Lulit Tesfaye explains how foundational knowledge management and knowledge engineering approaches can play a key role in ensuring enterprise Artificial Intelligence (AI) initiatives start right, quickly demonstrate business value, and “stick” within the organization. The presentation includes real world case studies and examples of how organizations are approaching their data and AI transformations through knowledge maturity models to translate organizational information and data into actionable and clickable solutions. Originally delivered at data.world Summit, Spring 2022.

1. Translating AI from Concept to Reality:
Five Keys to Implementing AI for Knowledge,
Content, and Data
Lulit Tesfaye
Partner and Division Director,
Data and Information Management
Enterprise Knowledge, LLC.

2. 10 AREAS OF EXPERTISE
KM STRATEGY & DESIGN
TAXONOMY & ONTOLOGY DESIGN
AGILE, DESIGN THINKING & FACILITATION
CONTENT & DATA STRATEGY
KNOWLEDGE GRAPHS, DATA MODELING, & AI
ENTERPRISE SEARCH
INTEGRATED CHANGE MANAGEMENT
ENTERPRISE LEARNING
CONTENT AND DATA MANAGEMENT
ENTERPRISE AI
Clients in 25+ Countries Across Multiple Industries
Lulit Tesfaye
Partner and Division Director, Data and
Information Management
KNOWLEDGE
MANAGEMENT
AGILE DELIVERY
PROGRAM MANAGEMENT
SEMANTIC MODELING
DATA & INFORMATION
MANAGEMENT
KNOWLEDGE GRAPHS
AI
Meet Enterprise Knowledge
HEADQUARTERED IN
ARLINGTON, VIRGINIA, USA
GLOBAL OFFICE IN BRUSSELS,
BELGIUM
Top Implementer of Leading Knowledge
and Data Management Tools
400+ Thought Leadership
Pieces Published

3. The Opportunity: In Numbers
Global business spending on AI will
grow by 21.3%to reach $62.5bn by
2022. Driven by growth in five
application areas: knowledge
management, virtual assistants,
autonomous vehicles, digital
workplace, and crowdsourced
data.
~ Gartner
Of the businesses using AI, 91%said
their ability to explain how AI
arrived at a decision is critical.
~ IBM
Only 8%of firms had practices that
enabled them to adopt and scale AI.
One of the top reasons why AI projects
get stalled being the lack of relevant or
usable data and having no clear
strategies for sourcing the
knowledge that AI requires.
~ McKinsey

4. Deconstructing Enterprise AI
AI for the enterprise 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.

6. The Journey Toward Enterprise AI
Phase I - 1960s
Phase II - Early 2000s
Phase III -
Now
Artificial Intelligence Enterprise KM
AI Inception
● Logic-based
problem solving
From Tacit to Explicit
● KM emerges linked to
economics (Druker)
● The “learning organization”
and IM/KM as an
“organizational resource”
The IBM Watson Era
● Question answering systems
● Early image classification and
processing
● Machine learning, NLP,
computer vision
The Era of IT and Tech Boom
● Rules-based expert systems
● The rise of the intranet
● Certification of knowledge
standards and frameworks
The Google and Alexa Era
● Higher computational power
● Natural language understanding and processing
(BERT)
● Cloud computing & open source tools
● Increased data availability
Digital Transformation Era -
● Digital transformations and KM practices
● AI and KM converge
● Collective knowledge, standards, and open data
1970s
1980s
1990s
2010s

7. Why Our Clients are Investing in AI
FIND NEEDED INFORMATION DISCONNECTED CONTENT & DATA DYNAMISM STANDING OUT FROM COMPETITION
AUGMENT KM AND DATA GOVERNANCE SECURITY AND COMPLIANCE STAFF RETENTION AND ENGAGEMENT
• Provides faster and easier ways
to surface content, identify
customer needs, and translate
them into additional
products/services
• Reduces the time it takes to find
the right information or experts
• Higher volume and dynamism of
organizational content/data
(both structured and
unstructured)
• Growing digitalization
• Democratization of data access
and connecting information to
train AI solutions
• Improved client intelligence
through holistic customer
conversations and efficient
inquiry response
• Shortened sales cycles and
improved revenue through
identification of large scale
patterns
• Relevant and timely decisions
• Automates classification and
labeling of content across silos
• Extends scope of data lineage
through machine reasoning on
contextual information
• Avoids additional governance
costs with data standards and
interoperability
• Risk reduction via governance
and access controls
• Threat prevention by
automating data capture,
enrichment, investigation,
archival, and retention
• Provides accurate and timely
responses to regulators with
available and legacy data
• Improved employee experience
with access to collective
knowledge
• Predicts capacity gaps and
training needs
• Provides personalized training
to reduce time-to-proficiency

8. FOUNDATIONS
PROTOTYPING
ENTERPRISE AI
Where Our Clients
are in Their AI
Journey
Evaluating and identifying and prioritizing AI
use cases and strategy
Conducting content/data gap and maturity
assessments and clean-up plans
Defining technical requirements, solution
architectures, and plans for a proof of concept
or pilot effort
Providing training around AI and its
foundations
Operationalizing a production
architecture for AI solution(s)
Incrementally expanding to
new use cases, data sources,
departments, and solutions on
a phased cadence
Embedding governance and
overall solution management
for ongoing evolutions and
releases
Enterprise AI strategy and
supporting organization
stature
Advanced architecture and
integration capabilities
Enterprise-wide data literacy
and management training
Defining organizational attributes and
developing starter metadata and data
models to create connections across
silos for prioritized use cases
Establishing their first implementable
information and architecture with
semantic models
Launching a clickable application to
demonstrate value and validate
process
Piloting products and tools
Defining a repeatable and incremental
process for future implementations
Developing a plan for staffing and
upskilling needs, adoption, and change
management
30%
50%
11%
PRE-AI
9%

9. The Five Keys to
Implementing
Enterprise AI

10. At the core of Enterprise AI are the foundational principles of knowledge management - people and
culture, content and contextual information, governance processes - all integrated with technology.
An Explainable Solution
05
Build solutions that allow users to create, modify, find,
and use changing data through explainable AI
Data Organization &
Connectivity
03
Connect data concepts to your content (data sets, text
documents, applications, etc.)
People & Domain
Knowledge
02 Define the knowledge important to your organization
The Problem or Use Case
01
Define the problem to be solved and the overarching
vision for AI
Data Enrichment
04 Enrich, add, and connect data concepts

11. Explainable Solution
05
Build solutions that allow users to create, modify, find,
and use changing data through explainable AI
Data Organization &
Connectivity
03
Connect data concepts to your content (data sets, text
documents, applications, etc.)
People & Domain
Knowledge
02 Define the concepts important to your organization
The Problem or Use Case
01
Define the problem to be solved and the overarching
vision for AI
Data Enrichment
04 Enrich, add, and connect data concepts
The Problem or Use Case
01
At the core of Enterprise AI are the foundational principles of knowledge management - people and
culture, content and contextual information, governance processes - all integrated with technology.
A clear problem statement and
definition of business value that AI will
solve - one that reflects stakeholders
interests.
The Problem or Use Case
01

12. Success Factors THE PROBLEM
OR USE CASE
A well defined use case looks like…
Construction Safety: Risk Management Recommender
As a civil engineer, I will be able to see the recommended remedies for a specific
construction site risk so that I can address it in my design.
Goal: Optimise building design through risk mitigation/elimination based on
regulatory requirements or past experience.
Input Data: Standards and Procedures (SOPs), Geo data
Available Source Systems: XQZ Modeling database, Access storage
Output: Relevant design codes, ……
For a recommendation engine:
Actionable use cases are written in user story format
and have the following characteristics:
Identify specific end users
Address a foundational business problem
Garner high participant interest
Communicate specific outcomes
Result in a “clickable” product
Are not overly complex to implement
Have the required resources readily available
Provide a repeatable approach for subsequent
implementations
What is the best
approach regarding
solutioning for this
risk? Have we done
this before?
I need data to support
my solution decisions.
Persona Requirements

13. Explainable Solution
05
Build solutions that allow users to create, modify, find,
and use changing data through explainable AI
Data Organization &
Connectivity
03
Connect data concepts to your content (data sets, text
documents, applications, etc.)
People & Domain
Knowledge
02 Define the concepts important to your organization
The Problem or Use Case
01
Define the problem to be solved and the overarching
vision for AI
Data Enrichment
04 Enrich, add, and connect data concepts
People & Domain
Knowledge
02
At the core of Enterprise AI are the foundational principles of knowledge management - people and
culture, content and contextual information, governance processes - all integrated with technology.
Ready access to people and domain
knowledge and the supporting
organizational functions.
People & Domain
Knowledge
02

14. Success Factors
PEOPLE & DOMAIN
KNOWLEDGE
Engage Domain Experts
and End Users
Organizational Skill Sets
and Project Teams
Budget for Adoption
and Governance
End users and SMEs help create a rich semantic
layer that captures key business facts and context
for AI. Facilitate engagements through:
● Interviews and focus groups
● Design-thinking and validation sessions
● Mockups for user stories
● Gold standards
● Gathering initial feedback in modeling
tools
Leverage a cross-functional team, including:
● Leadership / Sponsors
● Product Owner / Manager
● Business Function Leads / Subject Matter
Experts
● Technical / IT Support
● Project Delivery Team
● Governance Teams
AI and knowledge governance requires
collaboration across the organization. To achieve
this:
● Align with the strategic goals of the
organization
● Define KPIs or ROI for AI projects
through an iterative process
● Engage business users from the outset
and maintain their engagement and
adoption
● Define a formal communications strategy
and embed governance across the
organization
● Start small, develop, deploy, and get
feedback iteratively

15. Explainable Solution
05
Build solutions that allow users to create, modify, find,
and use changing data through explainable AI
Data Organization &
Connectivity
03
Connect data concepts to your content (data sets, text
documents, applications, etc.)
People & Domain
Knowledge
02 Define the concepts important to your organization
The Problem or Use Case
01
Define the problem to be solved and the overarching
vision for AI
Data Enrichment
04 Enrich, add, and connect data concepts
Data Organization &
Connectivity
03
At the core of Enterprise AI are the foundational principles of knowledge management - people and
culture, content and contextual information, governance processes - all integrated with technology.
Connecting data across multiple systems
enables traceable analysis, allowing you to
follow information across different sources,
different locations, data types, and across
time.
Data Organization &
Connectivity
03

16. Success Factors DATA ORGANIZATION
& CONNECTIVITY
The four “C”s of data organization:
Connectivity
How the data entities are
both related and
interconnected
Context
The conceptual and
contextual understanding of
data entities
Collaboration
Engagement across system
owners as well as between AI
and humans
Clean up (Complexity!)
Having controls and
processes in place to ensure
good data quality

17. Metadata and Taxonomy
Build a strong metadata and taxonomy foundation by
defining the concepts that are important to your
organization, how people talk about those concepts,
and how those concepts are grouped together.
You add new data concepts via taxonomy
development, data entry and mapping.
DATA ORGANIZATION
& CONNECTIVITY
How do we achieve these actions?
Connecting Data
Ontologies provide structure and a data schema for
expanding the metadata model to create an
interconnected web of information that conveys
machine understandable relationships and meaning.

18. Explainable Solution
05
Build solutions that allow users to create, modify, find,
and use changing data through explainable AI
Data Organization &
Connectivity
03
Connect data concepts to your content (data sets, text
documents, applications, etc.)
People & Domain
Knowledge
02 Define the concepts important to your organization
The Problem or Use Case
01
Define the problem to be solved and the overarching
vision for AI
Data Enrichment
04 Enrich, add, and connect data concepts
Data Enrichment
04
At the core of Enterprise AI are the foundational principles of knowledge management - people and
culture, content and contextual information, governance processes - all integrated with technology.
Enrich your content and data with
domain knowledge and context via
extraction of topics and text for
taxonomy enrichment, auto-tagging,
and classification of key concepts in
your data.
Engage SMEs and knowledge engineers
to set the groundwork for
human-in-the-loop development and
getting knowledge into a standard,
machine-readable format.
Data Enrichment
04

19. How do we achieve this? DATA ENRICHMENT
Extract + Understand
+
Standardize + Label
+
Finetune + Repeat
● Training data
● Text extraction and analytics (NLP)
● Alignment of labeling with
organization’s standardized
taxonomy/ontology
● Auto-tagging and classification
● Accuracy, fine tuning, and iterations
subject relationship object
customer has product
document isAbout topic
Person
Product
Document
Transaction
Topic
Customer

20. Success Factors
Data Stored with Context:
Store information the way people
speak, with context. Data
relationships are first class citizens,
giving a better understanding of
related data.
Outcome: Understanding of the
relationships between data
Structured and Unstructured
Information:
Allow for the integration of structured
and unstructured information so that
users can access relevant data and
content at the same time.
Outcome: Connect business knowledge
to data, get a 360-degree view of the
organization
Interoperability:
Information is created based on web
standards and security protocols. Making
information interoperable across multiple
systems.
Outcome: Flexibility to move data between
industry & current and future systems,
minimize limitations of proprietary solutions,
avoid vendor lock.
Aggregation:
Allow for consistent ingestion of diverse
information types from sources internal or
external to the organization (e.g. Linked Data,
subscriptions, purchased datasets, etc.).
Outcome: Handle large datasets coming from
various sources, including public sources and
boost knowledge discovery and efficient
data-driven analytics
DATA ENRICHMENT

21. Explainable Solution
05
Build solutions that allow users to create, modify, find,
and use changing data through explainable AI
Data Organization &
Connectivity
03
Connect data concepts to your content (data sets, text
documents, applications, etc.)
People & Domain
Knowledge
02 Define the concepts important to your organization
The Problem or Use Case
01
Define the problem to be solved and the overarching
vision for AI
Data Enrichment
04 Enrich, add, and connect data concepts
Explainable Solution
05
At the core of Enterprise AI are the foundational principles of knowledge management - people and
culture, content and contextual information, governance processes - all integrated with technology.
Combining the principles of semantic
modeling with knowledge management and
AI, a rich semantic layer captures key
business facts and solutions and is
understandable, reusable and interoperable.
An Explainable Solution
05

22. A solution that works -
Business stakeholders
understand how it
works & want to use it
1
Data or content that
represents your
business and is
“NERDy”
2
Scalable practices
with a portfolio of
initiatives that have
different milestones
on the horizon
3
Closing: Success Factors EXPLAINABLE
SOLUTION
If we do these things right: We will have…

23. Lulit Tesfaye https://www.linkedin.com/in/lulit-tesfaye/ https://enterprise-knowledge.com/
Thank you!