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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:
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.
Lillian Pierson, P.E., is an industry-renowned data- and AI-driven growth strategist, advisor and fractional CMO for B2B technology companies. She's also a licensed professional engineer who's supported the expansion of 10% of the Fortune 100 whilst educating over 2 million learners on topics of data strategy, data science, AI, and growth marketing.
Acknowledgments ix
About the Author xi
Introduction xiii
Part I The Data & AI Advantage in Modern Business 1
Chapter 1 Leveling the Playing Field with Data and AI 3
Chapter 2 Introduction to Data Strategy 17
Chapter 3 Types of Data-Intensive Use Cases Based on Business Objectives 32
Chapter 4 Data- and AI-Driven Product-Led Growth 49
Chapter 5 Amplifying Growth Marketing Outcomes with Data and AI 66
Chapter 6 Validating Product-Market Fit for Commercial Data and AI Products and Services 83
Part II The Data & AI Trifecta: Ethical Considerations, Deployment Tactics, and Competitive Analysis 105
Chapter 7 Complying with Regulatory and Ethical Standards 107
Chapter 8 Practical Tactics for Successful AI Deployments 121
Part III The Technical Foundation for Growth 139
Chapter 9 Surveying Your Industry and Organization 141
Chapter 10 Perform a Technical Assessment 160
Chapter 11 Stakeholder Engagement and Data Literacy 176
Chapter 12 Assessing Your Current State Organization 193
Chapter 13 Assessing Your Current State AI Ethics and Data Privacy 215
Part IV Formulating and Implementing an AI Strategy 235
Chapter 14 Selecting and Scoping a Winning Use Case 237
Chapter 15 Evaluating All Relevant Resources 249
Chapter 16 Data Strategy Recommendations for Reaching Future State Goals 277
Chapter 17 Finalizing Your Strategic Plan 299
Index 320
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.
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.
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.
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.
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.
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.
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.
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.
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.
From a strategic perspective,...
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