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Change management


DEMOCRATIZING TRANSFORMATION

Give your entire workforce the capacity to become innovators.
by
 * Marco Iansiti
   and
 * Satya Nadella

by
 * Marco Iansiti
   and
 * Satya Nadella

From the Magazine (May–June 2022) · Long read
Núria Madrid 
Spotlight Series / The Digitally Literate Organization

01
Democratizing Transformation
02
Developing a Digital Mindset
03
The Next Great Digital Advantage
Summary.    Many companies struggle to reap the benefits of investments in
digital transformation, while others see enormous gains. What do successful
firms do differently? This article describes the five stages of digital
transformation, from the traditional stage,...more
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 * Buy Copies
 * Print

IDEA IN BRIEF

THE PROBLEM

Many companies struggle to reap the benefits of investments in digital
transformation, while others see enormous gains. What do successful companies do
differently?

THE JOURNEY

This article describes the five stages of digital transformation, from the
traditional stage, where digital and technology are the province of the IT
department, through to the platform stage, where a comprehensive software
foundation enables the rapid deployment of AI-based applications.

THE IDEAL

The ideal is the native stage, whose hallmarks are an operating architecture
designed to deploy AI at scale across a huge, distributed spectrum of
applications; a core of experts; broadly accessible, easy-to-use tools; and
investment in training and capability-building among large groups of
businesspeople.

Over the past decade, Novartis has invested heavily in digital transformation.
As the Swiss pharmaceutical giant moved its technology infrastructure to the
cloud and invested in data platforms and data integration, it recruited AI
specialists and data scientists to build machine-learning models and deploy them
throughout the firm. But even as the technical teams grew, managers from across
the business—sales, supply chain, HR, finance, and marketing—weren’t embracing
the newly available information, nor were they thinking much about how data
could enhance their teams’ work. At the same time, the data scientists had
little visibility into the business units and could not easily integrate data
into day-to-day operations. As a result, the investments resulted in only
occasional successes (in some aspects of the R&D process, for example) while
many pilots and projects sputtered.





More recently, however, pilots targeting both R&D and marketing personalization
started showing business value and captured the attention and imagination of
some of Novartis’s more creative business executives. They became increasingly
excited about opportunities to deploy AI in various parts of the company and
began to earnestly champion the efforts. (Disclosure: We have both worked with
Novartis and other companies mentioned in this article in a variety of ways,
including board membership, research, and consulting.) They realized that
technologists and data scientists alone couldn’t bring about the kind of
wholesale innovation the business needed, so they began pairing data scientists
with business employees who had insight into where improvements in efficiency
and performance were needed.

Novartis also invested in training frontline business employees to use data
themselves to drive innovation. A growing number of teams adopted agile methods
to address all kinds of opportunities. The intensity and impact of
transformation thus accelerated rapidly, driving a range of innovation
initiatives, including digitally enabling sales and sales forecasting,
reconceiving the order and replenishment system for health-care-services
customers, and revamping prescription-fulfillment systems and processes.



THE DIGITALLY LITERATE ORGANIZATION: SERIES REPRINT

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The progress in digital transformation became invaluable as the company dealt
with the initial chaos of the pandemic. Novartis business teams partnered with
data scientists to devise models to manage supply-chain disruptions, predict
shortages of critical supplies, and enable quick changes to product mix and
pricing policies. They also developed analytics to identify patients who were at
risk because they were putting off doctor visits. As the Covid crisis wore on,
the value of AI became obvious to managers companywide.

Before this wave of AI adoption, Novartis’s investments in technology consisted
almost entirely of packaged enterprise applications, usually implemented by the
IT department with the guidance of external consultants, vendors, or systems
integrators. But to build companywide digital capability, under the leadership
of then chief digital officer Bertrand Bodson, Novartis not only developed new
capabilities in data science but also started to democratize access to data and
technology well outside traditional tech silos. The company is now training
employees at all levels and in all functions to identify and capitalize on
opportunities for incorporating data and technology to improve their work. In
2021, the Novartis yearly AI summit was attended by thousands of employees.

THE ELEMENTS OF TECH INTENSITY

To enable transformation, companies must create synergy in three key areas:
Capabilities Organizational ...



The potential for employee-driven digital innovation is impossible to calculate,
but according to the market research firm IDC’s Worldwide IT Industry 2020
Predictions report, enterprises across the global economy will need to create
some 500 million new digital solutions by 2023—more than the total number
created over the past 40 years. This cannot be accomplished by small groups of
technologists and data scientists walled off in organizational silos. It will
require much larger and more-diverse groups of employees—executives, managers,
and frontline workers—coming together to rethink how every aspect of the
business should operate. Our research sheds light on how to do that.


THE SUCCESS DRIVERS

When we started our research, we wanted to understand why many companies
struggle to reap the benefits of investments in digital transformation while
others see enormous gains. What do successful companies do differently?

We looked at 150 companies in manufacturing, health care, consumer products,
financial services, aerospace, and pharma/biotech, including a representative
sample of the largest firms in each sector. Some were failing to move the
needle, but many had made dramatic progress. Perhaps surprisingly, we found that
outcomes did not depend on the relative size of IT budgets. Nor were the success
stories confined to “born digital” organizations. Legacy giants such as
Unilever, Fidelity, and Starbucks (where one of us, Satya, is on the board)—not
to mention Novartis—had managed to create a digital innovation mindset and
culture.

Digital transformation requires that executives, managers, and frontline
employees work together to rethink how every aspect of the business should
operate.

Our research shows that to enable transformation at scale, companies must create
synergy in three areas:


CAPABILITIES.

Successful transformation efforts require that companies develop digital and
data skills in employees outside traditional technology functions. These
capabilities alone, however, are not sufficient to deliver the full benefits of
transformation; organizations must also invest in developing process agility
and, more broadly, a culture that encourages widespread, frequent
experimentation.


TECHNOLOGY.

Of course, investment in the right technologies is important, especially in the
elements of an AI stack: data platform technology, data engineering,
machine-learning algorithms, and algorithm-deployment technology. Companies must
ensure that the technology deployed is easy to use and accessible to the many
nontechnical employees participating in innovation efforts.


ARCHITECTURE.

Investment in organizational and technical architecture is necessary to ensure
that human capabilities and technology can work in synergy to drive innovation.
That requires an architecture—for both technology and the organization—that
supports the sharing, integration, and normalization of data (for example,
making data definitions and characteristics consistent) across traditionally
isolated silos. This is the only real, scalable way to assemble the necessary
technological and data assets so that they are available to a distributed
workforce.

Many large companies are making headway in each of these areas. But even leading
companies tend to underestimate the importance of getting employees to pull
transformation into their functions and their work rather than having central
technology groups and consultants push the changes out to the business. As Eric
von Hippel of MIT has advocated for many years, frontline users, who are closest
to the use cases and best positioned to develop solutions that fit their needs,
must take a central role, joining agile teams that dynamically coalesce and
dissolve on the basis of business needs.


BUILDING TECH INTENSITY

Our research unpacks how capabilities, technology, and architecture work
together to build what we call tech intensity. Derived from the economics
concept of intensive margin—how much a resource is utilized or applied—tech
intensity refers to the extent to which employees put technology to use to drive
digital innovation and achieve business outcomes. Our research found that
companies that made good investments in technology and made tools accessible to
a broad community of data- and tech-skilled employees achieved higher tech
intensity—and superior performance. Companies that failed to develop tech- and
data-related capabilities in their employees and offered only limited access to
technology were left behind.

See more HBR charts in Data & Visuals

We ranked the tech intensity of the 150 firms in our study and found that the
top quartile of the sample grew their revenues more than twice as fast as the
bottom quartile. (See the exhibit “Digital Transformation Pays Off.” To score
your firm’s tech intensity, go to www.keystone.ai/techintensity.) We also found
that technology, capability, and architecture indices correlated with other
measures of performance, from productivity and profits to growth in enterprise
value. Using an econometric technique known as instrumental variables, we also
found evidence that the relationship between tech intensity and performance was
causal: That is, greater intensity (especially investments in technical and
organizational architecture) powered higher revenue growth.


STAGING THE TRANSFORMATION

Our analysis confirms that just spending money on technology does not result in
more growth or better performance; in fact, in some cases it can actually damage
the business if it accentuates divisions and inconsistencies across groups.
Instead, it is the architectural, managerial, and organizational approaches to
transformation that best explain the substantial and enduring differences among
firms. We found that companies typically progress through five stages on their
transformation journey.

See more HBR charts in Data & Visuals


TRADITIONAL MODEL.

Not surprisingly, many companies fit what we consider to be the traditional
model of digital innovation, whereby digital and technology investments are the
province of the IT department (or other technical specialist groups) and impact
is scattered across groups, mostly in inconsistent ways. IT works with business
units to fund projects and manage implementation—say, for the deployment of an
enterprise application or a data platform technology. The projects and their
implementations are customized to the specific requirements of the individual
silos, business units, or functions. The result is that over time, the
technology and data infrastructure reflect the quirks of individual groups,
without any consistency and connectivity. This sort of disjointed approach makes
it virtually impossible to share, scale, or distribute innovation efforts across
the organization.

Many businesses in the traditional model still spend a great deal of money on
information technology. Consider a financial services firm we studied, whose
tech and analytics budget is among the top in its industry, in both absolute and
relative terms. The company has spent heavily on state-of-the-art data-platform
technology and hired thousands of IT specialists and data scientists, who sit
isolated in a separate IT group, while few (if any) employees on the business
side are involved in the organization’s digital innovation efforts. The company
thus lacks the architecture and capabilities required to foster any intensity in
tech adoption. Not surprisingly, the firm’s IT and data sciences efforts have
stalled, and business impact has been minimal.

Núria Madrid

A telltale sign that a company is in the traditional stage is that perceptions
of impact among technology and business employees are dramatically different.
The former perceive impact to be high (as measured by the effort they put into
their work), while the latter measure it as much lower (according to how their
everyday activities have benefited).


BRIDGE MODEL.

To break free of the traditional constraints of silos—organizational and
infrastructural—companies typically start by launching pilots that bridge
previously separate groups and developing shareable data and technology assets
to enable new innovations. They might first focus on specific functional
opportunities such as optimizing advertising, manufacturing, or supply-chain
capabilities. These companies are piloting not only technology but also a
fundamentally different model of innovation in which executives, managers, and
frontline workers from the business side work in collaboration with IT and data
scientists. Victor Bulto, Novartis’s head of U.S. pharmaceuticals, was
instrumental in launching early pilots (focusing, for example, on identifying
at-risk patients) and served as a champion for many initiatives as the
organization moved through the bridge stage. Lori Beer, JPMorgan Chase’s global
CIO, likes to talk about the demonstrated impact of piloting AI to simplify
expense reporting and approval—a process-improvement pilot that won over many
employees.


HUBS.

As more and more pilots demonstrate the success of the new approach,
organizations form data and capability hubs and gradually develop the capacity
to link and engage additional functions and business units in pursuit of
opportunities for transformation. As they progress down this path, leaders begin
to realize that the bottleneck in innovation has shifted from investments in
technology to investments in the workforce. The limiting factor at this stage is
the number of business employees with the capability—the know-how and the
access—to drive digital innovation. Companies thus need to invest in coaching
and training a much larger community of employees.

See more HBR charts in Data & Visuals

Fidelity strives to develop what it calls digital athletes. It began to build
hubs by creating centralized data assets (a companywide data lake, for example);
now it is scaling up training for thousands of business employees, giving them
the capacity to deploy digitally enabled solutions across the entire business.
Digitally savvy investment specialists and tax experts, for example, are working
closely with data scientists and technologists to create innovative solutions
with a special focus on personalization and tailored customer impact. They’ve
also created an app aimed at onboarding and engaging younger investors and
another app for delivering AI-powered recommendations to Fidelity financial
advisers, to name just a few examples.

Starbucks, too, is focused not only on technology and architecture but also on
developing broad-based, agile innovation skills in its employees to power its
hubs. CEO Kevin Johnson explains, “We’ve gone from large teams working in silos
to smaller, cross-functional teams [everywhere], and from evaluating every idea
as pass-fail to rapid iteration.” Starbucks is now a digital innovation
powerhouse, with sophisticated customer apps enabling remote ordering, loyalty
programs, and payment systems along with internal systems enabling AI-based
labor allocation and inventory management.


PLATFORM MODEL.

As companies enter the platform stage, data hubs merge into a comprehensive
software foundation that enables the rapid deployment of AI-based applications.
Firms focus on building sophisticated data-engineering capabilities and
encouraging the reuse and integration of machine-learning models.
Analytics-based prediction models are applied across the business, with an
increasing focus on the automation of basic operational tasks. Organizations
begin to function a bit more like software companies, developing comprehensive
capabilities that enable product and program management and rapid
experimentation.

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Over the past five years, Microsoft has gone through almost every stage of this
journey. Years ago, we were just as siloed as most companies, with each
product-based organization segregating its own data, software, and capabilities.
As we connected and normalized data from different functions and product groups,
we were able to deploy integrated solutions in areas ranging from customer
service to supply-chain management.

Next In
The Digitally Literate Organization
Developing a Digital Mindset



How to lead your organization into the age of data, algorithms, and AI



We integrated all our data in a companywide data lake, and we built what we call
a business process platform, which provides software and analytics components
that teams use to enable innovation in areas ranging from Xbox manufacturing to
managing advertising spend. We also invested in training programs for
nontechnical employees, cultivating data-centric and machine-learning
capabilities throughout the organization.


NATIVE MODEL.

The most successful companies among the 150 in our study have deployed an
entirely different type of operating architecture, centered on integrated data
assets and software libraries and designed to deploy AI at scale across a huge,
distributed spectrum of applications. Its hallmarks are a core of experts;
broadly accessible, easy-to-use tools; and investment in training and
capability-building among large groups of businesspeople. These companies are
approaching the capacity of digital natives such as Airbnb and Uber, which were
purpose-built to scale companywide analytics and software-based innovation.
Airbnb and Uber are certainly not perfect, but they come close to the native
ideal.

At Microsoft, we still have a lot to learn, but in some parts of the
organization we are starting to approach the native model. As is common in any
enterprise, the progress has not been uniform. Different groups have achieved
different levels of capability, but the results overall are encouraging, as we
see increasingly innovative solutions to internal and customer-facing problems.
Most critically, our companywide approach to understanding, protecting, and
working with data has progressed by light years.


THE IMPERATIVE FOR LEADERS

The mandate for digital transformation creates a leadership imperative: Embrace
transformation, and work to sustain it. Articulate a clear strategy and
communicate it relentlessly. Establish an organizational architecture to evolve
into as you make the myriad daily decisions that define your technology
strategy. Deploy a real governance process to track the many technology projects
underway, and coordinate and integrate them whenever possible. Champion agility
in all business initiatives you touch and influence. And finally, break free of
tradition. Train and coach your employees to understand the potential of
technology and data, and release the innovators within your workforce.

This mandate extends to technology providers. Despite much investment,
technologies are still too complex and are often too hard to use and deploy. We
need tools and technology that make driving transformation intuitive for
frontline workers while keeping data secure. Let’s not forget that until
recently many of us were relying on specialists in Fortran and Cobol to model
business problems and even to perform basic mathematical operations.
Spreadsheets brought about a revolution in mathematical modeling; we need
technology providers to bring the same revolution to AI and make using a
machine-learning application as easy as creating a pivot table.

Momentum is growing. But we must sustain the efforts to ensure that companies of
all stripes make it across the digital divide.

A version of this article appeared in the May–June 2022 issue of Harvard
Business Review.
Read more on Change management or related topics Analytics and data science,
Digital transformation, AI and machine learning and Information technology and
telecom sector
 * Marco Iansiti is the David Sarnoff Professor of Business Administration at
   Harvard Business School, where he heads the Technology and Operations
   Management Unit and the Digital Initiative. He has advised many companies in
   the technology sector, including Microsoft, Facebook, and Amazon. He is a
   coauthor (with Karim Lakhani) of the book Competing in the Age of AI (Harvard
   Business Review Press, 2020).
 * Satya Nadella is the chairman and CEO of Microsoft.

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Spotlight Series / The Digitally Literate Organization
01
Democratizing Transformation
02
Developing a Digital Mindset
03
The Next Great Digital Advantage
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Digital transformation, AI and machine learning and Information technology and
telecom sector


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