It's time for IT teams to digitize like the startups do

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Many IT leaders are desperately seeking digitization as a way to transform their companies, but century-old, nontechnology improvements can produce results that can impact operations across all lines of business.

Review any survey of senior business executives’ IT priorities conducted in the last few years and you’ll find digitization hovering at the top of each list. Executives see it as essential for growing their businesses, improving customer experience and upgrading their business models. Yet they admit they aren’t sure how to do it, as Gartner found when it surveyed CEOs. All the while in the same industries, thousands of venture-funded technology startups are discovering digitization opportunities. While business executives and their IT teams search fruitlessly, startups furiously launch pre-emptive digital offerings. 

This contradiction results from two vastly different approaches. Business execs search for opportunities to digitally automate their existing operations. But startups search for existing operations that are cumbersome and annoying for customers. They search first for nontechnology improvement opportunities. Once found, the startups simplify and standardize. Finally, they digitally automate. 

Back to the future 

For two decades, our company has analyzed hundreds of thousands of diverse job positions, from plant floor workers to hedge fund operations and marketing campaign organizations. The companies have included the largest Fortune 500 firms as well as businesses with as few as 2,000 employees. We have analyzed operations in more than 30 major countries throughout the Americas, Asia and Europe. We have documented our research in a standardized, comparative database. Our work demonstrates that 75 percent of all operational improvements available to businesses are nontechnology-dependent: work simplification and standardization. These improvements are overwhelmingly concentrated among organizations’ “knowledge workers.” 

[Related: How to succeed at digital transformation] 

Once known as white-collar, or office, workers, knowledge workers toil with their minds in sales, accounting, customer service, human resources, order management and innumerable other areas. Knowledge workers make up about half of the work force, with estimates ranging from 44 percent to 60 percent, depending on how the counting is done. 

As a conservative estimate based on our direct experience, at least 35 percent of employees in Fortune 500 businesses are knowledge workers – excluding service workers, such as retail and food service employees. By this definition, knowledge workers are businesses’ costliest, best-educated employees. Despite generations of heavy investment in office technology, however, close scrutiny reveals that knowledge work operations remain needlessly complex and overwhelmingly manual. 

Consequently, knowledge workers unintentionally squander 40 percent of their time on avoidable, repetitive tasks: error correction, customer over-service, sales downtime, duplication and similarly wasted efforts. The non-technology improvements to avoid these tasks are the digitization opportunities that executives in the surveys are struggling to find. Knowledge work is an overlooked diamond field of non-technology improvement and digitization opportunity. 

The cost of avoidable, repetitive knowledge work is staggering. We estimate from our research and public information that this cost reduced earnings for the Fortune 500 by at least 12 percent, or $212 billion, in 2014. This translates into roughly $4 trillion in shareholder value. That’s more than five times the market value of Apple Inc., the world’s most valuable company – and one that routinely steals valuable digitization opportunities from Fortune 500 peers. This potential market value fuels the outsiders’ insatiable search for digitization opportunities. 

Digitization begins with nontechnology ‘industrialization’ opportunities 

The outsiders’ secret: They simplify and standardize work so that it can be performed by machines – either mechanical or digital. We can call this process “industrialization.” It’s nothing new. For the past century, manufacturers have used industrialization to mechanically automate factories filled with hourly workers. They launched an Industrial Revolution that delivered history’s greatest increase in productivity and economic wealth. 

Maybe it is happening again. Today’s digital startups follow the same industrialization playbook. But they simplify and standardize the work of employees who were exempted from the last Industrial Revolution: knowledge workers. It’s not obvious, but the principles of industrialization can be applied to knowledge work. 

This is precisely what the digitization startups are doing, even if they don’t recognize it. They search out products and services that are manually intensive for established companies to deliver. They look for inconsistency, inconvenience and frustrated customers. For example, within financial services, products such as consumer mortgages, small business loans and insurance all fit this description. Not surprisingly, these are prime targets of the new “fintechs.” This is the name given to the more than 4,000 venture-funded startups now targeting the financial services industry. 

Once a fintech decides on an opportunity – mortgage loans for example – it begins industrialization. Its non-technology improvements resemble those of industrial engineers preparing a product for an assembly line. The startup reimagines every aspect of the product or service. It dissects every step. Wasted activity is designed away. Standardization further reduces complexity. The fintech is preparing to install a digital assembly line – one that often includes the customer as a voluntary self-service participant. 

Manufacturers have industrialized like this for generations, from the first mass-produced sewing machines of the 19th century to the cars now partially assembled by robots. Some call this process “design for machinability.” Digital startups are simply redesigning knowledge work for “digital machinability.” 

How knowledge workers justify ‘virtuous waste’ 

Can’t business executives and their IT teams do the same thing – design for machinability? Yes. Most already do it for hourly work but not for knowledge work. That’s because they view knowledge work and its “waste” as different from hourly work. For example, when new sales orders arrive with the usual missing and incorrect data, the knowledge work “factory” simply begins repair work – making corrections. No questions asked. Knowledge workers see these corrections as merely the unavoidable and negligible cost of doing business. But digital startups see a golden opportunity to industrialize and digitize away this costly, avoidable waste. 

Unlike a manufacturing plant, a knowledge work factory has no industrial engineers who recognize errors as valuable redesign opportunities. Instead, each employee in the knowledge work factory is expected to manage a dizzying array of one-off corrections. If they think of these corrections at all, they and their managers view these as valuable activity. After all, they are preserving revenue, making the sales force more effective and keeping customers happy. This is virtuous activity – “virtuous waste.” 

Despite its circular logic, the virtuous waste misperception provides an opportunity for knowledge workers to continue the status quo. Startups exploit this opportunity. 

Knowledge work today: automation without industrialization 

Imagine that you are a knowledge worker. After you digitally purchase your coffee on your way to work, you arrive at your office – the knowledge work factory. Take your place at your workstation. Now it’s time to slow down. You have manual work to do, although it will be performed on the latest office technology. A workflow system acts as a digital conveyor belt, delivering “work products” to your computer monitor. These might be orders, reports and requests for data analysis or similar items for you to process. 

If you were in a mechanical, industrialized factory, these items would arrive complete and in good order. Industrial engineers would have clearly defined your work tasks and the specs for your work products. Data would be kept in clean and orderly inventories. Standard instructions and procedures would be documented and easy to find. Your work load would be sequenced and scheduled for completion within documented standards and reasonable timelines. But that’s not how it works in the knowledge work factory. 

[Related: How digital transformation is disrupting IT outsourcing] 

In practice, the workflow system is often circumvented. Urgent, garbled requests for your work products – a management report, for example – arrive unexpectedly via email, phone or text message. As you begin your task, there is no clean and orderly inventory of the data you need. You must manually gather much of the data for that requested management report. The data will not be compatible or complete. Manual cleaning and reconciliation is needed to make the data usable. This practice is so widespread it has acquired the name “data wrangling.” 

Next, there are no standard specs for the design of the management report. There are no rules for naming it when it is complete. You consult your coworkers for unwritten “tribal knowledge” and design the report as best you can. You create your own, informal naming system. There is no master inventory for finished reports, so you squirrel it away where you, and only you, are likely to find it. 

Across the Fortune 500, over 9 million knowledge workers – 35 percent of total employees – are having similar workdays in marketing, customer service, finance, sales, product management and even IT. Forty percent of their time is lost to the lack of industrialization. We see this in company after company. 

Stop outsourcing industrialization and start digitizing 

When business executives and their IT teams struggle unsuccessfully to find digitization opportunities, they are effectively outsourcing this job to digital startups. Compared to startups, however, existing businesses hold all the advantages, both strategic and operational. They possess the lion’s share of opportunities and resources needed to win. Overcoming existing organizational misperceptions represents their greatest challenge. 

First, they have many more opportunities to try, fail and eventually become experts. A typical startup usually has only one chance. It must succeed or die. Among just the Fortune 500, however, the equivalent of three million employees’ worth of virtuous waste is ready to be digitized out of knowledge work factories. Each company has enough opportunity to establish a permanent “knowledge work industrialization factory.” This is not much different or any more difficult than establishing an industrial engineering group dedicated to knowledge work. Their goal would be to make mundane knowledge work tasks as simple as digitally ordering coffee. 

Second, existing businesses already have the resources that the startups need and crave: data, technology, experienced workers, customers and investment capital. From a knowledge work industrialization perspective, however, most of this is an undisciplined mess. Data standards are lax, effectively creating internal, unintended “data lakes.” Capabilities to collaboratively design knowledge work for “digital machinability” pale in comparison to those of even the most basic manufacturing operation. 

Suggest that executives invest to industrialize a knowledge work factory to eliminate virtuous waste and fend off digital startups. They scoff. This is “knowledge work.” It’s not manual labor. All the while, startups are cherry picking their most valuable opportunities for digitization.

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