As machines and social networks turn expertise into a commodity, knowledge work migrates from special Roles to nearly everywhere at work. Does this mean that everyone gets smarter, or does it just mean that organizations will be herding even more cats?

1. What’s Next for
Knowledge Workers
©2013 Malcolm Ryder / archestra
As you might expect, a good place to go look for a consensus definition of knowledge worker is
Wikipedia, the world's largest common ground for uncommon knowledge.
As cited there, Peter Drucker coined the term knowledge worker over forty years ago, referring to one who
works primarily with information or one who develops and uses knowledge in the workplace. The easy
idea that knowledge is developed from information makes its inventory of information tasks a safe point
of reference. However, even the provided evolving chart of knowledge worker roles blurs the distinction
between information processing and knowledge processing.
Here, "processing" means to make, find, collect, analyze, organize, provide, maintain and track.
Meanwhile, we know that information and knowledge are not the same thing. For actual knowledge
work, the essential difference between them must be something that is also created, supported, and
controlled in use.
The essential difference -- namely context -- is arguably what "knowledge work" is most distinctively
working on. High-value knowledge work makes the difference between recognizing and applying
contexts, or not doing so, in a managed way. Contexts are transformative of information, and contexts
are antecedents of knowledge.
In other words, knowledge workers perform transformations and create antecedents that result in the
availability of needed knowledge for operations. Thereafter, the managed use of available knowledge
extends the work.
All "processing" treats its subject as a material resource, and through processing, resources can become
commodities. Today, the combination of high-powered analytics by machines, with social networking,
means that vast amounts of information can be contextualized at extremely high speeds, effectively
making knowledge a commodity.
An industrial equivalent to this is found in the difference between mass transit and personal
automobiles. We might decide that neither mode is more capable of moving people than the other; but
the significant difference is not between commodity and non-commodity transportation, anyway. At a
given level of consumer cost, each mode commoditizes transportation. Instead, the significant
difference is between inconvenience and convenience. Industrialization of transportation commoditizes
convenience instead of leaving convenience as a luxury.

2. Machines and social networks are generating huge quantities of convenient knowledge, which mean
that as far as most knowledge consumers are concerned, the bulk of the labor value of knowledge
workers is necessarily now moving from knowledge origination to a different aspect of their labor:
selectivity.
Knowledge Labors
Knowledge
Information
Acquisition
Classification
Validation
Distribution
Sourcing
Transformations
Selections
Communication
Experts
(credentials)
Analysts
(methods)
Managers
(authorities)
Publishers
(reputations)
All work is labor. Typically, across the lifecycle of knowledge provision, the division of knowledge labor
has aimed for the efficient creation of knowledge that could be validated before distribution.
Heretofore, both efficiency and validation have relied on expertise. Expertise was relatively scarce (and
therefore a high-value premium), whereas communications technologies had far earlier made
distribution itself into a vehicle allowing prepared content to be abundant.
Now, things are different. Expertise itself is usually a major intersection of investigation (affecting
efficiency) and credibility (affecting validation). Importantly, both of those factors are subject to the
constraints or supports of cultural and practical influences -- such as cost allowances, politics, and
schedules. These factors could make expertise relatively (and sometimes greatly) inconvenient for some
organizations, while more convenient for others.
Because automation and social networks have each now radically improved the cost, transparency, and
speed of knowledge processing, the development of knowledge is becoming very broadly convenient,
and convenient expertise is becoming commoditized.
Described in traditional terms of organizational analysis, the quantum leaps in the convenience of
expertise translates knowledge work from a "vertical" function managed as a discrete operation, to a
"horizontal" function included as a competency within and across most goal-oriented procedures. The
customary terms for this – “decentralization” or “democratization” -- reflect new consensus
expectations. Those expectations may not be the default for the majority of knowledge work
implementations but they are increasingly the default for demand of access to knowledge.
An organization-level division of labor is always an exercise in specializations based primarily on three
things: labor efficiency, ownership of resource, or accountability of results. Any one of these factors may

3. be dominant singly, but generally they are all three combined. For example, efficiency in knowledge
work has been predicated on expertise; and investment in experts has been predicated on operational
outcomes encouraging sponsors. The combination supports “expertise” as a Role, by which sponsors get
a high level of accountability for outcomes.
But going forward, the organizational value of knowledge work is not going to be anchored so much in
the cultivation of superior distinct knowledge workers. Instead, it will be anchored in optimally
leveraging today’s tremendously expanded access to knowledge assets. The challenge is to define what
"leveraging" means.
Most general surveys of the concerns of business operations have been saying for years that the pace of
change in the operating environment is the dominant knowledge-related issue confronting competitive
organizations. Understandably, few information projects are now as important as the effort to predict
change early. Impact analysis of actual work is either equal to or right behind change prediction in
importance; but ongoing frequent change makes the focus of impact analysis far less about "quality of
execution" and instead about "relevance".
The business goal for using knowledge is to achieve sustained relevance in a constantly changing
environment of demand-related opportunities and risks. This requires two high-level efforts that
provide the necessary awareness of the environment and the logic for action. One is Business
Research, and the other is Business Development.
The outcome of "successful" Business R&D is timely alignment with environmental conditions, with
minimal confusion from the structural complexity of the environment and organization itself.
In that light:

Business Research consists primarily of mapping and monitoring the environment.

Business Development consists primarily of prioritizing and integrating production.
Knowledge–based
Function
Competencies
Items to Understand
RESEARCH
of the operating
environment
Mapping
logical entities, boundaries and locations
Monitoring
events, states, changes
DEVELOPMENT
of production
Prioritizing
policy, requirements, scope
Integrating
inventions (models, designs), transactions (agents, brokers)

4. This perspective clarifies an agenda for pursuing and processing knowledge, guiding the selection and
prioritization of key types of items to understand. Each different business division or operation must
identify the specific knowledge items related to its own conduct of competencies; but all the divisions
and operations will have the same purposefulness of knowledge effort in common. Through cooperating
with each other in the “business R&D” effort, organizations will collectively expose unsuspected patterns
of activity; operators will forecast impending opportunities and risks; managers will navigate trade-offs
of continuity and course corrections; and teams and partners will collaborate on creating paths to
market for new solutions. Those familiar objectives will model the shared perspective used to decide
what knowledge to obtain and apply for finding, following and meeting demand.