You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
First, assess whether the reference contains any valid content. If the reference contains no valid information, such as a 'page not found' message, then all statements should be considered 'unknown'.
If the reference is valid, for a given statement: if the facts or data it contains can be found entirely or partially within the reference, it is considered 'supported' (data accepts rounding); if all facts and data in the statement cannot be found in the reference, it is considered 'unsupported'.

You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
“Rolling Up the Leaf Node” To New Levels of Analysis:
How Algorithmic Decision-Making Changes Roles, Hierarchies, and Org Charts

Melissa Valentine
mav@stanford.edu
Rebecca Hinds
rhinds@stanford.edu

May 6, 2021

Abstract

This paper draws on a 10-month ethnography of a retail technology company to contrast two
conflicting ways the organization structured the work of an internal professional group. The
original structuring approach involved using bureaucratic decision structuring to coordinate the
collective decision-making of that group. This structuring set up the profession’s organizational
chart as a “decision tree”—but one that had an unexamined impact on decisions and outcomes
and was not expected to change very much or very often. In contrast, the second structuring
approach involved using algorithmic decision structuring to coordinate collective decisionmaking. This structuring approach involved explicitly measuring the impact of decisions on
outcomes—including measuring the impact of the overall decision structuring itself. This
approach also involved the expectation that the structuring would change frequently and
responsively. To use the new algorithmic tools, the organization had to reconfigure not just
single workflows, but the larger set of role relationships and hierarchies that were structured by
the entire organizational chart. Because organizational charts and bureaucratic decision
structuring have been a prevalent mode of production for many decades in many industries, we
propose that this difference between approaches to decision structuring offers an undertheorized
reason why algorithms bring about large-scale infrastructural change.

Huang Center for Engineering, 475 Via Ortega office 210, Stanford, CA 94305 T 650.725.5676

INTRODUCTION
Responding to the so-called ‘Big Data revolution’, many scholars have examined how
algorithms are reconfiguring professional expertise (e.g., MacKenzie, 2014; Christin, 2018;
Sachs, 2019; Brayne and Christin, 2020). However, to date, there are few theoretical accounts
considering how data and algorithms are changing the structuring of these professions, for
example, their role structures, professional hierarchies, or organizational networks (Bailey and
Barley, 2020). This gap is surprising because the main theories of technological change and its
consequences hold that technologies are occasions for structuring (Barley, 1986) and that
technological revolutions are revolutions precisely because, by changing the means of
production, they significantly alter how people interact within and between established
professions (Barley, 1990; Barley, 2015; Barley, 2020).
To date, the extant literature on algorithms and professional expertise has focused more
on how data and quantification are changing the subjective nature of professional expertise.
These studies note the juxtaposition of the abstract and almost “sacred” nature of professional
expertise against the perceived accountability and objectivity of algorithms (Christin, 2017: 13).
Professionals now heed the “rules” of algorithms, changing the perceived value and source of
their professional expertise: journalists are incorporating audience preferences in their reporting
(Christin, 2018), radiologists are leveraging recommendation for medical diagnoses (Lebovitz,
Lifshitz-Assaf, and Levina, 2019), and high-frequency traders are relying on algorithms to make
financial trades that balance profitability with low risk (MacKenzie, 2018).
Our paper examines instead how data is changing the structuring of professionals’
collective expert work practices, including their role relationships and professional hierarchies.
This question builds on research that has examined the implications of professionals being

employed in bureaucratic organizations (Huising, 2015; Anteby, Chan, and DiBenigno, 2016),
wherein their jobs are nested in roles or “systems of prescribed decision premises” (Simon,
1991). Those professional roles, in turn, are nested in managerial hierarchies and subject to
financial and bureaucratic controls that distribute responsibility for different decisions. While
scholars initially characterized “the profession” and “the bureaucracy” as different logics
(Freidson, 2001) and “antithetical” (Freidson, 1984: 10), they have since recognized that the two
divisions of labor are in fact compatible and increasingly seen structured together. Empirically,
professions have been embedded in bureaucratic systems ever since the World War II-era when
professionals started to become bureaucratized through salaried organizational employment and
vertical integration (Barley and Tolbert, 1991). Today, most professionals are now employed in
large bureaucratic organizations and subject to bureaucratic decision structures (Briscoe, 2007;
Noordegraaf, 2011; Huising, 2015).
In this paper, we argue that many changes are coming about precisely because
professional expertise is currently structured by and subject to those quantified bureaucratic
decision structures. We found these to be in tension with algorithmic approaches being
developed in data and technology-focused organizations. Our findings and argument are based
on a 10-month ethnography of a retail technology company. Our study revealed a contrast
between two conflicting ways that the company structured the expert work practices of a large
professional group, with one way coming to replace the other. The original structuring approach
involved using bureaucratic decision structuring to coordinate the collective decision-making of
a more-than-200-person department of fashion buyers. The second structuring approach involved
using algorithmic decision structuring to coordinate their collective decision-making.
Our data illustrate the tension and contrast between bureaucratic and algorithmic decision

structuring. The bureaucratic decision structuring, organized by the organizational chart,
structured decisions into roles and hierarchies as a single decision tree with unexamined impact
that was not expected to change very much or very often. In contrast, the algorithmic decision
structuring dynamically modeled and identified many ways of structuring decisions that were
often at odds with existing roles and hierarchical structures and were instead based on likely
performance impact. To resolve this tension and make use of the algorithmic tools, the buyers
and planners had to reconfigure the role relationships and hierarchies structured by the
organizational chart and disentangle them from the data structures. We propose that this
fundamental difference between bureaucratic and algorithmic decision structuring offers a yet
undertheorized reason why algorithms are bringing about large-scale structural change –
especially because organizational charts and bureaucratic decision structuring have been
prevalent modes of production for many decades in many industries.
THEORY
The structuring of professional expertise
Scholars have tracked the structuring of professional expertise through three key phases
over time. First, Abbott and others recognized the creation and credentialing paradigmatic
standalone professionals as a notable structuring of professional expertise. These practices by
members of the profession created powerful solo practices as prototypical forms of professional
employment (Cogan 1953; Goode 1957; Parsons 1968). Within those groups, members could
exercise expert decisions with only the oversight of other members of the profession.
In the 1960s, as occupational groups that were considered “marginal professions” started
to intensify their efforts to become “full-fledged professions”, scholars began to turn their focus
to professionalism (e.g., Hall, 1968). A key focus of this research on professionalism involved

how professionals establish divisions of labor by making claims to abstract knowledge and
negotiating those claims (Abbott, 1988). Beginning in the 1970s, scholars became interested in
how the power of the profession could explain the structural changes associated with the rise of
professionalism (Berlant 1975; Parry and Parry 1976; Larson 1977). To explain these changes,
these scholars drew on “market control theory”, which argued that professions sought market
monopoly and social closure in order to increase their status in society.
The structuring of professional expertise in bureaucratic organizations
Following this work, scholars also began to recognize that the standalone paradigmatic
professions that Abbott described were not characteristic of many ways that professional
expertise was structured. This second wave of research recognized that, in fact, many
professionals were employed by bureaucratic organizations, which meant that their expert
decision-making was actually shaped and influenced by bureaucratic structuring, rather than just
the self-monitoring and credentialing practices that Abbott described. Whereas scholars who had
studied professional divisions of labor had examined expertise and effectiveness as central
logics, those who had studied the bureaucratic division of labor acknowledged rationalization
and efficiency as its central logics (Bunderson, Lofstrom, and Van de Ven, 2000). Over time,
scholars have concluded that these different logics have implications for organizational design.
Whereas research focused on professionals has tended to center on professional identity,
standards, expert authority, and self-defined membership, research focused on the bureaucratic
division of labor has tended to focus on specialization, hierarchy, and rules.
While researchers initially characterized the professional and bureaucratic divisions of
labor as “antithetical” (Freidson, 1984: 10) and “necessarily conflictual” (Abernethy &
Stoelwinder, 1994: 4), they later recognized that the forms may, in fact, be “symbiotic” (Hall

1968; Davies, 1983; Barley & Tolbert 1991; Montgomery, 1997; Bourgeault, Hirschkorn, and
Sainsaulieu, 2011: 67). Abbott (1989: 279), for example, recognized that professionals and
divisions of labor “can be nested inside one another.” Weber foreshadowed this nesting by
acknowledging expertise as a key premise of bureaucratic decision making, as well as a key
determinant of organizational structure through “factoring” jobs into specific functions and roles
that required high levels of expertise in specialized areas of specific organizational tasks (Weber,
1958).
Especially as scholars devoted attention to de-professionalization and proletarianization,
they started to examine the implications of professionals being increasingly structured in
bureaucratic organizations (e.g., Wilensky, 1964; Hall, 1968; Barley and Tolbert, 1991).
Professions first gained prominence in bureaucratic organizations during the World War II-era
when professionals such as physicians and lawyers, long considered prototypical professionals,
transitioned from solo practitioners to working in bureaucratic organizations. From 1931 to 1980,
self-employment among American physicians and lawyers fell from 80% to approximately 50%,
and from 50% to less than 33%, respectively (Barley and Tolbert, 1991). Ultimately,
multinational firms created legal departments, as well corporate medical units that were
exclusively responsible for attending to their employees. Most professionals are now employed
in large bureaucratic organizations (Hinings, 2005; Briscoe, 2007; Noordegraaf, 2011).
Professional expertise as control metrics infuse in bureaucratic structures
A third phase in the evolving literature on professions and the structuring of professional
expertise relates to the fact that bureaucratic organizations have become increasingly
characterized by quantified metrics of control (Fligstein, 1990). Such control metrics are now
commonly used to structure, monitor, and evaluate the decisions of professionals. Fligstein

(1990) called this “conceptions of control”, defined as managerial paradigms of how best to
solve competitive problems. Fligstein argued that, as social, economic, and regulatory
environments in the U.S. have changed over time, different groups of professionals have sought
to establish “stable” divisions of labor that enabled them to gain control of corporations and also
exert control on their internal and external environments by establishing “legitimate action”
(Fligstein, 1990: 6). According to Fligstein, through their “conceptions of control”, different
professional groups have infused the modern bureaucratic organization with means of controls
that have impacted specific aspects of organizational design.
Fligstein asserted that four different conceptions of control have emerged over time. First,
in the years leading up to the twentieth century, the dominant conception of control involved
direct control of competition, which was realized through the establishment of monopolies or
cartels. Second, between 1900 and 1920, the “manufacturing conception of control” dominated
as industrial engineer professionals entered factories, and managers and engineers gained control
of firms. Trained in statistical and scientific methods focused on efficiency, these professionals
introduced changes to organizational structures through the establishment of hierarchies and
vertical integration. Third, during the Depression years, sales and marketing professions became
the top executives in their firms as the “sales and marketing conception of control” dominated.
During this time, firms shifted focus from price stability to product differentiation, and sought to
effectively harness sales and marketing expertise by developing merchandising functions to
coordinate production and organizing into divisions to enable the production of full product lines
rather than narrow ones that had been characteristic of the manufacturing conception of control.
Most recently, according to Fligstein, the “financial conception of control” has reigned
dominant as financial professionals, who are adept at managing firms as investment portfolios

and evaluating the profitability of different product lines, have taken control of the firm. Fligstein
demonstrated that, as part of the emergence of the financial conception of control, divisions were
tasked with reporting their own financial metrics and implemented financial controls and
advanced accounting systems. Today, most large organizations are dominated by the finance
conception of control and “emphasize control through the use of financial tools” (pg. x). As
Fligstein (1990:15) explained, “product lines are evaluated on their short-run profitability and
important management decisions are based on the potential profitability of each line”.

Bureaucratic rationalization reflected in the org structure
Taken together, the three streams of research reviewed above help us understand how
professional expertise has been structured in bureaucratic organizations. Early scholars showed
that, through the establishment of professional divisions of labor and jurisdiction boundaries,
professionals’ decision-making has been constrained to specific areas of expertise. Fligstein
(1990), Barley and Tolbert (1991), and others showed that, as professionals have entered
bureaucratic organizations, their expertise and decision-making structures have been subject to
vertical integration, as well as bureaucratic controls. Dividing up tasks and decisions in this way
is interpretable to humans. For example, the products that consumers purchase are produced by
individual product lines that are structured in divisions in bureaucratic organizations.

ALGORITHMS AND PROFESSIONAL EXPERTISE
A separate but related stream of research has explored how data and algorithms have
transformed the nature of professional expertise (e.g., MacKenzie, 2014; Christin, 2018;
Lebovitz et al., 2020). Yet, to date, many of these studies explore professionals’ expertise, but
have not yet specifically examined how data and algorithms are changing the structuring of these

professions such as their role structures or organizational hierarchies or networks (Barley
2020). Theories of technology and change argue hold that “revolutionary” technologies change
how established roles and occupations relate to each other, suggesting that more needs to be
understood about how these new data technologies are changing the structuring of professional
expertise (Barley, 1990; Barley, 2015; Barley, 2020).
As our review of the literature above suggests, bureaucratic and organizational structures
and financial controls have become primary to how professional expertise is organized in
society. In this paper, we propose that these bureaucratic structures and related control metrics
may be salient to how data and algorithms change the structuring of professional expertise in
organizations. Even though many studies predict that data technologies will occasion significant
organizational and societal change (e.g. ), few studies have predicted or focused on how the Big
Data revolution might affect these kinds of formal organizational structures. In fact, in 1978,
Meyer explicitly said that automation—the use of computers as a technical innovation—would
likely have little impact on formal bureaucratic structures. To date, few studies focused on data
and automation have departed from that assumption. However, in the present study, we find such
a departure. We studied the development of a new algorithm that automated work in a retail
company and found that it unintendedly, but directly, impacted the formal bureaucratic structure.
We analyze our findings to show how algorithms are now impacting formal bureaucratic
structures, as opposed to strictly the jobs and processes that unfold within those structures.

Our findings stem from the observation that the decision structuring of algorithms is
fundamentally different than that of bureaucratic systems. We define decision structuring as the
process wherein individuals’ decisions are shaped by 1) decision rules, which are practices that
prescribe what information to use when making a particular type of judgment, and 2) decision

premises, which are prescriptions for how to reason about problems (Cyert and March, 1963;
Simon, 1991). On one hand, bureaucratic decision structuring, as depicted in org charts, involves
dividing out responsibility for different decisions into different jobs that are nested into
managerial hierarchies. Bureaucratic decision structuring is driven by decision rules such as
standard operating principles and decision premises such as roles that prescribe a unitary way to
reason about problems. In contrast, algorithmic decision structuring involves decision rules and
premises that produce many ways to reason about problems. Importantly, algorithms not only
have the capability to newly measure and analyze the impact of the specific one way that the
organizational chart is segmenting decisions, but they can also identify optimal ways of
structuring the decisions, even as conditions change. Our findings, thus, illustrate the contrast
between bureaucratic decision structuring which establishes a single stable decision tree with
algorithmic decision structuring, which dynamically models and identifies many ways of
structuring decisions based on likely performance impact. We propose that this contrast between
bureaucratic and algorithmic decision structuring offers a yet undertheorized reason why
algorithms are bringing about large-scale structural organizational change.

METHODS
Research setting: AlgoCo
The research site for this study was a large “digitally-native” retail technology company,
to which we give the pseudonym AlgoCo. AlgoCo was founded in the early 2010s and, like
many digitally-native retail companies, believed that “hyper-personalization” was the “new
norm” in retail (Van Ossel, 2019). AlgoCo, however, differed from many online retail companies
in its commitment to using proprietary algorithms for a wide range of applications, including
predicting purchase behavior, forecasting demand, designing new apparel, generating marketing

strategies, and optimizing inventory. At the time of our study, AlgoCo employed over 100 data
scientists in a centralized, powerful “algorithms” department. The algorithms department had
developed and implemented more than 100 algorithmic capabilities that were “in production”
meaning recommending or automating work and decisions throughout the organization. Since its
founding, AlgoCo had focused significant resources on developing a proprietary data
infrastructure that included detailed information on distinct units for sale, or “Stock Keeping
Units” (SKUs). In addition to incorporating basic SKU data such as clothing material and size,
the data infrastructure also encompassed details on client preferences, including whether SKUs
aligned with the preferences of each individual consumer, as well as client feedback, including
ratings of how well a certain piece of clothing fit. One data metric that was important in this
study was fine-grained data on customer-item outcomes, for example, the “keep rate” of each
item, which calculated the number of times that a customer kept (versus returned) an item
divided by the number of times a customer was sent an item. This metric was fine-grained, in
the sense that it calculated the keep rate of items by different colors and different sizes. So, for
example, anyone could look on a main dashboard to see whether that the navy blue size XL
version of a sweater had a better keep rate than the green size S version of that same sweater.
We selected AlgoCo as the context for our study because it was well suited to developing
new theory about how organizational structures are impacted when a company relies extensively
on algorithmic capabilities. Our observations at AlgoCo began in 2017. At that time, AlgoCo had
almost 2000 employees, of which about 100 people comprised its “algorithms” department.
AlgoCo’s commitment to its proprietary algorithms was reflected in its organizational chart,
wherein the algorithm department was centralized rather than “folded” into other departments, as
is common among many other retailers. Unlike most retailers where data science and algorithm

departments—if they exist—work in service of other departments, at AlgoCo, the algorithms
department was managed by a Chief Algorithm Officer who, in turn, reported directly to the
CEO. The algorithms department worked autonomously, with each data scientist in the
department “owning” his or her own research project focused on developing transformational
algorithms. They owned the algorithms even as they were “put in production” meaning part of
the live set of platforms that the employees throughout AlgoCo used to do their work.
The Assortment Planning algorithm
The data science team that we studied included two experienced data scientists and a user
interface (UI) designer, and later grew to include two additional data scientists. Their work
involved developing new ways of mathematically modeling the assortment planning process –
which is the way that fashion buyers plan out the inventory that the company should develop,
produce, or purchase from vendors to then sell to customers. Even though the team we studied
was specifically focused on developing a new assortment planning algorithm, when they
explained their work to others, they often described how their algorithm interfaced with a whole
interconnected algorithm system that drew on centralized data (see Figure 1). The team was
trying to formalize the assortment planning process as a constrained optimization problem,
which would mean that their model could include and weigh all of the decisions about all items
of clothing in the same mathematical formula (which would, in turn, be embedded software
code) that could calculate how different decisions impacted the overall optimization score of a
set of inventory decisions. In parallel, while the data scientists were developing this
formalization of the problem, they were also meeting weekly with the UI designer who was
creating new user interfaces that the buyers could use to visualize this new formalization of the
assortment planning process. As we will elaborate in the findings, we did not see a lot of

resistance to using the tool based on occupational identity or autonomy issues as has been found
in prior studies (Christin, 2018; Kellogg, Valentine, & Christin, 2019), but we did observe
consistent mismatches between the algorithmic and bureaucratic approaches to understanding
and structuring decisions.
----------------------Insert Figure 1 here
----------------------Ethnographic data and analysis
We used an inductive, ethnographic research approach in this study. Our research design
was guided by the open-ended question of whether and how newly developed algorithms were
changing professionals’ work. Because this area of research is new and growing, an inductive
field-based approach was well-suited to the question (Edmondson and McManus, 2007). The
first author negotiated access to work as an unpaid program manager for the AlgoCo algorithms
department. The unpaid program manager position allowed access to company headquarters in a
large US city, and to all of the company’s internal data, communication, and coordination
platforms. It also involved going through the onboarding and socialization processes, and
joining the algorithms department community, including weekly happy hours, as well as the
company-wide community, including weekly company-wide meetings.
In addition to providing the program manager administrative services, the first author
negotiated access with the one specific data science team that was developing one specific
algorithmic capability. Embedding in their team involved attending local team meetings, and
their managers’ meetings and directors’ meetings, which gave us a broad view of the work of
many related data science teams. The algorithm that our data science team was developing,
described in detail below, was focused on a particular workflow (assortment planning, see Figure

2) within the job of fashion buyers at AlgoCo. Fashion buyers were paired with planners who
did much of the computation in support of the buyers’ job. Therefore, the first author also
identified a specific fashion buyer-planner pair who agreed to let us study their work before,
during, and after the algorithm was developed and deployed. This shadowing included following
the fashion buyer to a New York City buying trip where the fashion buyer met with vendors and
negotiated prices and orders with many vendors. The fashion buyer-planner pair was embedded
in a larger buying team, which allowed us to collect data from all of their weekly team meetings
and department meetings throughout the 10-month period that we observed. This focus allowed
for a clear analysis of the workflow before, during, and after the algorithm was developed.
----------------------Insert Figure 2 here
----------------------The data science team took a “human-centered design” approach to the tool development,
first observing the buyers’ work, and then engaging collaboratively with them to understand their
needs and mental models of their assortment planning work (e.g., Cooley, 1999; Norman, 2005).
This collaborative approach meant that we could also analyze extensive data on the crossfunctional interactions between the data scientist local team and fashion buyer pair, as well as
between the data science managers and buying directors. Many of these interactions took place
either at user testing meetings, cross-functional governance meetings for this specific algorithm,
or later user training meetings. Finally, as we began to understand that a key finding related to
the tension between the algorithmic and bureaucratic decision structuring, we also collected
archival data on the fashion buyers’ org chart or department structure over the decade that
AlgoCo had been in business.
Our main argument relates to the significance of changes that we observed over time.

Our analytical approach was therefore structured to characterize, substantiate, and illustrate
changes over time. We conducted a thorough analysis of all our observations of the buyers’ and
planners’ work as it was enacted using all of the AlgoCo data platforms before the new algorithm
was developed. We analyzed how decisions and metrics were used, discussed, and interpreted
during daily work and regular meetings. We also analyzed how change was understood and
accomplished within this earlier phase of how the buyers worked, because those within-phase
changes reveal many assumptions about why things “were the way they were”. Within this first
phase, before the algorithm was developed, people had a taken-for-granted way of making sense
of their decisions, jobs, metrics, and many of those taken-for-granted assumptions were surfaced
in discussions during minor changes to decision structuring within this phase. We also
conducted a thorough analysis of the many cross-functional interactions that played out as the
data science team developed the algorithm, in collaboration with the fashion buyers. These
interactions began to surface many of the tensions that are the focus of our paper. We analyzed
the discussions, tensions, and resolutions that played out in each meeting and interaction during
this period. Finally, the first author also returned to AlgoCo a year after leaving the field to
follow up on the adoption and use of the algorithm, and to see how some of the tensions had
evolved and resolved. The final phase included analysis of all of the interviews, meetings, and
observations conducted during the month of follow-up with the data science team and their
managers, and the fashion buyers’ teams and their managers.
FINDINGS
This section contrasts two different ways that AlgoCo structured the expert work
practices of a large professional group, retail fashion buyers, with one way aiming to replace the
other. The original structuring approach involved using bureaucratic decision structuring to

coordinate collective decision-making among the large group of professionals. In this system,
each professional occupied a role that had authority and accountability for a codified and specific
set of decisions, and then groups of such roles were nested in managerial hierarchies. The
managers had authority and accountability for the collective sets of decisions made by that group
of roles, as is typical in bureaucratic organizations. The organizational chart not only structured
jobs and hierarchies, but also financial and performance metrics, which were assigned to each
role and each manager and thus followed the structure of the org chart. Each role used their
metrics to guide their work practices on an ongoing basis, such that the metrics enabled the large
group to carry out coordinated decisions.
The second way of structuring the expert work practices was different and aimed to
replace this original way. The second structuring approach involved using algorithmic decision
structuring to coordinate collective decision-making among the large group of professionals. A
newly developed algorithmic tool modeled ‘scenarios’ – meaning it computed the likely
performance outcome of different decisions. At first, the algorithmic tool was used for
algorithmic decision structuring within each role; each professional could use it to optimize the
decisions they made for their own local metrics. However, this exercise began to show that the
way each role’s decisions and metrics were divided out also influenced how the optimal
outcomes were identified. This realization then began to call into question the way decisions and
metrics were structured among all of the roles and hierarchies – the structuring of the overall
organizational chart itself. The algorithmic tool newly revealed the performance impact of
different ways of dividing out and nesting the metrics among the different roles and hierarchies,
with two implications: first, the formal bureaucratic decision structuring process itself became
subject to new measurement and analysis. And second, the algorithmic tool not only

demonstrated the impact of the unitary way that the organizational chart was dividing out the
metrics, but it also modeled “arbitrarily many” ways of structuring the decisions and metrics,
helping identify many optimized ways of structuring the decisions – prompting a move towards
more dynamic, flexible, and responsive structuring. These findings reveal the contrast between
bureaucratic decision structuring – which sets up a single decision tree with unexamined impact
that is not expected to change very much or very often – and algorithmic decision structuring
which dynamically models and identifies many ways of structuring decisions based on likely
performance impact (see Table 1).
----------------------Insert Table 1 here
----------------------The rest of the findings more fully illustrate this contrast and the differences and tensions
between the two ways of structuring decisions. We first use our ethnographic data to explain
how a more-than-200-person merchandising department used bureaucratic decision structuring to
coordinate and accomplish “assortment planning,” a complex and collective decision-making
process. We then describe how data scientists developed the new algorithmic tool to support
this process, which began to surface the contrast and tension between bureaucratic and
algorithmic decision structuring. We conclude the findings by describing how data science and
merchant leaders worked to understand, disentangle, and make use of both the bureaucratic and
algorithmic decision structuring processes for assortment planning and related work.

Phase 1:
Professionals Use Bureaucratic Decision Structuring to Coordinate Collective Work
The merchandising department’s organizational chart and related metrics structured the
decisions domains and work practices of about 200 merchants in various ‘buyer’ and ‘planner’
roles and in different levels of hierarchy. Figure 3 depicts a stylized version of the org chart.

----------------------Insert Figure 3 here
----------------------Assortment planning process. There were many related processes involved in
developing and maintaining a well-performing inventory. Here we describe the specific
assortment planning process under bureaucratic decision structuring. About six months before a
fashion season began, the planning, strategy, and marketing executives determined a set number
of buys for all of the merchandising department and set the financial and performance metrics for
the entire group. We can narrate this process with falsified and simplified numbers, as illustrated
in Figure 4. The buying executive might be given 600,000 buys and a keep rate (KR) target of
75%. The work of selecting and developing the 600,000 buys was complex and, so, it was
further divided such that each buying director was allocated 100,000 buys each with KR targets
level-loaded across departments based on the expected performance of each department. This
process of dividing out the buys and level-loading the metrics continued to the “bottom” of the
organizational chart with the 100,000 units divided up among the front-line buyers who
negotiated with vendors. In our simplified example, the Plus buying and planning managers
decided to allocate 30,000 units to Wovens, 20,000 units to Knits, 10,000 units to Dresses,
20,000 units to Denim, and 20,000 units to Bottoms. This complex process was data-driven and
dynamic. Much of this work was accomplished using Excel (or Google) spreadsheets that had
been programmed with sophisticated macros (automated input sequences that calculate complex
formulas across different cells and tabs in a spreadsheet) that helped calculate the potential
impact of moving a set of buys from one product category (e.g., men’s denim) to another
category (e.g., women’s knits).
----------------------Insert Figure 4 here
-----------------------

Buyer-planner workflow in assortment planning. The assortment planning process
thus involved a “top-down” allocation of buys and targets that defined the work and targets of
fashion buyers at every level of the merchandising org chart. Continuing our stylized example
from above, this process meant that, at the beginning of the assortment planning process, the
buyer-planner pair who was in charge of the women’s plus-sized denim category would be given
20,000 units of denim “buys” for that quarter and a set of performance targets that they needed to
hit with those buys. They followed a somewhat similar process as the overall buying and
planning directors as they developed an assortment based on their 20,000 units—they all relied
on the similar spreadsheets that were pre-coded with macros, and they dynamically and
iteratively moved their buys across different product categories to see what the predicted impact
would be on performance targets. At the director level, this process meant seeing the predicted
impact of moving larger sets of buys between the women’s versus men’s department. At the
front-line buyer-planner level, this process meant seeing the predicted impact of moving sets of
buys between different kinds of plus-sized denim (e.g., capris versus boot cut) and between
different vendors.
Figure 5 offers a stylized illustration of what this local assortment process involved. The
buyer-planner pair met and discussed frequently how to allocate the upcoming seasons buys
across many different denim product categories, including style (capri, skinny, boot cut,
straight), color (white, light, medium, dark, stonewash), price point (under $50, $50-$80, $80+),
and vendor. Each of these product categories had different historical performance, which was
the source of data used to predict assortment plan performance. The role relationship involved
the buyer being more of an artist with an intuition for upcoming trends, and the planner making
the vision work by moving buys around between the different product categories.

----------------------Insert Figure 5 here
----------------------Organizational changes under bureaucratic decision structuring. During our data
collection, we observed or learned about three instances of organizational change related to the
buyers’ organizational chart. These instances provide a useful analytical lens for understanding
how the organizational chart was being used and its assumed purpose. The discussions around
these changes illustrate how the “org chart” and related bureaucratic decision structuring was
dividing up the large set of complex decisions and related tasks to be manageable and
interpretable for humans.
The first example involved creating a new role on the Plus buying team as the volume of
purchases in that customer segment grew. Originally, the Plus buying team had a buyer-planner
pair who planned and managed the assortment for “Tops.” As Plus sales volume grew, it became
infeasible for one buyer and planner team to make all the purchasing decisions for that category
and so the “Tops” category was split into two subcategories—“wovens” and “knits and
sweaters”. The Plus Buying Director explained the decision,
We split out Tops into someone who was responsible for wovens and someone who has
responsibility for knits and sweaters, just to make the scope of responsibility more
equitable and more manageable.
The decisions and targets for the Tops buyer were thus segmented into two buyer roles—one
buyer-planner team was responsible for developing the inventory for wovens, while another team
was responsible for knits and sweaters. Each buyer-planner pair was assigned their own volume
and targets. If the Plus Buying Team as a whole was allocated 100,000 units, the wovens buyer
might be allocated 20,000 units and the knits buyer might be allocated 20,000 units of her own.
There was no discussion of whether this division would impact the outcomes or targets, it was an

assumed, taken-for-granted division of labor based on the growing sales and need to split the
number of decisions for manageability.
The second example occurred when we were conducting our observations and involved
the company newly entering a new market in the European Union (EU). The merchandising
department expanded to include an EU department alongside the women’s, men’s, plus, and
kid’s departments. The EU executives who formed and structured the department decided to
structure the buying teams and roles based on how the customers might use the clothes, rather
than on the more standard product type. The EU buying team thus had an Evening Wear buyer,
a Casual Wear buyer, and a Workwear buyer. A buyer might include a dress in developing an
inventory for each of these categories because dresses might be appropriate for any of these uses.
In contrast, the US Women’s buying team had dedicated Dress buyers who would purchase all of
the evening wear, casual wear, and workwear dresses. As the EU buying department was being
structured, this non-standardized way of structuring the buying roles was easily accepted by the
merchandising department and executives. It was explained to be the way of structuring and
dividing out the decisions that was most manageable and useful for the EU buyers. Later, the
non-standard roles and product categories introduced complications for some of the data science
approaches, but under the original bureaucratic decision structuring work, this division was
straightforward. There was no discussion of whether this way of dividing out the decisions
would impact targets or outcomes.
The third example had happened a year before we began our field observation, but many
people discussed it in interviews, and we collected extensive archival data on this example. This
change involved a large “re-org” (an emic term, short for “re-organization”) that changed the
buying teams and reporting lines in the women’s department. This example again illustrates how

these bureaucratic structuring decisions were made based on the need for manageability of large
areas, and were made to support interpretability and clear accountability for a decision domain.
AlgoCo had been founded with only a Women’s department, then later added Men’s, Plus, Kids,
and EU departments. This comparatively long history meant they had higher sales volume in
this Women’s category. The higher sales volume had become unmanageable – every buyerplanner pair was overwhelmed by the amount of inventory they had to develop and manage. So
the executive who led the Women’s department felt it was time to divide the department into
smaller areas so that teams could more easily manage those smaller areas.
One merchandise executive reflected on a strategic opportunity to better use the data
insights as she thought through this re-org:
We were sub-optimizing the buyers’ decisions because they were gravitating toward the
average. But the average of a big base…That was not serving our clients, particularly
those at the bookends of the spectrum, whether it’s age, or price preference, or style.
Her sense was that defining a buyer role by product type meant they bought those products with
the average customer in mind. There were many conversations about how to use the re-org to
pursue this strategic opportunity of having the buyers focus more on specific customers. For
example, at a multi-day “off-site”, executives, merchant leaders, and data scientists all discussed
how to split up the Women’s department. The data scientists in the algorithm department wanted
to divide up the department by customer age segments so that the buyers could focus on
developing inventory specifically for different age groups. They defended this proposal by
arguing that age was the client attribute that most significantly predicted KR. They argued for
structuring based on which segmentation related to client outcomes, not based on how buyers
and planners think about or interpret their work. One data scientist explained,
I wanted to buy by age segment to introduce a source of diversity into our assortment...I
focused on Age because another Algos team had shown that Age was the client attribute

that most strongly conditioned Keep Rate.
In contrast, the merchandising team wanted to divide up the women’s department based on price
point. They thought that focusing buyers on developing inventory within “low price point
denim” would be a better approach for dividing up the department, and also for introducing more
diverse and targeted inventory. The merchandising teams’ reasoning was based on intuition.
One of their executives explained that she did not think that customers’ preferences were that
different based on their ages, so she thought developing inventory targeted to the ages would not
produce better inventory. In the end, the merchants’ authority for their own department and
workflows prevailed, and the Women’s department was divided up into bargain, general, and
luxury price points. The buying directors of these sub-departments reported to the Women’s
exec, and the “buys” and targets were divided out among these newly formed buying groups.
Figure 3 which shows a stylized version of AlgoCo’s “org chart” illustrates that the
Women’s department has one more level of hierarchy than the other departments. This re-org
was responsible for that additional layer in the Women’s department hierarchy. The overall org
chart of the full merchandising department involved non-standard categories and levels for
dividing up the decisions. But it provided relatively clear accountability for decision domains
within the large, complex, coordinated set of decisions involved in planning and managing a
massive inventory for a large and diverse set of customers.
Phase 2:
Tensions between Bureaucratic and Algorithmic Decision Structuring
We next describe the change process that unfolded as a data science team developed a
new algorithmic tool for the buyers to use during assortment planning. We observed a consistent
interaction pattern during this process that was especially evident during cross-functional
meetings such as user testing meetings, the weekly planning meetings, and later at the user

trainings. Over time, and through our analysis, we realized that this interaction pattern related to
fundamentally different ways that the buyers and data scientists were approaching the decision
structuring process. To explain this contrast, we will illustrate how this tension emerged within
individual buyer roles and then within the buyer hierarchy.
Algorithmic decision structuring in tension with the professionals’ roles
The tension that arose related to the buyers’ individual roles was related to their use of
non-standard and practical product categories to guide their decision-making. The categories
were non-standard in the sense that they were not the same across the different buyers’ roles.
That the categories were non-standard across buyers was not salient under bureaucratic decision
structuring, because the buyers were responsible for “hitting” higher-level targets so that the
focus was on hitting those metrics rather than the ways in which they went about this, which
were not attended to. In contrast, the data scientists were expecting more standard categories to
be built into the tool, and expected that the categories that would be preferred would be the ones
that predicted outcomes. The data scientists also expected that client categories (for example,
age segments), not just product categories (for example, denim styles) would shape decisionmaking. These tensions were apparent at the first user testing session and shaped most
interactions during the tool development process.
How to structure decisions. The first user prototyping session involved the buyer for
Men’s bottoms. Note that the Men’s department had not achieved high enough sales volume to
warrant a dedicated Men’s denim buyer as in the case of the Women’s bargain and general
departments, so this buyer covered and was responsible for all decision making related to Men’s
bottoms, including denim. The user testing session involved her inputting her buys into the tool
and getting the algorithmic recommendation for how she should allocate the buys. After she ran

the tool, she immediately noticed that the tool was splitting up recommendations in a way that
she did not typically do. Looking at the tool, she reflected,
Today how we buy is, for every style, we buy across all inseams. So this set of
recommendations is super different from what we’d actually do today. Rarely do we buy
one pant in one inseam. This recommendation is telling us that certain styles would be
better in certain inseams.
The data scientists’ initial response was representative of their approach. They first explored
whether this different way of splitting up the decisions (i.e., buying different styles in different
inseams rather than buying all inseams for any one style) might be predictive of outcomes. They
did not feel as constrained by the practical product categories the buyer was using, and were
expecting that something in the data was guiding this algorithmic recommendation. One of the
data scientists suggested that it was possible that on the client side that shorter clients (i.e.,
smaller inseam) preferred a certain color and style of pants and taller clients (i.e., longer
inseams). The buyer admitted, “It’s a valid question” acknowledging that this other set of client
categories (tall or short clients) might explain their buying patterns and therefore the
recommendation she was seeing. She then explained the practical reasons why she needed to
make the decisions in a way that did not split up buys across inseams and styles, including to
negotiate with vendors. Together, she and the data scientists decided that later they might be
able to investigate the question of vendor negotiations and vendor minimums, but for now, they
would try to support the buyers’ existing practice of buying all inseams across all styles; this
required adjusting both the model and the interface of the tool.
As they explored the issue, the data scientists also were learning that this issue of nonstandard and practical product categories would likely arise for every buyer. One of the data
scientists tried to discover how generalizable this inseam issue would be. He said, “Got it. So
(the tool is separating out styles and inseams) and that is a big blocker… Hmm. That’s for

men’s bottoms but not women’s…?” The buyer replied, “Yeah, women’s doesn’t use inseams.”
We can connect this example to the main tension. In the past, it did not matter whether
she separated out her buys by inseams and styles or whether she and the women’s denim and
pants buyers did that differently. What mattered was whether their part of the inventory was
profitable and performing well with clients. When the algorithmic tool recommended splitting
up inseams by styles, the data scientists guessed it was because of an underlying pattern in the
data where different clients with different heights preferred different styles and wanted to try
buying in a way that reflected that data pattern. The buyer could not accommodate that
recommendation because it did not fit with her practical reasons for buying all inseams in any
one style. This men’s bottom was the first user testing session, but each subsequent session
revealed additional practical and non-standard categories that were important in how the buyers
planned their assortments. Even the last user testing sessions that we observed in the last month
of our observation, which was for Plus dresses, involved new product categories. That buyer
wanted to be able to structure her assortment decisions across many dress silhouettes that had not
previously been raised by any other buyer.
Depth recommendations. This example relates to the broader tension that arose during
this phase of tool development, which related to differences in what the buyers assumed was a
good assortment versus what the algorithmic tool recommended as a good assortment. A
specific tension was around the “depth” of styles that the buyers assumed made up a good
assortment versus what the algorithm recommended. Each buyer was given buys and targets and
had to distribute those buys across a variety of styles. Each buyer used practical, intuitive—and
non-standard—product categories to divide out across their chosen number of styles (e.g.,
bootcut, straight, skinny, capri) and to the depth of buys in any one style to create what they

considered a good assortment. The algorithmic recommendations began to explicitly model and
measure the impact of their intuition of a good assortment.
The Plus wovens buyer’s first user testing session illustrates the tension. She and the data
science team had this exchange:
Buyer: …Because when I ran it the first time it basically told me I should buy every
single unit in this first style which, truthfully, isn't even that good.
Data scientist: Oh that's interesting. Which one was it that they've put too much into?
Buyer: (gesturing to screen) If I didn't put this 1000 unit threshold, what I was getting
was… it would say put 10,000 units into one style and put 10,000 into this other style.
Data scientist: Right, so that is, again, the expected behavior because that's ... if you
don't tell the tool to force some amount of breadth the tool is just going to be like, put
everything in the best one.
The data scientist was explaining that an optimization algorithm was going to optimize in a
simple or naïve way— it was going to find the item with the highest keep rate and recommend
that the entire set of buys be allocated there, because it would indeed optimize the keep rate. The
buyers’ concern was that putting 10,000 units to that one style that had performed well in the
past would not represent a good inventory. There were many conversations among the buyers
outside of the user testing sessions about how comical it was that the tool was telling them to buy
10,000 units of one style. The buyers and planners discussed these initial depth
recommendations as a big failure on the part of the algorithm.
The data scientists were agnostic as to whether 10,000 units of one style was a good
inventory or not; to them, that was a question that would be best answered by data. The ongoing
tension between the two approaches was not whether 10,000 units was the right number of units,
but the higher-level question of how to understand what a good assortment was. The data
scientists’ approach was to use historical data to introduce diversity into the assortment—they

wanted the optimization algorithm to be constrained by empirical data patterns such as customer
segments, seasonality, and vendor relationships. The buyers’ approach was to use their intuition
and experience to introduce diversity into the assortment—they wanted the optimization
algorithm to be constrained by their intuitive product categories such as what styles, colors,
silhouettes should be included each season’s assortment. The tension was not directly
interpersonally conflictual. Instead, it involved very different approaches to thinking about
assortment planning.
Product categories. Another example of this tension involved figuring out whether and
how customer categories should be used in the assortment planning process. As explained in the
first findings section, although buyers consulted customer data as they were planning, they did
not do so in a holistic or systematic way. Instead, they would look at the customer data
pertaining to any style that was performing in an unexpected way (recall the “under the covers”
phrase). The data scientists wanted to include customer categories in the constrained
optimization model for assortment planning. They thought that recommendations would be
more diverse in a way that reflected customer preferences if the assortment decisions were
constrained and optimized for customer segments. The data scientists wanted to include agerelated client segments in the calculation of the algorithmic recommendations. The following
example illustrates the tension. In a follow-up user testing session, the data science team
introduced the fact that they were including client segments by age in the recommendations now.
This conversation illustrates:
Data scientist 1: One slight change... I did add client segment into it. We do not have to
include that. Last time you said it does not change that much. Including it allows us to
see if that’s true. If we don’t have client segment in it, then KR will be biased to be
higher because it will take the 50 and over segment.
Data scientist 2: Because it’s maximizing KR and the older age segment has higher KR.

Buyer: I guess that’s important to know because right now we don’t sort too much by
age group. The [bottoms styles that we buy] that do well… do well across age group.
But knowing that would impact the end result is important to note for whoever is using it.

Her response was indicating her assumption that the way to use that information would be for the
stylists who decided what styles to send to what customers to know that a particular style might
perform well with different age groups. She did not necessarily agree that her assortment
planning process should include considerations of age. She continued, “I wonder if we even
don’t have metric this in…” meaning that the set of recommendations should not even be
informed by historical buying patterns separated out by client age.
Age was not the only client variable that predicted variance in terms of purchase patterns,
and the data scientists were interested in including other client segmentations in the assortment
planning process as well. For example, another data science team had used machine learning
algorithms across all customer and item interactions to analyze the latent factors that contributed
to a customer’s preference for an item. That team had determined four different clusters of
clients that determined variation in purchase patterns, including client styles such as “Casual” or
and “Classic”. The assortment planning data science team suspected that assorting based on
latent style (versus for the average customer, which is what they thought the baseline buying
process was aimed at) might improve inventory performance.
However, like age-based client segments, this segmentation increased complexity on the
part of buyers and planners and increased their workloads, even though it predicted more
variance. Earlier attempts – years before – to get the buyers to plan based on customer segments
had failed because the client segments had introduced too much complexity into the planning
process. The buyers still wanted to use their original product category strategies (“a well-

performing denim inventory includes skinny, straight, boot-cut, boyfriend”) so including
customer categories to them just meant three times the work for unclear benefit. Figure 6
illustrates the problem of trying to include client categories in the original process that used
spreadsheets for assortment planning. In describing this problem at a meeting with a large group
of merchants, a data scientist said, “Rebuys are really successful… when they are purchased for
client segments, they are extra successful. And not just for KR, but for lots of other aesthetic and
financial targets. But in the past, forcing buyers to think about client segments makes the (client
segment insights) less scalable.”
----------------------Insert Figure 6 here
----------------------Algorithmic decision structuring in tension with the professionals’ hierarchy
In addition to the tensions related to the buyers’ roles reported above, the second set of
tensions related to how the multiple and complex decisions involved in assortment planning
should be divided and aggregated. This related to the buyers’ organizational hierarchy structured
by the org chart. Again, the differing approaches were about practical, interpretable, and fairly
static structuring on the buyers’ side versus more flexible structuring that explicitly predicted
outcomes on the data science side. For the data scientists, the structuring that would be preferred
was always that which had predicted outcomes in the historical data.
The org chart is a decision tree, decisions made in ‘leaf nodes’. A key idea to
understanding this tension is to understand why the data scientists considered the merchandising
organizational chart to be a decision tree. Several data scientists in various meetings talked in
offhand ways (meaning most people there understood the point) about how the merchandising
department data structure and organizational chart, which were structured around the same
product taxonomy (recall Figure 3), could be understood as “decision trees.” They explained

that within the decision trees “the buying all happens at the leaf nodes.” One of the data
scientists elaborated on this point in an interview. He showed us a data interface that organized
all the items in the AlgoCo inventory. He showed earrings as an example product category,
So there's the ID, there's the name, there's a parent ID. So, earrings has ID 63 and has a
parent that is 9, follow that and. see earring's parent is jewelry which is ID 9. And
jewelry has a parent whose ID is 87… accessories.
See how earrings has a parent (jewelry) and jewelry has a parent (accessories) There are
some things that if you follow down, nothing has them as a parent. Those are leaf nodes.
He then emphasized, “So those (gesturing to leaf node) are the groups that actually go out and
buy things. And then the others are just roll-up groups.” He was referring to the fact that the
buyers who made buying decisions were at the “level” of jewelry. Actual purchasing decisions
were not made at the “roll up” levels like accessories. He explained further, gesturing to his
screen, “There are people here” (gesturing to the buyers) that actually buy stuff. And there are
people here (gesturing to another buyer in the same group) that buy stuff. But here (gesturing to
their manager and their manager’s manager) there's no one here that buys stuff.” He concluded,
“The budget for this leaf node (meaning the buyer) and this leaf node (the other buyer) roll up to
the budget for this parent node (the manager). But no buying happens here (at the manager
level).” One of the data scientists on the team we studied connected this idea to their algorithm:
If you have a hierarchy where information flows bottoms up and tops down like this,
where the decisions happen here, here, and here (indicating leaf nodes and the roll-up
teams) rather than side to side, you are naturally going to have workflows that have to
involve leaf nodes and these bottoms up decisions.
She further explained that other algorithmic design processes could look at “hooking in at other
places where the information might be flowing. But for us designing for this buying decision
meant designing at the leaf node.”
Org chart hierarchy was constraining optimization. The data scientists had chosen to

design at the leaf nodes because that was where the buying decisions were made. However, an
issue soon arose because it became clear that the structuring of the leaf nodes was somewhat
arbitrary but was influencing what the algorithm could recommend. We can report a simple
example to illustrate and then explore this insight and its implications more fully. To check our
understanding of this dynamic, we asked one of the data scientists in an interview,
Interviewer: OK so what you all are saying is… Consider two scenarios. In the first
you set up two buyers’ roles: 1) women’s workwear and 2) women’s casualwear
and give them each 1,000 buys… and then run the optimization algorithm on the
1,000 within workwear and the 1,000 within casualwear.
In the second scenario, you set up the two buyers’ roles as 1) women’s tops and 2)
women’s bottoms and give each of them 1,000 buys… and then run the
optimization algorithm on the 1,000 within tops and the 1,000 within bottoms.
You’re saying that in these two scenarios, you would get a different set of
recommendations… and you would stock a different inventory.
Data scientist: It seems most certain that you would.
Interviewer: And one way of doing it would produce better outcomes.
Data scientist: Right. And you could measure it.
As this quote illustrates, the data scientists and buyers began to realize that the org chart itself
was segmenting out decisions into jobs through the assigned buys and targets, and in ways that
influenced what an optimal set of decisions would be for that job. The merchandising org chart
was segmenting out decisions to be made in a way that, according to the data scientists’
approach, was unexamined and not optimized. Instead, the org chart was structuring the
individual buyers’ jobs and related buys and targets based on an interpretable and taken-forgranted product taxonomy (Figure 3). The directors would use the interpretable product
taxonomy to divide out targets, and the buyers would hit their targets. Most of the focus was on
whether the buyers were hitting their targets, not where the targets came from and whether a

different or better set of targets were possible. The algorithmic approach newly called into
question the process of dividing up the targets because that defined the space of decisions on
which the algorithm was optimizing. The algorithm would optimize within whatever set of
decisions it was given, and the buys and targets—following out the interpretable product
taxonomy of the org chart—were defining decision space that would be optimized.
This issue was not contentious between the two functions. Rather, both buying directors
and data scientists recognized it as a problem and discussed it in meetings and interviews. As an
example, a buying director said, “Segmenting our teams and inventory in this way doesn’t allow
for our algorithms to explore scenarios about our inventory and our clients in a multidimensional
way. It also does not let us optimize for multiple performance metrics.” Her point was that the
algorithmic tool was set to explore the 60,000 buys of one buyer. But that same approach could
be used to explore across 120,000 buys of two buyers, and so on, to see if there was an optimal
pattern across their buys. A data scientist expressed a similar idea this way: “We saw the
algorithm could explore a larger space for better results.” One of the executives said in a
strategic planning meeting after they were discussing the merchandising org chart constraining
the algorithmic exploration, “I fully understand the drawbacks of how we are currently
organizing the merchandising department. We are just now figuring out the better way.” A
final quote illustrates how this problem related to a core principle for the data scientists. Several
of the data scientists had heard in their disciplinary training the phrase, “Binning is sinning”
which referred to the idea that data should be modeled as a continuous distribution and that
imposing “bins” or categories on the data would introduce a lot of distortions and problems. One
of the data scientists suggested, “You’ve heard the phrase ‘binning is sinning?’ I wonder if this
is an artificial binning... We might be at a temporary period in the history of AlgoCo in which

we're artificially binning the way we are buying as opposed to buying for specific clients.”
Phase 3:
Reconfiguring Roles and Hierarchies to Accommodate Algorithmic Decision Structuring
The section above illustrated the tensions that arose as the data scientists’ approach to
decision structuring interfaced with the buyers’ bureaucratic approach to decision structuring.
Their mutual attempts to accommodate algorithmic decision structuring involved reconfiguring
role structures and organizational hierarchies – i.e., the kinds of major structural changes
predicted by theories of technological change for more “revolutionary” technologies (Barley,
1990; Barley, 2015; Barley, 2020). At the end of the two-year study period, the algorithmic tool
had been broadly adopted, some changes to the role structures and hierarchies had already been
accomplished, and other changes had been specifically planned and discussed. These involved
changes to the buyers’ role, their role structure with the planners, and the hierarchy that had
structured the merchandising department (see Table 2).
----------------------Insert Table 2 here
----------------------Reconfiguring the professionals’ role and role structure
As described in Phase 2, the buyers and data scientists differed in how they thought about
the relevant categories that should be used to diversify any one buyer’s assortment plan. This
tension resolved in how the data scientists designed the algorithmic tool, with implications for
both the buyers’ role and their relationships with planners.
The buyers’ role. The first tension related to the buyers’ use of non-standard and
practical product categories to guide their decision-making, when the data scientists expected
that the categories that would be preferred would be the ones that predicted outcomes. One
resolution to this tension involved the data scientists configuring the algorithmic tool to let the

buyers input any of the practical and non-standard categories that they wanted into the tool in the
form of constraints on the optimized recommendations. This change involved including more
data. As one data scientist explained, “We added whatever targets they asked for: brand, color,
silhouette, price, print, fit, inseam, rise, latent style. Every buying group wanted different ones.”
Accommodating the product labels meant that the buyers more easily transitioned to using the
tool, and it also helped the data scientists collect data on the different product attributes that the
buyers considered relevant to developing a well-performing inventory. This change then also
included changing the tool slightly to allow the buyers to input any of those product categories as
constraints on the recommendations. As an example, after the exchange reported above where
the data scientist explained to the buyer that the algorithm would allocate all units to the best
style if not forced to add some breadth of styles, she added, “And so that's like where you get to
be creative – to pick out what kinds of breadth do you want”, encouraging the buyer to input her
intuitive constraints to create her idea of what a good assortment would be. To support this
desired behavior from the buyers, the data science team configured the buyers’ interface so that
they could enter constraints such as Vendor A must supply 30% of inventory, the Bootcut style
must comprise 70% of inventory, or red-colored Denim must comprise 10% of inventory. This
functionality allowed the buyers to segment their assortment by any of these categories and
visualize how various depth and breadth structures impacted their targets. Similar to the product
category labels, this functionality was seen as fairly straightforward for the data scientists to add
to the tool. One of the data scientists explained this in a presentation at a cross-functional
meeting. She said, “We’ve gotten a lot of feedback along the way. The financial targets and
aesthetic and breath targets were easy to implement. We are all still learning how to combine
these to deliver assortments that match people’s intuition.”

Note one key implication of this reconfiguration of the tool. The feature that allowed the
buyers to input their constraints in order to structure the assortment the way that they wanted
meant that their intuitive structuring of their assortment became explicitly modeled and
measured. If they had an intuitive sense that structuring their assortment to include 30% of their
units in a certain category—say bootcut denim—that structuring decision was now recorded and
measured. In the past, they were accountable for whether they hit their targets, but with little
understanding of how they were structuring their assortments to hit their targets. Now, their
intuition pertaining to how to hit their targets was recorded and could be measured and analyzed
over time. Even though the tool allowed them to plan inventories based on practical, intuitive
categories, it also implicitly subjected those to the data science approach of assessing the impact
of that decision structuring on outcomes.

Recall that a second related tension was that the data scientists also expected that client
categories (for example, age segments), not just product categories (for example, denim styles)
would shape decision-making. The resolution to this tension involved the data scientists
including client segments “behind the scenes”, meaning client segments were added to the code
that produced the recommendations, but they were not apparent in the user interface that the
buyers interacted with. And, over time the buyers came to expect that the recommendations
included client segments. For example, towards the end of our study period, a buying director
was listening to a presentation by the algorithms team and asked unprompted, “The client
segment here is age?” The team of data scientists responded:
Data scientist 1: Yes. For this group, it’s four age segments. But that’s changeable! For
our longer-term vision, we can use the machine-learned personalized style for client
segmentation.
Data scientist 2: Yes. That’s a very important vision for where we are headed.

Data scientist 1: We will use the best client segment that gives us the best outcomes
The team further elaborated their approach. One data scientist said, “We keep it away from users.
We keep it ‘behind the scenes’ which means we could expand it to way more client segments”
(i.e., we could add a lot of complexity without that additional creating complexity for the users).
One of the merchandising team members asked, “So it could be multi-dimensional?” and the
data scientist confirmed, “Yeah, whatever, however we want to segment that.” Putting these
structuring decisions “behind the scenes” meant there was considerable flexibility in how the
structuring happened. The buyers would still get recommendations that were informed by this
extra layer, but they would not be aware of the complex structuring based on client
segmentations that was happening “behind the scenes” to inform their recommendations. The
data science team or the buying directors could select any client segmentation to use to inform
the recommendations and could also choose to change those segmentations frequently.
As the buyers and data scientists envisioned further changes to the buyers’ roles beyond
these changes of explicitly measuring the impact of their decisions and including more and
including more complex segmentations “behind the scenes” in the recommendations that they
received, they used language of buyers “curating” an algorithmically-recommended assortment,
rather than producing the assortment plan themselves. They expected that the buyers would start
to curate the algorithmic assortments for both context and for strategy. We saw examples of this
curation role when we returned to observe the buyers use the tool. They ran the tool, projected
the algorithmic recommendations on the screen and then as a group discussed removing different
recommendations. For example, one set of styles were removed because one of the buyers knew
that they were trying to reduce their work with a certain vendor. As another example, another set
of styles was swapped out because one of the buyers thought that their groups’ vision for that

season involved more of a pastel palette.
The buyers’ role relationship with the planner role. Another change emerged in the
buyers’ role relationship with the planners. Recall that the data scientists always wanted to link
the decision structuring to outcomes. They configured the assortment tool to allow the buyers to
automatically model and compare different assortment plans. The buyers could input many ways
of dividing out their buys (i.e., structuring their decisions) and easily “run” the optimization
algorithm to get different sets of recommendations. In the past, working in the spreadsheets, the
buyers tended to only have one assortment plan in mind. The planners and buyers shared
ownership of the spreadsheets, and the planners’ job was to model the buyers’ emerging
assortment plan and determine if they could make it work by moving the buys around. Within
this role relationship, and using the more static spreadsheets, it would have been extremely
complex to develop several different assortments plans and dynamically compare them.
One of the buying directors said in a cross-functional meeting, “Oh… this means we can
now compare assortments?” and an algorithms manager confirmed: “Right, now we can
compare assortments.” The tool interface was also visually appealing, and let the buyers
visualize their assortment as they were planning it. The buyers visualized the assortment on
many different dimensions using these various product categories using a dynamic pivot table
that displayed the different styles in small, medium, and large bubbles (see Figure 7). This new
data and the tool interface allowed the buyers to sort and visualize their planned inventory many
ways across many product dimensions. Thus, even though the tool was very deliberately
designed to support one workflow within the buyer “leaf nodes,” it ended up automating a lot of
the manual computation that the adjacent planner role had previously done.
----------------------Insert Figure 7 here

----------------------When we returned to AlgoCo a year after our first study period, we observed a buying
team go through an assortment planning process. They used the tool to easily and dynamically
model and curate algorithmic recommendations. They were able to save different versions of the
assortment plan and compare them. And the age segmentation was running in the background.
This functionality of dynamically modeling the assortment plan, including client segments, was
not possible in traditional spreadsheets, where the planners would have had to do extensive
manual work to calculate the full set of targets for each possible assortment. There, any changing
of units between different product categories – for example, between vendors or sizes – was
manual and laborious and it tended to be done more in trading units between styles or different
product categories. The new tool included as many product categories as were requested and the
powerfully flexible UI allowed the buyers to dynamically model many ways of dividing up their
units to model, compare, and choose among optimized assortment plans.
Reconfiguring the professionals’ organizational hierarchy
AlgoCo also worked to reconfigure the buyers’ organizational hierarchy as they came to
see the issues that were created by the way the org chart constrained and influenced the
algorithmic search space and related sets of recommendations. The buyers had structured the
“people structure” hierarchy using practical, interpretable, and fairly static structuring – e.g.,
their people structure tended to be represented in typical PDF organizational charts that did not
change very often. In contrast, the data scientists preferred flexible structuring that explicitly
predicted better outcomes. They had come to understand that the org chart as visualized in the
PDF also represented and constrained the way that the massive, centralized stores of data were
stored and structured, as well as the way that the budget (including the assigned buys, targets,

and metrics) was structured and allocated. Directors in both departments saw strategic
opportunities to separating out these the different structures and more flexibly and dynamically
modeling some of the decisions that were being constrained by the static org chart.
Work to decouple the data structure, budget structure, and org structure. Both
departments took on many related initiatives trying to figure out how to support a more flexible
and dynamic approach to structuring, especially regarding the intertwined data, budget, and
people hierarchical structures. One of the data scientists led the work of conceptualizing this
problem and of convincing other data scientists and AlgoCo leaders to work on decoupling the
separate but related functions that were all structured around the same org chart decision tree.
He gave a formal presentation focused on the data structure aspect where he explained,
There's only one data structure hierarchy, and it's currently doing three things. Focus on
two relevant things for now – “what is it” and “who bought it”. So, (gesturing to the
buying group level and related level in the data structure) this hierarchical level is
interpreted by us on algorithms as meaning something about “what is it” – “oh, it is a
women’s blouse.”
But what it really is really telling us, is actually “who bought it” – “oh this was bought by
the blouses buying group.”
His description was explaining that the data structure was in fact encoding the people structure,
rather than recording properties of the items themselves. To say it a different way, if anyone
looked in the data, they would find that all the clothing item IDs were nested in product category
IDs that mirrored the buying teams. But as described above, the segmenting out of the buying
teams was arbitrary and based on manageability and interpretability, so there was no reason to
use those hierarchical divisions to structure the data.
His proposal was to restructure the massive data store so that it was not hierarchically
segmented at all. He said in one of his presentations where he was explaining the problem to
other data science leaders, “I'm going to argue that not all of these things (the data, budget, and

people structures) are hierarchical in nature, in fact, I think only one of them is.” He argued that
it was much more consistent with data science approaches to store the data as “flat” and use
flexible “roll ups” instead of static hierarchical divisions. He explained:
So, the proposal is to build a new data model where the principles are ... that we want to
use hierarchy only for concepts that are truly hierarchical. When a hierarchy is not
unambiguous, tags are better than hierarchy.
My example here is, in old email clients there would be folders, and sometimes you could
have folders within folders and that is like a hierarchical way of grouping your emails. If
you had an email from your dad about buying a house, you would have to decide, "Does
this go in the family folder or does this go in the real estate folder?"
Then with Gmail, you just put tags on there. You don't have to make these choices about
where does it go; you just tag it with everything that's relevant.
The email example helped explain his vision for a better way of structuring their data if they
decoupled it from the way the merchandising org chart was structured. Every item would be
simply tagged with as many relevant tags as desired. And then “roll ups” could aggregate all
relevant items using tags depending on how the data were being used in that moment. In the
email “tag” example, one could easily look at every email that had been tagged “family” and
then separately look at every email that had been tagged “real estate”—the same email could
easily appear in both. In contrast, when the email had been stored in a hierarchical folder
structure, you could only see and understand the email as a family email or a real estate email.
His point was that, in following the organizational chart, the data structure was only letting
people see “women’s blouse” rather than letting that item be tagged and flexibly rolled up into
sets such as “any green item” or “anything from Vendor A” or “anything for millennial clients”.
The plan to change the data structure to a flatter and more flexible model where all items
were tagged rather than hierarchically stored was a huge undertaking, but also well-received
within both data science and merchandising. One of the buying directors explained it this way in

a meeting, “We are thinking about breaking the dependency of the data structure hierarchy and
how Merchandising organizes themselves to allow for more flexibility…” The data scientists
saw the flexibility in terms of the different analyses that could be done, and the buying directors
saw opportunities in terms of how the merchandising teams were staffed and structured.
Roll up the leaf node. The data science directors and buying directors were focused on
those strategic departmental structuring issues involving separating out the data structure from
the merch department structure. The data science team who had specifically developed the
assortment planning algorithm were also working on related issues connected to the merch
organizational hierarchy. Recall that they saw the org chart as a decision tree and considered the
structuring of the leaf nodes (the buyers’ jobs where the decisions were made) as arbitrary and as
unnecessarily constraining the search space in ways that were impacting outcomes. One phrase
that caught on referred to the idea to “roll up a leaf node” and run the optimization
recommendation algorithm there. Figure X visualizes what was meant by this idea. The original
configuration of the algorithmic tool was to produce an optimized set of recommendations at one
individual buyer’s set of buys. The individual buyers were the “leaf node” of the decision tree
and several buyers were together nested under a shared manager. “Rolling up the leaf node”
meant aggregating all the buys and targets of an entire team of buyers and running the
optimization algorithm across that level of buys (see Figure 8). This idea was the specific way of
allowing the algorithm to “explore a larger space for better results.” The data scientists
proposed “rolling up the leaf node” on the plus buying team. This proposal meant that the
manager and leaf node structure (typical org chart) would be reconfigured into a buying group
that collectively curated the whole plus assortment. The algorithmic tool would model many
Plus-wide assortments that could be compared, and the buying group would curate those group-

level recommendations for context and strategy. The Plus buying director was willing to try this
experiment and learn from the process of group-level algorithmic recommendation and grouplevel curation, which represents a significant shift from the traditional manager and leaf node
way structure.
----------------------Insert Figure 8 here
----------------------As the data scientists worked on this idea of rolling up the leaf node and recommending
and curating at that buying group level, they also started to think through and model other ways
that the decisions could be structured. As an example, the data science team kept on their team
roadmap charter the question of “planning at different levels of hierarchy” – which referred to all
of the different ways they could learn from “rolling up the leaf node”. They kept a brainstormed
list of all the ways to do this, including “Department, Class, Silhouette, etc.” One of the data
scientist’s strategic idea was to roll up decisions by client segments and organize the buyers into
groups around the client segments. She explained, “It kind of makes sense to me to have buying
groups organized around client segments” because client segments predicted variance in
outcomes.

These continuing discussions at AlgoCo about how they reconfigure the people

structure, the data structure, and the data tools is well-summarized by some educational materials
that one of the data scientists put together:
When it’s important to have the benefits of splitting finely while focusing on a small
number of relevant segments, this is a great opportunity to let humans and machines do
what they each do best.
Algorithms can be designed to segment the data to as fine a granularity as the data
supports. Then, algorithms can dynamically detect the key outlier segments that deserve
extra attention, and aggregate the other segments back into larger groups.
What gets surfaced to humans are the important findings about the forest, as well as
highlights of the handful of trees that matter right now. Such flexible segmentation

schemes enable people and algorithms to adapt together to changing data and changing
business priorities.
The data scientists recognized the value of the more bureaucratic approaches that are practical
and interpretable for people’s decision-making, and also the algorithmic approaches to dividing
and aggregating decisions and outcomes. Their aim was to flexibly balance these approaches
going forward, which as we have shown involved and will continue to produce many changes to
role structures and organizational hierarchies.

DISCUSSION
This 10-month ethnography of a retail technology company analyzes the work of retail
assortment planning before and after the development of a new algorithmic tool. The
comparison reveals why data scientists’ work to develop algorithmic tools is producing
significant changes to professionals’ roles, role structures, and organizational hierarchies, and
with what implications. Our data illustrate that the professionals’ original structuring approach
involved bureaucratic decision structuring to coordinate the collective decision-making of a
200-person department. This approach, which involved structuring a profession’s organizational
chart as a largely static “decision tree”, was in tension with the algorithmic decision structuring
enabled by the new algorithmic tool, which involved dynamically measuring the impact of
decisions on outcomes. To resolve these tensions to effectively use of the algorithmic tool, the
organization reconfigured roles, role structures, and hierarchies –significant structural
organizational changes consistent with theories of major technological change (Barley, 1990;
Barley, 2015; Barley, 2020).
Algorithmic disruption of bureaucratic decision structuring
Our society is on the brink of a myriad of different so-called technological “revolutions”.

From the Big Data Revolution (e.g., Kitchin, 2014) to the Fourth Industrial Revolution (e.g.,
Schwab, 2017) to the Control Revolution (e.g., Barley, 2020), technology is changing society in
fundamental ways. Yet new, transformative technology is not sufficient for spurring a revolution.
In order for a technological revolution to truly be a revolution, it must involve a fundamental
change in the technological infrastructure of a society (Barley, 2020). Algorithms constitute such
an infrastructural change. As they are increasingly incorporated into organizational routines,
algorithms are influencing authority (Pasquale, 2015), power dynamics (Kellogg, Valentine, &
Christin, 2019) as well as decision making (Shrestha, Ben-Menahem, and von Krogh, 2019). It is
critical for scholars and practitioners to understand how this algorithmic revolution is
fundamentally changing the bureaucratic organizations that are at the core of modern society.
Nearly a half-century ago, Simon (1973: 272) asserted, “[t]o design effective decisionmaking organizations...we must understand the decision-making tools at our disposal, both
human and mechanical”. More than ever before, the decision-making tools at our disposal are in
tension. It is critical to understand the nature of these tensions and how they might be resolved in
order to design effective organizations. Whereas bureaucratic decision-making, as depicted on
organizational charts, has long been considered rational (Weber, 1947), algorithms are
“supercarriers of formal rationality” and offer “augmented rationality” (Cohen, 2007: 504;
Lindenbaum, Vesa, and Den Hond, 2020: 250-259; Glaser, Valadao, and Hannigan, 2020). In
this study, we show how the tension between the two forms of rationality—in the case of
bureaucratic decision making, rationality based on human interpretability, and in the case of
algorithmic decision making, ‘advanced’ rationality that is unconstrained by human
interpretability—offers an as yet undertheorized reason why algorithms are bringing about large
scale infrastructural change.

This study, thus, contributes to research on bureaucracy and organizational design. A key
premise of bureaucratic decision-making is the belief that expertise is critical (Kweit and Kweit,
1980). Bureaucracies are structured to guide and “factor” decisions to effectively leverage local
expertise. Jobs are nested in roles, which are “systems of prescribed decision premises'' (Simon,
1991), that are constrained by rules and conceptions of controls. We show how, by facilitating
top-down decision-making, bureaucratic decision structuring is fundamentally at odds with
algorithmic decision structuring. Thus, contrary to the assumptions inherent in prior work (e.g.,
MacKenzie, 2014; Christin, 2018; Brayne and Christin, 2020), we illustrate how bureaucratic
structuring may hinder the effective structuration of expertise in bureaucratic organizations.
Contributions to theories of the structuring of professional expertise
This study also contributes to research on professions, addressing recent calls for a
reevaluation of the professions literature to account for the changing nature of work (e.g., Anteby
et al., 2016). Although scholars initially characterized “the profession” and “the bureaucracy” as
“antithetical” (Freidson, 1984: 10), they have since acknowledged that they are, in fact,
compatible. This compatibility involves professionals’ roles being nested in managerial
hierarchies through vertical integration and bound by the controls that distribute decision-making
responsibility. As professional expertise is being increasingly structured in bureaucracies
(Huising, 2015; Anteby, Chan, and DiBenigno, 2016), we have reached a critical moment in time
wherein organizations are moving towards automating bureaucratic structures and controls. We
show why and how the bureaucratic structuring of professional expertise is likely to drive
structural changes as algorithmic decision structures are increasingly implemented in
organizations.
To date, research has assumed that the expansion of professional expertise over particular

tasks and decisions occurs through jurisdictional claims, often against other occupations, that are
typically competitive and adversarial in nature. Our research addresses a yet unanswered key
question related to the professions: “How do occupational members actually coordinate with a
network of relations to collectively expand their scope of expertise?” (Anteby, Chan, and
DiBenigno, 2016: 220). We show how algorithms can redefine role relations and encourage
cooperative interaction between professional groups that have traditionally been separated in
bureaucratic organizations by jurisdictional claims. By occasioning new divisions of labor, role
relations, and ways of decision making, we show how algorithms can change the ways in which
professions are structured in organizations and expand professionals’ scope of expertise, such as
buyers and planners newly working together to expand their expertise in creating a highperforming inventory that accounts for non-practical and non-human-interpretable categories.
Third, we contribute to research on algorithms and professions. While organizations still
bear the imprints of prior conceptions of control, especially financial controls and related
metrics, they are being increasingly dominated by quantification and algorithms as metrics of
control (Fligstein, 1991; Espaland and Stevens, 2008; Kellogg, Valentine, and Christin, 2020). A
rich recent body of work has shown that quantification and algorithms change the nature of
professional expertise and, in particular, professionals’ jurisdictions and areas of discretion (e.g.,
Christin, 2017). As has been the case throughout the rise of bureaucratic structures, we show
how algorithms as new conceptions of control are not only changing the nature of professional
expertise, but also how professionals are structured in organizations.
We also illuminate why, in addition to understanding the “black box” or opaqueness that
is characteristic of algorithms (Faraj, Pachidi, and Sayegh, 2008), it is also critical for scholars
and practitioners alike to understand the “black box” of bureaucratic decision structuring—the

often taken-for-granted structures that are fundamental to bureaucratic structures. By gaining a
heightened understanding of how bureaucratic organizations structure decisions, practitioners
can better appreciate how to increase the adoption of algorithmic tools to resolve key tensions,
while scholars can adopt a new lens through which to study the implementation of algorithms.
While algorithmic technologies may be occasions for structuring (Barley, xx), that structuring
also depends on the bureaucratic structures that they may be aiming to displace.
Contributions to theories of organizational design
We also contribute to research on organizational design within the Carnegie School
tradition. Within this classic body of work, the information processing point of view holds that
effective organization design involves a division of labor that is able to factor the total system of
decisions that need to be made into relatively independent subsystems that “can be designed with
only minimal concern for its interactions with the others” (Simon, 1976). As Simon (1957)
explained, the division of labor in organizations has been necessary because humans’ processing
power and attention are limited. We show how algorithms may fundamentally change what we
consider to be ideal organizational forms as algorithmic technology enhances humans’
processing power and supports ‘arbitrarily many’ decisions.
Building on Simon’s (1962) assertion that complex organizations perform better when
they have a hierarchy and “near decomposable structures,” a rich body of work has investigated
modular organizational designs, often in the context of M-form organizations (e.g., Galunic and
Eisenhardt, 2001). Yet, with modular systems, there is a tradeoff between the breadth of search
and the speed of search (Ethiraj and Levinthal, 2004). While prior research has shown that
organizations leverage modular forms to realign organizational structures to an evolving mix of
product markets by competing for resources and charters, we show how organizations leveraging

algorithmic decision structuring may fundamentally change role structures to enable the
collaborative pursuit of new charters.
This study offers a contrast to prior work that has shown how algorithms enable new
ways of coordination by managing task decomposition, sometimes as part of a divide-and-assign
strategy (Zammuto et al., 2007; Faraj, Jarvenpaa, & Majchrzak, 2011; Boudreau & Lakhani,
2013). Our research shows how, while “organizing no longer needs to take place around
hierarchy” (Zammuto et al., 2007: 749), it, in large number, still does, with the implication that
the impact of bureaucratic structuring on algorithmic structuring ought to be considered.
“Moneyball” for the organizational chart
As we conclude the current paper, we note a connection to a well-known example of data
analytics changing the way that decisions were made, as popularized in the book-turned-movie,
Moneyball, by Michael Lewis. We came to refer to this paper as “Moneyball for the org chart”
and found that metaphor informative, so wanted to draw that connection here. Lewis (2004)
traces the introduction of “sabermetrics” to baseball, which involved the use of statistical
methods to evaluate player performance. Prior to the use of sabermetrics—originally defined as
“the search for objective knowledge about baseball” (Hirsch & Hirsch, 2014), baseball scouting
was based on methods of intuitive categorization of players. Scouts were instructed to seek out
players based on several categories of capabilities—for example, strength or a short stride—that
intuitively reflected the characteristics of a great baseball player, especially his power. Scouts
used a 2-to-8 scale to quantify the value of a player, with 5 representing the average skill of a
major-league player. With the use of sabermetrics, the methods of categorization and
commensuration changed. Lewis recounts how Billy Beane, the General Manager of the Oakland
A’s, started to develop new metrics such as on-base percentage (OBP) and the slugging

percentage that did not emphasize the physical power of an athlete and did not correspond to
human interpretable categories. As well, with Moneyball, staffers began to structure roles
differently. They no longer tried to pick the best individual players, but instead, tried to seek the
best aggregation of players for the money by determining which players contributed more to
winning than their salaries suggested they could. Thus, they started to select for wins, rather than
individual player performance. An analogy can be drawn to the current study and our findings
that show how new analytical techniques fundamentally transformed how role relations and
hierarchies were structured at AlgoCo, enabling its members to dynamically make decisions
based on outcomes, rather than human-interpretable roles as structured by the organizational
“roster”, otherwise known as the org chart.

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Table 1. Comparison of Bureaucratic and Algorithmic Decisions Structures

Number of decision
structures in use
Criteria for
deciding between
decision structures
Relevant
technologies

Models of change

Bureaucratic
Decision Structures
Typically one at a time

Algorithmic
Decision Structures
“Arbitrarily many”

Interpretability
Feasibility

Predicts variance in data
Optimized outcomes

● PowerPoints and PDFs
with images of
organizational charts
● Excel spreadsheets with
Macros

● Dynamic user interface that
allows for modeling and
curation
● Backend that integrates
many data and algorithmic
systems (e.g., Figure 1)

Engage in traditional change
management:
● update all PowerPoints
and spreadsheets
● change reporting
relationships and
groupings of individuals

Local users learn to use dynamic
decision structures:
● users learn to run different
scenarios,
● data scientists learn to
display different
information
Organization learns to
accommodate disrupted
bureaucratic decision structure
(Barley, 2020)
● reimagine roles
● reimagine hierarchy
● reimagine accountability

Table 2. Reconfigured Roles, Role Structures, and Professional Hierarchies

Buyers’ Role

Original

Reconfigured

Using bureaucratic decision
structuring
Buyers use non-standard and practical
product categories to guide decision
making.

To accommodate algorithmic
decision structuring
Buyers visualize impact of nonstandard and practical product
categories using tool that
accommodates various constraints.

Buyers’ intuition and impact of
decisions on outcomes is not
explicitly measured.
Buyers’ role involves being an artist
with intuition for upcoming trends.

BuyerPlanner
Role
Structure

Buyers ‘hand off’ vision for wellperforming inventory to planners.
Planners roles’ involves carrying out
buyers’ visions and making numbers
work.
Clear division of labor between
buyers and planners.

Buyer
Professional
Hierarchy

BuyerPlanner
Hierarchy

Buyers’ intuition and impact of their
decisions on outcomes become
recorded, measured, and analyzed
over time.
Buyers’ role involves becoming a
curator of algorithmicallyrecommended assortment, curating
for context and strategy.
Buyers begin to dynamically compare
multiple assortment plans and, in turn,
make decisions that previously
involved planners’ manual
computation.
More flexible division of labor
between buyers and planners.

Hierarchy is structured using
practical, interpretable, and static
categories.

Manager and leaf node structure
reconfigured into a buying group that
collectively curates entire assortment.

Buyers’ jobs are “leaf nodes” where
decisions are made, with clear and
codified managerial hierarchy.

New flexible hierarchies are
considered that enable buyers and
algorithms to adapt together.

Planning director sets top-down
allocation of buys and targets and
“sends them down” the static
structured org chart.

Planning directors look to redefine
their expertise in setting department
wide strategies.
New opportunities are identified in
terms of how buyer-planner teams are
staffed and structured.

Figure 1. Mockup of Data Scientists’ Explainer of their ‘Algo’ in the Larger System

Figure 2. Mockup of Assortment Plan: Styles to Develop and Stock to Sell to Clients

Figure 2. Professionals’ Organizational Chart (stylized) with non-standard categories

Figure 3. How Targets were Structured by Org Chart: Department to Buying Groups

Figure 4 (con’t). Buying Groups to Buyers

Figure 5. Example of Spreadsheets for a Buyer-Planner Pair

Buyers and planners would be given a set of buys that they would distribute across these spreadsheet
cells to produce a diverse and well-performing inventory.

Figure 6. Including Client Segments for a Buyer-Planner Pair

Including insights on client purchasing patterns in this process, when using the spreadsheets, meant
multiplying the amount of work to calculate the predicted performance of the assortment

Figure 7. Mock-up of Algorithmic Tool User Interface

Figure 8. “Roll up the leaf node”
</reference>

<statements>
1. In white-collar administrative environments, AI technologies transform the sociology of managerial control.
2. Kellogg, Valentine, and Christin examine how algorithmic systems construct a new contested terrain of control, in which digital platforms automate the classical supervisory triad of direction, evaluation, and discipline.
3. In professional domains ranging from clinical nursing to corporate accounting, these automated affordances circumscribe human discretion, standardizing workflows and eroding traditional professional autonomy.
4. Finally, the expansion of algorithmic management and automated screening systems requires modernized regulatory architectures to protect worker agency and prevent systemic discrimination.
5. Furthermore, labor laws must evolve to incorporate algorithmic governance into collective bargaining frameworks, ensuring that frontline workers retain codetermination rights regarding how automated tracking, predictive evaluations, and automated task-allocation systems are implemented across the enterprise.
6. Concurrently, predictive scoring architectures introduce structural disparities in credit markets, while algorithmic platforms automate workplace management and candidate screening.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.