AWS Cloud Enterprise Strategy Blog

Mental Models to Clarify the Goals of Digital Transformation, Part 1

As we’ve all realized by now, the term digital transformation has become slippery and overused. Something big is going on today in the way technology is used in large enterprises, a change that seems to have something to do with digital technology, digital ways of working, and the increasing importance of digital interactions in our lives. Digital transformation is the name we give to that vague complex of stuff that all enterprises know they must confront today. But it’s difficult to construct a transformation strategy because the term means so many different things.

The term is out of our control; there’s no help for it. We won’t be able to give it a workable definition. Instead, let’s build a few mental models that will help us see and conquer digital transformation in its various aspects. I’ll propose eight mental models covering different aspects of what we mean by digital transformation. I’ll cover the first four in this blog post and the rest in a post to follow.

  1. Gaining Speed
  2. Using Digital Technology
  3. Interacting Digitally
  4. Becoming Customer-Centric
  5. Being Data-Driven
  6. Increasing Resilience
  7. Becoming Future Ready
  8. Building the Enterprise of the Future

Model 1: Gaining Speed

A visualization of digital data moving at speed down a highwayThe digital world is a world of speed, in more ways than is obvious. Challenges to established enterprises appear quickly: true. And speed-to-market for new products or product features is increasingly important as advantages are quickly competed away: also true. Customer tastes change quickly; geopolitical situations change quickly; pandemics and trade wars emerge and must be responded to. All true.

But speed is also important because we’ve learned that we can harness it to improve quality, security, stability, reliability, financial controls, and innovation. Some of that is counter-intuitive but stems from the idea of fast feedback and adjustment. Innovation is increased because we can quickly try out plausible new ideas at low risk and low cost using the cloud and DevOps. Then, depending on what we learn, we can double down, dismiss, or modify the idea. Financial control, similarly, is increased because instead of taking large risks based on all the uncertainties of a business case, we can instead make small, incremental investments and see results from each one before making the next investment.

Speed improves security because we can respond more quickly to changes in the threat landscape, and we can repeatedly, quickly, and with low overhead test our systems as they are being built and after they are deployed. The same applies to stability and resilience. We’ve moved from a world where we mitigate risk through detailed advance planning (which never worked well in IT) to one where we mitigate it through agility, speed, small investments, and rapid feedback.

For enterprises, this requires a broad shift in orientation. Most of us have optimized our businesses for infrequent, extensively planned change. Today’s model is no less risk-mitigating (more, in fact) but requires that we set ourselves up to move quickly. That means a change in how we govern and oversee, select investments, design technical architectures, and plan initiatives.

Model 2: Using Digital Technology

Abstract representation of digital transformationAnother way to think about digital transformation is as the introduction of certain new technologies that broadly expand the scope of what we can expect our systems to do. These new technologies not only pose new opportunities for revenue growth and cost management, but also change what customers expect and what competitors have access to.

Perhaps the deepest transformation today is coming from machine learning. Machine Learning (ML) has been around for five or six decades, but only recently became practical for widespread business application due to faster processors and improved algorithms. It’s important because it greatly expands what we can call on technology to do. Many areas that were just too complex for traditional programming—where the programmer has to specify, in detail, exactly what the computer should do—are now possible by guiding the machine in learning on its own.

It’s not just that ML has become practical—it’s also that it’s been democratized, meaning that it is now easily within the reach of any company. Until recently, a business would have had to hire hard-to-come-by experts in ML. Today, Amazon Web Services has made ML models easily available in the cloud to everyone, even people with no experience at all in the field. AWS’s pre-trained models and tools abstract away many of the complexities of ML. Organizations can build prototypes quickly. And since cloud costs depend on the volume of usage, these prototypes are also relatively affordable. Getting started with ML is low risk, inexpensive, and fast.

Other technologies that were accessible to only a few people are now common in businesses, such as virtual and augmented reality, and software services to manage IoT sensors and robots. A vast range of new analytics is possible, as technology now makes it easier for us to process large amounts of data, unstructured data like image, video, and natural language text, and timestream data. New devices like Alexa make conversational interactions with customers feasible.

So in this mental model, transformation is about using new tools in new ways to get business results. Because they’re new, using these tools requires creativity and leadership. It also requires new ways of thinking about how to solve problems and design systems. But it’s not really optional. Businesses that can’t or won’t use these new tools are liable to be disrupted by those who can and will—and already are. And it’s not a one-time matter: every business must learn to continually seek out new technologies and apply them strategically, because the pace of innovation is unlikely to ebb.

Model 3: Interacting Digitally

A view of the Earth from spaceAnother mental model for digital transformation is that it’s about learning to interact digitally—particularly with customers, but also with suppliers, regulators, and tastemakers on social media. Customers have changed; they now expect digital services, and they have a high bar. They’re looking for frictionless, service-oriented, flexible ways to get things done, and they should be able to do them, for the most part, from their phones and other devices. Much of their time is spent interacting online with very service-oriented companies who constantly innovate new modes of user-friendly interaction. Anything less provokes frustration and brands a company as outdated.

Companies, on their part, have found that digital interactions can have other substantial benefits for them. They can gather data on customers and their behavior, drive purchasing behavior, reduce costs, and try out new ideas less expensively than they could in in-person interactions.

But digital interactions are quite different from in-person interactions, and again a shift in mental models is necessary. Customers expect digital interactions to be an effortless part of their lifestyles; your interactions with them happen in a time and place of their choosing. Serving customers with technology is very different from providing employees with tools, which is what information systems have traditionally done. You can train your employees, but you can’t train customers. Your employees will put up with certain inconveniences, but your customers will simply switch to one of your competitors if there are too many barriers to their enjoyment.

Model 4: Becoming Customer-Centric

Network of business conceptTraditional products were designed under the auspices of a product manager, who took input from the market and combined it with judgment, experience, the company’s product strategy, and a pinch of magic pixie dust to come up with a new product, which would either succeed or fail in the market.

In the digital world, we essentially co-design our products and their features together with customers. We use minimum viable products to gauge customer interest, and then tweak the product in rapid iterations, gathering feedback from the way customers respond to continue evolving the product. The goal is to treat any ideas we have—no matter how experienced we are in the market, or how high in the org the person who thought of the idea is, or how obviously correct the idea is—as a hypothesis that must be tested with customers.

True, companies have thought of themselves as customer-centric in the past, but the digital world is different—a digitally savvy company works backward from a customer need and continually tests to see whether they are satisfying that need. The competitor who wins is the one that better meets the customer need (where price is part of the customer need). The good news is that in the digital age, it’s possible to learn quickly from customers using a fast, iterative model.

The big change in mindset in this mental model is the emphasis on learning from customers—not just by asking them, but by trying things and measuring their impact.

A Preview

In the next blog post in this series, I’ll cover the other four mental models. The last one, “Building the Enterprise of the Future,” weaves together aspects of the preceding seven to show how together, they are changing the nature of enterprises in ways that we can only vaguely glimpse.

Twitter | LinkedIn | Blogs


More on this topic

Mental Models to Clarify the Goals of Digital Transformation, Part 2, Mark Schwartz

Digitally Transforming What Exactly?, Phil Le-Brun

Mental Models for Your Digital Transformation, Joe Chung

Tuning Up the High-Frequency Enterprise, Phil Potloff

The Future of Faster Enterprises, Miriam McLemore

Mark Schwartz

Mark Schwartz

Mark Schwartz is an Enterprise Strategist at Amazon Web Services and the author of The Art of Business Value and A Seat at the Table: IT Leadership in the Age of Agility. Before joining AWS he was the CIO of US Citizenship and Immigration Service (part of the Department of Homeland Security), CIO of Intrax, and CEO of Auctiva. He has an MBA from Wharton, a BS in Computer Science from Yale, and an MA in Philosophy from Yale.