There's No AI Without IA (Information Architecture)

Copyright © 2019 Earley Information Science, Inc. All Rights Reserved.
www.earley.com
GILBANE'S DIGITAL EXPERIENCE CONFERENCE
TUESDAY, APRIL 30TH, 11:45 A.M. - 12:30 P.M.
There's No AI Without
Information Architecture
(IA)
Seth Earley, Founder & CEO, Earley Information Science
seth@earley.com
781-820-8080
www.earley.com
www.linkedin.com/in/sethearley

Copyright © 2017 Earley Information Science, Inc. All Rights Reserved. 2
1994
YEAR FOUNDED.
Boston
HEADQUARTERED.
50+
SPECIALISTS & GROWING.
Earley Information Science is a specialized information agency. We support measurable
business outcomes by organizing your data, content and knowledge assets.
Our proven methodologies are designed specifically to address product data, content
assets, customer data, and corporate knowledge bases. We deliver scalable governance-
driven solutions to the world’s leading brands, driving measurable business results.
We make information more
useable, findable, and valuable.

AWARDS & RECOGNITION
2018 100 Companies that Matter in KM
2014 Trend-Setting Products Award
2013 Trend-Setting Products Award
2008 Trend-Setting Products Award (Wordmap)
2013 Applied Materials’ added to
InformationWeek 500 List of Business
Innovators
• “Cognitive Search Is Ready To Rev Up
Your Enterprise’s IQ”
• “Google-ize Your Site-Search Experience”
• “Polishing Up Your Products —
Why PIM Really Matters”
• “Artificial Intelligence Solution Landscape”
ANALYST MENTIONS
• “Unlocking the Hidden Value of
Information (Applied Materials)”
2015 KM Reality Award
(Allstate Business Insurance, ABIe project)

Further Reading/Future Reading
The Problem With AI
There’s No AI Without IA
February 2020

Three take aways
1. Artificial Intelligence is an evolution of technologies that have
been around for decades
2. Artificial Intelligence requires a solid foundation in information
management principles
3. Knowledge engineering can produce ROI today while preparing
for an AI future
5

AI as Evolution
6
Principles of machine learning have been
embedded in software we have used for years

7
“ When [AI] finally works, it
gets co-opted by some other
part of the field. So, by
definition, no AI ever works;
if it works, it’s not AI.
Source: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-825-
techniques-in-artificial-intelligence-sma-5504-fall-2002/lecture-notes/Lecture1Final.pdf
“

8
Spell check Speech recognition

• Mitigation of compliance risks
• Removal of Personally Identifiable
Information (PII)
• Removal of Redundant, Outdated and
Trivial (ROT) content
• Protection of intellectual property
• Identification of patterns of fraud
• Detection of customer sentiment
• Prediction of credit risks
• Feature extraction from product data
Text Analytics,
a long time staple
of content
management, is
now called “AI”

combined with a sophisticated UX”
Source: https://www.theregister.co.uk/2017/01/02/ai_was_the_fake_news_of_2016/
“The definition of “AI” has been stretched
so that it generously encompasses pretty
much anything with an algorithm”
vast knowledge
“What seems to be AI, is ,

Managing vast amounts of knowledge
requires a Knowledge Engineering approach
11

The Customer Experience and
Personalization

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How
customer-
oriented
are we?
13
Personalization has been
the big promise for the
past 15 years.
The problem is that this
vision is still a long way
from reality.
What Customers Want…
• Answers to their problems
• The right product
• Options and meaningful choices
• Help with their goal
• Assistance with a task
• Recommendations for a solution
• Expertise that they trust
• Responses that move them forward
• A “rewarding” experience
• Convenience, speed, efficiency
• Ease of doing business
What They Actually Get…
• Convoluted navigation
• Search results that are difficult to filter
• Too many choices
• Endless phone menus
• Call center reps w/o requisite knowledge
• Confusing content
• Marketing language that tries to sell them
• Incomplete information
• Frustrating, disconnected interactions
• Impediments, lack of responsiveness
• High friction processes

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Promise of Personalization
14
Personalization has been the big promise for the past 15 years. The problem
is that this vision is still a long way from reality.
• High fidelity journey models reflecting
audience goals based on buying stage
• A multi dimensional audience model
that describes their intent in data
terms along their journey
• Product data models aligned with
user buying criteria
• A messaging architecture to allow for
refactoring of offering components
• Componentized content that can be
reassembled to test offer and message
variations
• Knowledge and insights from across
the organization to articulate customer
needs and company solutions
Meaningful personalization requires

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Product Information, Content and the Customer Journey
Internal audiences need to easily find,
share and reuse content, data and
insights to support the external
customer experience
Merchandizers
Product managers
Category owners
MARKETING PROMOTION / PLANNING
PRODUCT DEVELOPMENT
Product Data/Content Product Content / Product Assets
PIM
PRODUCT ONBOARDING
PIM
Manager
Catalog
Manager
Merchandizer
Product Information Management
Campaigns
Email Marketing
Social media
Promotions
DEMAND GENERATION
$
Marketing managers
Marketing analysts
CONTENT STRATEGY
Editorial manager
Content manager
Category manager
Product content
Product assets
Marketing plans
ECOMMERCE
PERSONALIZATION
STRATEGIES
Purchase history
Demographics
Interest profile
Buyer persona
CUSTOMER SUPPORT
Call Center
Agents
Documentation
Warranty
Knowledgebase
Content/data source
Person/role
Collaboration
PROCESS
Support managers
K-base owner
CUSTOMER SELF
SERVICE
Reviews
Manuals
Knowledgebase
Regional managers
Market Analyst
Merchandizer
Market data
Regional demographics
Store sales
PROMOTIONS
Collaboration, Insights and Knowledge Sharing
Content Optimization
Customer Journey

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Intelligent Content Lifecycle
Internal audiences need to easily find,
share and reuse content, data and
insights to support the external
customer experience
Merchandizers
Product managers
Category owners
MARKETING PROMOTION / PLANNING
PRODUCT DEVELOPMENT
Product Data/Content Product Content / Product Assets
PIM
PRODUCT ONBOARDING
PIM
Manager
Catalog
Manager
Merchandizer
Product Information Management
Campaigns
Email Marketing
Social media
Promotions
DEMAND GENERATION
$
Marketing managers
Marketing analysts
CONTENT STRATEGY
Editorial manager
Content manager
Category manager
Product content
Product assets
Marketing plans
ECOMMERCE
PERSONALIZATION
STRATEGIES
Purchase history
Demographics
Interest profile
Buyer persona
CUSTOMER SUPPORT
Call Center
Agents
Documentation
Warranty
Knowledgebase
Content/data source
Person/role
Collaboration
PROCESS
Support managers
K-base owner
CUSTOMER SELF
SERVICE
Reviews
Manuals
Knowledgebase
Regional managers
Market Analyst
Merchandizer
Market data
Regional demographics
Store sales
PROMOTIONS
Collaboration, Insights and Knowledge Sharing
Content Optimization
Customer Journey
Product Data Maturity
Content Optimization Maturity
Knowledge Engineering Maturity
Customer Experience Maturity
Monitored by Metrics and
Governance Playbook to Track
Progress, ROI and Course
Corrections

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Multiple Components of Personalization
Content
Customer
Product Data
Knowledge

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Maturity of Capabilities
Personalization programs based on machine learning and intelligent content are part of a larger
ecosystem of tools, processes, methodologies and collaboration.
18
KNOWLEDGE ties
together all of the other
pieces – it is the human
element of judgement,
expertise and creativity
that is harvested from
experts and embedded in
data models and
processes.
CONTENT to engage the
customer must be findable,
and it must be relevant in the
moments that matter.
Content components and
snippets are the building
blocks for machine optimized
offerings
CUSTOMER DATA
needs to be consistent,
harmonized from different
systems and modeled to
provide and respond to
signals from interactions
both upstream and
downstream.
PRODUCT DATA
models must be
complete and aligned
with attributes and details
that are important to the
customer’s decision
making criteria.
METRICS DRIVEN
GOVERNANCE
measures ROI of projects
and provides feedback for
course corrections and
fine tuning of user
experience, product data,
and content decisions and
is the foundation for
automation
Maturity is required across all dimensions for long term success

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Modeling the Customer
Static Customer Data
Dynamic Customer Data

PRODUCT ARCHITECTURE
COMPONENTIZED CONTENT
CUSTOMER DATA FOUNDATION
DELIVERING PERSONALIZED CUSTOMER EXPERIENCES AT SCALE
What does it take to do this right?
advanced services & expertise
unified models standardized platforms & processes
20
• enriched customer journeys
• product attribute model & corresponding
taxonomies
• data intake, clean-up, aggregation.
• analysis, recommendation & decision
making
(ML, data science, human judgment)
• process setup (continuous or periodic)
• standard pipeline for insight
delivery to marketing teams
KNOWLEDGE & INSIGHTS
• product data with e-catalog and display
hierarchies optimized for customer
journeys
• back end product information onboarding
process aligned with customer
experience practices
• metrics driven decision making
• merchandizer collaboration with product
and solution experts
• configure price quote and recommendation
tools aligned with user personas and pain
points
• product information management
ecosystem aligned with rich media
• cross sell and upsell relationships
• merchandizing and solution bundles
• optimized content structure
• component architecture aligned with
messaging architecture
• content attribute model & corresponding
taxonomies
• omnichannel offer recommender
• dynamic offer generator
• content assembly based on offering
architecture and baseline hypotheses
tested against target outcomes
• recombination tested continuously using
changing messaging architecture
• component content management
system
• content production workflows
• content standards & governance
• high fidelity customer journeys with
augmentation and automation
opportunities
• customer attribute model &
corresponding multi-dimensional
audience taxonomies
• profile standardization
• pattern recognition
• customer signal reconciliation across
upstream platforms
• machine learning development & training
• customer data platform
• customer data modeling
• cross system normalization
• metrics aligned data governance
decision making

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Using a “High Fidelity” Journey Map
21
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Understand the customer journey
Identify details of the customer
Define content needed White Paper Product compare tool
Installation guide
Static Customer Data Dynamic Customer Data

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high-fidelity customer journey model
What does it take to do this right?
customer model
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
22
INTELLIGENT
PERSONALIZATION
Component content model
User journey/customer model
Product data model
Knowledge architecture
Static customer data:
industry, role, interests
Dynamic metadata identifies changing, real time. signals about customer
goals and intent while they go through their journey
Customer Data
Platform
Download white paper
Product compare &
purchase
Download installation guide Open email & click offer
Dynamic customer data
Dynamic customer data:
campaign responses, click
through, recent purchases
Delivering Personalized Customer Experiences – At Scale

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customer model
CMS and PIM
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Content
23
INTELLIGENT
PERSONALIZATION
Product data model
Customer Data
Platform
Top of funnel content
White Paper
Basic Widget
Product
New customer offer
1
What does it take to do this right? Dynamic metadata identifies changing, real time. signals about customer

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customer model
CMS and PIM
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Content
24
INTELLIGENT
PERSONALIZATION
Product data model
Customer Data
Platform
White Paper
Basic Widget
Product
Middle of funnel content
Product compare tool
Deluxe Widget
New customer offer
New customer offer
Product compare &
purchase
2

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customer model
CMS and PIM
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Content
25
INTELLIGENT
PERSONALIZATION
Product data model
Customer Data
Platform
White Paper
Basic Widget
Product
Deluxe Widget
Post purchase content
Installation guide
New customer offer
New customer offer
Product compare &
purchase
Download installation guide
3

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customer model
CMS and PIM
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Content
26
INTELLIGENT
PERSONALIZATION
Product data model
Customer Data
Platform
White Paper
Basic Widget
Product
Deluxe Widget
Post purchase content
Installation guide
Super Widget
Post purchase nurture
content
User tips
Promotion for upsell
New customer offer
Existing customer offer
New customer offer
Product compare &
purchase
Download installation
guide
Open email & click offer
4

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Personalization Example: Cross/Up-sell
27

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AI-driven personalization
requires
• meaningful knowledge and
content assets
• analytics to understand
and model customer
behaviors
• consistent architecture
across product information,
customer and content
models
• knowledge of problems to
anticipate needs
AI promises to help businesses meet / anticipate
customers’ needs
Product relationships
based on knowledge of
solutions
Browse history and
other constraints
(products available in a
region)
Configurations and
answers to questions
Product – review
relationships: what to
show, sorting rules
CHATBOTS
REVIEWS
CUSTOMER ALSO
VIEWED
RELATED
PRODUCTS

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Watson Intelligent Assistant

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Example: Amazon Alexa Skills
Skills are tuned for specific use cases. They use AI, but are backed up by a vast set of APIs and
knowledge. Skills surround a set of limited use cases (e.g., buy a movie ticket, order a pizza,
etc.), and all the AI, content and engineering are tuned for those specific interactions.
30

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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
31
31
Task Complexity versus Domain Complexity
“Helper bots”
“Configuration bots”
“Transaction bots”
Don’t start here
High domain complexity/
High task complexity

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Basic
Search Engine
Knowledge
Portal
Virtual
Agent
Intelligent
Assistant
Knowledge
Base
Any text
Multiple sources
Multiple sources, separate
Ontologies and schemas
Domain specific
Highly curated sources
Dynamic. Info enrichment
improves with interaction
Search
Interaction
Keyword or full text
query
Full text query or
Faceted Exploration
Query, explore facets
Offers related info
Implicit query / recommends
based on users’ history
Information
Architecture
None necessary, but
Improves with metadata
Ontologies, clustering,
classification
classification, NLP
classification, NLP,
personalization
User
Experience
Search box, documents list Role-Based Conversational
Conversational,
personalized,
contextual
Enabling
Technology
Search box, documents list
Search, classification,
databases
NLP, search, classification
Process engines
NLP, search, classification
Machine Learning
IA of Increasing Importance Moving to AI
Increasing Functionality / Increasing Information Richness
32

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1-Unpredictable 2-Aware 3-Competent 4-Synchronized 5-Choreographed
Metadata
Processes
Chaotic tagging practices
with no taxonomy
Business metadata in use, with
controlled vocabularies in isolation
Enterprise taxonomy is replicated
across sites and platforms, and
metadata use is manual but consistent
Content schemas with clear metadata
requirements are used effectively to
group, aggregate, sort, and filter
assets in the interface
Contextualized and/or personalized
metadata with auto-population of
values and ongoing quality audits
IA / User
Experience
Haphazard creation of content and
sites with inconsistent content models
and poor site experience
More consistency across sites and
platforms, but with little cross-site
structure
Consistency across sites in how
content is displayed, particular in
regards to metadata relevant to
specific groups and audiences
Enforced metadata standards provide
a context-dependent user experience
for content management and receipt,
in a Web-page-like experience
Integration of structured and
unstructured information from
multiple systems dynamically
presented to support user tasks in an
application-like experience
Search
Integration
Random document generator
Some tuning of search with
individualized content tagging
Scope-able search with consistent but
uncontrolled tagging
Faceted search with taxonomy-
governed tagging; metadata available
for use within the search experience
Search-based application with
associative relationships (related
search), tuned algorithms, facets, and
metrics-driven use cases
User
Proficiency &
Content
Practices
Poor or minimal usage, lack of
awareness of capabilities or content
practices
Early adopters and power users using
out-of-box features, little control of
content
Departmental collaboration with basic
content control
Cross-team project-oriented
collaboration with information
lifecycle management
Automated workflows and reporting
for compliance with enterprise
content standards
Governance
Information sprawl, lack of vision, no
intentional decision making, minimal
centralized architectural support
Awareness of challenges; activity is
monitored but not constrained;
analytics data exist but are not
reviewed and/or managed
consistently
Assigned responsibilities, oversight
and communications infrastructure in
place, best practice documentation is
published and available
Intentional decision making, resource
allocation, change controls in effect;
quality metrics are regularly reviewed
Agenda-driven business leadership
and stakeholder engagement to effect
continuous process improvement
through measurement and
collaboration
Technology
Business groups use their own
organically developed approaches;
business requirements are
undocumented. No global search.
Business groups follow improvised
processes within established tools;
multiple work-arounds used to
improve information findability
Global systems processes are
supported by home-grown or external
systems for tracking, managing, and
finding content
Dedicated, managed systems enable
multiple distinct workflows and
processes that business groups
understand, follow, and find
information
Systems are architected to share
information within and across teams
and divisions, to find content with high
relevancy
Building Maturity current target
priorities
33

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The Knowledge Management challenge is usually put into
language that confuses the issue:
Vendors say that they need to “train the AI”
What do you “train the AI” with?
…high value knowledge assets (quality data and curated content)
The Knowledge Management Challenge
34

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THE BOTTOM LINE
“Training data” is the biggest challenge to artificial intelligence program
development
The knowledge that artificial intelligence driven bots need is the same
knowledge that humans need
By building knowledge management programs to support employees, the
organization is preparing for improved access through emerging AI and Bot
technologies
35

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EIS Bot Factory workstreams
• Content type and
variable definitions
• Classification schema
design
• Feature engineering
• Vocabulary development
• Associative relationship
mapping
• Deconstruction and
componentization of
FAQ’s,
troubleshooting
guides, reference
materials, e-learning
modules, etc.
• Content refactoring
and component
tagging
• Integration of
component models
with user experience
• Crowdsourcing of
phrase variations for
intent triggers
(utterances)
• Classification of intent
using customer issue
and query data
• Entity extraction
training and tuning
• Escalation and handoff
model
• Feedback workflow
design: utterance,
intent and knowledge
• Success metric design
• Governance and
accountability model
• Speech to text conversion
• Text mining/ analytics on
call logs / support content
• Search analytics
• User journey mapping
• Scenarios and use cases
• Identification of
repeatable, unambiguous
processes
• Deconstruction of user
journeys into dialogue
components
• Precoordinated intent
design
• Disambiguation models
• Intent entity extraction
• dialogue context tagging
model
PROCESS
ANALYSIS
DIALOGUE DESIGN &
INTENT CLASSIFICATION
CONTENT ANALYSIS,
DOMAIN MODELING
& ONTOLOGY DESIGN
COMPONENTIZATION OF
KNOWLEDGE CONTENT
TRAINING DATA
CORPUS
DEVELOPMENT
HYBRID LEARNING &
CONTINUOUS
IMPROVEMENT MODEL
CREATION
36

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Standardized/Normalized Content is Portable and Reusable
37
Standardized domain
specific schemas for
reuse
Field 1
Field 2
Field n
…
Field 1
Field 2
Field 3
Field n
…
ELearning, FAQ’s,
Troubleshooting
charts, support
articles
Componentized
content
Tagging for ingestion
Componentized content can be
repurposed across tools and
technologies Improved CSR
Information Access
Faster time to value for all information
access scenarios
Portability across AI and
Chatbot systems
Improved customer self
service
Metrics aligned with specific
content performance
COMPONENTIZATION OF
KNOWLEDGE CONTENT

Agent: “I need to determine liability coverages for employee
actions for a collection agency in Massachusetts”
Agent: “Hi, I need some help with a policy”
Semantic deconstruction of utterance
Bot: “OK. Can you tell me what kind of policy?”
Topic = “liability coverage”
Product = “employee practices liability”
Nature of business = “collection agency”
Region = “Massachusetts”
Content type = “Guideline”
Entity derivation
Context derivation
Audience = “Certified agent”
Topic
Product
Nature of business
Region
Content type
Audience
Faceted retrieval from
knowledge base
Returns content tagged
with appropriate metadata
38

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Domain modeling, knowledge mapping
ABI manual maps – Concept Topics
Job Aid maps - Process Topics
FAQ maps - QA Pairs
39
CONTENT ANALYSIS,
DOMAIN MODELING
& ONTOLOGY DESIGN

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Allstate Strategy
ABI Objectives
ABI Revenue
Call Center Traffic Agent Book of Business
Business Processes
ABIe Effectiveness Content Quality
DITA Content
& Taxonomy
ABIe Stats,
Google
Analytics
Search
Test
Cases
Processes enable
objectives
L
I
N
K
A
G
E
Search Quality
Grow top line Allstate revenue
Data supports
(and measures)
processes
Objectives align
with strategy
Data Sources
Working here
(search, content,
IA, taxonomy,
metadata, etc.)
Measuring here
(Process
measures)
Measuring here
(ABI KPIs –
Results
Measures)
Aligning enterprise strategy with virtual assistant metrics
CEO: “Show me how will this project increase our revenue.”
ABIe Utilization
HYBRID LEARNING &
CONTINUOUS
IMPROVEMENT MODEL
CREATION

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Search
Analysis
• Identify new taxonomy terms
• Identify new auto-completes
• Identify missing content
Behavior
Analysis
• Analyze & Remediate Top Best Bets
• Analyze & Remediate Top Searches
• Identify missing content
Utilization
Analysis
• Correlate ABIe usage to call center volumes
• Gather and analyze VOC
• Identify action plans
Content
Analysis
• Measure compliance with editorial guidelines
• Measure compliance with tagging guidelines
• Remediate issues
Metrics-driven Governance Processes
HYBRID LEARNING &
CONTINUOUS
IMPROVEMENT MODEL
CREATION

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Contextual customer
information
Customer/Bot/Agent
interaction
Performance
Metrics/Trends
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Customer question
Bot interpretation of
customer intent
46

Human agent ability to
correct intent
classification
47

Bot candidate response
with confidence score
Human agent accepts bot
answer
48

Customer’s follow on
question
Bot’s lower confidence
score for answer

Bot’s recommended
content
51

Human agent selects
bot response for edit
54

Human agent updates
response
55

Updated response sent
to customer
Update becomes
part of bot
learning
56

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TRAINING
AIDS
CALL LOGS CUSTOMER
PROFILE DATA
ANALYTICS
& ACTIVITY
SOCIAL
NETWORKS
DEMOGRAPHIC &
ETHNOGRAPHIC DATA
SENTIMENT
ANALYSIS
SERVICES &
OFFERS
CUSTOMER EXPERIENCE ENRICHED
BY KNOWLEDGE
BOT MATURITY &
SCALABILITY
Combining Platform Independent Knowledge with
Agent-Bot Collaboration for Scalability & Customer Experience
58
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Diving Deeper: Enabling A Digital Architecture
59
CONTEXTUALIZED USER EXPERIENCE
Context Aware Information Architecture
Content Model Taxonomy Metadata
Structured
(Operational) Data
Unstructured
(Big) Data
Information Infrastructure
Marketing
Data
User
Data
Product
Data
Historical
Data
Operating
Content
Information Management Platforms
PIM DAM CMS ECM CRM ERP
Customer
Personalization
Content
Publishing
Site
Merchandizing
Product Info.
Management
Digital Commerce
Business
Intelligence
Knowledge
Management
Enterprise Search
Content
Management
Digital
Workplace

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Always remember – “There is no AI without IA”
• It’s only AI if we don’t know how it works
• Simplicity is hidden complexity
• Clean data is the price of admission
• Identify user journeys, data sources and data owners
• Define governance, curation, and scalable processes
60

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Suggested Resources
61
Allstate’s ABIe project case study
http://www.earley.com/knowledge/case-
studies/allstate%E2%80%99s-intelligent-agent-reduces-call-
center-traffic-and-provides-help
Cognitive Computing Consortium
http://www.cognitivecomputingconsortium.com/
Enterprise Search: 14 Industry Experts Predict the Future of
Search http://www.docurated.com/enterprise-
search/enterprise-search-14-industry-experts-predict-
future-search
Evaluating Enterprise Virtual Assistants
http://info.intelliresponse.com/rs/intelliresponse/images/O
pus_EvaluatingEnterpriseVirtualAssistants_Jan2014%20(2).p
df
Characteristics of Highly Effective Enterprise Virtual
Assistants
http://www.slideshare.net/intelligentfactors/characteristics
-of-highly-effective-enterprise-virtual-assistants
The Knowledge Graph and Its Importance for Intelligent
Assistance
http://opusresearch.net/wordpress/2016/01/12/the-
knowledge-graph-and-its-importance-for-intelligent-
assistance/
Making Intelligent Virtual Assistants a Reality
http://info.earley.com/make-intelligent-virtual-assistant-
reality-whitepaper
Cognitive Search – The Next Generation of Information
Access http://www.earley.com/blog/cognitive-search-
next-generation-information-access
Earley Executive Roundtable - Training the Robots:
Evolving Virtual Assistants and the Human Machine
Partnership http://info.earley.com/roundtable-virtual-
assistant-human-machine-partnership
Earley Executive Roundtable Understanding virtual agents
– what's needed to make them a reality?
http://info.earley.com/roundtable-intelligent-virtual-
agents-reality
Vendor Landscape: Knowledge Management For Customer
Engagement
https://www.forrester.com/report/Vendor+Landscape+Kn
owledge+Management+For+Customer+Engagement/-/E-
RES119672

Thank You!
CONTACT US
Seth Earley
Founder & CEO
Earley Information Science
seth@earley.com
781-820-8080
62

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