In this webinar Seth Earley establishes the formula for AI success, demystifies the topic for executives and provides actionable advice for data strategists. Key Takeaways: **AI-Powered solutions begin with a focus on business goals **Successful AI requires a semantic data layer built on a solid enterprise information architecture. **Instrumenting measuring ROI should be part of every AI program

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WEBINAR
WEBINAR
SPEAKERS
Building theAI Powered Enterprise
Artificial Intelligence BeginsWith Information
Architecture
SETH EARLEY
FOUNDER & CEO
EIS
DAVE SKROBELA
MANAGING DIRECTOR
EIS
THANK YOU

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Today’s Speakers
SETH EARLEY
Founder & CEO
Earley Information Science
@sethearley
https://www.linkedin.com/in
/sethearley/
Seth@earley.com
DAVE SKROBELA
Managing Director
Earley Information Science
@daveskrobela
https://www.linkedin.com/in
/skrobela/
Dave.Skrobela@earley.com
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BeforeWe Get Started
WE ARE RECORDING SESSIONWILL BE
50 MINUTES PLUS
10 MINUTES FOR
Q&A
YOUR INPUT IS
VALUED
Link to recording will be
sent by email after the
webinar
Use the Q&A box to
submit questions
Participate in the polls
during the webinar
Feedback survey afterward
(~1.5 minutes)
Thank you to our media partner2 : CMSWire & Marketing AI Institute
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Agenda
What is an “AI Powered Enterprise”? How should an AI strategy be developed?
Ontologies, knowledge graphs and data quality
Business case, investment justification and ROI
Getting started or moving forward on your journey
Objective:
Establish the formula for AI success, demystify the topic for executives and provide actionable
advice for data strategists.
Take aways:
AI-Powered solutions begin with a focus on business goals
Successful AI requires a semantic data layer built on a solid enterprise information architecture.
Instrumenting measuring ROI should be part of every AI program
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*https://www.accenture.com/us-en/blogs/intelligent-functions/scaling-ai-how-to-make-it-work-for-your-company
**https://www.mckinsey.com/featured-insights/artificial-intelligence/ai-adoption-advances-but-foundational-barriers-remain
Business Readiness forAI
75% of business
leaders feel that they
will be out of business in
five years if they can't
figure out how to scale
AI*
5
But 92% believe they
have not comprehensively
mapped the opportunities
for AI adoption**

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A recent survey of Fortune 1000 data
executives from NewVantage
Partners* found that more than 90%
were increasing their investment in data
and AI.
At the same time, only 26% have AI
systems in widespread production. One
of the blockers is a lack of data quality.
*https://www.newvantage.com/_files/ugd/e5361a_ad5a8b3da8254a71807d2dccdb0844be.pdf
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Discussion
7

8. 8
TOPIC
Defining the AI
Powered
Enterprise
FIRSTMARK CAPITAL – MAD LANDSCAPE 2021

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AUTOMATE ROUTINETASKS
• Robotic Process Automation (RPA)
• Reduce the need for human input and
save time on routine tasks
• Increasing use of conversational
systems (bots) to handle routine task
management
• Reduce administrative work, allow for
higher value work
• Increase focus on supporting people,
enabling more effective collaboration
What CanAI Do?
ANALYSIS OF PATTERNS
• Predictive Analytics
• Identify trends and anomalies across
customers, products, programs,
employees
• Churn patterns, proactively identify
problems with equipment, installation,
service
• Monitor multiple variables in customer
interactions
• Surface hidden factors buried in large
amounts of data
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“Cognitive”AI
• Reduces the “cognitive load” on humans
• Surfaces information in anticipation of a task or need
• Provides conversational access to knowledge
(processes, procedures, status inquiries, etc.)
• Accelerates time to productivity
Leverages a Knowledge Architecture
10

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Information Retrieval Continuum
BASIC
SEARCH ENGINE
KNOWLEDGE
PORTAL
VIRTUAL
AGENT
INTELLIGENT
ASSISTANT
KNOWLEDGE
BASE
Any text
Multiple sources
Keyword or full text
query
None necessary, but
Improves with metadata
Search box,
documents list
Search
Multiple sources, separate
taxonomies and schemas
Full text query or
Faceted exploration
Taxonomies, clustering,
classification
Role-Based
Search, classification,
databases
Domain specific ontologies
Highly curated sources
Query, explore facets
Offers related info
Conversational
NLP, search, classification
Process engines
Dynamic info enrichment
improves with interaction
Implicit query /
Recommends based on
users’ history
Conversational,retains
context, personalized
NLP, search, classification
Machine Learning
Ontologies, clustering,
classification, NLP
Ontologies, clustering,
classification, NLP, personalization
SEARCH
INTERACTION
INFORMATION
ARCHITECTURE
USER
EXPERIENCE
ENABLING
TECHNOLOGY
Increasing functionality

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Complex Advisory/ Diagnosis
Product Support
Product Configuration
Judgment Based
Domain
Complexity
Transaction Support Knowledge Retrieval
Information/
status inquiries/
order processing
Task/dialogue Complexity
12
12
Task Complexity versus Domain Complexity
“Knowledge bots”
“Configuration bots”
“Transaction bots”
Don’t start here
High domain complexity/
High task complexity

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Poll #1
13
1. High level executive vision
2. Defined at a functional level (sales, customer service, etc.)
3. Cross functional level
4. IT/data science owned research initiatives
How well defined is your AI strategy?

14. 14
TOPIC
Role of Data
Quality &
Architecture
WAVESTONE I DATA AND AI LEADERSHIP EXECUTIVE SURVEY 2022

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“Sound bite” definitions
• A Taxonomy is a list of terms that enable classification of information
• Method used to organize Subject/Topic metadata
• Typically expresses hierarchical relationships (parent/child)
• Emphasizes context
• A Thesaurus is a specialized taxonomy
• Equivalence relationships (synonyms)
• Associative relationships (related terms – “see also”)
• Preferred terms, variant terms
• An Ontology is a collection of related taxonomies and thesauri
• A body of knowledge is represented by multiple lists of categories
• Categories of various types are conceptually related
• Typically uses a full range of logical expressions (not just parent/child) to show relationship
@sethearley

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ONTOLOGY IS
THE CONTEXTUAL
AND SEMANTIC
FRAMEWORK
FOR THE
ENTERPRISE
Knowledge Graphs make
data more accessible and
usable by the entire
enterprise using the
ontology framework.
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Taxonomy
Development
Taxonomies for:
• Entities
- Customers
- Products
• Conditions
- Environments
- Industries
• Activities
- Tasks
- Processes
DEPARTMENTS
INDUSTRIAL
DIST
• Fastenal
• Grainger
• MSC
• Wolseley
• …
ENVIRONMENTS
• Marine
• Underground
• Confined Space
• …
PROCESSES
• Rough Cut
• Finish Cut
• Polishing
• Coating
• ...
TASKS
• Extraction
• Fabrication
• Joining
• Separating
• …
PRODUCTS
• Abrasives
• Clamping
• Fasteners
• …
INDUSTRIES
• Mining
• Food Processing
• Healthcare
• …
CUSTOMERS
• Hitachi
• Schlumberger
• Toyota
• …
INTERESTS
• Prototyping
• MRO
• Replenishment
• …
• Tech Support
• Merchandising
• Sales
• …
ROLE
• Design engineer
• Maintenance engineer
• Procurement Mgr
• ...
Industrial DistributorTaxonomies
DOCUMENTTYPES
• Installation guides
• Manuals
• Marketing plans
• …
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Industrial Distribution Ontology
Industrial
Distributors
Departments
Industries
Interests
Processes
Customers
Products
• Tech Support
• Merchandising
• Sales
• …
• Abrasives
• Clamping
• Fasteners
• …
• Marine
• Underground
• Confined Space
• …
• Rough Cut
• Finish Cut
• Polishing
• Coating
• ...
• Extraction
• Fabrication
• Joining
• Separating
• …
• Mining
• Food Processing
• Healthcare
• …
• Prototyping
• MRO
• Replenishment
• …
Environments
Tasks
Document
Types
ABCo
Competitors
ABC Company
H
H
A
A
A
A
A
A
A
A
A
H
E
• Fastenal
• Grainger
• MSC
• Wolseley
• …
• Hitachi
• Schlumberger
• Toyota
• …
• Installation guides
• Manuals
• Marketing plans
• … 18

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Industrial Distribution Knowledge Graph
Customers
Tasks
ABC Company
Document Types
Industries
Products
Processes
Roles
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Applications for the Ontology and KGraph
• Taxonomies and metadata models allow for faceted retrieval and
personalized recommendations
• Graph relationships between products, customers, roles, interests, tasks,
processes and industry support personalization and disambiguation
• Procurement managers are interested in total cost of ownership, supply
chain reliability, product lifecycle, replacement parts and other reliability data
• Design engineers are looking for innovative products that meet their design
objectives and specifications
• Search terms can be disambiguated using industry as an additional signal
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Poll #2
21
1. Revenue generation
2. Cost savings (reducing spend)
3. Cost containment (doing more with same spend)
4. Customer satisfaction
5. Improved brand equity
6. Risk mitigation
What high level business benefits are driving
your AI initiatives?

22. 22
TOPIC
Getting
Measurable ROI
32%
of companies
reported being able to
realize tangible and
measurable value from
data
Only
ACCENTURE – CLOSING THE DATA VALUE GAP

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Measuring here
(business outcomes)
Measuring here
(process indicators)
Enterprise Strategy
Business Unit Objectives
Likelihood to Recommend
Customer Sat Scores Renewals
Business Processes First call resolution Knowledge base usage
Search
Digital Content
Working & Measuring
here (knowledge,
architecture, taxonomy,
search, etc.) Trouble
Ticket System
Knowledge
Base
Processes enable
objectives
L
I
N
K
A
G
E
Time per incident/AHT
Improved recurring revenue
Content supports
processes
Objectives align
with strategy
CEO: “How will this program contribute to
increased revenue?”
Abandonment
Data Scorecards
Process Scorecards
Outcome Scorecards
Accuracy Knowledge quality
Digital Team: “How do I know architecture / AI tool/ knowledge / search is
working?”
MeasuringValue
Search relevance
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Data/Technology Scorecards
Process Scorecards
Outcome Scorecards
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Poll #3
25
1. We are not collecting the data necessary to measure results
2. The data is not yet showing meaningful improvements
3. We are showing some value, but not enough to justify
additional investments
4. We are showing clear ROI that has justified our investments
How are tracking return on AI programs?

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Questions to ask
• What is the real business problem that the
solution is attempting to solve?
• What is the quantifiable impact? How can
it be measured?
• How will the organization adapt to and act
on this new information?
• Where will the data come from? Is it of
sufficient quality?
• Will a proof-of-concept scale? What was
required to make it work?
ENGAGING INTHIS DIALOGWILL HELPTO
MANAGE EXPECTATIONS
26
• How will data issues be addressed
upstream?
• Is the process clear? What aspects of the
process will AI improve?
• Who will own the solution?Who else will
be impacted? Who will fund continued
development?
• Has the organization been correctly
informed about expected capabilities?

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Get StartedWith an EISAI ReadinessAssessment
27
We can help get you there.
Through a combination of interviews, questionnaires, surveys and
working sessions, the EIS AI Readiness Assessment:
1. Educates executives and stakeholders about types of AI technologies
in layperson terms
2. Outlines success factors and approaches for achieving real business
value from AI
3. Examines four critical areas of the enterprise for AI readiness:
• Organizational design
• Business alignment and process clarity
• Data readiness and technology infrastructure
• Ongoing governance, decision making and success measures
4. Summarizes the current state in an executive working session
designed to identify gaps, set realistic goals and prioritize actions.
THE OUTPUT OF THE EIS
AI READINESS
ASSESSMENT IS AN
ACTIONABLE ROADMAP
FOR AI CAPABILITY
DEVELOPMENT AND
DEPLOYMENT

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Thanks!
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Resources
29
[EIS]The Role of Ontology and Information
Architecture in AI
https://www.earley.com/insights/role-ontology-
and-information-architecture-ai
[EIS]There’s No AIWithout IA: 5 InsightsYou
Don’tWant To Miss
https://www.earley.com/insights/theres-no-ai-
without-ia-5-insights-you-dont-want-miss
[Accenture] Scaling AI: How to make it work
for your company
https://www.accenture.com/us-
en/blogs/intelligent-functions/scaling-ai-how-to-
make-it-work-for-your-company
[McKinsey] AI adoption advances, but
foundational barriers remain
https://www.mckinsey.com/featured-
insights/artificial-intelligence/ai-adoption-
advances-but-foundational-barriers-remain
[NewVantage] Data and AI Leadership
Executive Survey 2022
https://www.newvantage.com/_files/ugd/e5361
a_ad5a8b3da8254a71807d2dccdb0844be.pdf
[Accenture] Closing the DataValue Gap
https://www.accenture.com/_acnmedia/pdf-
108/accenture-closing-data-value-gap-fixed.pdf

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CONTACT US
CONTACT US
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Thank you for your time.We’d love to
hear from you!
Earley Information
Science
www.earley.com
Seth Earley
Seth@earley.com
Dave Skrobela
Dave.Skrobela@earley.com