Make AI Work For Manufacturing By Building A Strong Foundation

Copyright © 2020 Earley Information Science, Inc. All Rights Reserved.
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AI MANUFACTURING 2020
AUGUST 27, 2020
Making AI Work for
Manufacturers: Building a
Strong Foundation
Chantal Schweizer, Principal Taxonomist
Dave Skrobela, Managing Director
Earley Information Science

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About the Speakers
Chantal Schweizer
Principal Taxonomist
@ladyschweizer
Dave Skrobela
Managing Director
@daveskrobela
2

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www.earley.com Copyright © 2020 Earley Information Science, Inc. All Rights Reserved.
The AI Powered Enterprise
3
Available on Amazon 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
DistinguishedProfessor 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. Insightfrom
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

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Making AI Work
for
Manufacturers:
Building a Strong
Foundation
Elements of successful AI
programs
The role of data quality and
data architecture
Methods for finding and
prioritizing AI applications
4

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50% of organizations
consider AI a priority
– International Data Corporation, Jul 2020
5

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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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– Markets and Markets Report, June 2020
The AI in manufacturing
market is expected to be
valued at $1.1 billion in 2020,
$16.7 billion by 2026.
Drivers: Industrial IoT and
automation, improving
computing power, and
increasing venture capital
investments.
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Elements of
successful AI
programs
STRATEGY
• Get buy-in and find quick wins
• Align AI to priority use cases
DATA ARCHITECTURE
• Understand and map out data quality of
various sources
• Build (or extend) taxonomies and
ontologies relevant to AI strategies
MEASUREMENT & GOVERNANCE
• Determine how will you measure the
success of an AI project
• Educate your team on how AI relates to
them
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Getting Buy-in
• What goals do you want to define and measure?
• Not just “AI for AI sake”
➢ Can the solution make measurable impact on business?
➢ AI is just an enabling capability not the end goal
• Roadmaps are great but don’t hold up the process of getting started
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Building Early Momentum
It’s a good candidate for AI if…
✓ There is a quick win
✓ You are addressing a repetitive process
✓ Humans consistently follow best practice
✓ There are addressable costs to reduce with informed decisions
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Use Cases
1. Solve immediate business problems
2. Provide clear and measurable ROI and
3. Justify investment in a data foundation that can be leveraged across multiple initiatives.
Application Impact Technology Data sources
Configure Price Quote Reduced call center
volume
Configuration bot PIM, CPQ
Pricing, Availability Improved support
productivity
Transaction bot Ecomm, PIM
Knowledge Access Reduce access time,
reduce downtime
Helper bot Componentized
knowledge assets
Traffic flow
optimization
Improve efficiencies IoT analysis Sensor data
Demand forecasting Reduce inventory Customer analytics Financial, web traffic
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Data Models: The
role of data
quality and data
architecture
The Information architecture and
knowledge structure challenge is
usually put into confusing language:
“Training the AI”
What do you “train the AI” with?
…high value data
and knowledge assets
AKA: Quality data and
structured/curated content
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Information Principles and AI
• Successful AI projects combine domains of data, content and knowledge assets
• If the source(s) of information is of poor quality, poorly organized or in the wrong
format, AI will fail
• Advanced functionality requires an advanced information architecture
• “Training the AI” means ingesting the correct, high quality knowledge assets. The
knowledge required to train AI is the same that is required to train humans
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State
Transaction type
Nature of Business
Certification
Topic
Product
Content Type
…
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Future State Vision
Marketo
eDiagnostics
PdM
ERP
Sharepoint
Corporate
Tech Pubs
Library
PBG
Libraries,
Web, LN,
LiveLink
Engineering
Portal
AI Application
Collaboration
Blog, Chat
Email
Customer
Portal
Website
PIM EC
Portal CMMS
DAM
Syndication
Oracle
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System Data Management
ERP System
Item Master
Pattern Data
Inventory &
Transaction
Data
Vendor Data
Configuration
Tool
MTO Product
Data
MTO Pricing
Customer
Data base
Customer
Order History
PIM System
Product Data
Pricing
Product
Content
Product
Relationships
DAM System
Product
Images
Videos
PDFs
CMS
PDP
Romance
Content
Search
InDesign
Tear Sheets
Price Sheets
16

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Taxonomy Relationships
17
Electrical
Lighting
Lightbulbs
LED Bulbs
SKU:4D560L
LED Lightbulb
PIM
[Company]
[Business Unit]
[Product Line]
[Product Series]
SKU:4D560L
LED Lightbulb
ERP
Products
Lighting & Ceiling Fans
Lightbulbs
Lighting
SKU:4D560L
WEBSITE
Grainger
Lighting
Bulbs & Lamps
SYNDICATION
Electrical
System
Lighting
Smart Home
Smart Bulbs
Amazon
Tools & Home Improvement
Lighting & Ceiling Fans
Lightbulbs
PurchaseHistory
CRM
CUSTOMER
SKU:4D560L
SKU:4D560L
SKU:4D560L
SKU:4D560L
SKU:4D560L
AI Application

Customer: “I need to know the replacement LED bulb for a
aspirating smoke alarm in a D300 fire system in California”
Customer: “Hi, I need some help with a product”
Semantic deconstruction of utterance
Bot: “OK. Can you tell me what kind of product?”
Accessory = “replacement LED bulb”
Base Product = “aspirating smoke alarm”
Product Line= “D300 fire system”
Region = “Califronia”
Content type = “Guideline”
Entity derivation
Context derivation
Audience = “Customer” -> Specific
Customer History
Accessory
Base Product
Product Line
Region
Content type
Audience
Specific Customer
History
Faceted retrieval from
knowledge base
Returns content tagged
with appropriate metadata
18

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Metrics-Driven
Governance
19
How will you measure success of your
AI initiative?
• through definition of the right KPIs
• and an effective program to govern
the program

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Measuring here
(business outcomes)
Measuring here
(process indicators)
Enterprise Strategy
Business Unit Objectives
New Business Opportunities
Average Order Size Total Account Revenue
Business Processes Site Traffic Search Relevance
Search
Digital Content
Working & Measuring
here (content, IA,
taxonomy, search, data
fill, etc.) Product
Content
CRM
Processes enable
objectives
L
I
N
K
A
G
E
Leads
Revenue Growth
Content supports
processes
Objectives align
with strategy
CEO: “How will this increase revenue?”
Conversion
Data Scorecards
Process Scorecards
Outcome Scorecards
CTR Fill Rate Content Quality etc.
Digital Team: “How do I know information architecture / data/ search is working?”
Measuring ROI and Justifying Investments
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Search
Analysis
• Identify new taxonomy and ontology terms
• Identify new auto-completes
• Identify missing content
Behavior
Analysis
• Analyze & Remediate Top Best Bets
• Analyze & Remediate Top Searches
• Identify missing content
Utilization
Analysis
• Correlate AI usage to expected application volumes
• Gather and analyze VOE (voice of employee)
• Identify action plans
Content
Analysis
• Measure compliance with editorial guidelines
• Measure compliance with tagging guidelines
• Remediate issues
Metrics-driven Governance Processes
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In Summary
Page
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“There is no AI
without IA”
• It’s only AI if we don’t know
how it works
• Simplicity is hidden complexity
• Clean data is the price of
admission
• Define processes, data sources
and data owners
• Define governance, curation,
and scalable processes
23

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Three take aways
Socialize, measure, and
make necessary course
corrections.
Start with narrow,
clearly defined process
aligned with business
needs.
Fix your data to produce
short term ROI while
preparing for an AI
powered future
START SMALL GET THE DATA RIGHT KEEP IT GOING
24

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Suggested Resources
Allstate’s ABIe project case study
http://www.earley.com/knowledge/case-
studies/allstate%E2%80%99s-intelligent-agent-reduces-call-
center-traffic-and-provides-help
Cognitive Computing Consortium
http://www.cognitivecomputingconsortium.com/
Enterprise Search: 14 Industry Experts Predict the Future of
Search http://www.docurated.com/enterprise-
search/enterprise-search-14-industry-experts-predict-
future-search
Evaluating Enterprise Virtual Assistants
http://info.intelliresponse.com/rs/intelliresponse/images/O
pus_EvaluatingEnterpriseVirtualAssistants_Jan2014%20(2).p
df
Characteristics of Highly Effective Enterprise Virtual
Assistants
http://www.slideshare.net/intelligentfactors/characteristics
-of-highly-effective-enterprise-virtual-assistants
The Knowledge Graph and Its Importance for Intelligent
Assistance
http://opusresearch.net/wordpress/2016/01/12/the-
knowledge-graph-and-its-importance-for-intelligent-
assistance/
Making Intelligent Virtual Assistants a Reality
http://info.earley.com/make-intelligent-virtual-assistant-
reality-whitepaper
Cognitive Search – The Next Generation of Information
Access http://www.earley.com/blog/cognitive-search-
next-generation-information-access
Earley Executive Roundtable - Training the Robots:
Evolving Virtual Assistants and the Human Machine
Partnership http://info.earley.com/roundtable-virtual-
assistant-human-machine-partnership
Earley Executive Roundtable Understanding virtual agents
– what's needed to make them a reality?
http://info.earley.com/roundtable-intelligent-virtual-
agents-reality
Vendor Landscape: Knowledge Management For Customer
Engagement
https://www.forrester.com/report/Vendor+Landscape+Kn
owledge+Management+For+Customer+Engagement/-/E-
RES119672
25

THANK YOU
Contact Us:
www.earley.com
Chantal Schweizer
chantal.schweizer@earley.com
Dave Skrobela
dave.skrobela@earley.com

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