployments
Important Considerations When Deploying AI
Choosing the Right Technology Stack for Your Deployment
Cost Considerations: Build vs. Buy
Speed and Efficiency
Deploying AI with Agile and DevOps Principles
Common Pitfalls and Best Practices
Considerations When Building with Foundation Models
Considerations When Deploying Retrieval-Augmented Generation
Recommended Reading
Part III The Technical Foundation for Growth
Chapter 9 Surveying Your Industry and Organization
Generative AI-Powered Market Research
Conducting a Quick Tech Assessment
Identifying Appropriate Case Studies
Researching and Identifying Appropriate Use Cases
Extracting Relevant Information from Existing Documentation and Interviews
Identifying Your Best-Fit Use Cases
Additional Considerations for Generative AI Use Cases
Chapter 10 Perform a Technical Assessment
Evaluating Existing Data Inventory for Efficacy and Efficiency
Reviewing and Assessing Existing Reference Architecture
Identifying Major Technology Gaps Via a Preliminary Technology Gap Analysis
Considerations for Foundation Models in Data Infrastructure
Chapter 11 Stakeholder Engagement and Data Literacy
Defining, Segmenting, and Prioritizing Stakeholders
Gathering Requirements and Conducting Stakeholder Interviews
Reviewing and Assessing Executive-Level and Company-Wide Data Literacy Needs
Chapter 12 Assessing Your Current State Organization
Producing a Basic Process, Organization, Technology, and Information Model That Reflects Your Current State Organization
Assessing the Data Maturity of Your Organization
Conducting a Data Skills Survey and Audit
Evaluating Skill Set Needs for Managing and Fine-tuning Foundation Models
Conducting and Producing a Data Resources Audit
Assessing Infrastructure and Compute Costs for Foundation Models
Chapter 13 Assessing Your Current State AI Ethics and Data Privacy
Reviewing Your Company’s Legal and Regulatory Frameworks
Gap Analysis for Ensuring AI Accountability, Explainability, and Unbias
Real-World Implications and Case Studies
Strategies for Improving Overall Adherence and Compliance
Notes
Part IV Formulating and Implementing an AI Strategy
Chapter 14 Selecting and Scoping a Winning Use Case
Documenting Your Potential Use Case Options
Selection Criteria for Foundation Models
Identifying and Recommending Your Winning Use Case
Defining the Scope, Schedule, Stakeholders, and KPIs for the Intended Data Initiative
How to Write a Project Charter
How to Pitch Your Project to High-Influence Stakeholders
Chapter 15 Evaluating All Relevant Resources
Clarifying Intended Users and Their Needs
Evaluating Data Resource Needs
Evaluating the Data Architecture That Is Relevant to the Project
Addressing Data Skills and Literacy Gaps
Developing Training Plans
Formulating Hiring Recommendations
Assessing Vendor Relationships and Recommending a Plan for Vendor Management
Addressing Ethical and Societal Implications of Foundation Models
Monitoring and Maintenance Challenges When Building with Foundation Models
Advanced Risk Mitigation and Management in Data Projects
Chapter 16 Data Strategy Recommendations for Reaching Future State Goals
Exploring the Anatomy of a Technical Strategic Plan
Deep Diving into the Foundational Overview
Defining Your Technical Vision
Chapter 17 Finalizing Your Strategic Plan
Creating an Implementation Road Map
Compliance Requirements
Resource Allocation and Budget
Stakeholder and Departmental Alignment
Training and Hiring Recommendations
Risk Management and Contingencies
Monitoring and Management Mechanisms
Advanced Project Management for Data Strategies
Wrapping Up Your Strategic Plan
Tools, Technologies, and Resources for Streamlined Implementation
This Is Just the Beginning
Index
End User License Agreement
List of Illustrations
CHAPTER 2
Figure 2.1 The STAR Framework’s four-phase approach.
Figure 2.2 How to apply the STAR Framework.
Figure 2.3 Traditional LLM vs. RAG-powered LLM.
Figure 2.4 Delivering an evergreen data strategy.
CHAPTER 4
Figure 4.1 The basic mechanics of PLG.
Figure 4.2 A simple feature adoption chart.
Figure 4.3 How Spotify uses recommenders to drive PLG.10
Figure 4.4 How Humanic uses LLMs to drive PLG.
CHAPTER 5
Figure 5.1 LLM support of the marketing function.
Figure 5.2 An example of dynamic pricing for a large-scale retailer.
Figure 5.3 RAG-powered content creation.
Figure 5.4 Conceptual schematic for a RAG-powered AI tool that automates social media mar...
Figure 5.5 End-to-end content creation automation by Predis.ai.
Figure 5.6 Use of RAG-powered AI chatbots for more efficient and accurate customer suppor...
CHAPTER 6
Figure 6.1 A market validation flowchart.
Figure 6.2 The road map to achieving product-market fit.
Figure 6.3 A high-level example of customer persona documentation.
CHAPTER 7
Figure 7.1 Stable Diffusion’s output for the one-shot prompt “AI start-up f...
CHAPTER 8
Figure 8.1 ML engineers vs. AI engineers.
CHAPTER 10
Figure 10.1 An example of a simple reference architecture.
CHAPTER 12
Figure 12.1 Stages of a data maturity model.
CHAPTER 13
Figure 13.1 Explainability for LLMs.
CHAPTER 15
Figure 15.1 An example of reference architecture.
Figure 15.2 A training flowchart.
CHAPTER 17
Figure 17.1 Adding a graphic to your implementation road map.
List of Tables
CHAPTER 2
Table 2.1 Fine-tuning vs. RAG
CHAPTER 3
Table 3.1 Examples of Operational Improvements Use Cases
Table 3.2 Examples of Data-Intensive Product-Led Growth Use Cases
Table 3.3 Examples of Growth Marketing Use Cases
Table 3.4 Examples of Data-Intensive Financial Improvements Use Cases
Table 3.5 Examples of Data Monetization Use Cases
CHAPTER 4
Table 4.1 Metrics to Measure PLG Performance
Table 4.2 Data-Driven Product Functions
CHAPTER 5
Table 5.1 AI-Enabled Decision-Support for Marketing Strategy Development
Table 5.2 Predictive Analytics for Common Types of Marketing Campaigns
CHAPTER 6
Table 6.1 Free or Low-Cost Sources for Market Research
Table 6.2 A Simple Multi-Criteria Matrix for Selecting Optimal Product or Service Ideas
CHAPTER 7
Table 7.1 This, Not That, of Data and AI Compliance
CHAPTER 8
Table 8.1 Cost Factors Associated with AI Deployment
Table 8.2 Common Pitfalls Related to AI Deployment and Best Practices
CHAPTER 9
Table 9.1 Traditional Internet Research vs. Generative AI-Enabled Research
Table 9.2 Case Types by Business Objective
CHAPTER 11
Table 11.1 An Example of Overlapping Stakeholder Interests
CHAPTER 12
Table 12.1 An Example of a POTI Table
CHAPTER 15
Table 15.1 Training Plan Inclusions
CHAPTER 16
Table 16.1 Key Characteristics of an Effective Organizational Chart
Table 16.2 What to Include in the Business Units and Intended Users Section
CHAPTER 17
Table 17.1 Resource Allocation and Budget Components
Table 17.2 Stakeholder and Department Alignment Components
Table 17.3 Risk Management and Contingencies Components
Table 17.4 Components of an Effective KPI
Table 17.5 Management Approaches for Data Intensive Projects
Table 17.6 Conclusion Section Components
Guide
Cover
Table of Contents
Praise for Data & AI Imperative
Title Page
Copyright
Dedication
Acknowledgments
About the Author
Introduction
Begin Reading
Index
End User License Agreement
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Praise for Data & AI Imperative
Consider the landscape of AI projects. More than half will end in failure. And for these doomed projects, they will be lucky if it happens quickly. Others will burn slowly before they run out of cash. Technology will be blamed, but in truth, the writing was already on the wall.
These projects fail because they did not have a proper data strategy. In Data & AI Imperative, industry veteran Lillian Pierson highlights what companies need to be successful. In many cases, the issues plaguing beleaguered start-ups are not the tech: it’s bad market fit, misunderstanding the customer, over promising, and under delivering.
But by reading this book, companies can get AI right from the get-go, and propel their ideas into exponential growth. In the hyper competitive world of data, Pierson provides the winning strategies.
—Jordan Goldmeier
Author of Bestselling Book, Becoming a Data Head
With Data & AI Imperative Lillian is addressing an important problem that many companies are now facing, how to derive value from data and AI in practice. Today most companies and organizations realize the transformational power of data and AI, and by reading this book, I believe that companies will have a better chance at deriving this value.
—Ulrika Jägare
Head of AI, Data, and Architecture at Scania
Data & AI Imperative is a must-read for data leaders aiming to make real business impact with AI. Lillian breaks down complex strategies into simple, actionable steps, making it easy to align AI projects with business goals. Whether you’re just starting out or already deep into data transformations, this book offers practical advice and insights that you can put to work right away.
—Kate Strachnyi
Founder, DATAcated and Author of ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color
We are living in the fastest changing business environment in history. Businesses must adopt tools and processes that drive insight and transformation exponentially faster. Lillian Pierson’s Data & AI Imperative is an essential read for business consultants who are working to navigate the complexities of AI-driven transformation. Her insights into aligning data strategies with business objectives are both practical and forward-thinking. They offer a clear path for consultants to help their clients harness the full potential of AI. This book is a valuable resource for anyone advising organizations on how to turn data and AI into a sustainable competitive advantage.
—Jim Harris
Leading Keynote Speaker at the WEF and AI Consultant to Fortune 500 Companies
Lillian Pierson’s book offers an in-depth guide on how modern product, engineering, and GTM teams can harness data and AI. From discovering product-market fit to utilizing product analytics for PLG strategies, to all aspects of modern marketing and sales use cases. This book covers it all.
—Gururaj Pandurangi
CEO, ThriveStack
In Data & AI Imperative, Lillian Pierson offers keen insights on data strategy as it relates to AI. She delves into the value proposition of investing internally and partnering externally to deliver near-term business value and sustained business growth with data and AI/ML. She offers real-world examples of best practices and common pitfalls organizations encounter when developing a data strategy. Data & AI Imperative is a must-read for data professionals and business leaders.
—Heather A. Smith MPA, PMP, CSM
AI & ML Innovation Leader and Former Associate Managing Director, Innovation & Data Science
Lillian is passionate about Data & AI and is a gifted marketer. Her easy style and insightful description of complex topics makes her book a must read for those who don’t want to get replaced by AI.
—Arjun Saksena
Founder/CEO, Humanic
Lillian Pierson’s Data & AI Imperative is a game-changer for data leaders navigating this complex and exciting landscape of data and AI. Her insightful approach bridges the gap between technical implementation and strategic vision, offering a comprehensive playbook for leveraging data to drive organizational success. This book is an essential resource for anyone looking to harness the full potential of data and AI in their company.
—Hana Khan
CEO, Trending Analytics
Founder of The Art of Communicating Data Podcast
Data & AI Imperative is an essential guide for AI leaders and professionals who often face challenges in influencing organizational strategy and driving real value. Lillian Pierson’s deep expertise shines through as she offers practical advice on how to overcome those barriers, helping professionals not only understand how to align AI with business objectives but also how to navigate the complex dynamics that often limit their impact. It’s a goldmine for anyone looking to drive value with AI.
—Maria Vechtomova
Manager & MLOps Tech Lead, Ahold Delhaize
Data & AI Imperative
Designing Strategies for Exponential Growth
Lillian Pierson, P.E.
Copyright © 2024 John Wiley & Sons, Inc. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
ISBNs:
Paperback: 9781394251957 ePDF: 9781394251971 epub: 9781394251964
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Library of Congress Cataloging-in-Publication Data
Names: Pierson, Lillian, author. | John Wiley & Sons, publisher.
Title: Data & AI Imperative: designing strategies for exponential growth / Lillian Pierson.
Other titles: Data and AI imperative
Description: Hoboken, New Jersey: John Wiley & Sons, Inc., [2025] | Includes bibliographical references and index. | Summary: "This book covers data landscape analysis, use case identification, planning and strategy development, implementation oversight, and evaluation and scaling of data initiatives. It focuses on applying pre-trained generative AI models rather than developing custom LLMs, which are costly and complex. Using a tested framework, the book guides you in aligning data resources with business objectives, selecting impactful use cases, crafting effective strategies, and ensuring successful execution and scaling of AI initiatives"– Provided by publisher.
Identifiers: LCCN 2024037615 (print) | LCCN 2024037616 (ebook) | ISBN 9781394251957 (paperback) | ISBN 9781394251964 (epub) | ISBN 9781394251971 (ebook)
Subjects: LCSH: Business–Data processing. | Artificial intelligence–Economic aspects. | Artificial intelligence–Financial applications. | Business planning.
Classification: LCC HF5548.2 .P4987 2025 (print) | LCC HF5548.2 (ebook) | DDC 658/.05–dc23/eng/20240924
LC record available at https://lccn.loc.gov/2024037615
LC ebook record available at https://lccn.loc.gov/2024037616
Cover image(s): Graph Paper: © xxmmxx/Getty Images,
Terrazzo Tiles: © Yuri Parmenov/Getty Images
Cover design: Wiley
To the decades of pioneers and innovators of data and AI technologies, whose hard work, persistence, and courage have paved the way for the growth of all modern companies. May your contributions inspire a new era of good in the world.
To Vitaly, Ariana, Stanislav, Irina, and Hadas: I’m forever grateful. Your unwavering support has been the foundation upon which I could write this book—the most powerful publication of my career.
Acknowledgments
I’ve had the great good fortune of working with some truly talented individuals from the Wiley team to produce this book.
Jim Minatel, Associate Publisher at John Wiley & Sons, led the effort and encouraged me in the writing of this book. It was a pleasure to work with John Sleeva, Project Editor, who helped bring the book from raw manuscript to final production. Thanks to Ashirvad Moses, Managing Editor, for bringing such ease to the final stages of the publishing process.
I appreciate the input of my dear friend Jay, who encouraged me and served as a sounding board as I approached this massive undertaking.
Huge thanks to Damian Wolfgram, who reviewed and contributed from his vast expertise in product leadership and management within the Fortune 500.
Finally, thank you to Satwik Mishra, who reviewed and contributed his AI engineering subject-matter expertise to this book.
—Lillian Pierson
About the Author
Lillian Pierson, P.E. is the founder and fractional CMO at Data-Mania, as well as a globally recognized growth leader in technology.
Through best-in-class marketing strategy, leadership, and advisory support, Lillian helps B2B tech start-ups, scale-ups, and consultancies achieve consistent and predictable revenue growth, all without the full-time CMO price tag.
As a fractional CMO for B2B tech ventures, Lillian specializes in go-to-market and product-led growth strategies, particularly for high-growth data and AI start-ups. Her expertise has supported the expansion of 10% of Fortune 100 companies.
Lillian has a formidable reputation as a data science and AI educator and consultant. She’s educated approximately 2 million learners through the dozens of books and courses she’s authored as part of deep partnerships with notable publishers such as John Wiley & Sons and LinkedIn Learning, to name two.
She’s been a licensed Professional Engineer in good standing since 2014, with approximately 20 years of experience developing in-house strategies for multinational corporations and organizations as large as the US Navy.
Furthermore, Lillian has been a strategic marketing and growth advisor for tech start-ups since 2018. Prior to this, she spent 12 years spearheading marketing campaigns for a diverse range of B2B technology companies, from VC-backed SaaS start-ups like Domino Data Lab to leading enterprise software companies like BMC software, as well as major corporations including IBM, Intel, and Dell.
Introduction
Late-breaking advancements in data science and artificial intelligence have fundamentally and irrevocably changed the face of business. These innovations open many new, untapped avenues for business growth at exponential scale, while simultaneously creating legions of hidden pitfalls for unknowing business leaders.
A methodically prepared, evidence-backed strategy lays a rock solid foundation for driving measurable and predictable business growth from your company’s investment into data and AI technologies—investments that are certainly necessary to remain commercially viable now and for the foreseeable future. Well-formed, meticulously executed data and AI strategies deliver an extensive array of benefits, including:
A dramatic increase in organizational productivity through automation
, so you can keep your focus on strategic decision-making and innovation.
Consistent bottom-line revenue growth
that ensures financial stability and supports long-term investments.
Improved customer retention and loyalty
that drives long-term business relationships and decreases customer acquisition costs.
Establishment of a clear and unquestionable competitive advantage across the market
, so you can lead in your industry with confidence and foresight.
What Does This Book Cover?
This book guides you through the following topics:
Data landscape analysis—
You’ll start by identifying the data resources, technologies, and skill sets that your organization currently has, what it needs, and how you can align these with your business objectives.
Use case identification and evaluation—
From a plethora of possibilities, you’ll learn how to identify the most impactful “Potential Use Cases” and eventually zero in on the “Winning Use Case” to implement. You’ll do this through a carefully designed and well-tested framework that I’ll be providing in this book.
Planning and strategy—
You’ll develop a data or AI strategy, complete with a technology assessment, skill gap analysis, and stakeholder engagement strategies.
Support for overseeing the implementation—
You’ll go beyond theories and simulations and gain the knowledge you need to oversee a responsible execution of your data or AI strategy, in the process transforming it into an operational product or program.
Launching and validating in the market—
You’ll learn how to bring commercial data and AI solutions to market, validate product-market fit, and scale through powerful AI-driven approaches to growth marketing and product-led growth.
Evaluation and scaling—
You’ll learn how to measure the impact of your data initiatives, optimize them for better return on investment (ROI), and identify means by which to scale them effectively.
With respect to AI strategy development, this book specifically focuses on the application of pre-trained generative AI models, not on building bespoke LLMs from scratch.
Developing custom LLMs is complicated, novel, and very expensive. It’s also risky.
Take, for example, BloombergGPT; a custom LLM that Bloomberg developed. It was trained on its own in-house proprietary financial data. The project required significant investment, to the tune of tens of millions of dollars, and yet recent studies at the Department of Electrical and Computer Engineering & Ingenuity Labs Research Institute Queen’s University indicate that both ChatGPT and GPT-4 performed better than BloombergGPT on financial natural language processing tasks.
The AI strategy-building guidance I provide within the pages of this book supports you in leveraging more accessible and versatile pre-trained generative AI models. It provides you with practical insight into the current landscape of generative AI, without digging into the complexities involved in the custom development of generative AI models themselves.
Who Should Read This Book
As implied in its title, this book is written for people who want to drive massive business growth by strategically harnessing data and AI technologies. Naturally, this group of people includes technical founders and leaders, non-technical founders who have sufficient grasp of broad concepts in data and AI, or even data professionals and developers who seek to level up their strategic thinking and approach.
If you’re reading this book, I assume you have:
Some form of expertise in data science methodologies, tools, and techniques.
A fundamental understanding of the mechanics involved in AI and machine learning.
Strong analytical skills and some hands-on experience working with data.
After reading this book, you can expect to come away with:
A holistic understanding of how to align data and AI initiatives with your organization’s broader goals and objectives.
A solid approach for identifying “Potential Use Cases” and rigorously selecting the “Winning Use Case” for optimal ROI.
Confidence in developing and presenting a data strategy that commands stakeholder buy-in and allocates resources efficiently.
Preparedness for overseeing the execution of data and AI strategies that deliver measurable bottom-line revenue gains.
By the end of this book, you’ll be equipped to think like a strategic driver of data and AI initiatives, you’ll have seen how to transform data into immediate business value, and you’ll know how to do this in a way that aligns with your organizational objectives while maximizing ROI.
Key Terms
The following key terms are used consistently throughout this book as I work to steer you through each and every nuance involved in data and AI strategy development. Please take note now, as your understanding of these terms is critical to your success with this book.
Current-state—
This refers to the existing conditions and operations of a business at a specific point in time. It serves as a baseline for analysis and future planning.
Future-state—
This refers to the envisioned or targeted conditions and operations of a business, representing desired improvements and goals from the current state.
Use case—
This is an example of how data could be used to achieve a particular business objective. It serves as a blueprint for a data project, outlining what needs to be done, what resources are required, and what outcomes to expect.
Potential use case—
This refers to a use case that has been preliminarily assessed as highly relevant and feasible for your organization but hasn’t yet undergone an in-depth analysis. It aligns well with the organization’s current capabilities, business objectives, and data resources. These are considered “low-hanging fruit” use cases that offer a high likelihood of quick success and clear business value. By designating a use case as a “potential use case,” you signal that it meets certain initial criteria and is worthy of further investigation. This serves as a filtering step in the process of choosing the best data initiatives to pursue.
Winning use case—
This refers to the single use case that has been thoroughly evaluated and ultimately selected for implementation. It’s the use case that, upon rigorous analysis, has been deemed to have the highest likelihood of delivering the most value in terms of meeting business objectives, utilizing available resources efficiently, and being aligned with the technical and skill capabilities of your team.
“Implement a use case”—
This refers to the set of actions and processes undertaken to deliver a specific use case—in this book, that use case will be your “winning use case.” This includes detailed planning, execution, monitoring, and eventually scaling the solution. The implementation phase is where the rubber meets the road: transforming ideas and analyses into tangible, operational data initiatives.
Growth—
For purposes of this book, the word
growth
refers to general bottom-line revenue growth and, more specifically, product-led growth and growth marketing strategies. These are the types of growth driven by the data and AI strategies discussed in this book.
Reader Support for This Book
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How to Contact the Author
I appreciate your input and questions about this book! Email me at [email protected], or send me a direct message through my website contact form. (https://www.data-mania.com/contact-our-fractional-cmo-for-hire/).
Part IThe Data & AI Advantage in Modern Business
Chapter 1Leveling the Playing Field with Data and AI
Data is the language of the powerholders.
– Jodi Petersen
It was a Tuesday morning, mid-March of 2023. With a warm cup of joe in one hand and my cell phone in the other, I saw an Instagram post from a content repurposer I like to work with. I can’t recall the exact contents of the post, but the gist was that something big had happened in the artificial intelligence (AI) space and that it would change our lives forever.
Driven by rabid curiosity, I immediately dug deeper and discovered that OpenAI had just “unleashed” its large language model, GPT-4, onto the world. (The word “unleashed” is a dead ringer for GPT-4 generated content, so pardon the pun.)
You know, we who operate in the AI industry have seen it coming for a long time, but the fact that they actually pulled it off was still the wake-up call of a lifetime. Even for those of us who’ve been working in the data science industry from its inception, the implications were shocking. This was the day that changed everything.
Evolving Business at Breakneck Speed
We’re in the midst of a never-before-seen acceleration of business change, the majority of which has been fueled by advancements in data science, data engineering, and AI. While generative AI technologies, like GPT-4, have radically extended the boundaries of what’s possible, they’ve also served as a warning shot in the dark for all businesses to either get on board or get left behind in the dust.
The transformative potential of data and AI cannot be overstated. Data and AI must take center stage when it comes to how your company drives improvements in growth, operational efficiency, customer engagement, product innovation, and strategic decision-making. Traditional strategies will no longer suffice. You need a dedicated, up-to-date data or AI strategy that’s laser-targeted to meet your business’s growth objectives in furtherance of the company’s mission and vision.
That said, it’s no easy feat to make effective use of data and AI technologies. You need a strategy, but building successful data strategies requires one to have a combination of strong technical expertise, business acumen, and astute leadership capabilities. These people are few and far between. My goal for this book is to equip you, the reader, with the strategy development know-how that you need in order to leverage your existing data expertise to drive reliable business growth.
With the extent of digital disruption we’re facing, one data strategy seldom suffices. It’s highly likely that your company will need a broad overarching strategy to guide the development of high return on investment (ROI) data initiatives across the organization, as well as composite data strategies for each of the use cases that are included within that overarching strategy.
Tip
The methods I’m covering in this book show you how to go about developing a data or AI strategy for a single use case. You can repeat the process for multiple use cases, but if you do, it’s highly advisable to map back and optimize the projects against one another in a top-level plan that governs your company-wide data and AI strategy.
A well-built data and AI strategy acts as a road map. It acts as a lighthouse to guide your company through the vast and often overwhelming complexities involved in digital transformation. It’s designed to directly transform technological investments into tangible business outcomes, such as improved customer experiences, streamlined operations, or even new revenue streams.
How to Use This Book
Let’s look a bit at what this book is meant to be and how to go about getting maximum value from your time within its pages.
My assumption is that, if you’re reading this book, you have a solid background in and understanding of data science, analytics, data engineering, and AI. Having a background in strategy development is icing on the cake, but if you don’t, that’s okay, too.
This book is written in narrative format, yes—but it’s more than just a narrative that describes data and AI strategy. Parts I and II are written as an educational primer to supplement and bolster your existing knowledge of applied data, AI, and growth that’s required to perform effectively in the data strategist role. Parts III and IV are meant to be used as a step-by-step instruction manual on how to go about building high ROI data strategies.
While Parts I and II detail the foundational knowledge that you should have prior to initiating a data strategy-building effort, these chapters will not be of equal importance to all readers. If you find some areas are less relevant to your current role, you’re pretty safe skipping around to other parts of the section. That said, for Parts III and IV, I advise you to follow the instructions as they are presented, in a step-by-step methodical manner.
Caution
Data strategy is a big money game; if your project fails, it could cost the company millions of dollars. Following the meticulous steps. I’ve laid out for you in great detail throughout Parts III and IV is the most sure-fire way I know to safeguard the success of your data initiatives.
The focus of this book is on business growth and the data and AI strategies that drive it. For this reason, it’s essential that we examine two of the biggest growth drivers in modern data-intensive businesses: product-led and growth marketing, introduced in Chapters 4 and 5, respectively. The recent explosive growth of generative AI start-ups also necessitates that we address the basics of ideation and validation around commercial AI products and services. That’s covered in Chapter 6.
If you’re a product, marketing, or start-up leader, then Chapters 4 through 6 will likely resonate with you. But if your background is mostly in data implementation, then you may prefer Chapters 7 and 8 on ethical and implementation-relevant concerns that are related to data strategy. If you’re looking to develop a strategy around the use of generative AI technologies, I’ve also laid out the implications of working with foundation models for you within Chapters 7 and 13.
How This Book Benefits You
Whether you’re a business leader, a product or program manager, or an individual contributor in the tech space, this book is designed to equip you with the insights and strategies you need to harness data and AI innovation to drive growth for your company.
If You’re a Business Leader or Executive
Whether you’re a Chief Technology Officer, a Chief Financial Officer, a Chief Marketing Officer, or any other type of CXO, you’re responsible for the growth and operational health of a core business function. And if you’re a Chief Executive Officer, then you know exactly how much of your organization’s success is riding on your shoulders.
In all the preceding scenarios, it’s imperative that you know the ins and outs of data and AI strategy so that you can oversee such strategies in driving the growth your company needs to stay competitive in today’s AI-imbued business environment. In this book, I’ve included all the insights and strategies you need to do just that.
If You’re a Product or Program Manager
Data and AI technology are the basis of growth for a modern organization. Customers and users expect that products and services are delivered with the efficiency advantages that only data and AI can deliver. Not every Product or Program Manager needs to become a technology expert, but you do need to know enough to steer your product and program road maps in the right direction. By reading this book, you’ll learn what you need to know to do that.
If You’re a Data or Technology Professional
Data scientists, data analysts, data engineers, machine learning (ML) engineers, AI engineers, and software developers–I’m looking at you. Without brilliant individual contributors like yourselves, the data and AI industry would never be where it is today. Though, one challenge most executional team members face is that they aren’t in the position to see how the work they do on a daily basis actually drives business growth.
As you read this book, you’ll get a clear picture of how what you do each day– all the technical bits and pieces–plays such an important role in the success of the final product that’s sent out to the market.
At first glance, the audience that I’m speaking to within the pages of this book may seem excessively broad, but here’s the thing: recent developments in AI have radically changed the game for all types of knowledge workers. Every role is impacted. Moreover, business executives, product leaders, and executional team members all have seats at the strategic table here. With its strong focus on data and AI strategies to drive exponential business growth, my goal for this book is to bridge the strategic gap that formerly lay between these diverse roles. By the end of this book, you’ll have the solid foundation in data and AI strategy that you need to start leading solutions that drive growth for your company and industry.
To the Business Leaders and Executives
From a strategic perspective, there’s never been a time when it’s more important for business leaders to truly understand how to harness data and AI to drive growth. The elephant in the room, of course, is generative AI. Entire industries are being upended by the radical change that generative AI has spawned. Chances are, your business and industry are affected, too.
Why Leaders Need a Data or AI Strategy
If your company is not leveraging generative AI to at least streamline its marketing and growth operations, then you’re already operating at a major disadvantage. But to make the most of any investment into generative AI projects, you must first have a strategy in place to support that project.
The need for data and AI strategy goes beyond novel generative AI projects in the marketing and growth domains. Competitors are capitalizing on new-found efficiencies across every business function—from software development to customer service, and from financial analysis to product design and innovation. This book helps you lead and champion a data or AI strategy that results in greater efficiencies within your business, regardless of the domain for which the strategy is built.
Caution
The need for astute data leadership is nothing new. Your organization’s data initiatives should be designed from the ground up to support one overarching goal. That goal is to fulfill your company’s mission. If you’ve got data operations that are disconnected from that mission, there’s a pretty big chance that you’re spinning wheels and achieving subpar returns on your investment into data technologies, skill sets, and resources. This book shows you how to align your data initiatives with core business objectives.
To drive business value from data and AI, you need to do so responsibly to safeguard the reputation of your company and the bottom line of its investments. To do that, your company’s approach to data and AI must be both ethical and compliant with laws and regulations. Chapter 7 of this book provides you with the foundational knowledge you need about ethical considerations as they relate to data and AI projects. Chapter 13 describes how to go about assessing the current state of your company’s AI ethics and compliance.
Lastly, your talent needs your leadership support in helping them upskill and successfully transition to a new data-driven landscape. Chapter 11 will show you the most efficient ways to assess data skill gaps across your organization. Chapter 15 educates you on how to develop effective training programs and make optimal selections for any new hires that might be required for the execution of your data strategy.
Steering the Data and AI Revolution
Not too long ago, generative AI capabilities were mere mental constructs in the minds of only the most creative of innovators. Today, these technologies are facilitating the type of vast data processing and pattern recognition that was only formerly available to big tech companies with the resources and know-how to build these capabilities from scratch in-house.
Like it or not, modern business leaders need to employ AI across the enterprise to achieve both productivity gains and competitive advantages. Naturally, this process involves addressing extremely complex challenges like the technology’s potential for making costly errors or its reliance on large volumes of data. To squarely meet these responsibilities, business leaders must be data literate to their core, but that isn’t enough. You also need strategies to steer your company’s data and AI developments to protect investment while also growing its bottom-line revenue. My goal with this book is to provide you with an approach and methodology on how to do it.
I also emphasize this a lot: modern business leaders must have a fundamental understanding of what's involved in implementing data and AI technologies. Within this book, I’m covering these fundamentals to the extent I am able, but I’m assuming that you’re coming to it with at least a basic understanding of data engineering, analytics, data science, and AI.
As you know, integrating AI in business operations requires far more than just technical implementation capabilities. Business leaders need the durable soft skills to lead large-scale technical initiatives in the face of internal resistance to change. Throughout this book, I share leadership tips on how to finesse the finer nuances involved in delivering data and AI strategies that drive growth.
Lastly, leaders need to know how to identify winning use cases for their projects. Winning use cases are the low-risk, high-reward cases around which successful data and AI strategies are built. To drive true competitive advantage, your strategies must be based on an exhaustive assessment of the current state and a thorough analysis of use case alternatives before deciding which one is optimal for your growth goals at this time. Chapters 9 through 14 guide you through the process of selecting a winning use case for strategy development and implementation.
By following the comprehensive approach I’ve laid out for you in meticulous detail throughout Chapters 9 through 17, I’m confident that you’ll quickly be on your way up the path toward leveraging data and AI to drive growth, innovation, and competitive differentiation in your industry.
Two Case Studies to Inspire Your Vision
To inspire your vision, I’m sharing two recent and powerful case studies that were massively effective in driving growth in the telecom and financial services industries. The following Vodafone case study illustrates a data-intensive strategic win that was achieved by Vodafone Italy.
Growth Marketing Case Study
Title: Vodafone Italy’s Conversion Boost of 42% with AI-Driven Creativity1
Company Name: Vodafone Italy
Industry: Telecom Services
Situation Summary
Vodafone Italy faced the challenge of enhancing customer experiences and loyalty through digital communication channels like mobile push notifications and SMS. The marketing team knew they needed to increase the impact of their marketing messaging to increase customer lifetime value, prevent churn, win back former customers, and boost customer satisfaction through loyalty campaigns.
Challenges
The company struggled with delivering the right message to the right people at the right time. It, also struggled to publish content that resonated with diverse customer types, and this led to missed opportunities in upselling, cross-selling, and customer retention.
Solution
With the goal being to boost their marketing efficiency and results, Vodafone Italy turned to Persado’s AI platform. Persado specializes in deploying machine learning and AI to generate optimized marketing creative. The platform analyzes and predicts the effect that specific words and emotions would have on customer decisions. In this way, Persado enabled Vodafone to craft more precise messages to significantly increase the quality and effectiveness of its marketing campaigns across its primary digital channels.
Results
Compared to conventional methods, Vodafone Italy saw a 42% lift in conversion rates for CRM campaigns and a 9% increase for win-back campaigns. The loyalty campaigns saw a 60% average increment in redemption rates at a relatively much lower cost. This strategic partnership improved Vodafone Italy’s marketing efficiency and contributed to significant business growth and customer engagement improvements.
Bonus resource
For help in pinpointing the areas where your company’s marketing effectiveness could be improved through the strategic use of data and AI like that described previously, I invite you to use my KPI Scorecard and Pipeline Tracker that I’ve made freely available to all readers here: www.data-mania.com/book.
Another powerful case study that I’m certain business leaders will appreciate is the decision-support win that was recently achieved by DHFL.
Decision-Support / Operations Case Study
Title: DHFL Streamlines Customer Onboarding from 40 Days to just 7 Days2
Company Name: DHFL (Dewan Housing Finance Corporation Ltd.)
Industry: Financial Services
Situation Summary
DHFL is committed to providing affordable housing finance in India’s semi-urban and rural areas, but it faced challenges in managing its customer onboarding process. Due to the rapid growth of its customer base, the company experienced delays in loan application processing. This decreased customer satisfaction rates across the board. To improve customer satisfaction, DHFL knew it needed more efficient operations.
Challenges
The primary challenge was in analyzing vast operational data to identify causes for delays in customer onboarding. The process was cumbersome and spanned 35–40 days. At these rates, DHFL was unable to deliver timely financial services. Operational bottlenecks across multiple geographies further complicated the problem. The company needed a solution to streamline the entire onboarding process, despite its complex geographical dependencies.
Solution
DHFL partnered with Gramener to develop visual analytics dashboards using Gramex, Gramener’s proprietary development platform. DHFL gained an all-around visibility of its operational KPIs and metrics through the solution. With Gramex, it was able to get a clear picture across different geographies and regions (with different levels of geographical granularity) with regard to cases that were either in processing, pending, or awaiting resolution. Gramex dashboards also enabled DHFL executives to make data-driven decisions and identify and address operational bottlenecks on time.
Results
DHFL saw a dramatic improvement in operational efficiency. Customer onboarding time was reduced by 65%, from 35–40 days to just 7–8 days. Loan application pendency decreased by 52%. Significantly reduced processing times directly improved customer satisfaction. It also reinforced DHFL’s commitment to its mission of providing accessible housing finance.
To the Product and Program Managers
The strategic use of data and AI has immense potential for product and program management. In direct correlation with the effective usage of data and AI, product and program outcomes are increasingly data-driven, customer-centric, and efficient. Let’s explore just a few of the ways that the data and AI strategy know-how that’s imparted in this book is already helping product and program managers score massive wins for their organizations.
Data- and AI-Enabled Product and Program Wins
An effective data or AI strategy has the power to positively transform how your company develops, manages, and evolves its products and services. Decision support, operations, and product growth are often full of quick wins that can be achieved by product and program managers with the help of this book.
Decision-Support Systems That Drive Product and Program Wins
One of the primary ways that you can use data and AI to achieve quick wins in product and program management is by using data to predict future trends and behaviors to improve decision-making processes. AI algorithms help analyze vast amounts of data to predict future trends, customer behavior, and potential market shifts. By leveraging these predictive capabilities, product and program managers can:
Make better-informed, more timely decisions
More accurately anticipate market and customer needs
Proactively adjust strategies based on large bodies of evidence, rather than making strategic decisions on a reactive, ill-informed basis
Develop tailored product and service recommendations that increase customer satisfaction, build loyalty, and improve customer retention
You can look forward to Parts III and IV, where I share a foolproof, strategic approach to building an effective data strategy around a decision-support use case.
Caution
If you’re not leveraging insights from predictive analytics to drive more effective resource allocation, product development prioritization, and risk management strategies, then you’re already operating at a disadvantage. This is a pretty standard use case that’s already in play at most data-mature organizations. More on data maturity is coming up in Chapter 12.
Automations That Increase Operational Efficiency
Generative AI applications are already being used to automate a wide range of routine tasks, including data entry, customer service, and even complex operational decision-making. These types of automations free up product and program managers to focus on their more strategic and creative requirements. They also reduce the likelihood of human error and improve the overall quality of your product and program management processes. This often results in a faster time-to-market for the new products and features you’re building. Not to mention that this will also improve the agility of your organization to respond to market changes much more swiftly.
AI-Enabled Product Growth
Integrating AI in product and program management has transformed growth strategies, particularly product-led growth. Product-led growth strategies often use AI to enhance customer engagement by providing personalized experiences, predictive future needs, and automated customer support. AI-driven analytics offer deeper insights into how users interact with the product, which in turn enables continuous improvement and innovation based on actual user behavior and feedback in near real-time.
For more insights on AI-enabled product-led growth gains, be sure to study up on Chapter 4.
To the Data and Technology Professionals
Let’s be real. If you’re reading this book, then the chances are pretty high that you’re a data or tech professional working on executional requirements that, hopefully, are part of a coordinated data or AI strategy designed to support your business in reaching its objectives in a timely manner.
Simply put, the work you do on a daily basis is the foundation of all data- and AI-enabled growth. Let’s take a look at how your work drives the greatest period of technological advancement ever known to humanity.
Developing Ground-Breaking Innovation at the Speed of Light
Data professionals are tasked with massive responsibilities associated with the development of AI applications, machine learning models, and the data infrastructure that supports these. Your expertise in data science, analytics, data engineering, computer vision, and natural language processing can help you turn manual data strategies into practical applications that dramatically increase efficiency, decision-making, and innovation across your industry.
This book is a data and AI strategy development resource manual. While it does not cover implementation-level details related to building data and AI solutions, Chapter 8 provides useful and practical insights into real-world tactics for a successful AI deployment.
Programming Ethics and Compliance Into Massively Scalable Technologies
As AI systems become an integral part of our daily lives, data and tech professionals must address and implement ethical design principles for responsible use. This demands that you build AI systems that are transparent, fair, and accountable.
While ethicists, legal experts, and policymakers establish guidelines and standards that govern data and AI usage, you’re tasked with actually embedding these ethical principles into the design and deployment of data and AI technologies in order to mitigate any privacy, bias, and security issues. The outcome of this collaboration, of course, being data and AI technologies that grow your business bottom line, while also benefiting society as a whole and minimizing potential harms.
Warning
Considering the scale at which many commercial data and AI tools are being adopted, the ethical considerations of your work cannot be understated. As builders, you have the power to transform lives, rewrite history, and shape the future of your company. Build wisely!
Cross-reference
Be sure to read Chapters 13 and 15 to learn the finer points involved in building strategies for ethical, responsible, and compliant data products and services.
How This Book Benefits Individual Contributors
As mentioned at the beginning of this chapter, this book provides individual contributors with the vital perspective that you need to see how what you’re building on a daily basis actually drives growth for your company. Gone are the days when you’re building with the blinders on, unsure about how a data project you’re working on exactly amounts to massive gains for the organization at large.
If you aspire to move into a product or program management role, the data and AI strategy knowledge that’s imparted in this book provides a strong foundation for doing so. But, there’s more … the knowledge of data and AI strategy you’ll get within the pages of this book empowers you to:
Start asking better-informed questions of leaders and stakeholders, so that you have a clearer picture of outcomes your work should enable.
Have a much clearer idea of the tools, methods, and approaches you take toward building products.
Understand your requirements much better by considering the views of multiple stakeholders that are involved in the strategy development process.
Look at the road map and the tasks you’re allotted both from a technical standpoint and a strategic lens—helping you align better with the overall goals of an organization.
Truly appreciate how your contribution in each step of the development process results in an end product or service that dramatically improves the lives of other people!
Now that I’ve provided clarifications and communicated expectations as to who this book is written for and how it will help you, let’s move into Chapter 2, where you’ll learn about the fundamental and introductory prerequisite concepts you need to know in order to make your grand entrance to the data strategy arena.
Notes
1
. Persado + Vodafone (2024).
Persado.
https://www.persado.com/resource-library/articles/
2
. Gramener (2024).
Gramener
.
https://gramener.com/case-studies/data-driven-operational-excellence/
Chapter 2Introduction to Data Strategy
Data is great, but strategy is better.
– Steven Sinofsky
Data & AI Imperative is a strategic playbook written to equip you with the tools, frameworks, and strategic expertise you need to build and implement a robust data strategy for your organization.
As you know, data is the linchpin of your business operations, its decision-making, and ultimately, your success. Throughout the pages of this book, I’ll be guiding you through the A to Z of transforming raw data into actionable insights that drive immediate business value.
Introduction to the STAR Framework™
Throughout this book, I’m going to use my STAR Framework to walk you through each and every step of the data strategy-building process.
