Artificial intelligence (AI) is getting lots of attention but one key aspect is often overlooked, understated, or underestimated: the quality of “training” information and the structure of that information – the Information Architecture or “IA”. AI only works when it has the data it needs to spot trends, identify patterns and provide functionality – especially when it comes to chatbots and other so called “cognitive” technologies. While many recent high profile attempts at chatbots have failed, they are getting better and one day will be indispensable. Organizations need to do certain things to prepare for a future of bots and AI-driven processes. This session will outline what that looks like and how organizations can solve problems today while preparing themselves for a future where businesses will succeed or fail based on the power of their bots.

1. No AI Without IA: Information
Architecture as a Critical Enabler
Mapping enabling processes, content & data to improve
efficiencies today while paving the way for AI-driven
personalization tomorrow

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We make information findable, useable and valuable. 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 ORGANIZE
DATA AND
CONTENT TO
DRIVE MEASURABLE
BUSINESS IMPACT.@EarleyInfoSci

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What customers
really 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
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What they
actually get…
• Convoluted navigation
• Search results that are difficult to filter
• Too many choices
• Endless phone menus
• Call center reps without requisite knowledge
• Confusing content
• Marketing language that tries to sell them
• Incomplete information
• Frustrating, disconnected interactions
• Impediments, lack of responsiveness
• High friction processes
4

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Definition of Personalization
“Virtue means doing the right thing, in
relation to the right person, at the right
time, to the right extent, in the right
manner, and for the right purpose.”
“Nicomachean Ethics” Aristotle
350 B.C.E
5

6. April, 1974

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Promise of Personalization
7
Personalization has been the big promise for the past
(23)15 years. The problem is that this vision is still a
long way from reality.
Meaningful personalization requires
• meaningful knowledge and content
assets
• the use of analytics to understand and
model customers
• prediction to anticipate what they need
• principles of AI to fulfill the promise

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Think Bigger: Customer Journeys
It is hard work seeing
the connected journey
from any one point!
business
data flow
product
engagement
lifecycle
product
information
lifecycle
content
lifecycle
CUSTOMER JOURNEYS
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Understand the customer Lifecycle Stages
Identify target roles and personas
• Role is generalized user accomplishing a particular set of tasks
(buyer, customer service agent, salesperson)
• Persona is a representation of a specific user
Define actions, touch points, pain points, opportunities at
each stage
Determine content and information sources and lifecycles
Identify technologies that support tasks and interactions
Align “signals” with data architecture
Deconstructing the Journey Map
OPPORTUNITY
PAIN POINT
DATA
ARCHITECTURE
TOUCHPOINT
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Data is the Key Enabler of AI and Machine Learning
Enriched data and decisioning
criteria to guide personalized
customer experiences (i.e.
search, merchandizing, etc.)Knowledge Content
• Portion of revenue from high
value customers
• Time between purchases
Sales analysis
• High value customers
• One time buyers
• Lapsed customers (retargeting)
• Tasks, solutions, interests
Customer Profiles
• Keyword searches and
subsequent behaviors
(conversions vs abandonment)
Web Behaviors
• Hi value product bundles,
product bundles
• Segment and product
bundle relationships
Product Data
• Organizing principles and
related content
Competitors / Suppliers
PRODUCT DATA
ENHANCEMENT
DATA MINING
DATA SOURCES
EXISTING ECOMMERCE PLATFORM
+
Data & Pattern
Analysis
Statistical
Modeling
Machine Learning
EXPLANATION-BASED RECOMMENDATIONS
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Context-Aware Information Architecture
• For digital technology to be effective, it must deliver content in context requiring an
understanding of the full ecosystem
• Data and content provide the greatest value to the enterprise when viewed holistically
across information silos where it can be organized, structured, harmonized and tagged to
deliver more meaningful analytics and attributes that represent contextual relationships
CONTEXT-AWAREINFORMATION INFRASTRUCTURE
For any digital technology to be effective, it must deliver content
in context. After all, customers only care about what they care
about. And employees only care about information that is
relevant to the business problem they are solving.
This may sound simple and obvious when expressed in this way,
but contextualization is actually a very complex information
architecture challenge. It requires very sophisticated content
modeling and relationship mapping between information types
and categories with supporting governance and change
management processes.
Contextualization requires a strategic understanding of
customers, employees, products, data and content. It’s the glue
Earley Information Science, Inc. All Rights Reserved. 4
architecture challenge. It requires very sophisticated content
modeling and relationship mapping between information types
and categories with supporting governance and change
management processes.
Contextualization requires a strategic understanding of
customers, employees, products, data and content. It’s the glue
that brings information together in a way that delivers true value
and creates a meaningful customer experience.
Context enhances everything, whether it’s the relevance of
content marketing and social media campaigns, the impact of
related product recommendations, the publication of targeted
product information, the accuracy of search or the precision of
business analytics. Without context, content loses value and its
impact on the performance of your digital business will be
diminished.
Knowledge is Power: Context-Driven Digital Transformation
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EIS Reference Architecture
12
Marketing
Data
User
Data
Product
Data
Historical
Data
Operating
Content
Information Infrastructure
Customer
Personalization
Content
Publishing
Site
Merchandizing
Product Info.
Management
Business
Intelligence
Knowledge
Management
Enterprise
Search
Content
Management
Information Management Platforms
PIM DAM CMS ECM CRM ERP
Digital
Commerce
Digital
Workplace
Contextualized User Experience
Context Aware Information Architecture
Content Model Taxonomy Metadata
Unstructured
(Big) Data
Structured
(Operational) Data

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Domain Data Required For Contextual Experience
PRODUCT DOMAIN
How your product data is initially captured and
organized changes how your e-customers can
search and interact with your website
MARKETING & SUPPORT DOMAIN
Marketing takes raw product details and
combines with customer insights to decide how
to present products to your customers online
CONTENT DOMAIN
Designing great product content for e-
commerce takes alignment – with Product,
Marketing and other areas of your company
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Product Data
Management
PRODUCT DATA is
business critical… and
messy. It serves many
different needs, and flows
to many different places.
Knowledge
Engineering
KNOWLEDGE
MANAGEMENT requires
structured information and
context… but knowledge isn’t
structured.
Content
Optimization
CONTENT is king… but it
must be found, and it must
be relevant in the moments
that matter.
Customer
Engagement
CUSTOMER DATA should
be centralized… but
customer engagement is
not.
Facets of a Successful B2B Digital Experience
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Brushless DC motorProducts Services Find a Store
BizCo
Motors > DC Motors > Brushless DC Motors
Hello John My Cart (0) Orders Log Out
Go
Pr oduct Name: ______________________________
Br and Name: ______________
98 cust omer r evi ews | 83 answer ed quest i ons
Zoom
Pr i ce: $___. __ / each
Qty: 1 Buy
+ Add to cart
Specifications
Datasheet:
Drawing:
Manual:
Catalog page #: __I t em Number : _______________
Mf r . Par t Number : __________
Shi ppi ng Wei ght : ___________
Count r y of Or i gi n: _________
I n st ock
Check avai l abi l i t y i n your
ar ea
ZI P Code:
90210 Find
Item _____________________________
Motor Application _____________________________
Motor Sub-Application _____________________________
Motor Design _____________________________
Motor Design Enclosure _____________________________
HP _____________________________
Nameplate RPM _____________________________
RPM Range _____________________________
Bearings _____________________________
Service Factor _____________________________
Ambient Temperature _____________________________
Frame Material _____________________________
Overall Length _____________________________
Length Less Shaft _____________________________
Motor Shaft Design _____________________________
RoHS Compliance _____________________________
Related Products
Pr oduct Name: ______
I t em Number : _______
Br and Name: ________
Pr i ce:
$___. __ / each
1 Buy
Pr oduct Name: ______
I t em Number : _______
Br and Name: ________
Pr i ce:
$___. __ / each
1 Buy
Pr oduct Name: ______
I t em Number : _______
Long Descr i pt i on: __________________________________________________________
___________________________________________________________________________
___________________________________________________________________________
_________________________________________________
• ________________
• ________________
• ________________
See an er r or ? Pr i nt t hi s page Shar e
Specifications
Drawing:
Manual:
Shi ppi ng Wei ght : ___________
Count r y of Or i gi n: _________
Item _____________________________
Motor Application _____________________________
Motor Sub-Application _____________________________
Motor Design _____________________________
Motor Design Enclosure _____________________________
HP _____________________________
Nameplate RPM _____________________________
RPM Range _____________________________
Bearings _____________________________
Service Factor _____________________________
Ambient Temperature _____________________________
Frame Material _____________________________
Overall Length _____________________________
Length Less Shaft _____________________________
Motor Shaft Design _____________________________
RoHS Compliance _____________________________
Customers Also Viewed
Pr oduct Name: ______
I t em Number : _______
Br and Name: ________
Pr i ce:
$___. __ / each
Pr oduct Name: ______
I t em Number : _______
Br and Name: ________
Pr i ce:
$___. __ / each
Pr oduct Name: ______
I t em Number : _______
Br and Name: ________
Pr i ce:
$___. __ / each
Pr oduct Name: ______
I t em Number : _______
Br and Name: ________
Pr i ce:
$___. __ / each
1 Buy
Pr oduct Name: ______
I t em Number : _______
Br and Name: ________
Pr i ce:
$___. __ / each
1 Buy
Pr oduct Name: ______
I t em Number : _______
Br and Name: ________
Pr i ce:
$___. __ / each
1 Buy
Long Descr i pt i on: __________________________________________________________
___________________________________________________________________________
___________________________________________________________________________
_________________________________________________
• ________________
• ________________
• ________________
Reviews
Cust omer : _______________
Dat e: ____________
Revi ew Text : __________________________________________________________
___________________________________________________________________________
___________________________________________________________________________
_________________________________________________
Helpful Not Helpful
Cust omer : _______________
Dat e: ____________
Revi ew Text : __________________________________________________________
___________________________________________________________________________
___________________________________________________________________________
_________________________________________________
|
Chat
This motor is
great for
motion control!
<
<
Hi Amanda
Personalization Example: Cross/Up-sell
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The role of AI in Personalization
AI is responsible for specific content areas on the site and needs inputs
• For “Related Products” AI needs product relationships, knowledge of processes,
industries, shopper profile info (e.g., preferences), purchase history, region, etc.
• For “Customers Also Viewed” AI needs browse history and any other
constraints e.g., only show products that are salable in region, sorting rules (e.g.,
based on margin?)
• For ”Reviews” AI needs product – review relationships and rules about what to
show, as well as sorting rules (e.g., best first, most recent first, most relevant first,
etc.)
• For “Chat-Bots” AI needs a knowledge-base with product relationships,
recommendations, phrases, use cases, terminology, training data (e.g., prior chat
logs, search history), dialog snippets, etc.
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What about AI?
17
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 ,

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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.
18

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Identify competitive differentiators,
strategic initiatives, priority
categories.
5 – 10 target processes
Products grouped to support
task, process or solution
MERCHANDIZERS
Target categories
Target processes
Intelligent NLP
USE CASES TARGET PROCESSES
PRODUCT
COMBINATIONS
KNOWLEDGE AND
EXPERTISE CONTENT
Customer Support Content
Maintenance manuals
Key Opinion Leaders
What products are used in combination?
Supports “skills”, surfaces
expertise and related
content for personalization
RELATED
CONTENT
19
Use Cases and the Associated Content for Personalization

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IA of Increasing Importance Moving to AI
BASIC
SEARCH
KNOWLEDGE-BASED
SEARCH
VIRTUAL AGENT
(CHAT-BOT)
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
SEARCH
INTERACTION
INFORMATION
ARCHITECTURE
USER
EXPERIENCE
ENABLING
TECHNOLOGY
Increasing Functionality / Increasing Information Richness
Ontologies, clustering,
classification, NLP, personalization
20

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Where Have We Already Done This? SEO
High page rank improves the visibility of a
website and leads to a higher web traffic,
conversion, revenue, etc.
Google does its best to level the field, and
content providers do everything they can to get
their content noticed.
Organizations can only improve their SEO by
investing in their information architecture (IA):
content and data.
Although Google is the AI, and it’s pretty neat, it
would not be nearly as useful without the $72
Billion* SEO industry.
The Google revolution is powered by a nearly
unending investment in content organization,
quality, structure, metadata, semantics etc.
IA improves Google search AI, the same holds
true for other AIs as well
Search engine optimization (SEO): The process of optimizing web pages so that they perform well
in organic search
*https://www.forbes.com/sites/tjmccue/2018/07/30/seo-industry-approaching-80-billion-but-all-you-want-is-more-web-traffic/#285186b87337
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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
22

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What customers want What they get What they should get
Answers to their problems Convoluted navigation Navigation that matches their mental model
The right products Search results that are difficult to filter Search that anticipates their needs
Options and meaningful choices Too many choices Products selectively presented
Help with their goal Endless phone menus Phone menus that match their need
Assistance with a task Call center reps without requisite
knowledge
Knowledgeable representatives that understand
them
Recommendations for a solution Confusing content Content correctly integrated into the user context
Expertise that they trust Marketing language that tries to sell them Credible knowledge bases that have their answers
Responses that move them forward Incomplete information Thorough, detailed answers
A “rewarding” experience Frustrating, disconnected interactions Consistent, seamless experience across channels
Convenience, speed, efficiency Impediments, lack of responsiveness Immediate answers, simple interactions
Ease of doing business High friction processes Low friction, minimal effort interactions
The Desired State of Customer Experience
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Suggested Resources
24
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/Opus_EvaluatingEnterpriseVirtualAssistants_Jan2014%20(2).pdf
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+Knowledge+Management+For+Customer+Engagement/-/E-RES119672

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THANK YOU!
Dino Eliopulos
Managing Director
dino@earley.com
773-383-2359