Process Knowledge Management, Part III
How We Lost Our Way
Introduction
In the first two essays of this series, we established that process knowledge—the understanding of process knowledge management, workflows and procedures—represents a distinct and critical form of organizational intelligence. We explored how to collect this knowledge from practitioners, organize it into coherent structures, and encode it for both human and computational consumption. Throughout, we emphasized that process knowledge exists across multiple registers: tacit and explicit, operational and strategic, embodied in both human expertise and formal representations.
But there is a darker chapter in this story that must be told, and merits a retrospective, in order to understand the domain of process and procedural knowledge. For the past four decades, American and Western companies have systematically outsourced not just manufacturing work, but the entire socio-technical ecosystem that generates, maintains, and transmits process knowledge. What began as a rational economic decision to reduce costs became a wholesale abandonment of the cultural and institutional practices that make process knowledge legible, valuable, and actionable. We outsourced what we dismissively called “the boring stuff”—the manufacturing, the execution, the grunt work—without understanding that we were outsourcing the very capacity to understand how things get built.
This essay examines this history of outsourcing through the lens of process knowledge management. It argues that what was lost was not simply jobs or manufacturing capacity, but something more fundamental: the socio-technical ethos that treated documentation, apprenticeship, and the systematic capture of procedural knowledge as integral to the work of building things, not as an afterthought or administrative burden.
When we sent manufacturing to China, India, and the Philippines, we divested from the opportunity to learn, to iterate, to fail and improve. We eliminated critical feedback loops, a requisite for capturing and documenting procedural knowledge. We dissolved communities of practice that essential sources for process knowledge. And crucially, thanks to gapping holes in the end-to-end process knowledge fabric, we stopped investing in the knowledge infrastructure required to capture and maintain our understanding of how complex systems actually work.
The Great Unbundling
The story begins in the 1880s and accelerates through the 1990s and 2000s with what business strategists celebrated this as “disaggregation” and “core competency focus.” Companies would concentrate on their “core” activities—typically defined as customer-facing brand management, product design, and strategic decision making—while outsourcing everything else. Manufacturing was among the first to go, particularly in electronics, textiles, and eventually more sophisticated dry and wet goods.
But the outsourcing movement didn’t stop at physical manufacturing. By the early 2000s, a new category emerged: Knowledge Process Outsourcing (KPO). Unlike traditional Business Process Outsourcing (BPO), which focused on routine transactional work like call centers and data entry, KPO involved outsourcing knowledge-intensive activities that required specialized expertise and analytical skills1. Legal research, financial analysis, market research, engineering design, pharmaceutical R&D, the very activities that generated and required deep process knowledge, were increasingly sent offshore to providers in India, the Philippines, and China.
The logic made sense. Why maintain expensive in-house capabilities when you could access global talent pools at a fraction of the cost? Why invest in training and developing institutional memory when specialized KPO firms could provide on-demand expertise? The KPO industry exploded. By 2006, India’s KPO sector alone was estimated at $1.5 billion, growing to over $12 billion by 2015.2 The Philippines positioned itself as a hub for “non-voice” back office services. China became the world’s factory, but increasingly also its laboratory for manufacturing process innovation.
What went largely unexamined was what happened to process knowledge when these activities migrated. The assumption was that process knowledge could be cleanly separated from execution—that “knowing how” could remain in Western headquarters while “doing what” happened elsewhere. This assumption proved catastrophically wrong. Look no further than current struggles in developing knowledge infrastructures in technology organizations and massive failures of agentic AI systems. (see my series, “Why AI Isn’t Autonomous (Yet)”).
Shenzhen and Process Knowledge
To understand what was lost, we must first understand what was gained. Dan Wang’s Breakneck provides the most compelling account of how China, and Shenzhen in particular, transformed manufacturing offshoring into a comprehensive accumulation of process knowledge.3 I adore Wang’s book so much, I have read it twice, and highly recommend.
Shenzhen first become a place where Western designed products were assembled. The city evolved into what Wang calls a “community of engineering practice” where tacit knowledge about how to actually build complex electronics circulates through dense networks of workers, engineers, entrepreneurs, and suppliers. Someone might work at an iPhone plant one year, move to a rival phone maker the next, and then start their own drone company, due to a rich socio-technical ethos invested in process knowledge.4 This creates a positive feedback loop of process knowledge accumulation, synthesis and codification. Ultimately, process and procedural knowledge entrenches embodied understandings of what works, what fails, how to troubleshoot, how to improvise, how to improve and optimize.
This is process knowledge in its richest form: a living ecosystem where knowledge moves through human relationships, apprenticeships, formal and informal collaborations, and the constant iteration between design and manufacturing. And this ecosystem extends across physical and digital worlds as that is the very essence of knowledge, a real manifestation of human-in-the-loop (HITL).
When a Shenzhen engineer encounters a problem, they have immediate access to a community that has solved similar problems. They can walk down the street and consult with specialists in adhesives, specialists in precision machinery, specialists in quality control. The knowledge exists both in documented procedures and in the practiced hands and pattern-recognition wisdom of experienced workers.
In 1890, economist Alfred Marshall wrote about the social and economic consequences of outsourcing skills and processes in his 1890, in his book Principles of Economics, stating, “When an industry has thus chosen a locality for itself, it is likely to stay there long: so great are the advantages which people following the same skilled trade get from near neighbourhood to one another. The mysteries of the trade become no mysteries; but are as it were in the air, and children learn many of them unconsciously.” 5 Indeed, skills, trades and knowledge are entangled, becoming deeply embedded in human identities and community identities, and therefore part of a societies brain trust of knowledge.
Shenzhen represents Marshall’s industrial skills and trades hypotheses at unprecedented scales. China now employs over 100 million people in manufacturing—eight times the numbers employed United States.6 These imbalances are significant, as aside from the obvious, such as labor capacity for skilled trades related to manufacturing, the process knowledge associated with how to build and maintain outsourced systems resides in the heads and hands of communities of people and plants and systems responsible for manufacturing.
Because many of the outsourced physical components and digital systems tend to be related to each other, entire networks of process knowledge are concentrated within communities such as Shenzhen. When you have that many people solving related problems, process knowledge compounds because most innovation is synthetic hybridization, not mutational deviation. Improvements propagate quickly. New capabilities emerge from unexpected combinations. With process and procedural knowledge baked into the ethos of a society, standardization of procedures support progress, and from these roots, innovation accelerates, built on the backs of reliable, repeatable procedures and workflows.
From Engineering State to Lawyerly Society
What happened in the United States during this same period? Wang argues that America transformed from an “engineering state” to a “lawyerly society”—a shift with profound implications for process knowledge management.7 Follow along, as I lay out how this came to be.
In an engineering state, the cultural and institutional focus is on building, optimizing, and documenting how things work. Engineers value process knowledge because they understand that it’s the substrate for continuous improvement. The ethos becomes iterative and standardized: build something, see how it fails, document the failure, redesign, build again. Process knowledge is valued culturally because the people doing the work understand that today’s insights become tomorrow’s foundation.
But starting in the 1960s, America’s elite shifted. Legal expertise became ascendant. Five out of the last ten U.S. presidents attended law school. At least half of Congress holds law degrees, while barely a handful studied science or engineering.8 The priorities of this lawyerly society turned toward litigation, regulation, and oversight—legitimate concerns, but ones that treat process knowledge very differently than an engineering culture does.
Lawyers are trained to manage risk and are not primarily trained to optimize processes, safe for legal libraries. They often create policies to limit document retention, to create legal defensibility, and are not primarily concerned with knowledge accumulation or transfer. In fact, their usual tendency is to avoid both. The kind of knowledge they value is precedent-based and tends towards the adversarial.
When lawyers run organizations, the impulse is to standardize procedures for compliance and risk-mitigation purposes, such as not retaining records of technology and process invention, for fear of disclosure in intellectual property disputes. However, capturing the rich tacit knowledge is what makes procedures actually work in practice. We often see this reflected in Governance programs, where the primary focus is on compliance, not things like data quality, information and knowledge management.
This cultural shift coincided with—and likely accelerated—the outsourcing wave. When American companies decided what to keep and what to send offshore, they kept the functions that their leadership understood and valued: engineering design, legal, financial structuring, brand management, strategic planning. The surface stuff and things that matter for go-to-market strategies. C-suite execs and investors sent away the “execution” or “boring stuff”, not recognizing that execution is where process knowledge lives.
