Outsourcing and Process Knowledge Loss
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description: What Foundation Capital calls a context graph is, in the disciplinary vocabulary of information science, a procedural knowledge graph grounded in formal ontologies, with provenance, temporal validity, entity resolution and controlled vocabularies underneath. We already have the methods. PROV-O models provenance. SKOS handles vocabulary control. OWL provides the reasoning structures. The toolkit predates the marketing
is part of: The Context Gap
text: What Foundation Capital calls a context graph is, in the disciplinary vocabulary of information science, a procedural knowledge graph grounded in formal ontologies, with provenance, temporal validity, entity resolution and controlled vocabularies underneath. We already have the methods. PROV-O models provenance. SKOS handles vocabulary control. OWL provides the reasoning structures. The toolkit predates the marketing. What is missing is not tooling but the recognition that this is a knowledge management problem, requiring knowledge engineers, information architects, ontologists and the cultural conditions that allow their work to be valued and sustained as part of the operational infrastructure. The Foundation Capital thesis is therefore best read not as a revelation but as a confession. The market is admitting, in the language it understands, that the operational knowledge required to run modern enterprises has gone missing. The 'context' being chased is the work that was sent away and outsourced in favor of products, features, solutions and marketing magic. The graph being built is an attempt to reconstruct, from telemetry and decision traces, what should have been captured natively through documentation, apprenticeship and disciplined knowledge engineering - and would have been, in an engineering state that had not transformed itself into a lawyerly society. That the industry needed a venture capital essay to name this problem tells us how far the disciplinary erosion has gone. 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. Why every organization is scrambling to wrap their arms around process knowledge, now known as context graphs, with the objective of recording, documenting and eliciting meaning from decision traces and execution traces. For the past four decades, American and Western companies have systematically outsourced not just manufacturing work…

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ThoughtSpot
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The Context Gap
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about: Context gap
about: Decision trace
about: Process knowledge
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about: Institutional memory
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about: Community of practice
about: Outsourcing
about: Knowledge Process Outsourcing
about: Business Process Outsourcing
about: Process mining
about: PROV-O
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about: OWL
about: EU AI Act
about: Context engineering
about: Agentic AI systems
about: Semantic Web
about: Linked Data
about: WebDAV
about: Virtuoso WebDAV publication
about: Risk mitigation
about: Multidimensional view
abstract: A critique of the enterprise AI context graph thesis as a rediscovery of process knowledge, procedural knowledge, institutional memory, and knowledge management disciplines weakened by outsourcing.
article body: A New Name for an Old Wound In December 2025, Foundation Capital published an essay titled 'AI's Trillion-Dollar Opportunity: Context Graphs,' which has since become one of the most-discussed pieces in enterprise AI. The thesis holds that agents are hitting a wall that governance alone cannot solve, and the wall is not missing data - it is missing decision traces. Foundation Capital proposes the 'context graph' as the answer, characterized as a living record of decision traces stitched across entities and time, where precedent becomes searchable. Within a month, Dharmesh Shah of HubSpot was calling context graphs 'a system of record for decisions, not just data,' Aaron Levie of Box declared we had entered 'the era of context,' and Arvind Jain of Glean wrote that the concept 'finally has a name.' In other words, a rebranding of a discipline lost. Allow me a moment of professional indignation. The thing being so deftly branded is process knowledge, procedural knowledge and institutional memory, core tenants of library and information science and the knowledge management. And the organization of information and knowledge is exactly what American and Western companies systematically dismantled over decades of outsourcing. 2026 is the year venture capital rebranded the absence of knowledge management as a trillion-dollar opportunity. I want to be careful here as the Foundation Capital authors are not wrong about the problem. They have correctly diagnosed that enterprise AI agents fail in the gap between what happened and why it was allowed to happen - the gap between systems of record and the reasoning that connects inputs to outputs. They are also correct that 'the context that justified [a decision] isn't preserved,' that 'you can't replay the state of the world at decision time,' and that without that capacity, agents inherit their parent systems' blind spots. This is true. It is also exactly what knowledge engineers, ontologists and information architects have been s…
author: Jessica Talisman
citation: AI's Trillion-Dollar Opportunity: Context Graphs
citation: Context Graphs, One Month In
citation: Context Data Platform
citation: Process Knowledge Management
citation: Why AI Isn't Autonomous Yet
citation: The Context Gap on Intentional Arrangement
citation: Breakneck: China's Quest to Engineer the Future
citation: Goliath's Curse: The History and Future of Societal Collapse
citation: Knowledge Process Outsourcing
citation: Business Process Outsourcing
citation: PROV-O: The PROV Ontology
citation: SKOS Simple Knowledge Organization System Reference
citation: OWL 2 Web Ontology Language Document Overview
comment: Kingsley Uyi Idehen comment
comment: Charles Ivie comment
comment: Jessica Talisman comment
comment: Fred Lardaro comment
comment: Andrei Bâcu comment
comment: Andreas Schurch comment
comment: Andi Willmott comment
comment: Shashi Bhatnagar comment
comment count: 10
date modified: 2026-05-19T18:30:55+00:00
date published: 2026-05-19T18:30:41+00:00
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AI's Trillion-Dollar Opportunity: Context Graphs
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The Knowledge Engineer
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a2
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China
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A New Name for an Old Wound
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is part of: The Context Gap
text: A New Name for an Old Wound In December 2025, Foundation Capital published an essay titled 'AI's Trillion-Dollar Opportunity: Context Graphs,' which has since become one of the most-discussed pieces in enterprise AI. The thesis holds that agents are hitting a wall that governance alone cannot solve, and the wall is not missing data - it is missing decision traces. Foundation Capital proposes the 'context graph' as the answer, characterized as a living record of decision traces stitched across entities and time, where precedent becomes searchable. Within a month, Dharmesh Shah of HubSpot was calling context graphs 'a system of record for decisions, not just data,' Aaron Levie of Box declared we had entered 'the era of context,' and Arvind Jain of Glean wrote that the concept 'finally has a name.' In other words, a rebranding of a discipline lost. Allow me a moment of professional indignation. The thing being so deftly branded is process knowledge, procedural knowledge and institutional memory, core tenants of library and information science and the knowledge management. And the organization of information and knowledge is exactly what American and Western companies systematically dismantled over decades of outsourcing. 2026 is the year venture capital rebranded the absence of knowledge management as a trillion-dollar opportunity. I want to be careful here as the Foundation Capital authors are not wrong about the problem. They have correctly diagnosed that enterprise AI agents fail in the gap between what happened and why it was allowed to happen - the gap between systems of record and the reasoning that connects inputs to outputs. They are also correct that 'the context that justified [a decision] isn't preserved,' that 'you can't replay the state of the world at decision time,' and that without that capacity, agents inherit their parent systems' blind spots. This is true. It is also exactly what knowledge engineers, ontologists and information architects have been s…

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a10
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text: The thread asks whether companies can rebuild the communities of practice behind the graph or will keep trying to buy the artifact without restoring the discipline, while also showing how practitioners can publish and inspect graph artifacts directly.

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The Great Unbundling
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description: When we sent manufacturing to China, India and the Philippines, we divested from the opportunity to learn, iterate, 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 invest
is part of: The Context Gap
text: When we sent manufacturing to China, India and the Philippines, we divested from the opportunity to learn, iterate, 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, …

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text: This is brilliant articulation of risks we face - and what to do to influence the mitigation. A very multidimensional view can be formed from the same article.

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text: Very astute observations Jessica Talisman. It reminds me of so many decisions that are made by every government. Quick decisions are made with unknown consequences yet to be realised. Often resulting in the cure being worse than the original pathogen. We are, as a species, so destined to find a quick fix, only to realise it cases a slow decline.

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Link comments and institutional feedback
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text: What is lost is found again - just repackaged. Jessica Talisman, really enjoyed your observations linking the past days to today - hugely astute.

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Andreas Schurch comment
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text: Jessica, love this! The phrase "context graph" is useful, but your point that the market is rediscovering process knowledge, ontology, and institutional memory is spot on. Curious how others here are thinking about the cultural side of this: can companies rebuild the communities of practice behind the graph, or will they keep trying to buy the artifact without restoring the discipline? We have seen this movie before.

Jessica Talisman comment
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parent item: Fred Lardaro comment
text: Fred knowledge and wisdom!

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a9
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Kingsley Uyi Idehen comment
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text: Jessica, here is a knowledge graph, deployed as a Semantic Web, constructed from notes generated by my AI agent from this post. Publication for Web access and upload to the underlying Virtuoso-hosted knowledge graph is achieved by mounting a Virtuoso WebDAV folder to the local operating system, copying local files into the mounted folder, and letting Linked Data principles provide Web-scale data access by reference. Links include https://linkeddata.uriburner.com/DAV/demos/daas/context-gap-jessica-talisman-gpt5-1.html and hashtags #AgenticWeb #ContextGraph #KnowledgeGraphs #LinkedData #SemanticWeb #HowTo #UseCase.

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Context Graphs, One Month In
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about: PROV-O
author: W3C
description: W3C provenance ontology named in the article as a method for modeling provenance.
is part of: Reference corpus for The Context Gap

Tony Baer
type: Person
description: Co-host associated with the It is About Data media reference.

Codex desktop workspace
type: SoftwareApplication
description: Generation environment used to create and validate this RDF-backed bundle.

The Context Gap RDF Turtle companion
type: DataDownload
encoding format: text/turtle
is part of: The Context Gap RDF/HTML/Markdown/JSON-LD bundle

Jessica Talisman comment
type: Comment
about: Context gap
about: Process knowledge
author: Jessica Talisman
date created: 3h
is part of: Visible LinkedIn comment thread
parent item: Charles Ivie comment
text: Charles outsource or automate without much in between. Currently reading Goliath's Curse by Luke Kemp which I think you'd appreciate

Fred Lardaro comment
type: Comment
about: Context gap
about: Process knowledge
author: Fred Lardaro
comment: Jessica Talisman comment
date created: 58m
is part of: Visible LinkedIn comment thread
text: Jessica Talisman!!! Everything old is new again. If all our experts disappeared... would our decision making systems be able to explain how decisions were made? One consolation is that we are pretty good at gaining wisdom after the fact.

Decision trace
type: DefinedTerm
description: Evidence of why a decision was allowed to happen at a particular time.
in defined term set: Glossary: context gap and process knowledge

Jessica Talisman
type: Person
description: Author of The Context Gap; information systems, taxonomy, ontology, and knowledge graph practitioner.

a1
type: Answer
is part of: FAQ: The Context Gap
text: The context gap is the difference between knowing what happened and knowing why a decision was justified, permitted, and executed in a given operational moment.

Capture decision traces at execution time
type: HowToStep
is part of: How to close the context gap
text: Record the decision context, inputs, actors, approvals, constraints, and alternatives while work is performed.

The Context Gap knowledge graph
type: Ontology
description: RDF source of truth for an HTML and Markdown collection derived from the article and visible comments.
comment: Ontology and artifact graph for Jessica Talisman's LinkedIn article and visible comment thread.

Risk mitigation
type: DefinedTerm
description: Comment-thread framing that the article can inform mitigation of multidimensional organizational and AI risks.
in defined term set: Glossary: context gap and process knowledge

The Data and AI Chief. How Semantic Layers and Ontologies Create Trusted AI
type: VideoObject
about: Procedural knowledge graph
about: OWL
description: LinkedIn publishing embed for the ThoughtSpot podcast with Cindy Howson referenced in the source article.
embed url: https://www.linkedin.com/embeds/publishingEmbed.html
is part of: The Context Gap
name: How Semantic Layers and Ontologies Create Trusted AI

Normalize entities and vocabularies
type: HowToStep
is part of: How to close the context gap
text: Use entity resolution and controlled vocabularies so people, systems, decisions, processes, and evidence can be joined reliably.

Glossary: context gap and process knowledge
type: DefinedTermSet
description: Defined terms extracted from the article and visible comment thread.
has defined term: Context graph
has defined term: Context gap
has defined term: Decision trace
has defined term: Process knowledge
has defined term: Procedural knowledge graph
has defined term: Institutional memory
has defined term: Knowledge management
has defined term: Community of practice
has defined term: Outsourcing
has defined term: Knowledge Process Outsourcing
has defined term: Business Process Outsourcing
has defined term: Process mining
has defined term: PROV-O
has defined term: SKOS
has defined term: OWL
has defined term: EU AI Act
has defined term: Context engineering
has defined term: Agentic AI systems
has defined term: Semantic Web
has defined term: Linked Data
has defined term: WebDAV
has defined term: Virtuoso WebDAV publication
has defined term: Risk mitigation
has defined term: Multidimensional view
is part of: The Context Gap

Shenzhen and Process Knowledge
type: ArticleSection
description: 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
is part of: The Context Gap
text: 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...///continue reading on my Substack, Intentional Arrangement

Why AI Isn't Autonomous Yet
type: Article
about: Agentic AI systems
about: Knowledge management
author: Jessica Talisman
description: Related Substack series linked in the article when discussing knowledge infrastructure failures and agentic AI systems.
is part of: Reference corpus for The Context Gap

O Reilly Media
type: Organization
description: Publisher and media organization associated with the Data Superstream reference.

It is About Data. Knowledge Graphs, Ontologies and Context
type: VideoObject
about: Context graph
about: OWL
description: LinkedIn publishing embed for the It is About Data episode referenced in the source article.
embed url: https://www.linkedin.com/embeds/publishingEmbed.html
is part of: The Context Gap
name: Knowledge Graphs, Ontologies and Context (Ep 56 with Jessica Talisman)

What is a procedural knowledge graph?
type: Question
accepted answer: a8
is part of: FAQ: The Context Gap

The Context Gap Markdown companion
type: DigitalDocument
encoding format: text/markdown
is part of: The Context Gap RDF/HTML/Markdown/JSON-LD bundle

Process Knowledge Management
type: WebPage
description: Process Knowledge Management

a6
type: Answer
is part of: FAQ: The Context Gap
text: The article mentions PROV-O for provenance, SKOS for vocabulary control, and OWL for reasoning structures.

Multidimensional view
type: DefinedTerm
description: Comment-thread idea that the same article can support many perspectives when represented as a graph.
in defined term set: Glossary: context gap and process knowledge

Virtuoso WebDAV publication
type: DefinedTerm
description: Workflow where files copied to a mounted Virtuoso WebDAV folder become Web-accessible resources and graph inputs.
in defined term set: Glossary: context gap and process knowledge

a5
type: Answer
is part of: FAQ: The Context Gap
text: Software alone cannot restore documentation practices, apprenticeship lineages, communities of practice, or cultural respect for knowledge engineering work.

Andrei Bâcu
type: Person
description: Commenter building knowledge graph and ontology work around EU AI Act compliance.

O Reilly Data Superstream. The Ontology Pipeline for AI Systems
type: VideoObject
about: Procedural knowledge graph
about: Institutional memory
description: LinkedIn publishing embed for the O Reilly Data Superstream reference in the source article.
embed url: https://www.linkedin.com/embeds/publishingEmbed.html
is part of: The Context Gap
name: Knowledge Infrastructures and the Ontology Pipeline for AI Systems with Jessica Talisman

What is the context gap?
type: Question
accepted answer: a1
is part of: FAQ: The Context Gap

Knowledge Process Outsourcing
type: WebPage
description: Knowledge Process Outsourcing

OpenLink Virtuoso
type: SoftwareApplication
description: Hybrid relational/RDF database and Linked Data server platform on which URIBurner runs.

Shashi Bhatnagar
type: Person
description: Commenter connecting the article to risk mitigation and multidimensional views.

OWL 2 Web Ontology Language Document Overview
type: TechArticle
about: OWL
author: W3C
description: W3C OWL 2 overview named in the article for reasoning structures.
is part of: Reference corpus for The Context Gap

Dan Wang
type: Person
description: Author of Breakneck, cited for China, Shenzhen, and engineering-state process knowledge.

URIBurner
type: SoftwareApplication
description: Linked Data resolver and SPARQL entry point used for entity hyperlinks.
is based on: OpenLink Virtuoso

Andrei Bâcu comment
type: Comment
about: Context gap
about: Process knowledge
author: Andrei Bâcu
date created: 2h
is part of: Visible LinkedIn comment thread
text: Jessica Talisman I really appreciate that you are sharing all this valuable information in an usable format. Have started building a knowledge graph and an ontology for the EU AI Act at Antifragile.AI, for streamlining compliance & cybersecurity and helping systems to become antifragile in time. Context graphs and context engineering are core components for providing evidence-based assertions and remediations, advanced observability and even inputs for RL of Agents.

Context Data Platform
type: Article
about: Context graph
about: Context engineering
author: Glean
description: Glean article referenced for Arvind Jain and the claim that the context graph concept finally has a name.
is part of: Reference corpus for The Context Gap