Recently, I’ve been dipping in and out of Edward Tenner’s provocative 1996 book, When Things Bites Back. In following one of Tenner’s footnotes I came across a fascinating 1992 academic study from the National Review of Productivity, authored by the Georgia Tech economist Peter G. Sassone.
The paper has an innocuous title, “Survey Finds Low Office Productivity Linked to Staffing Imbalances,” but its findings are profoundly relevant to our recent discussion of attention capital theory, and the value of deep work more generally.
Beginning in 1985, Sassone began a series of twenty office productivity case studies spread over different departments in five major U.S. corporations. His initial goal was to measure the bottomline benefits of the front office computer systems that were new at the time, but as he notes, this soon changed:
“[I]t became apparent that [my] data collection and analysis techniques were yielding important productivity insights beyond the cost justification of office computer systems.”
Deploying a technique called work value analysis, Sassone measured not only the amount of work conducted by his subjects, but also the skill level required for the work. He found that managers and other skilled professionals were spending surprisingly large percentages of their time working on tasks that could be completed by comparably lower-level employees.
He identified several factors that explain this observation, but a major culprit was the rise of “productivity-enhancing” computer systems. This new technology made it possible for managers and professionals to tackle administrative tasks that used to require dedicated support staff.
The positive impact of this change was that companies needed less support staff. The negative impact was that it reduced the ability of managers and professionals to spend concentrated time working on the things they did best.
Among other examples uncovered in his case studies, Sassone highlighted:
- A corporate marketing department where senior marketing professional were spending more than a day per week of their time preparing charts and graphs for presentations.
- A large commercial bank where corporate bankers were devoting more than a quarter of their time to handling routine interactions with clients.
This reduction in the typical deep-to-shallow work ratio (see Rule #1 in Deep Work) became so pronounced as computer technology invaded the front office that Sassone gave it a downright Newportian name: The Law of Diminishing Specialization.
What makes Sassone’s study particularly fascinating is that he used rigorous data collection and analysis methods to answer the question of whether or not this diminishing specialization was a good trade-off from a financial perspective.
His conclusion: no.
Reducing administrative positions saves some money. But the losses due to the corresponding reduction in high-level employees’ ability to perform deep work — a diminishment of “intellectual specialization” — outweighs these savings.
As Sassone explains:
“The results of a comparison of a ‘typical’ department, with a department with a reasonable high level of intellectual specialization were startling. The typical office could save over 15 percent of its payroll costs by restructuring its staff and increasing the intellectual specialization of its workers.”
To make this more concrete, he calculated:
“[T]he typical office can save about $7,400 [around $13,200 in 2018 dollars] per employee per year by restructuring its office staffs and improving its levels of intellectual specialization.”
In other words, Sassone found that the corporate divisions he studied could produce the same amount of valuable output by reducing the number of managers and professionals while increasing the number of administrative staff.
This rebalancing works because more administrative support means the higher level employees can spend more time working deeply on the activities that produce the most value. Because the former are cheaper to hire than the latter, the result is the same work for less total staffing costs.
An important lesson lurks in these results that’s just as relevant now as it was then, back in the early days of the front office IT revolution: optimizing people’s ability to create value using their brains is complicated. Just because a given technology makes things easier doesn’t mean that it makes an organization more effective, you have to keep returning to the foundational question of what best supports the challenge of thinking hard about valuable things.