Streamlining Information Flows In The Digital Workplace

Copyright © 2020 Earley Information Science, Inc. All Rights Reserved.
Streamlining Information Flows in the
Digital Workplace: The Role of Artificial
Intelligence and Knowledge Engineering
Digital Workplace Experience
October 14, 2020
WWW.EARLEY.COM
Seth Earley
Earley Information Science
@sethearley
seth@earley.com
www.linkedin.com/in/sethearley

SETH EARLEY - BIOGRAPHY
CEO and Founder
Earley Information
Science
@sethearley
seth@earley.com
www.linkedin.com/in/sethearley
Over 20 years experience
Current work
Co-author
Editor
Member
Former Co-Chair
Founder
Former adjunct professor
Speaker
AIIM Master Trainer
Course Developer & Master Instructor
Data science and technology, content and knowledge
management systems, background in sciences (chemistry)
Enterprise IA and Semantic Search
Information Organization and Access
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
Editorial Journal of Applied Marketing Analytics
Data Analytics Department IEEE IT Professional Magazine
Practical Knowledge Management from IBM Press
Cognitive computing, knowledge and data management systems,
taxonomy, ontology and metadata governance strategies

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The AI Powered Enterprise
3
Available now
https://www.amazon.com/AI-Powered-
Enterprise-Ontologies-Business-
Profitable/dp/1928055508/
“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
“Read this book to learn how leaders
and companies are using AI with
structured data to transform business.
Insight from real world examples,
combined with a proven methodology,
will arm the reader with the knowledge
and confidence necessary to drive AI
in any organization”.
– Barry Coflan, SVP & Chief
Technology Officer, Schneider Electric
– Digital Energy

Three take aways
1. A seamless customer experience is dependent upon upstream and internal processes If employees
cannot locate what the need or have to go through acts of heroics to do so, at some point the customer
will feel this. Acts of heroics do not scale
2. Information Architecture is still needed. IA provides the knowledge, content and data scaffolding for the
organization and for AI technologies.
3. AI offers the opportunity to speed up information flows and surface appropriate knowledge and content
in the correct context but does not do so by itself. For AI speed the information metabolism of the
enterprise, we have to teach the AI about the enterprise.
4
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Supporting a Seamless Experience
5
“Acts of heroics do not scale”

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Orchestration of the Customer Journey with Data, Content and Knowledge
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

Upstream Friction Impedes the User Experience
7
• Inconsistent data architecture, terminology, naming conventions
• Inability to locate high value assets – (assets created from scratch
rather than reused)
• Manual processes – handoffs, imports and exports, data
conversions, clean up
• Missing expertise – loss of tribal knowledge
• Overlapping and duplicate functionality
• Lack of measurement (of outcomes, changes, new technologies)

A Streamlined Customer Experience
8
If internal processes require acts of heroics, the customer experience will inevitably suffer
…Requires a Streamlined Employee Experience
Humans enable the experience
As customers traverse their journey

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CUSTOMER JOURNEY: LIFECYCLE/ENABLING TECHNOLOGIES
LEARN CHOOSE PURCHASE USE MAINTAIN SUPPORT
MARKETING SALES DISTRIBUTION SERVICE FINANCE SUPPORT
Marketing
Communications
B2B/Channel
Partners
B2C/Retail
Fulfillment
Inventory
management
Product
performance
Billing & payment
Credit & collections
Help & complaints
Repair & returns
ENTERPRISE PROCESSES: DEPARTMENTS/FUNCTIONAL AREAS/ACCOUNTABILITIES
Technologies
Departments
Processes
Accountabilities
Marketing ops
Product marketing
Marketing comm
Digital marketing
Training
Retail/dealers
Web marketing
Channel management
Telemarketing
Sales support
Logistics
Installation
Activation
Service operations
Applications
Quality assurance
Finance
Billing operations
Credit & collections
Customercare
Executive escalations
Call center operations
• Bots (chat, helper,
virtual assistants)
• Event management
• Webinar tools
• Promotion management
• Social media
• Marketing resource
management
virtual assistants)
• Ecommerce
• CRM
• Web content management
• Sales management
• Marketing resource
management
virtual assistants)
• Inventory management
• Supply chain
• Logistics and distribution
• Point of sale and systems
virtual assistants)
• Knowledge base
• Online documentation/
help systems
virtual assistants)
• Ecommerce
• CRM
• Billing system
• Web content management
• ERP/accounting
• Credit card
authorizations/EFT
virtual assistants)
• CRM
• Knowledgebase/
unsupervisedsupport
• Online documentation/
help systems
• Call centercall tracking
• Trouble ticketing
Data/Technology Scorecards
Process Scorecards
Outcome Scorecards
Journey Stage

The Role of IA*
10
There’s No AI Without IA*
*IA = Information Architecture

Broad Classes of Artificial Intelligence
11
Machine learning and predictive analytics find patterns based on large
amounts of structured or semi-structured data
• Predicting credit worthiness, identifying fraud, predicting maintenance
Cognitive computing applications – intelligent virtual assistants, conversational
commerce, chatbots, semantic search
• Siri, Alexa, Watson
• Seek to reduce the “cognitive load” on humans by personalizing and
contextualizing information

Information Architecture for Machine Learning
12
“Not all machine learning requires taxonomy,
ontology or reference data. The AI figures it out”.
For example, a machine learning vision system to identify defective
parts simply needs examples of good parts and bad parts.
True, but what about the results of the analysis?
What part, product line, and manufacturing process?
Which quality control programs are impacted?
What needs to change? Who makes the change? How are processes updated? And so on.
The algorithm may not need the information
architecture, but applying the results do.

13
Department
Object Type Location
Collection
Example Ontology for Museum Sensor (IoT) Analysis
Example Courtesy of Pandata

State
Transaction type
Nature of Business
Certification
Topic
Product
Content Type
…
Example Ontology for Insurance Company Virtual Assistant

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Chatbots are a
channel
(… to knowledge, content, data, information…)

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Information Retrieval Continuum
BASIC
SEARCH ENGINE
KNOWLEDGE
PORTAL
VIRTUAL
AGENT
INTELLIGENT
ASSISTANT
KNOWLEDGE
BASE
Any text
Multiple sources
Keyword or full text
query
None necessary, but
Improves with metadata
Search box,
documents list
Search
Multiple sources, separate
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

“But even those personalities required
proficiency in other facets of the technology
such as an expertly developed domain
model”
“Because intelligent virtual assistants are
focused within a domain model, they benefit
from a clearly defined knowledge base and are
able to go much deeper and stay within those
bounds…”
Source: Analyst Gigaom Research https://gigaom.com/2014/09/01/the-next-step-for-intelligent-virtual-assistants-its-time-to-consolidate/
“…domain models and ontologies are important”
Teaching the AI About the Business
17

AI is Not Magic
18
You cannot automate what you don’t understand.
You cannot automate a mess.

AI Is Only Part of the Answer
19
Many applications for AI are attempts to make up for our past sins in poor data hygiene
There is no magic, AI does not take away the hard work needed to optimize the digital workplace
such as understanding user needs and mapping business processes
The tools can help, but human insight, judgement and expertise is always needed
If humans cannot understand the business process and user needs, the AI will not.
AI can speed the “information metabolism” of the
enterprise, but it is a tool, not the answer by itself.

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

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Digital Maturity Across Multiple Areas
21
KNOWLEDGE ties
together all of the other
pieces – it is the human
element of judgement,
expertise and creativity
that is harvested from
experts and embedded in
data models and
processes.
CONTENT to engage the
customer must be findable,
and it must be relevant in the
moments that matter.
Content components and
snippets are the building
blocks for machine optimized
offerings
CUSTOMER DATA
needs to be consistent,
harmonized from different
systems and modeled to
provide and respond to
signals from interactions
both upstream and
downstream.
PRODUCT DATA
models must be
complete and aligned
with attributes and details
that are important to the
customer’s decision
making criteria.
METRICS DRIVEN
GOVERNANCE
measures ROI of projects
and provides feedback for
course corrections and
fine tuning of user
experience, product data,
and content decisions and
is the foundation for
automation
A streamlined Digital Workplace requires
orchestration of many capabilities

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Book Excerpt: Will Your Company Make It Into the AI-
Powered Future?
https://tdwi.org/articles/2020/03/17/adv-all-ai-powered-future.aspx
Ecommerce Times “The Architectural Imperative for
AI-Powered E-Commerce”
https://www.ecommercetimes.com/story/86530.html
Information Week “AI Hot Spots: Where Is Artificial
Intelligence Heading Now?”
https://www.informationweek.com/big-data/ai-machine-learning/ai-
hot-spots-where-is-artificial-intelligence-heading-now/d/d-
id/1337237?page_number=1
Forbes Magazine “Why 'Ontology' Will Be A Big Word
In Your Company's Future”
https://www.forbes.com/sites/cognitiveworld/2018/07/20/why-
ontology-will-be-a-big-word-in-your-companys-future/
Further Reading
“If you're serious about
harnessing the power of AI in your
business — and you should be —
this book will show you how to
make it an operational reality.”
– Scott Brinker, VP Platform
Ecosystem, HubSpot, Editor,
chiefmartec.com

Seth Earley
CEO
Earley Information Science
Seth@earley.com
781-820-8080
https://www.linkedin.com/in/sethearley
IEEE IT Professional Magazine articles:
“There’s No AI without IA”
“The Problem with AI”

Streamlining Information Flows In The Digital Workplace

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