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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
2
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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
3
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The AI Powered Enterprise
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
5
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AI as Evolution
Principles of machine learning have been
embedded in software we have used for years
6
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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.
9
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
11
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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
13
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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
14
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The strategic and tactical importance
of AI for the Customer Experience
15
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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
16
Current State of AI
Capabilities
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What is realistic?
17
• 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?
18
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
19
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Practical Versus Possible
20
• 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
21
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
22
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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
24
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
25
Search relevance
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Why AI Projects Fail
26
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50% of organizations
consider AI a priority
– International Data Corporation, Jul 2020
27
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77% of organizations
report that business
adoption of AI initiatives
remains a major challenge
– Forbes Technology Council, Mar 2020
28
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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
29
YOU CAN’T AUTOMATE A MESS
YOU CAN’T AUTOMATE WHAT YOU
DON’T UNDERSTAND
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Why Do AI Projects Fail?
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
30
THERE’S NO AI WITHOUT IA*
* Information Architecture
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Why Do AI Projects Fail?
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
31
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
32
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Developing a Unified Language
33
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Language, terminology and meaning
For AI success, need to address both semantic architecture and data architecture
34
• 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
36
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
37
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
…
38
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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
39
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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
41
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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
42
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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
44
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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
45
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Information Retrieval Continuum
46
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
47
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How Humans Find Answers Now – Read The Manual
48
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
48
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The Time For Smart Chatbots Is Now
49
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.
49
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Current Approaches to Bot Content Design
50
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
50
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Build A Bot To Find The Answers
51
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
51
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Enter Component Content
52
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
52
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Transforming Content for Multiple Channels
53
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
54
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
56
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Michelin Tire Selector
57
• 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
59
• 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
60
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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The AI Powered Enterprise
61
“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
62
Copyright © 2021 Earley Information Science, Inc. All Rights Reserved.
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www.earley.com
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
63
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64
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“There’s No AI without IA”
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Earley Information Science is a professional services firm focusing on architecting and
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Our proven methodologies are designed to address product data, content assets,
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Prerequisites for Effective and Meaningful Automation

  • 1. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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
  • 2. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 2
  • 3. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 3
  • 4. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. The AI Powered Enterprise 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
  • 5. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 5
  • 6. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com AI as Evolution Principles of machine learning have been embedded in software we have used for years 6
  • 7. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. 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
  • 8. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. “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
  • 9. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com “Cognitive” AI Improved usability = reduced mental work to perform a task. No “cognition” – machine does not “understand” or think like a human. 9 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
  • 10. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. 10 Technology has caused job disruption throughout history
  • 11. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 11
  • 12. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 12 Source: https://singularityhub.com/
  • 13. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 13
  • 14. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 14
  • 15. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com The strategic and tactical importance of AI for the Customer Experience 15
  • 16. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 16 Current State of AI Capabilities
  • 17. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. What is realistic? 17 • 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
  • 18. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. How can AI help? 18 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
  • 19. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 19
  • 20. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. Practical Versus Possible 20 • 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.
  • 21. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 21 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
  • 22. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Foundation for Success 22
  • 23. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. 23 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
  • 24. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. The Promise of AI 24 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
  • 25. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 25 Search relevance
  • 26. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Why AI Projects Fail 26
  • 27. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. 50% of organizations consider AI a priority – International Data Corporation, Jul 2020 27
  • 28. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. 77% of organizations report that business adoption of AI initiatives remains a major challenge – Forbes Technology Council, Mar 2020 28
  • 29. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 29 YOU CAN’T AUTOMATE A MESS YOU CAN’T AUTOMATE WHAT YOU DON’T UNDERSTAND
  • 30. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Why Do AI Projects Fail? 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 30 THERE’S NO AI WITHOUT IA* * Information Architecture
  • 31. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Why Do AI Projects Fail? 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 31 AI PROJECTS WITHOUT SUCCESS MEASURES WILL NOT PROVIDE BUSINESS VALUE
  • 32. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 32
  • 33. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Developing a Unified Language 33
  • 34. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Language, terminology and meaning For AI success, need to address both semantic architecture and data architecture 34 • 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
  • 35. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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
  • 36. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Example (Ontology) Framework 36 Ontology Taxonomy: Hierarchical list of agreed upon “official” terminology Ontology: All of the taxonomies in the enterprise + relationships between concepts Knowledge Graph: Ontology + Data
  • 37. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Taxonomy Facets for Content Findability 37 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
  • 38. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com State Transaction type Nature of Business Certification Topic Product Content Type … 38
  • 39. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 39
  • 40. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 40 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)
  • 41. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Business Processes and Organizational Knowledge 41
  • 42. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. 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 42
  • 43. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. 43 Orchestration Requires Maturity in Many Areas Content Customer Product Knowledge
  • 44. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 44
  • 45. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 45
  • 46. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Information Retrieval Continuum 46 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
  • 47. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 47
  • 48. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. How Humans Find Answers Now – Read The Manual 48 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 48
  • 49. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com The Time For Smart Chatbots Is Now 49 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. 49
  • 50. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Current Approaches to Bot Content Design 50 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 50
  • 51. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Build A Bot To Find The Answers 51 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 51
  • 52. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Enter Component Content 52 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 52
  • 53. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Transforming Content for Multiple Channels 53 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
  • 54. 54 Source: Customer Contact Week LV, June 2021 Tech Style Fashion Group/SmartAction
  • 55. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 55 Source: Customer Contact Week LV, June 2021 Tech Style Fashion Group/SmartAction
  • 56. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Example of Focused Bot Application 56
  • 57. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com Michelin Tire Selector 57 • Clear task objective • Unambiguous questions • Clear steps • No open-ended questions • Ability to escalate to agent Positives:
  • 58. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 58 • Lack of simple utterance resolution • Loss of context upon web page hand off Negatives: Michelin Tire Selector
  • 59. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. Ask these critical questions 59 • 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.
  • 60. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 60 YOU CAN’T AUTOMATE A MESS; YOU CAN’T AUTOMATE WHAT YOU DON’T UNDERSTAND. THERE’S NO AI WITHOUT IA
  • 61. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. The AI Powered Enterprise 61 “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
  • 62. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 62
  • 63. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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 63
  • 64. www.earley.com www.earley.com Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. 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”
  • 65. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 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. 65
  • 66. Copyright © 2021 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com 66 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 UNRIVALED CUSTOMER EXPERIENCE, SMART ECOMMERCE, AND ACCELERATED BUSINESS DECISION-MAKING.