Enterprises are increasingly recognizing the critical need for knowledge management (KM) to power cognitive AI. In fact, KM and AI are two sides of the same coin. Training a chatbot requires the same organized information that we use to train a human. When you engineer knowledge correctly, you serve the needs of people today and prepare for greater automation in the future. In fact, the long term success of the organization will depend on doing just that – especially when the competition builds high functionality bots that will produce lower costs and better customer service. Those without the capability will not be competitive. In this panel discussion, our experts discuss examples and approaches that show how KM supports AI and how to ensure the success of your KM initiative. Knowledge management and AI People and cultural considerations Business justification for long term investment

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WEBINAR
WEBINAR
SPEAKERS
The Knowledge Management Imperative:
Why KM is Essential toAI
SETH EARLEY
FOUNDER & CEO
EIS
DAN MILLER
LEAD ANALYST & FOUNDER
OPUS RESEARCH
THANK YOU
JEN SNELL
VP GTM CONVERSATIONAL AI
VERINT

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Today’s Speakers
SETH EARLEY
Founder & CEO
Earley Information Science
@sethearley
https://www.linkedin.com/in/set
hearley/
Seth@earley.com
DAN MILLER
Lead Analyst & Founder
Opus Research
https://www.linkedin.com/in/danmiller/
@dnm54
@opusresearch
dmiller@opusresearch.net
Jen Snell
VP, GTM Conversational AI
Verint
https://www.linkedin.com/in/jennifersnell1/
@JenniferLSnell
@Verint
jennifer.snell@verint.com

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BeforeWe Get Started
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Participate in the polls
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Feedback survey afterward
(~1.5 minutes)
Thank you to our media partner2 : CMSWire & Marketing AI Institute

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Agenda
4
Panel Introduction
Perspectives
• Seth Earley
• Dan Miller
• Jen Snell
Discussion
• Knowledge management and AI
• Business justification for long term investment
Q&A

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Perspective –
Seth Earley,
Earley
Information
Science
Cognitive AI Runs on
Knowledge and
Content

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Fragmented Content Operations
6
Engineering
PIM, PLM, PCM,
CPQ
Proposal Processes
Content authoring, content
management systems
Marketing
Campaign management,
go to market planning,
offerings development
Sales
Proposal development,
presales support
Support
Self service content, call
center support content
Demand Gen
Content authoring, content
management systems
Presales Support
Pitch decks, demonstration
scripts
Collateral Development
Sales asset management
Content Operations
Content authoring, content
management systems
Email Marketing
Content authoring, content
management systems
Product Data Operations
Product content, product
information
Digital Operations
Content authoring, content
management systems
Field Service
Field service management
systems
Self Service Support
Knowledge curation, knowledge
bases
Promotion Management
Regional Marketing
Engineering Content
Operations
Lifecycle mgt, support,
manufacturing processes
Call Center
Content authoring, content
management systems
AI/ CognitiveAssistant
Content Operations

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Consolidated Content Operations
7
Engineering
Content Operations
PIM, PLM, PCM,
CPQ
Content authoring, content
management systems
Sales
Marketing
Campaign management, go
to market planning, offerings
development
Proposal development,
presales support
Support
Self service content, call
center support content
Create Once, Publish Everywhere
(COPE)

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Chatbots are a channel…
…TO KNOWLEDGE, DATA AND CONTENT

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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
Ontologies and schemas
Full text query or
Faceted Exploration
Ontologies, clustering,
classification
Role-Based
Search, classification,
databases
Domain specific
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
9

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A complete
Information
Architecture can be
quite complex
10

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ControlledVocabularies –The
“official” terms used to describe
things
11
A Knowledge retrieval bot uses
simpler subset of design elements
based on focused use cases.

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Transforming Content for Multiple Channels
12
Knowledge
Architecture enables
the correct structure for
components
Field 1
Field 2
Field n
…
Field 1
Field 2
Field 3
Field n
…
Product guides,
specification sheets,
troubleshooting
information, FAQ’s and
related support content
Converted to “bite-sized”
chunks to answer
questions
Componentized content can serve
multiple purpose
Componentized content can be
repurposed across tools and
technologies Improved call center
efficiencies
Powering conversational
applications and virtual
assistants
Customer self service
KPI’s and Metrics monitor the value of knowledge assets
for ongoing improvement

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Poll
13
1. There have been prior efforts, but they have not been
sustainable
2. We have had pockets of deployments in specific high value
areas that are being maintained
3. Our knowledge processes are mature but at the department
or BU level
4. We have mature enterprise knowledge operations
5. None of the above
Where are you on your knowledge journey?

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Perspective –
Dan Miller,
Opus Research 2022 Major Trends
Review

15. 15
Reminder: Who we are
• Founded in 1986
– Coined “Conversational Commerce”
– Compiled first map of IA Ecosystem
– Analysis of Conversational Intelligence, IAs and IAuth
• Advisory Services, Publications & Events
– Strategy sessions
– Advisories, Impact Briefs, Vendors that Matter
– Webcasts and Focused Events

16. 16
Major Trends (2022 Preview)
• “Conversational AI” becoming a commodity
• Conversations replacing Contact Centers
• Smart search supplanting Knowledge Management
• Conversational Intelligence acting as Voice of the
Customer
• Automation driving ROI and Vice Versa

17. Euro Tour 2021 17
The Conversational Cloud

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Poll
18
1. There is no connection to knowledge operations – KM for AI
is a siloed AI project effort
2. The organization is able to reuse knowledge for retrieval by
cognitive assistants but through a separate process
3. Knowledge is created once and published everywhere –
including through cognitive assistants - using component
content
4. None of the above
How would you describe KM for Artificial
Intelligence in your organization?

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Perspective –
Jen Snell,
Verint Consumer ShiftsWiden
the Engagement
Capacity Gap

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Recent Changes Will Have Deep and Lasting Impact
Workforce
Work from Anywhere
Generational Shifts
Workforce of Humans & Bots
Consumer
Elevated Expectations
Rise of Self-Service
Choice of Journeys
Shift
to Digital

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Increasing Interactions and
Elevating Expectations
Time
The Engagement
Capacity Gap™
Budget &
Resources
21
Consumer Shifts Widen the Engagement Capacity Gap

22. © 2022 Verint Systems Inc. All Rights Reserved Worldwide. 22
Brands Require a Whole New Set of Capabilities
to Close the Engagement Capacity Gap
Embrace an
Enterprise-wide
CX Culture
Power Today’s
Evolving and
Distributed
Workforce
Embrace
Digital-First
Engagement
Embrace the
Enterprise
Ecosystem
Harness Data,
AI and
Analytics to
Drive Action

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Discussion
23

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Knowledge management andAI
24
Consumer centric approach
What is conversational intelligence?
How does KM support AI projects?

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Business justification for long term investment
25
What is the business case for investment?
How can the organization be sure that investments make sense and
are producing value?
How can ROI be measured over the long term when a great deal of
foundational work needs investment?

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Q&A
TRENDS & PERSPECTIVES
26

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Resources
27
[Blog] Knowledge Management’s
New Renaissance - Solving
Problems Today While Preparing
for an AI Powered Future
https://www.earley.com/insights/bui
lding-knowledge-systems-solve-
problems-today-and-prepare-future
[Blog] The Role of Ontology and
Information Architecture in AI
https://www.earley.com/insights/rol
e-ontology-and-information-
architecture-ai
[eBook] AI Powered Enterprise –
Chapter 1
https://www.earley.com/the-ai-
powered-enterprise-chapter-1
[Blog] There’s No AI Without IA: 5
InsightsYou Don’t WantTo Miss
https://www.earley.com/insights/the
res-no-ai-without-ia-5-insights-you-
dont-want-miss

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CONTACT US
CONTACT US
Thank you for your time.We’d love to
hear from you!
28
Earley Information
Science
www.earley.com
Seth Earley
Seth@earley.com
Opus Research
https://opusresearch.net
Dan Miller
dmiller@opusresearch.net
Verint
https://www.verint.com/
Jen Snell
jennifer.snell@verint.com

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Measuring here
(business outcomes)
Measuring here
(process indicators)
Enterprise Strategy
Business Unit Objectives
New Business Opportunities
Average Order Size Total Account Revenue
Business Processes Site Traffic Search Relevance
Master
Data
Data and Content
Working & Measuring here
(data quality, IA, taxonomy,
search, data fill, etc.) Product
Content
CRM
Processes enable
objectives
L
I
N
K
A
G
E
Leads
Revenue Growth
Data supports
processes
Objectives align
with strategy
CEO: “How will this increase revenue?”
Conversion
Data Scorecards
Process Scorecards
Outcome Scorecards
Heuristics Fill Rate Content Quality etc.
Digital Team: “How do I know data/ taxonomy / content / search is working?”
Using Metrics & KPIs to Focus Governance

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Data/Technology Scorecards
Process Scorecards
Outcome Scorecards