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The 8 Laws of Digital Transformation

Finally, the Holy Grail. Nirvana. Serendipity's Golden Opulence Sundae (nice). Shangri-La. The Cubs winning the World Series. The "ultimate operative state" every modern organization seeks to achieve…Digital Transformation. But what exactly is Digital Transformation?

Digital Transformation is the creation of a continuously learning and adapting, AI-driven, and human-empowered business model that seeks to identify, codify, and operationalize actionable customer, product, and operational insights (propensities) in order to optimize (reinvent) operational efficiency, enhance customer value creation, mitigate operational and compliance risk, and create new revenue opportunities.

In this chapter, we'll pull together all the aspects of digital transformation we've discussed so far—with the concept of "human empowerment" to be covered in Chapter 9, Creating a Culture of Innovation Through Empowerment—into an actionable yet pragmatic plan to help organizations digitally transform their business and operational models. Digital Transformation is especially crucial as world events and developments, such as the COVID-19 pandemic, highlight the need for organizations to strive towards digital transformation expeditiously in order to survive.

Unfortunately, many organizations think that Digital Transformation simply means digitalizing their customer engagements and business operations; that is, replacing human analog processes with device or application digital processes. But that's not nearly sufficient to truly achieve digital transformation.

Organizations that are going to survive, and thrive, in the 21^st century must go beyond just "digitalizing" their customer engagements and business operations. Organizations must transition to a business model that proactively seeks to uncover, codify, and leverage granular customer, product, and operational insights (propensities) around which they can reinvent key business processes, reduce operational and compliance risks, uncover new revenue opportunities, and create a more compelling, differentiated customer experience.

To help guide organizations along their digital transformation journey, I have come up with these "Digital Transformation (DX) Laws"; laws based on repeated observations that describe or predict a range of natural phenomena. These "Digital Transformation Laws" come courtesy of several customer engagements; engagements where organizations are pursuing digital transformation but get waylaid by unexpected obstacles along that journey (picture the "Jason and the Argonauts" movie…the original not the awful remake).

DX Law #1: It's About Business Models, Not Just Business Processes

Digital Transformation is about reinventing and innovating business models, not just optimizing existing business processes.

Solely optimizing existing business processes is a "paving the cow path" mindset, where organizations simply apply new digital technologies to replace existing human-intensive operational processes, without taking into full consideration where and how new sources of customer, product, and operational value can be created. And while "paving the cow path" can yield marginal improvements in your business model (a Horizon 1 effect that we will discuss later in the chapter), marginal improvements won't win the day from a business model reinvention and digital transformation perspective.

Digital Transformation is about creating and leveraging new digital assets (data, analytics, and insights or propensities about customers, products, and operations) to reinvent your business model and create new sources of competitive differentiation. Organizations who have committed to Digital Transformation are looking to leverage these digital assets to create "economic moats."

Warren Buffett, the investor extraordinaire, popularized the term "economic moat" to refer to a business' ability to maintain competitive advantages over its competitors (through process and technology innovation and patents) to protect its long-term profits and market share from competing firms; to reinvent industry value chains and disintermediate competitors' customer relationships. Organizations who are not able to create economic moats, well, it doesn't end well for them (see Figure 8.1).

Figure 8.1: Digital Transformation Means Business Model Reinvention

Beware of the senior management team who believes that digital transformation will not affect their industry; that they are somehow magically protected by the artificially defined Standard Industrial Classification (SIC) industry borders. Any industry that relies on customers should be concerned about digital transformation. There are suppliers, channel partners, competitors from nearby industries, and even former customers who are ignoring those artificial industry borders and seeking to derive and drive new sources of value across the entirety of the customer journey, to identify opportunities to reinvent their customer value creation processes and disintermediate competitors' customer relationships. And yep, that means if your company doesn't undertake digital transformation, then your company could be the next victim (see Figure 8.2).

Figure 8.2: Digital Transformation…Whose Industry is Next?

DX Law #2: It's About Digital Transformation, Not Digitalization

Digital Transformation is about reinventing your customer engagements and business operations with continuously learning AI capabilities to derive and drive new sources of customer, product, service, and operational value.

Digital Transformation is more than just digitalization, which is the integration of digital technologies such as web-based apps, mobile devices, and sensors into existing operational processes. digitalization enhances or replaces human-centric processes with digital technologies, such as transmitting current patient health and wellness data to the cloud using mobile devices, apps, and sensors on a real-time, granular basis instead of requiring patients to physically travel to a care facility on an as-needed basis and have their vital health and wellness numbers manually recorded by a nurse (see Figure 8.3).

Figure 8.3: Digitalization versus Digital Transformation

Digital Transformation, on the other hand, leverages the growing wealth of Big Data and Internet of Things (IoT) with continuously learning AI to uncover new customer, product, service, and operational insights (propensities) to reinvent the organization's business models, to derive and drive new sources of customer, product, and operational value. Digital Transformation means creating a culture of continuous learning and operational adoption fueled at the front lines of customer and operational engagement, thereby creating an AI-powered organization whose value creation processes start at the organizational front lines.

DX Law #3: It's About Speaking the Language of Your Customers

Digital Transformation is about empathizing, ideating, validating, and quantifying the creators and inhibitors of customer value; it's about reinventing your business model to expand upon, exploit, and monetize those sources of customer value creation while eliminating the inhibitors of value creation.

Let's say that you are in the retail industry and looking to identify opportunities to combine digital technologies with customer usage insights (propensities) to eliminate barriers to customer value creation. That retailer would want to invest the time to empathize, ideate, validate, and quantify the sources and impediments of value creation for their customers. Figure 8.4 provides an example of how a retailer could reinvent the customer value creation process by emphasizing the sources and eliminating the inhibitors of value creation.

Figure 8.4: Traditional versus Digitally Transformed Customer Value Chain

The top part of Figure 8.4 highlights today's analog-intensive customer engagement process. To demonstrate an example of this traditional customer journey, let's say I head to the pantry to grab a box of my favorite cereal, CAP'N CRUNCH'S^© PEANUT BUTTER CRUNCH, only to discover to my utter dismay that the last box has been consumed by the kids. Trudging through the traditional customer journey to pick up another box, I'd encounter many inhibitors of value creation (represented by multiple red down arrows) as well as a single step that is a creator of customer value (represented by a single green up arrow). The result? When I'm out of the CAP'N and navigating the traditional customer journey, there ain't no joy in Mudville.

Now, take the bottom part of Figure 8.4: The Digitally Transformed Customer Journey, a simplified process where I say to my AI virtual assistant, "Hey knucklehead, please deliver two boxes of CAP'N CRUNCH here at the house pronto," and I'd pay for express service and receive the retailer's delivery within 30 minutes. Or better yet, what if the retailer developed an AI-based, continuously learning analytic model that interacts with my Analytic Profile comprised of my product usage propensities and preferences to proactively predict and prescriptively deliver 2 boxes of the CAP'N before I realize that I'm out!

The key point about Figure 8.4?

It isn't about just optimizing the existing ordering process; it isn't about just "paving the cow path." Figure 8.4 demonstrates that digital transformation requires the complete rewiring (or reinvention)—of the organization's value creation process: from demand planning to procurement to quality control to logistics to inventory management to distribution to marketing to store operations to customer experience. And that complete rewiring or reinvention starts by developing an intimate understanding, appreciation, and empathy for your customers (and sometimes, even the customers of your customers).

What we're talking about here is learning to "speak the language of your customers." We will deep dive into this topic in Chapter 9, Creating a Culture of Innovation Through Empowerment, because the ability to "speak the language of your customers" may be the most important and fundamental step in realizing digital transformation success.

DX Law #4: It's About Creating New Digital Assets

Digital Transformation is about creating new digital assets—Analytic Profiles and analytic modules—that leverage customer, product and operational insights (propensities) to drive granular decisions and hyper-individualized prescriptive recommendations.

Organizations need to build new digital assets—Analytic Profiles (or Digital Twins) and composable, reusable, continuously learning analytic modules—that codify the customer, product, and operational insights (propensities) that provide the fuel for the organization's digital transformation.

As I discussed in Chapter 3, A Review of Basic Economic Concepts, Analytic Profiles capture detailed predictive insights (propensities) for each of the organization's key business or operational entities or assets that facilitate the delivery of hyper-individualized, predictive customer engagement and product usage and performance recommendations. For example, in the healthcare industry, Patient Analytic Profiles could digitally transform the healthcare industry by capturing granular patient, disease, treatment, medicine, fitness, wellness, doctor, and hospital analytic insights around which healthcare providers could digitally re-engineer and reinvent hyper-personalized healthcare services. These hyper-personalized healthcare services could include precision medicine, individualized welfare, remote diagnostics, predictive world population health, AI-driven digital wellness advisors, and prescriptive health and wellness recommendations (see Figure 8.5).

Figure 8.5: Sample Healthcare Patient Analytic Profile

The use of granular and predictive Patient Analytic Profiles could lead government healthcare agencies to replace blanket healthcare operational and policy decisions with hyper-personalized healthcare and wellness recommendations, which is the heart of the Economic Value of Data Theorem #5 "Do more with less" that we discussed in Chapter 5, The Economic Value of Data Theorems.

And as I discussed in Chapter 6, The Economics of Artificial Intelligence, Analytic Modules are prebuilt, composable, reusable, continuously learning analytic assets that produce predefined business or operational outcomes built on a layer of technology abstraction that enables the orchestration and optimization of the underlying machine learning, reinforcement learning, and deep learning frameworks. Analytic Modules provide granular, predefined business and operational outcomes such as anomaly detection, customer propensity to buy, customer retention at-risk, predictive maintenance, operator skill evaluation, and scheduling optimization (see Figure 8.6).

Figure 8.6: Composable, Reusable, Continuously learning Analytic Modules

Analytic Profiles and Analytic Modules are assets that economically behave like no other asset we have ever seen. These digital assets never wear out, never depreciate, and can be used across an unlimited number of use cases at near-zero marginal cost. And on top of that, these digital assets become more valuable the more they are used as their usage drives continuous learning and adaptation that leads to improvements in the predictive reliability, accuracy, and operational effectiveness of these digital assets.

DX Law #5: It's About Transitioning from Predicting to Prescribing to Autonomous

Digital Transformation is about predicting what's likely to happen, prescribing recommended actions, and continuously learning and adapting (autonomously) faster than your competition.

Digital Transformation is about creating an organization that continuously explores, learns, adapts, and relearns. Wash, rinse, repeat. Every customer engagement is an opportunity to learn more about the preferences and behaviors of that customer. Every product interaction or usage is an opportunity to learn more about the performance and behaviors of that product. Every employee, supplier, and partner engagement provides an opportunity to learn more about the effectiveness and efficiencies of your business operations.

As we discussed with the Big Data Business Model Maturity Index in Chapter 1, The CEO Mandate: Become Value-driven, Not Data-driven, to create a continuously learning intelligent organization, organizations need to master the transition from reporting to predicting to prescribing to autonomous analytics. That means proactively navigating up the three levels of the Analytics Maturity Curve (see Figure 8.7).

Figure 8.7: Analytics Maturity Curve: From Descriptive to Autonomous Analytics

This analytics maturity curve provides a guide to help organizations transition through the three levels of analytics maturity—from reporting to autonomous:

• Level 1: Insights and Foresight. This is the foundational level of advanced analytics that includes statistical analysis as well as the broad categories of predictive analytics (for example, clustering, classification, regression) and data mining. Level 1 leverages descriptive and explorative analytics to uncover the customer, product, and operational insights (propensities) buried in the data. The goal of Level 1 is to quantify cause-and-effect, determine confidence levels, and measure the goodness of fit with respect to the predictive insights.
• Level 2: Optimized Human Decision Making. The second level of advanced analytics includes supervised machine learning and unsupervised machine learning. Level 2 leverages predictive and prescriptive analytics that seek to predict what's likely to happen and then prescribe recommended or preventative actions. The goal of Level 2 is to create analytics that can learn and codify trends, patterns, and relationships; build predictive models that explain the trends, patterns, and relationships harvested from the data, and deliver prescriptive recommendations and actions without having to pre-program the business with static, operational (if-then type) rules.
• Level 3: The Learning and Intelligent Enterprise. The third level of advanced analytics includes artificial intelligence, reinforcement learning, and deep learning/neural networks. Level 3 leverages automation and autonomous analytics; analytics that continuously learn and adapt with minimal human intervention. These analytics seek to model the world around them—based upon the objectives as defined in the AI Utility Function—by continuously taking action, learning from that action, and adjusting the next action based upon the feedback from the previous action. Think of this as a giant game of "Hotter and Colder" where the analytics are continuously learning from each action and adjusting based upon the effectiveness of that action with respect to the operational goals (maximize rewards while minimizing costs) all with minimal human intervention.

Understanding and navigating the 3 levels of the Analytics Maturity Curve is likely a book in of itself. But remember that without the guiding principles of the Big Data Business Model Maturity Index and mastering the economic (wealth creation) aspects of the Schmarzo Economic Digital Asset Valuation Theorem against which to measure business and operational effectiveness, these advanced analytic technologies are nothing more than just shiny enabling technologies. These technologies are only the enablers of value creation; by themselves, they do not provide any value.

DX Law #6: It's About AI-driven Autonomous Operations and Policies

AI can enable more granular, relevant operational and policy decisions by continuously learning and adapting based upon most current environment situations…with minimal human intervention.

Policies are the foundation for any successful organization. They document the organizational principles, best practices, and compliance guidelines that aid decision-making in supporting the consistent and repeatable operations of the business. But most organization's policies are static, documented like a series of static if-then rules that are difficult to manage and even more difficult to update based upon changing business, economic, societal, cultural, and environmental conditions.

What if organizations could replace those static if-then types of policies with AI-based, continuously learning and adapting algorithms that learned and evolved based upon the constantly evolving state of the environment in which the business operates? The result would be an organization as nimble as the market and the world conditions dictate and would super-charge the organization's digital transformation (see Figure 8.8).

Figure 8.8: Transition from Static Rules to AI-based Learning Policies

"AI-driven Policies" are operational policies maintained by AI agents that take actions (or make decisions) that continuously seek to optimize, automate, adapt, and operationalize (scale) an organization's business and operational standard operating procedures within a constantly evolving environment with minimal human intervention.

Using deep reinforcement learning, we can transition from static rules to continuously learning and adapting autonomous policies that learn how to map any given situation (or state) to an action to reach a desired goal or objective with minimal human intervention. These autonomous policies would dynamically learn and adapt in response to constantly changing external factors such as pandemics, climate change, economic conditions, trade wars, and international conflicts, and give digitally transforming organizations a significant competitive advantage over organizations that operate their businesses using static policies.

DX Law #7: It's About Identifying, Codifying, and Operationalizing Sources of Value

The heart of Digital Transformation is the ability to identify, codify, and operationalize (scale) the sources of customer, product, and operational value within an environment that is continuously learning and adapting to ever-changing customer and market needs.

Digital Transformation knows no artificially defined industry borders. It seeks to uncover intimate and actionable "customer" insights no matter where that customer might be on their personal journey and use those customer insights to reinvent the organization's value creation processes (see Figure 8.9).

Figure 8.9: Digital Transformation Value Creation Mapping

Digital Transformation requires organizations to master four fundamental aspects of value creation:

• Fundamental #1: Identify Sources of Value Creation. Identify the sources of value creation with a customer-centric perspective (think "outside-in" view) that applies basic economic concepts to identify, validate, value, and prioritize the sources of customer (and market) value creation.
• Fundamental #2: Codify Sources of Value Creation. Use advanced analytics (AI, deep learning, machine learning, reinforcement learning) to codify customer, product, and operational insights (propensities) into digital assets – Analytic Profiles and composable, repeatable, continuously learning Analytic Modules.
• Fundamental #3: Operationalize Sources of Value Creation. Operationalize (scale) the sources of value creation through a production-centric perspective (think Michael Porter value chain analysis) that focuses on embedding the customer, product, and operational insights (propensities) into the organization's operational systems.
• Fundamental #4: Continuously Learn and Adapt to Sources of Value Creation. Create a continuously learning operational (with AI) and cultural (with empowered front-line employees) environment where the organization learns and adapts with every front-line customer engagement or operational interaction.

To execute against Figure 8.9, organizations will need to master a combination of design thinking (to identify the sources of value creation), economics (to value the sources of value creation), data science (to codify the sources of value creation), value chain analysis (to operationalize the sources of value creation), AI (to continuously learn and adapt to the sources of value creation), and cultural empowerment (which we will cover in Chapter 9, Creating a Culture of Innovation Through Empowerment).

DX Law #8: It's About the 3 Horizons of Digital Transformation

Create an Aspirational Vision to focus and prioritize the organization's immediate and long-term investments in customer, product, and operational value creation.

Unfortunately, most organizations do two things very poorly—prioritize and focus. Too many organizations "peanut butter" their precious transformational resources across too many "strategic" initiatives. Organizations don't fail due to a lack of "strategic" initiatives; organizations fail because they have too many. Which brings us to the final Digital Transformation Law and its importance in setting that Aspirational Vision towards which to direct and focus the organization's precious digital transformation resources and investments.

In 2000, McKinsey & Co proposed the "Three Horizons Portfolio Management Framework" as an approach that allows companies to manage a portfolio of projects for current and future growth. Then in 2015, Geoffrey Moore wrote his management strategy book "Zone to Win" that proposed a management framework to prioritize projects with the goal of sustaining the current business while investing in future businesses.

I've blended these two marvelous pieces of work (and I won't pretend to represent the depth of their work, so check out the footnotes for more details) into the 3 Horizons of Digital Transformation. These 3 horizons in Figure 8.10 provide a critical guide in helping organizations master the delicate balance between investing in today's business (which keeps the lights on) while simultaneously investing in tomorrow's digitally transformed business (which makes it worthwhile to keep the lights on today).

Figure 8.10: 3 Horizons of Digital Transformation

The 3 Horizons Framework is designed to ensure that organizations don't get caught flat-footed by competitors (or aggressive, aspirational partners, suppliers, and even former customers) who are "thinking differently" about how digital transformation can reinvent their organization's business model based upon deriving and driving new sources of customer, product, and operational value.

Let's do a quick review of each of the horizons:

• Horizon #1: Optimize Your Current Operations. Horizon #1 seeks to optimize current operational processes, reduce compliance and management risks, and create a more compelling customer experience in order to maximize current business success. It employs descriptive and explorative analytics to uncover the customer, product, and operational insights (propensities) buried in the data. Horizon 1 is focused on making money today, so the successful execution of your Horizon #1 initiatives is critical for not only keeping the lights on but also provides the resources (money, people, and learning) that fund the organization's transition into Horizon #2.
• Horizon #2: Digitalize Your Current Operations. Horizon #2 seeks to integrate new digital technologies with new customer, product, and operational insights (propensities) to dramatically improve the operational effectiveness of your current business model. Horizon 2 applies predictive and Prescriptive analytics that seek to predict what's likely to happen and prescribe recommended or preventative actions. Horizon 2 strives to master the creation of new digital assets (Analytic Profiles and Modules) that accelerate the ability to explore, learn, and exploit changing customer, market, and operational needs.
• Horizon #3: Digitally Transform (Reinvent) Your Business Model. Horizon #3 seeks to reinvent the organization with a continuously learning and adapting business model that uncovers and acts upon the new customer, product, and operational insights (propensities) with minimal human intervention. Horizon 3 exploits the game-changing potential of automation and autonomous analytics that continuously learn, adapt, and reinvent the organization's business model as well as the industry's value chain. Horizon 3 seeks to create "economic moats" through superior customer, product, and operational insights (propensities) to disrupt current business models, re-engineer industry value chains, and disintermediate competitors' customer relationships.

Let's make this "3 Horizons of Digital Transformation" concept come to life with a simple example of how an Agriculture Equipment Manufacturer (since I am from Iowa) might leverage the 3 horizons to guide their Digital Transformation journey (see Figure 8.11).

Figure 8.11: "3 Horizons of Digital Transformation" – Agricultural Equipment Manufacturer

Let's triage what the Agricultural Equipment Manufacturer's "3 Horizons of Digital Transformation" journey might look like:

• Horizon 1 would focus on applying descriptive and exploratory analytics to the organization's existing customer, product, and operational data to understand the drivers of business performance. Horizon 1 would integrate the resulting customer, product, and operational insights (propensities) with an operational framework (like Six Sigma given this is a manufacturer) to optimize operational use cases such as zero unplanned operational downtime, predictive maintenance, resource scheduling, asset utilization, "first-time fix" management, demand forecasting, energy optimization, waste minimization, fraud/theft reduction, and inventory optimization. However, with the changing market dynamics and the creation of new business models from start-ups, being the most efficient Horizon 1 manufacturer is no longer sufficient. Focusing on "paving the cow path" with Descriptive and Exploratory analytics is a great way to ensure irrelevance in Horizon 2.
• Horizon 2 would be about architecting and delivering on the promise of "Digital Farms". Horizon 2 would couple Design Thinking (to understand the customers'—and potentially customers' customers'—sources of value creation) with data science (to codify the customer, product, and operational insights (propensities)) to create a "Digital Farming" business model that monetizes equipment, worker, soil, crop, weather, commodities pricing, and economic and operational insights. Horizon 2 would focus on "digitalizing" the farm; integrating customer, product, and operational insights (propensities) with new digital technologies such as IoT, robotic process automation, 5G, augmented reality, virtual reality, 3D printing, and blockchain to replace or augment human-intensive tasks. These new digital technologies unleash high volumes of new customer, product, and operational data that could be mined with AI, ML, and DL to uncover new customer, product, and operational insights (propensities) that can be further monetized. The Agricultural Equipment Manufacturer at Horizon 2 would create Analytic Profiles or Digital Twins of key operational entities such as tractors, farming equipment, compressors, livestock, workers, and technicians to dramatically improve, accelerate, and augment operational decision making with fine-grained, hyper-individualized predictive and prescriptive analytics.
• Horizon 3 would be about the creation of a continuously learning and adapting "Autonomous Farming" business model. In Horizon 3, our Agricultural Equipment Manufacturer would master automation and autonomous analytics to create continuously learning and adapting (autonomous) products, services, and policies that adapt and evolve with minimal human intervention, to exploit superior customer, supplier, equipment, worker, soil, crop, weather, commodities, economic, and operational insights (propensities) to disrupt competitors' business models, re-engineer industry value chains, and disintermediate competitors' customer relationships.

Horizon 3, and ultimately Digital Transformation, is about reinventing and re-innovating your business model, not just simply "paving the cow path" by re-engineering your existing business processes. Sorry, but that's just shuffling the chairs on the Titanic.

Summary

Evolutions are nice and safe (unless you are the dodo bird). Revolutions, on the other hand, totally disrupt business models while seeking to disintermediate competitors' customer relationships and reinvent industry value chains. To survive the revolution, organizations must seek to reinvent how they create new sources of customer, product, and operational value. If you want to exploit the "8 Laws of Digital Transformation," organizationally, that means:

• Transitioning from siloed business functions to interdisciplinary collaboration.
• Moving from seniority-based, leader-driven decision making to data and analytics-driven decision-making at the front lines of customer engagement and operational execution.
• Transforming from a rigid and risk-averse culture, to an agile, experimental, learning, and adaptable culture.
• Becoming value-driven within the frame of customer journey centricity.

Now we are ready to move to the final chapter, which covers the single most important determinant of your organization's digital transformation success…your organizational culture and team empowerment. While it's been famously said that "software is eating the world," in the end, culture eats everything. But first, some more homework.

Further Reading

1. Evan Tarver, What Are the Primary Activities of Michael Porter's Value Chain?, Investopedia, May 26, 2019: https://www.investopedia.com/ask/answers/050115/what-are-primary-activities-michael-porters-value-chain.asp
2. McKinsey & Company, Enduring Ideas: The three horizons of growth, December 1, 2009: https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/enduring-ideas-the-three-horizons-of-growth
3. Geoffrey Moore, "Zone to Win: Organizing to Compete in an Age of Disruption," 2015

Homework

1. Does your organization differentiate between Digitalization and Digital Transformation?

   

2. How fluent is your organization in "speaking the language of the customer?"

   

3. Where does your organization sit on the Analytics Maturity Curve?

   

4. What is the status of your "3 Horizons of Digital Transformation" journey?