EKAM™ – Enterprise Knowledge Architecture Model
Creating trusted semantic foundations for Enterprise AI.
EKAM transforms business knowledge into structured semantics which enable knowledge graphs and lead to trusted AI
Our Services
From Enterprise Discovery to Trusted AI
Most AI and data programmes begin with technology.
We begin with enterprise understanding.
Before introducing platforms, AI or knowledge graphs, we discover how the organisation actually works.
We identify:
- how work is performed
- how decisions are made
- how information flows
- how business concepts relate
- how enterprise knowledge is created and reused
This becomes EKAM™—a structured enterprise knowledge model that provides the semantic foundation for knowledge graphs, AI and intelligent decision-making.
The EKAM™ Methodology
Our Methodology: Business First. Knowledge Second. Technology Third.
We discover the enterprise before modelling it.
We model the enterprise before implementing technology.
This ensures AI is grounded in trusted enterprise knowledge rather than disconnected data.
1. Discover BEM – Business Event Modelling
We start with observable business behaviour.
We identify:
- Business events
- People involved
- Information consumed and produced
- Operational outcomes
Examples:
- Tax Return Submitted
- Asset Inspected
- Claim Approved
- Waste Transfer Recorded
These events reveal how the organisation actually operates.
2. Understand Enterprise Knowledge Discovery
From events, we define a shared business vocabulary.
We establish consistent meaning across the organisation:
- Customer
- Citizen
- Asset
- Contract
- Product
- Case
This becomes the enterprise vocabulary from which semantic models are built..
3. Structure Semantic Architecture
We formalise meaning into machine-interpretable models.
We define:
- concepts
- relationships
- hierarchies
- business rules
- governance constraints
using standards such as RDF, OWL and SKOS.
This ensures meaning is consistent, traceable and reusable.
4. Connect (Enterprise Knowledge Graph)
Enterprise knowledge is transformed into machine-readable semantic models.
We define:
- concepts
- relationships
- classifications
- business rules
- governance policies
using recognised standards including RDF, OWL, SHACL and SKOS.
This creates a consistent, governed and reusable enterprise knowledge model.
5. Enable (Trusted Knowledge Retrieval)
Enterprise knowledge is connected across business domains, applications and information assets.
This enables:
- enterprise navigation
- cross-domain relationships
- lineage and provenance
- impact analysis
- enterprise reasoning
6. Accelerate (Enterprise AI)
AI agents operate on enterprise knowledge rather than disconnected information.
The result is:
- reduced hallucinations
- greater regulatory confidence
- improved enterprise reasoning
- better operational decisions
- trusted intelligent automation
Enterprise AI does not begin with algorithms. It begins with enterprise knowledge.
Why Choose iTeQ Consulting
Enterprise Knowledge First. AI Second.
Successful AI does not begin with models.
It begins with understanding the enterprise.
Before organisations can trust AI, they must first understand how their business works, how knowledge is created, and how meaning is shared across the enterprise.
That’s where we start.
We build the semantic foundations that enable AI, knowledge graphs and intelligent systems to reason over your organisation with confidence rather than guess from disconnected information.
Our approach combines
- Enterprise Knowledge Discovery – understanding how work is actually performed.
- Business Event Modelling – capturing enterprise behaviour through business events.
- Enterprise Semantic Architecture – creating a common enterprise language.
- Ontology & Semantic Engineering – formalising enterprise knowledge using open standards.
- Knowledge Graph Architecture – connecting enterprise knowledge across domains.
- GraphRAG & AI Enablement – grounding AI in trusted enterprise knowledge.
- Governance by Design – embedding provenance, lineage, traceability and policy from the outset.
The Result
Rather than building AI on disconnected data, we help organisations build AI on trusted enterprise knowledge.
The outcome is more explainable, more governable and more reliable AI that supports better decisions across the enterprise.
Case Studies
Enterprise Knowledge in Practice
Although the term Enterprise Knowledge Architecture is new, the underlying discipline has shaped our work for many years.
Across Government, Healthcare, Financial Services, Construction and Risk Management, we have consistently helped organisations understand how they operate before introducing new technology.
Each engagement followed the same fundamental journey:
Enterprise Discovery → Enterprise Knowledge → Semantic Architecture → Knowledge Graph → AI Enablement
HMRC
Building Enterprise Knowledge for Future AI
We developed a structured enterprise knowledge foundation to support governance, analytics and future AI adoption across one of the UK’s largest and most complex tax environments.
Delivered
- Enterprise capability models
- Canonical business concepts
- Metadata governance
- Information lineage
- AI-ready knowledge architecture
bet365
Establishing Trusted Enterprise Knowledge
We created a consistent semantic foundation across analytical domains to support cloud migration, governance and future AI capability.
Delivered
- Canonical business definitions
- Enterprise semantic models
- Metadata standards
- Governance controls
- AI-ready analytical foundations
NHS Staff Bank
Creating a Shared Workforce Knowledge Model
We established a common enterprise understanding of workforce operations across healthcare services.
Delivered
- Workforce knowledge model
- Capability mapping
- Enterprise semantics
- Governance framework
Home Office
Trusted Enterprise Knowledge
We strengthened confidence in operational and analytical information by establishing common enterprise semantics and governance across multiple transformation programmes.
Delivered
- Enterprise data standards
- Metadata framework
- Lineage models
- Governance controls
DEFRA
Regulatory Enterprise Knowledge
We created structured enterprise knowledge models supporting regulatory reporting across national waste tracking initiatives.
Delivered
- Regulatory event models
- Enterprise information structures
- Governance frameworks
- Traceability foundations
ISG
Connected Enterprise Knowledge
We improved visibility across project delivery, commercial management and governance by creating connected enterprise knowledge structures.
Delivered
- Project information models
- Commercial relationships
- Governance structures
- Reporting frameworks
Control Risks
Enterprise Risk Knowledge Architecture
We structured enterprise risk information into a connected, governed knowledge model supporting operational and strategic decision-making.
Delivered
- Risk domain models
- Operational event models
- Stakeholder relationships
- Governance frameworks
A Proven Enterprise Knowledge Architecture Approach
Every organisation is different.
The underlying challenge is the same.
Before AI can reason about an enterprise, the enterprise must first understand itself.
That is the purpose of Enterprise Knowledge Architecture.
Across every engagement we follow the same repeatable approach:
Enterprise Discovery
Understanding how the organisation actually works.
↓
Enterprise Knowledge
Capturing business capabilities, concepts, events and relationships.
↓
Semantic Architecture
Creating a common enterprise language.
↓
Knowledge Graph
Connecting enterprise knowledge across domains.
↓
Enterprise AI
Enabling trustworthy AI built upon governed enterprise knowledge.
Technology changes. Enterprise knowledge endures.
iTeQ Insights
Why AI Needs Enterprise Knowledge
Artificial Intelligence is transforming how organisations operate.
Yet many AI initiatives struggle to move beyond demonstration into trusted enterprise deployment.
The reason is simple.
AI does not understand your organisation.
It does not know:
- your business capabilities
- your business terminology
- your operational processes
- your governance policies
- your organisational relationships
- your decision context
Without this enterprise knowledge, AI can produce responses that appear convincing but are not reliably grounded in organisational reality.
From Information to Enterprise Knowledge
Enterprise knowledge is rarely held in one place.
It is fragmented across:
- business processes
- enterprise applications
- operational data
- documents
- people
- regulatory frameworks
EKAM™ transforms these disconnected sources into a structured enterprise knowledge model that reflects how the organisation actually operates.
This creates a trusted semantic foundation from which knowledge graphs, intelligent retrieval and Enterprise AI can operate with confidence.
A Different Starting Point
Many organisations begin AI programmes by selecting technology.
We begin by understanding the enterprise.
Before implementing AI, we discover how the organisation works, how knowledge is created and shared, and how meaning is applied consistently across the business.
Only then do we create the semantic foundations that enable trustworthy Enterprise AI.
Technology evolves rapidly.
Enterprise knowledge endures.
Enterprise AI begins with Enterprise Knowledge.
EKAM Framework
EKAM™ – Enterprise Knowledge Architecture Model
A semantic operating model for Enterprise AI
EKAM™ (Enterprise Knowledge Architecture Model) is a structured methodology for discovering, organising and operationalising enterprise knowledge.
It enables organisations to transform fragmented business information into a connected enterprise knowledge model that provides the semantic foundation for Knowledge Graphs, Enterprise AI and intelligent decision-making.
What is EKAM?
EKAM is not a software platform.
It is not a modelling tool.
It is not another Enterprise Architecture framework.
EKAM is an Enterprise Knowledge Architecture.
It defines:
- how the enterprise operates
- how business knowledge is created
- how meaning is shared
- how relationships are connected
- how AI can reason over trusted enterprise knowledge
It provides the semantic foundation for:
- Enterprise AI
- Knowledge Graphs
- Intelligent Search
- Automation
- Decision Intelligence
- Enterprise Governance
The EKAM Architecture
1. Enterprise Discovery
Understanding how the organisation actually works.
Using Business Event Modelling, capabilities, business processes and enterprise interactions, we discover the knowledge that already exists across the organisation.
2. Enterprise Knowledge
Business events reveal the concepts that define the organisation.
Examples include:
- Customer
- Citizen
- Asset
- Product
- Contract
- Case
- Supplier
These become the canonical enterprise vocabulary shared across people, systems and AI.
3. Semantic Architecture
Enterprise knowledge is formalised into machine-readable semantic models.
EKAM defines:
- concepts
- relationships
- hierarchies
- business rules
- governance policies
using recognised semantic standards including RDF, OWL, SHACL and SKOS.
4. Enterprise Knowledge Graph
Enterprise knowledge is connected across business domains into a single knowledge graph.
This enables:
- enterprise-wide relationships
- contextual navigation
- lineage
- provenance
- enterprise reasoning
- impact analysis
5. Trusted Enterprise Retrieval
Applications and AI retrieve enterprise knowledge rather than isolated documents.
Using GraphRAG and semantic retrieval, responses become:
- explainable
- traceable
- context-aware
- policy-aware
- grounded in enterprise knowledge
6. Enterprise AI
AI agents operate using governed enterprise knowledge.
The result is:
- reduced hallucinations
- improved explainability
- stronger governance
- consistent decisions
- trusted intelligent automation
Why EKAM Exists
Most organisations do not have a data problem.
They have a knowledge problem.
Business knowledge is distributed across applications, documents, people, policies and processes.
Without a shared enterprise understanding, organisations experience:
- inconsistent reporting
- duplicated business definitions
- fragmented governance
- disconnected systems
- unreliable AI
EKAM addresses this challenge by establishing a trusted semantic foundation that represents the enterprise as connected knowledge.
Traditional Enterprise Architecture vs EKAM
Traditional Enterprise Architecture asks:
“How is the enterprise built?”
EKAM asks:
“How does the enterprise understand itself?”
Traditional architecture focuses on systems.
EKAM focuses on enterprise knowledge.
Traditional architecture connects applications.
EKAM connects enterprise meaning.
Traditional AI works on data.
Enterprise AI works on trusted enterprise knowledge.
Business Outcomes
Organisations implementing EKAM establish:
- trusted enterprise knowledge
- shared business meaning
- Knowledge Graphs
- explainable Enterprise AI
- regulatory traceability
- enterprise-wide consistency
- scalable automation
- better decision support
EKAM as an Enterprise Operating Model
Enterprise systems answer different questions.
ERP
“What happened?”
CRM
“Who are our customers?”
EKAM
“How does the enterprise understand itself?”
EKAM becomes the System of Understanding that connects enterprise knowledge across business, technology and AI.
The EKAM Method
Every implementation follows the same repeatable journey:
Enterprise Discovery
↓
Enterprise Knowledge
↓
Semantic Architecture
↓
Knowledge Graph
↓
Enterprise AI
This ensures that AI is built upon trusted enterprise knowledge rather than disconnected information.
Strategic Value
EKAM enables organisations to:
- prepare confidently for Enterprise AI
- reduce dependence on tribal knowledge
- establish a common enterprise language
- improve governance and traceability
- support explainable automation
- create reusable enterprise knowledge assets
- build a long-term semantic foundation for digital transformation
A living white paper exploring the evolution of Enterprise Knowledge Architecture (EKAM). Updated regularly as the methodology, industry models and reference architecture evolve.
Why Enterprise Knowledge?
Enterprise Knowledge Architecture Series
White Paper | Version 1.0
Published: July 2026
Last Updated: 10 July 2026
Reading Time: 8 minutes
Audience: CIOs • Chief Architects • CDOs • Transformation Directors • Enterprise Architects • AI Leaders
Author: Bill Grant
Chief Knowledge & AI Architect
iTeQ Consulting
The defining challenge of the twenty-first century enterprise is no longer acquiring information. It is transforming information into trusted knowledge, and knowledge into intelligent action.
For decades organisations have invested billions in digital transformation, enterprise architecture, cloud migration, data platforms and artificial intelligence. Yet many still struggle to answer fundamental questions about themselves.
- What work do we actually perform?
- Which business capabilities create value?
- How are our applications, data and processes connected?
- Where are the risks?
- What knowledge does the organisation possess?
- How can AI make reliable decisions if the enterprise cannot describe itself?
The problem is no longer one of technology.
It is one of knowledge.
From Enterprise Data to Enterprise Knowledge
Traditional Enterprise Architecture documents systems.
Data Architecture organises information.
Knowledge Architecture explains how the enterprise actually works.
Enterprise Knowledge Architecture (EKAM) creates a structured understanding of the organisation by connecting people, processes, applications, information, events, decisions and business outcomes into a coherent knowledge model.
Rather than producing static documentation, EKAM builds a living representation of the enterprise that can be continuously enriched, governed and understood.
Knowledge becomes an organisational asset rather than a collection of disconnected documents.
Why This Matters
Artificial Intelligence is only as good as the knowledge it can access.
Without trusted enterprise knowledge, AI systems frequently:
- hallucinate
- misunderstand business context
- duplicate effort
- create inconsistent recommendations
- overlook dependencies
- increase operational risk
By contrast, AI grounded in enterprise knowledge can reason using trusted business context rather than isolated datasets.
This transforms AI from an interesting technology into a dependable enterprise capability.
Introducing Enterprise Knowledge Architecture
Enterprise Knowledge Architecture is an emerging discipline designed to discover, model, govern and operationalise enterprise knowledge.
The Enterprise Knowledge Discovery Framework (EKDF) provides a structured approach to:
- Understand the Work
- Discover Enterprise Knowledge
- Model Relationships
- Create Semantic Consistency
- Build Knowledge Graphs
- Enable Trusted AI
- Deliver Evidence-Based Decision Making
It bridges the traditional disciplines of Enterprise Architecture, Data Architecture, Information Architecture, Knowledge Management and Artificial Intelligence into a single integrated methodology.
The Enterprise Knowledge Paradigm
The enterprise of the future will not be defined by the systems it owns, the processes it executes, or the data it stores.
It will be defined by its ability to understand itself.
Organisations that understand themselves can:
- make better decisions
- respond faster to change
- reduce delivery risk
- improve governance
- accelerate transformation
- enable trustworthy AI
Knowledge becomes the strategic asset from which every other capability grows.
Our Vision
At iTeQ Consulting we believe Enterprise Knowledge Architecture represents the next evolution of enterprise transformation.
Our mission is simple:
Helping organisations understand themselves before enabling AI.
Because intelligent organisations are built on trusted knowledge—not assumptions.
AI-Ready Enterprise Discovery
Why AI Cannot Understand a Business It Has Never Discovered
Enterprise Knowledge Architecture Series
White Paper | 002 Version 1.0
Published: July 2026
Last Updated: 24 July 2026
Reading Time: 5 minutes
Audience: CIOs • Chief Architects • CDOs • Transformation Directors • Enterprise Architects • AI Leaders
Every organisation wants AI.
Few have first asked a much simpler question.
Does AI actually understand our business?
For most organisations, the answer is no.
Not because today’s AI models lack intelligence, but because the enterprise itself has never been systematically discovered.
The Missing Step
Organisations typically approach AI in this order:
- Purchase an AI platform
- Connect corporate documents
- Index data
- Build a chatbot
- Hope it understands the business
Unfortunately, the business itself has never been modelled.
The AI therefore encounters thousands of disconnected documents, inconsistent terminology, duplicated concepts and conflicting definitions.
It is expected to infer meaning from information that even the organisation itself has never fully connected.
Discovery Before AI
Before an enterprise can become AI-ready it must first understand itself.
Enterprise Discovery captures:
- what work is performed
- who performs it
- why it is performed
- what information is created
- what decisions are made
- how knowledge flows
- where governance exists
Only once this understanding exists can technology accurately represent the enterprise.
Discovery Creates Knowledge
Traditional discovery often produces documents.
Enterprise Knowledge Discovery produces something far more valuable.
It creates a reusable knowledge asset describing how the enterprise actually operates.
This knowledge becomes the semantic foundation from which:
- Canonical Business Models emerge
- Enterprise Ontologies are derived
- Knowledge Graphs are generated
- AI Agents obtain trusted context
Discovery is therefore not the beginning of a project.
It becomes the beginning of enterprise intelligence.
AI Requires Business Context
Large Language Models understand language.
They do not inherently understand:
- your organisation
- your terminology
- your governance
- your operating model
- your business rules
Without business context, AI generates statistically plausible responses.
With enterprise knowledge, AI can reason within the context of your organisation.
The difference is profound.
One generates text.
The other supports decisions.
Enterprise Discovery as Strategic Capability
Many organisations still regard discovery as a project activity.
We believe it should become an enduring enterprise capability.
As businesses evolve, the enterprise knowledge model evolves with them.
Every change improves the organisation’s understanding of itself.
AI therefore becomes progressively more accurate because the knowledge on which it depends becomes progressively richer.
Looking Ahead
Enterprise Discovery is only the first step.
Once enterprise knowledge has been discovered, it must be organised into a common language.
That is the role of the Canonical Business Model, explored in the next Insight.
Key Takeaway
AI cannot understand an enterprise that has never understood itself. Enterprise Discovery creates the trusted knowledge foundation upon which explainable, governed and scalable AI is built.
From Enterprise Discovery to Enterprise Understanding
Enterprise Knowledge Architecture Series
White Paper | 003 Version 1.0
Published: July 2026
Last Updated: 28 July 2026
Reading Time: 15 minutes
Audience: CIOs • Chief Architects • CDAOs • AI Leaders • Enterprise Architects • Transformation Directors
The premise
For decades, organisations have designed architectures around applications, infrastructure, processes and data.
These disciplines remain essential.
But Artificial Intelligence introduces a different requirement.
AI does not simply need access to enterprise information. It needs to understand what that information means, how it relates to the organisation, where it came from, what rules govern it and the context in which it can be trusted.
This creates an architectural challenge that sits between traditional Enterprise Architecture, Data Architecture, Knowledge Engineering and Artificial Intelligence.
We call this:
Enterprise Knowledge Architecture.
Enterprise Knowledge Architecture provides a structured representation of how an organisation understands itself.
It connects business behaviour, enterprise concepts, relationships, rules, evidence and context into a coherent knowledge architecture that can be understood by people, systems and AI.
From Data Architecture to Knowledge Architecture
Enterprise Data Architecture answers important questions:
What data do we hold?
Where is it stored?
How does it move?
Who owns it?
How is it governed?
Enterprise Knowledge Architecture asks additional questions:
What does this information mean?
What enterprise concept does it represent?
How are those concepts related?
What happened to create or change them?
What evidence supports what we believe?
Which rules apply?
What context determines interpretation?
And critically:
Can an AI system understand these relationships well enough to reason about the enterprise?
This is not a replacement for Data Architecture.
It is the next semantic layer above it.
The Enterprise Knowledge Problem
Most organisations already possess enormous amounts of knowledge.
The problem is that it is fragmented.
Knowledge exists within:
business processes
applications
data platforms
documents
policies
people
regulations
architecture models
operational decisions
Each may describe part of the enterprise.
Few describe the enterprise as a coherent whole.
A Customer may have different definitions across Finance, Sales, Operations and Compliance.
A Product may be represented differently by different applications.
A business rule may exist within a policy document but be implemented differently within several systems.
A decision may be recorded without preserving the evidence and assumptions upon which it was made.
For people, these inconsistencies are often resolved through experience and institutional knowledge.
AI cannot safely rely on that.
From Enterprise Discovery to Enterprise Knowledge
This is why Enterprise Knowledge Architecture begins with Enterprise Discovery.
Rather than starting with systems or technology, discovery begins with the enterprise itself.
We identify:
business capabilities
business events
actors
enterprise concepts
information flows
decisions
rules
policies
relationships
evidence
Observable business behaviour provides a particularly powerful starting point.
Consider:
Claim Approved
That event immediately raises questions.
What is a Claim?
Who approved it?
What Customer does it relate to?
Which Policy governs it?
What evidence supported approval?
Which business rules were applied?
What happened before approval?
What happens afterwards?
The event exposes the enterprise knowledge surrounding the activity.
By repeating this process across business domains, an organisation begins to construct a representation of how the enterprise actually operates.
Establishing Shared Enterprise Meaning
Discovery alone is not enough.
Different parts of an organisation will inevitably use different terminology and conceptual structures.
Enterprise Knowledge Architecture therefore establishes a shared semantic model.
Concepts such as:
Customer
Citizen
Product
Asset
Contract
Supplier
Case
are defined independently of the applications that happen to contain them.
Relationships are then established between those concepts.
A Customer holds an Account.
A Supplier provides a Product.
A Contract governs a Service.
A Case concerns a Citizen.
An Asset is subject to an Inspection.
The enterprise is no longer represented simply as datasets.
It becomes a network of meaning.
From Meaning to Machine-Interpretable Knowledge
The semantic model can then be formalised using established knowledge representation standards.
RDF represents relationships.
OWL provides richer semantic meaning and inference.
SKOS supports controlled vocabularies and taxonomies.
SHACL allows semantic structures and constraints to be validated.
Together, these allow enterprise knowledge to become machine-interpretable.
This is an important transition.
The organisation is moving from:
information that AI can retrieve
to:
knowledge that AI can interpret in context.
The Enterprise Knowledge Graph
The resulting semantic architecture can be operationalised through an Enterprise Knowledge Graph.
Rather than information remaining isolated within individual applications and domains, relationships become traversable across the enterprise.
A user—or an AI agent—might move from:
Customer
to:
Contract
to:
Product
to:
Supplier
to:
Risk
to:
Policy
to:
Decision
to:
Evidence
The graph preserves context that conventional document retrieval can easily lose.
This enables questions that span traditional organisational boundaries.
Enterprise Knowledge and AI
This is where Enterprise Knowledge Architecture becomes particularly important.
Large Language Models are exceptionally capable at interpreting language.
They are not inherently authoritative sources of enterprise truth.
Retrieval-Augmented Generation improves this by grounding AI responses in organisational information.
Enterprise Knowledge Architecture takes grounding further.
Instead of retrieving only relevant text, AI can retrieve:
entities
relationships
business context
governed definitions
provenance
rules
evidence
GraphRAG and semantic retrieval can therefore ground AI in a structured representation of the enterprise.
The question changes from:
What documents mention this subject?
to:
What does the enterprise know about this subject, how is that knowledge connected, and why should it be trusted?
A System of Understanding
Enterprise technology already contains many systems of record.
ERP records transactions.
CRM manages customer interactions.
Data platforms consolidate information for analytics.
Document repositories preserve organisational content.
Enterprise Knowledge Architecture introduces something different:
a System of Understanding.
It does not replace existing systems.
It provides the semantic architecture through which their information can be understood as part of the wider enterprise.
This creates a foundation for:
trusted Enterprise AI
Knowledge Graphs
intelligent retrieval
decision intelligence
impact analysis
automation
governance
enterprise reasoning
EKAM™
EKAM™ — the Enterprise Knowledge Architecture Model — is being developed by iTeQ Consulting as a structured approach to implementing Enterprise Knowledge Architecture.
It connects:
Enterprise Discovery
↓
Enterprise Knowledge
↓
Semantic Architecture
↓
Knowledge Graph
↓
Enterprise AI
The objective is not to create another technology platform.
It is to establish a repeatable architecture through which organisations can discover, structure, govern and operationalise their knowledge.
The Architectural Shift
The evolution can be expressed simply.
Traditional Enterprise Architecture asks:
How is the enterprise built?
Enterprise Knowledge Architecture asks:
How does the enterprise understand itself?
Data Architecture determines how enterprise information is structured and managed.
Enterprise Knowledge Architecture determines how that information acquires shared meaning and context.
AI Architecture determines how intelligent systems are designed.
Enterprise Knowledge Architecture provides the enterprise understanding upon which those systems can operate.
These disciplines are complementary.
Together they provide the foundations for trustworthy Enterprise AI.
Conclusion
The next stage of Enterprise AI will not be determined solely by increasingly capable models.
Organisations must also improve the quality of the enterprise knowledge those models can access.
That requires moving beyond fragmented information towards shared meaning, connected knowledge, provenance, context and governance.
Enterprise Knowledge Architecture provides a way of making that transition.
Before AI can understand the enterprise,
the enterprise must first understand itself.
Enterprise Knowledge Architecture Series
This paper forms part of the Enterprise Knowledge Architecture (EKAM)
research programme developed by iTeQ Consulting.
Publications include:
- Paper 001 – Why Enterprise Knowledge?
- Paper 002 – AI-Ready Enterprise Discovery
- Paper 003 – Enterprise Knowledge Architecture
- Paper 004 – Enterprise Knowledge Discovery Framework [planned]
- …