Prerequisites for Effective and Meaningful Automation

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Prerequisites for Effective &
Meaningful Automation
Harness the Power of Artificial
Intelligence to Drive Extraordinary
Competitive Advantage
CUSTOMER CONTACT WEEK
Seth Earley
CEO – Earley Information Science
AUTHOR – The AI-Powered Enterprise
________________________________________________
Cell: 781-820-8080
Email: seth@earley.com
Web: www.earley.com

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2 Goals For Today
1. Understand what needs to be in place to be
successful
2. Identify how AI can provide a significant competitive
advantage
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Seth Earley - Biography
CEO and Founder
Earley Information
Science
@sethearley
seth@earley.com
www.linkedin.com/in/sethearley
Over 25 years experience
Current work
Award winning author
Past Editor
Member of Editorial Board
Former Co-Chair
Founder
Former adjunct professor
Speaker
Data science and technology, content and knowledge management
systems, background in sciences (chemistry)
Industry conferences on knowledge and information management
Northeastern University
Boston Knowledge Management Forum
Academy of Motion Picture Arts and Sciences, Science and
Technology Council Metadata Project Committee
Journal of Applied Marketing Analytics
Data Analytics Department IEEE IT Professional Magazine
The AI Powered Enterprise
Cognitive computing, knowledge and data management systems,
taxonomy, ontology and metadata governance strategies
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4
“A great resource to separate the
hype from the reality and a
practical guide to achieve real
business outcomes using AI
technology.”
—Peter N Johnson, MetLife
Fellow, SVP, MetLife
“I do not know of any books that have
such useful and detailed advice on the
relationship between data and
successful conversational AI
systems.”
—Tom Davenport, President’s
Distinguished Professor at Babson
College, Research Fellow at MIT
Initiative on the Digital Economy, and
author of Only Humans Need Apply
and The AI Advantage
Winner of the Axiom
Silver Award for
Business and AI

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Agenda
• AI as evolution in technology
• The strategic and tactical importance of AI to Call Centers
• Prerequisites for Success
• Why AI Projects Fail
• How to establish a unified digital language and data standardization
• Mapping business processes and structuring organizational knowledge
• Example focused bot application
• Fine tuning your BS detector vendor and technology evaluation process
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AI as Evolution
Principles of machine learning have been
embedded in software we have used for years
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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.
7
“
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
“
AI in 2002

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“The definition of “AI” has been
stretched so that it generously
encompasses pretty much anything
with an algorithm”
8
Source: https://www.theregister.co.uk/2017/01/02/ai_was_the_fake_news_of_2016/
AI in 2017

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“Cognitive” AI
Improved usability = reduced mental work to perform a task.
No “cognition” – machine does not “understand” or think like a human.
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Chatbots, Intelligent Virtual Assistants,
Conversational Commerce apps, etc., facilitate
conversations to reduce cognitive load on the
audience while enhancing interactivity
MAKES IT EASIER FOR THE HUMAN –
AS HAS EVERY TECHNOLOGY
INITIATIVE THROUGHOUT HISTORY

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Technology has caused job disruption throughout history

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Poll (show of hands)
WHERE ARE YOU ON YOUR (CALL CENTER) AI JOURNEY?
1. We are at the initial stages of investigation
2. We use limited AI in pilot situations
3. AI is operationalized for some aspects of call center operations
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Source: https://singularityhub.com/

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In the next several years…
WE WILL SEE EXPONENTIAL INCREASES IN THE
CAPABILITIES OF COGNITIVE ASSISTANTS
MANY ORGANIZATIONS WILL USE THESE TOOLS TO BE A
PRIMARY MECHANISM FOR CLIENT COMMUNICATIONS
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In the next several years…
THOSE THAT DO NOT
WILL BE AT A SIGNIFICANT DISADVANTAGE.
ORGANIZATIONS CAN BUILD A FOUNDATION THAT
SOLVES PROBLEMS TODAY AND
SETS THE STAGE FOR FUTURE CAPABILITIES
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The strategic and tactical importance
of AI for the Customer Experience
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Due to lack of:
• Clarity about technology
capabilities versus business
needs
• Quality production data
sources
• Understanding of core
processes and customer
needs
• Continue to be overhyped
• Executives have been burned
• Millions of dollars have been wasted
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Current State of AI
Capabilities

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What is realistic?
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• Improve the productivity of call
center reps
• Improve time to productivity for
new hires
• Allow reps to spend more time
on the human relationships
• Handle complex tasks
• Replace people
AI powered virtual assistants and chat bots can:
Other AI and machine learning tools can
improve the quality of customer interactions
NOT

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How can AI help?
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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
AUTOMATE ROUTINE TASKS
• 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

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How can AI help?
COGNITIVE AI
• Refers to approaches for reducing the “cognitive
load” on humans
• Surfaces information in anticipation of a task or
need
• Provides conversational access to knowledge
(processes, procedures, status inquiries, etc.)
• Can accelerate time to productivity for new team
members
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Practical Versus Possible
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• Conversation balance
• Level of customer stress
• Tone of agent
• Load balancing
• Scheduling
• Assessments of knowledge
• Soft skill gaps
Real-time sentiment analysis Custom eLearning
Workforce optimization
Allows real time
intervention and
improved feedback.
Uses data on external
events such as weather-
related disruptions.
Creates tailored training
for remediation.

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Practical versus Possible
Machine learning assessments:
• Identify best candidates
• Predict likely success
AI-powered simulations and assistance:
• Simulations reduce time to competency
• Helper bots provide answers in context
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Candidate screening Faster time to competency
One large call center services firm, was
able to predict success in a job with
90% accuracy.
Combined with customized training, helper
bots reinforce learning with scripts and
suggestions in the context of
conversations

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Foundation for Success
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AI has tremendous promise, however too many organizations
are beginning the journey without a solid foundation.
To make AI work, you will need:
• Clarity of business purpose
• Detailed understanding of the processes
• The correct, quality data sources structured for the application
• A culture that is open to new ways of working
• An understanding of which aspects of a process can be improved
or automated using AI technology

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The Promise of AI
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Beginning the journey with the right preparation will lead to
success instead of disappointment
You will also need:
• A strong sponsor with social capital
• Adequate resources and funding
• The right supporting processes
• A way of measuring results – upstream and downstream

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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?”
Measuring Value
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Search relevance

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Why AI Projects Fail
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50% of organizations
consider AI a priority
– International Data Corporation, Jul 2020
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77% of organizations
report that business
adoption of AI initiatives
remains a major challenge
– Forbes Technology Council, Mar 2020
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Why Do AI Projects Fail?
Misalignment with the business
• Incorrect expectations – marketplace hype, management by magazine article
• Excessively broad scope and poorly defined outcomes
• Confusing, ill-defined processes
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YOU CAN’T AUTOMATE A MESS
YOU CAN’T AUTOMATE WHAT YOU
DON’T UNDERSTAND

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The data management challenge
• Lack of training data – what is the nature of training data, anyway?
• Differences between pilot data and the deployment environment
• Missing reference architecture – if data is inconsistently defined, it cannot be leveraged
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THERE’S NO AI WITHOUT IA*
* Information Architecture

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Lack of governance and socialization
• Underestimating the role of culture – adoption and change does not happen by itself
• Trust in the results – people will not trust what they do not understand
• Lack of success measures – without metrics, we cannot judge the value of results
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AI PROJECTS WITHOUT SUCCESS
MEASURES WILL NOT PROVIDE
BUSINESS VALUE

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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?
• 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?
ENGAGING IN THIS DIALOG WILL HELP TO
MANAGE EXPECTATIONS
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Developing a Unified Language
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Language, terminology and meaning
For AI success, need to address both semantic architecture and data architecture
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• Language is ambiguous, meaning contextual
• Need agreed upon terminology and structures to remove friction from
information flows
• This is especially true for Cognitive AI – bots, virtual assistants and
conversational access

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A single concept can have different
Expressions
Person we do business with
• Cust_Name
• Cust_ID
• Customer ID
• Customer
• Client
Person who writes a document
• Contributor
• Author
• Creator
What we buy or sell a product for:
• Price
• Cost
Pitch
• the property of sound
• the throwing of a baseball
• a vendor's position (especially on the sidewalk)
• sales talk
• degree of deviation from a horizontal plane
• dark heavy viscid substance
• a high approach shot in golf
• a card game
• abrupt up-and-down motion
• the action of throwing something
• …
A single expression can represent different
Concepts
Data Architecture Semantic Architecture
Example from Fred Liese 35

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Example (Ontology) Framework
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Ontology
Taxonomy: Hierarchical list of agreed
upon “official” terminology
Ontology: All of the taxonomies in the
enterprise + relationships between
concepts
Knowledge Graph: Ontology + Data

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Taxonomy Facets for Content Findability
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Product
• Select
• Affordable Health
• Basic Medical
 Comprehensive Medical
• Executive Medical
• …
Treatment Topic
• Addiction
• Allergy & Immunology
• Accidents / Emergencies
 Acupuncture
• Anesthesiology
• …
 Search results
match content tags:
Comprehensive Medical
Acupuncture
Authorization
Product
Treatment Topic
General Topic
General Topic
 Authorization
• Benefits
• Claims
• Codes

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State
Transaction type
Nature of Business
Certification
Topic
Product
Content Type
…
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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
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CONTEXTUALIZED USER EXPERIENCE
Consistent 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
Future State Reference Information Architecture (IA)

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Business Processes and
Organizational Knowledge
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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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Orchestration Requires Maturity in Many Areas
Content
Customer
Product
Knowledge

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Access to knowledge is critical for internal organizational efficiencies
Profile customers
Evaluate leads
Target prospects
Analyze on site
behaviors
Optimize search
Differentiate the
experience
Evaluate promotions
Create cross sell
relationships
Personalize offers
Optimize self service
Improve knowledge retrieval
Evaluate product usage
Analyze sentiment
Measure community engagement
Understand loyalty drivers
To support the external customer (knowledge) journey, internal
stakeholders have specific knowledge needs
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Business Processes and Knowledge
• A business process is embedded organizational knowledge
• The business competes on knowledge
• AI can improve processes by enhancing knowledge access
• Chatbots are a knowledge access mechanism
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Information Retrieval Continuum
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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
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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How Humans Find Answers Now – Read The Manual
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We can break the process down of
referencing a manual like this:
Scenario 1 – Access
• Here is the manual
Scenario 2 – Generalized retrieval
• “Look in chapter 4”
Scenario 3 – Specificity of the answer
• Here is the specific answer to your
question from that manual
Scenario 4 – Contextualized knowledge
• Here is the specific answer from the
manual and related information based on
your exact product configuration and
context
MANUALS COMPILE
KNOWLEDGE FOR TECHNICAL
SUPPORT
BUT…
• They require study
• And it takes too long to find
answers to specific questions from
large manuals.
HIGH VALUE USE CASES FOR
CHATBOTS AND VIRTUAL
ASSISTANTS
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The Time For Smart Chatbots Is Now
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75% of a call center agents time is taken by manual research and
knowledge retrieval
https://www.ibm.com/blogs/watson/2017/10/10-reasons-ai-powered-automated-customer-service-future/
A recent customer service study revealed that 72% of
Millennials believe a phone call is not the best way to resolve
their customer service issue.
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Current Approaches to Bot Content Design
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Typically entail:
• Re-creation of large
quantities of content to be
“AI ready” (referred to as
“training the AI”)
• Dedicated groups or
outside resources to
develop or refactor content
Instead, content should be
designed with specific use
cases, audiences and tasks in
mind.
Component content can
enable publish once, use
anywhere
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Build A Bot To Find The Answers
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Componentized content can be repurposed:
• channel partners
• marketing campaigns
• customer self service
• contact center agents
• field support
• embedded product knowledge
Extract the human intelligence to find the answer and embed that
intelligence into the system.
Human intelligence must be broken into chunks and structured using a
Knowledge Architecture
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Enter Component Content
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Typical use cases include:
• Translation into other languages
• Localization of content to meet regional needs
• Management of complex product configuration materials
The same structure can be ingested into intelligent
applications:
• Reduces the time and cost of “training” AI and cognitive applications
• Preserves existing organizational boundaries and business processes
• Reduces internal training costs
Summary
FAQ
Installation
Configuration
Troubleshooting
Product Manual
Reusable components
Breaking manuals and large documents into reusable pieces
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Transforming Content for Multiple Channels
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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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Source: Customer Contact Week LV, June 2021 Tech Style Fashion Group/SmartAction

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Source: Customer Contact Week LV, June 2021 Tech Style Fashion Group/SmartAction

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Example of Focused Bot Application
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Michelin Tire Selector
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• Clear task objective
• Unambiguous questions
• Clear steps
• No open-ended
questions
• Ability to escalate to
agent
Positives:

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• Lack of simple
utterance resolution
• Loss of context
upon web page
hand off
Negatives:
Michelin Tire Selector

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Ask these critical questions
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• How do you define AI?
• Why did you choose AI for your solution?
• What ROI are your clients seeing?
• How was your model trained?
• What training data do we need?
Courtesy Cal Al-Dhubaib Pandata
• How often do you update your model?
• How often will we need to train our model?
• How explainable is your solution?
• How do you manage risk?
• What are the technical qualifications of your
team?
When Selecting AI Technology
DEMO’S NEED TO BE BASED ON YOUR USE
CASES AND YOUR DATA.
DON’T BUY FEATURES, BUY OUTCOMES.

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Summary
• AI is a natural extension of technologies that have been around for many years
• AI enabled applications are another tool in your toolkit
• Focus on business alignment, value and processes
• Data architecture and quality data is a core requirement
• Define governance, curation, and success measures
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YOU CAN’T AUTOMATE A MESS; YOU CAN’T
AUTOMATE WHAT YOU DON’T UNDERSTAND.
THERE’S NO AI WITHOUT IA

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“A great resource to separate the
hype from the reality and a
practical guide to achieve real
business outcomes using AI
technology.”
—Peter N Johnson, MetLife
Fellow, SVP, MetLife
“I do not know of any books that have
such useful and detailed advice on the
relationship between data and
successful conversational AI
systems.”
—Tom Davenport, President’s
Distinguished Professor at Babson
College, Research Fellow at MIT
Initiative on the Digital Economy, and
author of Only Humans Need Apply
and The AI Advantage
Winner of the Axiom
Silver Award for
Business and AI

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Additional Resources
Knowledge is Power: Context-Driven Digital Transformation
https://www.earley.com/knowledge/white-paper/knowledge-power-context-driven-digital-transformation
Searching for Gold: Harnessing the Power of Taxonomy and Metadata to Improve Search
https://www.earley.com/knowledge/white-paper/searching-gold-harnessing-power-taxonomy-and-metadata-improve-search
New Age of Knowledge Management
https://www.earley.com/blog/new-age-knowledge-management
A New Approach to Data, Content and Knowledge Management - Do it Right
https://www.earley.com/blog/new-approach-data-content-and-knowledge-management-do-it-right
Why Information Taxonomy Must Represent the Landscape of the Business
https://www.earley.com/blog/why-information-taxonomy-must-represent-landscape-business
How to bring a Google-like search experience to the enterprise
https://www.earley.com/training-webinars/how-bring-google-search-experience-enterprise
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Additional Resources
Making Intelligent Virtual Assistants a Reality
https://www.earley.com/knowledge/white-paper/making-intelligent-virtual-assistants-reality
Knowledge Management's Rebirth as Knowledge Engineering for Artificial Intelligence
https://www.earley.com/blog/knowledge-managements-rebirth-knowledge-engineering-artificial-intelligence
Knowledge Engineering: Structuring Content for Artificial Intelligence
https://www.earley.com/ke-for-ai
Four Critical Elements of Metrics-Driven Information Governance
https://www.earley.com/blog/four-critical-elements-metrics-driven-information-governance
How Ontologies Drive Digital Transformation
https://www.earley.com/training-webinars/how-ontologies-drive-digital-transformation
Knowledge Management and User Engagement – Weaving the Experience into Work Practices
https://www.earley.com/blog/knowledge-management-and-user-engagement-weaving-experience-work-practices
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Thank you
64
Seth Earley
CEO
Earley Information Science
Seth@earley.com
781-820-8080
https://www.linkedin.com/in/sethearley
@sethearley
IEEE IT Professional Magazine articles:
“There’s No AI without IA”
“The Problem with AI”

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1994
YEAR FOUNDED.
Boston
HEADQUARTERED.
50+
SPECIALISTS & GROWING.
Earley Information Science is a professional services firm focusing on architecting and
organizing data – making it more findable, usable, and valuable.
Our proven methodologies are designed to address product data, content assets,
customer data, and corporate knowledge bases. We deliver scalable solutions to the
world’s leading brands, driving measurable business results.
We make information more
useable, findable, and valuable.
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Our Services
PRODUCT DATA
• Omnichannel
Taxonomy & Attribute
Design
• PIM Selection &
Deployment
• Product Catalog
Optimization
KNOWLEDGE
• Knowledge
Management Strategy
• AI, Ontology, and
Knowledge Graph
Design
• Information Architecture
Strategy
CONTENT
• ECM Strategy for
Unified Commerce
• Content Search &
Findability
• OmniChannel Content
Marketing
CUSTOMER DATA
• Customer Experience
Strategy
• CDP Selection &
Deployment
• Change Management &
Governance
WE BUILD THE INFORMATION ARCHITECTURE THAT POWERS
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ACCELERATED BUSINESS DECISION-MAKING.

Prerequisites for Effective and Meaningful Automation

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Prerequisites for Effective and Meaningful Automation