With the explosive popularity of ChatGPT, organizations are throwing massive budgets and executive attention at the implementation of AI technologies. Making these solutions work for the enterprise can deliver competitive advantage and open up new solutions and business opportunities that were never before possible. However, without the right Information Architecture (IA) foundations, these projects are bound to fail. In this presentation, Marino and Galdamez provided practical, actionable steps around IA that organizations can take in preparation for future AI solutions. In this session, attendees: - Reviewed key elements of IA and discovered how their successful design and implementation can lay the foundations for AI; - Learned basic terminology surrounding AI, as well as different techniques and applications of AI in enterprise environments; - Gained a deeper understanding of the feedback loops between IA and AI and the corresponding implications on user experience; and - Received practical advice on IA design to facilitate its implementation and the success of AI efforts.

1. Designing IA for AI
Information Architecture Conference 2024

2. Designing IA for AI ^
Information Architecture Conference 2024

3. Designing IA for AI
Overview of IA and AI
Practical steps to prepare content and data
Q&A

4. Today’s Presenters
Chris Marino
(HE/HIM)
Principal Solution Consultant
Guillermo Galdamez
(HE/HIM)
Principal Consultant

5. ENTERPRISE KNOWLEDGE
ESTABLISHED 2013 – OUR FOUNDERS AND PRINCIPALS HAVE BEEN PROVIDING
KNOWLEDGE MANAGEMENT CONSULTING TO GLOBAL CLIENTS FOR OVER 20 YEARS.
10 AREAS OF EXPERTISE
KM STRATEGY & DESIGN TAXONOMY & ONTOLOGY DESIGN
TECHNOLOGY SOLUTIONS AGILE, DESIGN THINKING, & FACILITATION
CONTENT & BRAND STRATEGY KNOWLEDGE GRAPHS, DATA MODELING, & AI
ENTERPRISE SEARCH INTEGRATED CHANGE MANAGEMENT
ENTERPRISE LEARNING CONTENT MANAGEMENT
80
+ EXPERT
CONSULTANTS
HEADQUARTERED IN WASHINGTON, DC, USA
PRESENCE IN BRUSSELS, BELGIUM
AWARD-WINNING
CONSULTANCY
KMWORLD’S
100 COMPANIES THAT MATTER IN KM (2015, 2016, 2017, 2018,
2019, 2020, 2021)
TOP 50 TRAILBLAZERS IN AI (2020, 2021)
CIO REVIEW’S
20 MOST PROMISING KM SOLUTION PROVIDERS (2016)
INC MAGAZINE
#2,343 OF THE 5000 FASTEST GROWING COMPANIES (2021)
#2,574 OF THE 5000 FASTEST GROWING COMPANIES (2020)
#2,411 OF THE 5000 FASTEST GROWING COMPANIES (2019)
#1,289 OF THE 5000 FASTEST GROWING COMPANIES (2018)
INC MAGAZINE
BEST WORKPLACES (2018, 2019, 2021, 2022)
WASHINGTONIAN MAGAZINE’S
TOP 50 GREAT PLACES TO WORK (2017)
WASHINGTON BUSINESS JOURNAL’S
BEST PLACES TO WORK (2017, 2018, 2019, 2020)
ARLINGTON ECONOMIC DEVELOPMENT’S
FAST FOUR AWARD – FASTEST GROWING COMPANY (2016)
VIRGINIA CHAMBER OF COMMERCE’S
FANTASTIC 50 AWARD – FASTEST GROWING COMPANY
(2019, 2020)
EK at a Glance

6. Information Architecture

7. ENTERPRISE KNOWLEDGE
Information Architecture (IA)
Information is presented, tagged,structured, and organized within a system to help users find the resources they need
to achieve their desired tasks and discover the knowledgethey can use to improve their performance.
CREATE
MAINTAIN
PROTECT
FIND
UNDERSTAND
USE
IA

8. ENTERPRISE KNOWLEDGE
Layers of Information Architecture (IA)
IA comes into play at different levels within solutions within an organization. The following diagram illustrates some of the
key elements within IA.
PRESENTATION
LAYOUT ⬢ NAVIGATION ⬢ INTERACTIVITY
SEMANTIC
METADATA ⬢ TAXONOMIES ⬢
ONTOLOGIES ⬢ KNOWLEDGE GRAPHS
PHYSICAL
DATA STORES ⬢ CONTENT REPOSITORIES ⬢
KNOWLEDGE BASES ⬢ FILE SYSTEMS ⬢
ACCESS CONTROL
▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲
GOVERNANCE

9. Artificial Intelligence

10. What is Enterprise AI?
Enterprise AI entails
leveraging machine
capabilities to discover and
deliver organizational
knowledge, data and
information in a way that
closely aligns with how we
look for and process
information.
ENTERPRISE KNOWLEDGE

11. ENTERPRISE KNOWLEDGE
Why AI?
Teach computers to perform
tasks to complement human
efforts and produce efficiency as
well as save time.
Provide more opportunities for
humans to do the hard things
that require critical thinking.
Summarize a document
Label a piece of content
Predict the next activity
Translate to a different language
Recommend a similar article
Answer a question
ENTERPRISE KNOWLEDGE

12. Enterprise AI
Reasons Why AI Initiatives Fail
Lack of clear
business
applications and
relevant use cases
Assumption that
AI is a “Single
Technology”
solution
Automation
requires subject
matter expertise
to scale effectively
Enterprise
information and
data is not ready
for AI
ENTERPRISE KNOWLEDGE

13. ENTERPRISE KNOWLEDGE
Pulling Structure
from Unstructured
AI works well with structured content. The key is to extract the
structure (tags, labels, metadata) from your unstructured
content (PDFs, Office, content).
ID Topic Year Author
1 AI 2024 Emily Crockett
2 KM 2023 Gui Galdamez
… … … …
How to Prepare Content for AI
“Artificial Intelligence (AI) enables organizations
to leverage and manage their content in exciting
new ways, from chatbots and content
summarization to auto-tagging and
personalization. ..”
From Data to Knowledge… Strategy
“At EK, we help organizations across multiple
industries and geographic locations tackle a wide
range of challenges in dealing with the data,
information, and knowledge that supports their
strategic goals. …”
ID: 1
Topic: AI
Year: 2024
Author: Emily Crockett
ID: 2
Topic: KM
Year: 2023
Author: Gui Galdamez

14. ENTERPRISE KNOWLEDGE
Generative AI
A form of artificial intelligence
that creates novel data based
on requests/inputs.

15. AI readiness
checklist
Define a vision and prioritize use cases
Design a taxonomy to describe content, people, and other concepts
Inventory and clean priority knowledge, information, and data
Identify and define access privileges over data
Content is properly and consistently tagged to provide structure
Establish links and relationships across related data and information
Ensure content is componentized to benefit from granularity
Foster trust by leveraging explainable AI and AI literacy

16. ENTERPRISE KNOWLEDGE
Define a vision and prioritize use cases
1. Establish a clear vision and strategy for the implementation and use of AI
across the organization
2. You have clearly outlined the scope for AI, and the priority use cases you
aim to address
3. For each of the use cases, define measurable success criteria
4. Communicate your intent and progress frequently with key stakeholders
EXAMPLE SUCCESS CRITERIA
⬡ Increase in findability of content
and data
⬡ % accuracy in recommendation
of relevant content
⬡ user satisfaction with natural
language search and chatbots
⬡ Data governance in place
Outcomes
⬢ Your key stakeholders understand the meaning behind your efforts and
the value that the organization and individuals can expect from AI
⬢ Use cases are aligned to the business value, and are supported by the
planned or existing technical and individual capabilities within the
organization
⬢ Efforts produce the evidence to get buy in from additional stakeholders,
and strengthen the support from existing champions

17. ENTERPRISE KNOWLEDGE
Design a taxonomy to describe content, people, and
other concepts
1. Analyze the needs to categorize and describe contentfor AI
2. Collect existing taxonomies, controlled vocabularies, and lists of terms from around the
organization and its systems
3. Consolidate analysis results into a unified taxonomy
4. Validate the taxonomy for completeness,usability, and alignment
Outcomes
⬢ The taxonomy will be foundational for subsequent activities in preparing
content and IA for AI, including inventories,
⬢ The taxonomy becomes a consistent thread weaving together content,
stakeholders, and other concepts relevant to the business

18. ENTERPRISE KNOWLEDGE
Inventory and clean priority knowledge,
information, and data
1. Determine what content inputs are needed for the AI use cases previously defined, and where
they can be sourced from
2. Assess the content’s state, structure, and sensitivity
3. Identify who is responsible or accountable for the content
4. Remediate content by removing duplicates, near-duplicates, or otherwise redundant information,
update or archive outdated information, and remove trivial content that is not adding value to
the organization
5. Ensure content can be ingested from the prioritized sources
Outcomes
⬢ Establish a basis for analyzing the gap between the content you have
and the content you need for your use cases
⬢ Assess the reliability of the information within a source
⬢ Identify content for revision, remediation, removal, and migration
⬢ Bring awareness to the types of content that may be under- or over-
represented and introduce biases to your AI models

19. ENTERPRISE KNOWLEDGE
Identify and define access privileges over content
1. Ensure you have awareness of access and protection needs over the content needed for
your AI use cases
2. Document the access needs and privileges as part of an access framework
3. Obtain approval from the adequate leaders and buy-in from stakeholders to adhere to the
access framework when implementing access controls and security provisions on your AI
solution
4. Arrange and structure information in a way that access can be enforced - by moving,
tagging, or breaking down into components
Outcomes
⬢ Content access follows a consistent, predictable, and enforceable pattern for
solutions to be able to ingest them and process them programmatically
⬢ Access frameworks provide clarity in who can access different content
⬢ Stakeholders around the organization have the confidence that sensitive data
is secure

20. ENTERPRISE KNOWLEDGE
Content is properly and consistently tagged to
provide structure
1. Define and enforce the basic metadata needed to describe, categorize, manage, and present
information for your prioritized use cases.
2. Leverage a taxonomy management system (TMS) for curating the taxonomy and publishing
terms consistently across different solutions
3. Implement auto-tagging and synchronization with TMS and source systems
Outcomes
⬢ Providing structure to unstructured content improves processing by AI algorithms
⬢ Infer new information from the additional metadata

21. ENTERPRISE KNOWLEDGE
Establish links and relationships across related data
and information
1. Design an ontology to model the key concepts, processes, and people within your organization.
Hint: Focus on the questions your users are finding difficult to answer
2. Instantiate the ontology into a knowledge graph by mapping the different entities, their properties,
and their relationships to different content sources in the organization.
3. Leverage the taxonomy to make sure that different concepts in the knowledge graph are
consistently described and categorized.
Outcomes
⬢ Build a user- and machine-readable graph of interrelated content
⬢ Create rich, descriptive, associations between the information, data, and
people in your organization
⬢ Surface “hidden” relationships between different business concepts and
entities

22. ENTERPRISE KNOWLEDGE
Ensure content is componentized to benefit from
granularity
1. Identify candidate content for components especially high value content and hierarchical
content
2. Break down content into smaller, distinct, and semantically meaningful blocks
3. Leverage Componentized Content Management Systems (CCMS) to be able to store and
manage individual units of information
4. Enrich the content blocks with the use of taxonomy and metadata
5. Connect content blocks to related data and information through a graph
Outcomes
⬢ Increase control over how units of information are created, managed,
assembled, and presented including to AI models.
⬢ Deliver hyper-targeted and relevant information to users, and be able to
do it at scale
⬢ Create reusable content that can easily be leveraged in other systems
and applications

23. ENTERPRISE KNOWLEDGE
Foster trust by leveraging explainable AI and AI literacy
1. Offer support to your organization in outlining the types of AI, their use cases, risks, and
limitations.
2. Implement systems which put limits or guardrails on the AI model and honors access controls
on content - Retrieval Augmented Generation (“RAG”)
3. Provide contextual information to enable them to make judgements on the data before
them:
⬢ Where information is sourced from and who created it
⬢ How information was processed
⬢ How recent the information is
⬢ How information was processed and delivered
Outcomes
⬢ Empower users to assess information they are consuming
⬢ Trace answers back to their source
⬢ Help users understand when, how, and why information was generated
⬢ Support security rules established by organization for providing access
to content to only those that should have access

24. Bringing it together
BETTER INPUTS = BETTER AI OUTPUTS
Foster trust by leveraging explainable AI and AI literacy
Design a taxonomy to describe content, people, and other concepts
Content is properly and consistently tagged to provide structure
Establish links and relationships across related data and information
Identify and define access privileges over data
Ensure content is componentized to benefit from granularity
Inventory and clean priority knowledge, information, and data
▲▲▲▲▲▲▲▲▲
GOVERNANCE
Define a vision and prioritize use cases

25. ENTERPRISE KNOWLEDGE
Thank you!
Want to keep in touch or learn more about our work?
visit us at enterprise-knowledge.com
Q & A
Chris Marino
https://www.linkedin.com/in/chris-j-marino/
Guillermo Galdamez
https://www.linkedin.com/in/guillermogaldamez/