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S U M M I T
SYDNEY
A Data-Driven
Roadmap to
Enterprise AI Strategy
Yun Zhi Lin, Head of Engineering at Contino
Cloud Platform
Build & Migration
Enterprise DevOps
Transformation
DevSecOps and
Cloud Security
Cloud Native
Software Development
Data Platforms
& Analytics
I’m Yun,
Head of
Engineering.
Key Takeaways
01 | Enterprise AI in 2019, Trends & Challenges
02 | Practical steps to drive Enterprise-Wide AI Strategy
03 | Reference Framework for Execution and Scale
Enterprise AI in 2019,
Trends and
Challenges
Source: still from Blade Runner 1982
Our Definition of Artificial
Intelligence
Using a computer to interpret data and apply
knowledge, logic and understanding.
Hmm learn all
the things!
KNOWLEDGE
DATA
We are living the Age of Implementation where
AI is an Equalizer allowing all companies to create
unique Virtuous Cycle and Defensive Data Moats
Better
Product
More Quality Data More Happy Users
V
AWS
but Generate Value and form Defensive Businesses
New Product
or CX
More Intelligent
Product or CX
Hyper Automation
& Optimisation
1 2 3
Most AI Use Cases are Grounded & “Non-Exotic”
9
2019 - Enterprise AI Roadblocks
Trust my
“gut feel”!
2019 - Enterprise AI Roadblocks
Have more
Strategy!
Execute
without
Strategy!
Analysis
paralysis or
duplicate
initiatives
Misaligned
pocket AI
initiatives
Need
more Skills
Unknown
Unknowns!
Experiment!
I have
Bad
Data
2019 - Enterprise AI Roadblocks
Patterns?
Automation?
2019 - Enterprise AI Roadblocks
Business
vs IT
Compliance as an
Afterthought? Ethics?
Consumer Data Right?
Enterprise AI challenges are not really about
algorithms or technology.
Coherent Strategy Operating Model Execution
The key ingredients of AI adoption are simple:
IT Business
1 2 3
Practical steps to drive
Enterprise-Wide
AI Strategy
Remember these are our Challenges
Need: Align Al Products to Corporate Strategy
Vision
New Product or Service More Intelligence Experience Defensive Data Moat
Corporate Strategy
IT Strategy
Data Strategy Digital Strategy
AI Strategy
Smarter Business
Processes, Maintenance,
WFM and Automation
AI Product 3AI Product 2AI Product 1
AI Strategy
Objectives Goals
Tactic 1 Measure 1
Objectives Goals
Tactics Measures
Objectives Goals
Tactics Measures
Tactic 2 Measure 2
Corporate Strategy
Objectives Goals (O KPI)
Tactics Measures (T KPI)
Vision
AI Product 1
Translate & Align Objectives, Goals, Tactics and Measures
AI Product 2
Need: Operating Model for Enterprise-Wide AI
Innovation, Collaboration and Consumption
Business IT
AI Product 3AI Product 2
Process
Ethics &
Legal
People
3Cs
Customers
Culture
Comms
AI Product 1
Data Assets
Technology Assets
AI Product 4
TACTICS
Products we can build to reach the goals.
MEASURES
Tactics KPIs
OBJECTIVES
Overarching pursuits over the long term
GOALS
Objectives KPIs
PEOPLE ETHICS & LEGAL
Topic: Contributors: Sponsor: Date:
1
2
3
4 5
PROCESSES TECH ASSETS7 86 9
AI STRATEGIC CANVAS
DATA ASSETS
ALIGN IT
Objectives &
Goals
PLAN IT
PROVE IT
SCALE IT
IMPROVE IT
Stakeholder
Education
AI Maturity
Assessment
Tactics and
Measures
AI Strategy
Development
MVP Products
(Lighthouses)
AI Community Of
Practice
Improve or
Fail Fast
Continuous
Training
Measures
Validation
AI
Innovation
& Consumption
Model
Consolidate
Artefacts and
Platform
Defensive
AI Asset
Product Driven AI Transformation Road Map
CYCLE OF
SCALING
INNOVATION
AI Business
Case
Revise AI
Strategy
New Product
Features
New MVPs
Vision
Alignment
Photo from Contino Exec Data Roundtable
2019
ALIGN IT
Telco X Executive Summary
Use of AI to measure Social Sentiment and Fault Prediction
Concierge for increased customer interaction and provide low cost solution for CSR
Rapidly innovate on future Enhancements and Products ideas such as auto fault remediation, workforce management
1
2
3
Current Corporate Strategy Document: 52 pages excluding 400 page appendices
Summary: Telco X is to looking to build AI-driven products to enhance CX and Automation. Long term goal is to prepare
for greater competition due to Consumer Data Right.
Short term is to increase NPS from 70% to 80% for FY20. In order to do this, Telco X thinks the following initiatives are
important:
DATA ASSETS
TACTICSOBJECTIVES
AI Driven CX and Automation
GOALS
FY20 NPS Increase 70% → 80%
PEOPLE
Exec Sponsor: CDO
ETHICS & LEGAL
1
2
3
4 5
PROCESSES TECH ASSETS7 86 9
AI STRATEGIC CANVAS AI Driven CXTopic: Head of AIContributors: CDOSponsor:
MEASURES
May 2019Date:
Planning
& Assessment
PLAN IT
Picking the Right Lighthouse MVP
LargeLow
Low
Small
Long
High
Short
Business
Sponsorship
Duration
Importance
Project Size
Pick this project
Data
Quality
Multi Disciplinary AI Squad
Engineers
Data
Scientists
UX
Business SMEs Customers
Site Reliability
Engineer“Brent”
ETHICS & LEGAL
MEASURES
TACTICS
DATA ASSETS
OBJECTIVES
AI Driven CX and Automation
GOALS
FY20 NPS Increase 70% → 80%
PEOPLE
Exec Sponsor: CDO
1
2
3
4 5
PROCESSES TECH ASSETS7 86 9
AI STRATEGIC CANVAS AI Driven CXTopic: Head of AIContributors: CDOSponsor:
Categorise
Positive and
Negative
Sentiments
85% Accuracy
over 48 hrs
Network Fault
Prediction MVP
Rank: 1 Size: M
Social Sentiment
Analysis MVP
Rank: 2 Size: S
Chatbot (Later)
Rank: 4 Size: L
May 2019Date:
DATA ASSETS
ETHICS & LEGAL
• Privacy and PII
• Opt In Opt out
• 7 Laws of AI (EU)
• Diversity & Bias
MEASURES
TACTICSOBJECTIVES
Predict Faults in advance using Weather and
Network Usage data
GOALS
85% accuracy for predictions
over a 48 hours time frame
PEOPLE
• Exec Sponsor: CDO
• Owner: Head of Operations
• Team: DaVinci AI Squad
• Customers: Castle Hill Beta
Users
1
2
3
4 5
PROCESSES TECH ASSETS7 86 9
AI PRODUCT CANVAS Network Fault PredictionTopic:
IoT Sensor
Data
Ingestion
Weather
Data
Ingestion
Fault
Model
Sensor
(IoT)
BOM
Data
(HTTP)
Fault
Forecast
(Deep
AR)
Legal & Compliance Review
Training
Client Engagement
Communications Strategy
Sales & Marketing Team
CHANGE MANAGEMENT
Ingest
Weather
data per
hour
Ingest
Sensors
data per
min
85%
Accuracy
for 48
hours
May 2019Date:
Reference
Framework for
Execution and Scale
PROVE IT SCALE IT IMPROVE IT
Treat SageMaker like Cattle! Not Pet!
Machine Learning Life Cycle
Product
OGTM
Split Data
Train ModelTest ModelDeploy Model
Monitor and
Validate
AI Problem
Definition
Validation Data Training DataTest DataLive Data
Pretrained
Model / Service
Collect and
Prepare Data
Self Service and Automation Patterns
SRE / DevOpsSelf Service and Governance
ML Engineers / Data ScientistsApplication Engineers
Application
Products
SageMaker
Products
Model Notebook
Training
JobAPI Gateway Inference
Lambda
Client
De-Identified Data Lake
Schedule Cleanup
Lambda
Endpoint
Cognito Auth
Encryption
Key
Service
Catalog
Trained
Model
Inference
Image
Deploy
Endpoint
Training
Image
MEASURES DATA ASSETS
ETHICS & LEGAL
• Privacy and PII
• Opt In Opt out
• 7 Laws of AI (EU)
OBJECTIVES
AI Driven CX and Automation
GOALS
FY20 NPS Increase 70% → 80%
PEOPLE
• Exec Sponsor: CDO
• AI COP: DaVinci + Tesla
• Customers: Castle Hill
1
2
3
4 5
PROCESSES
• Internal Open Source Policy
• Asset Custodians
• Change Management
TECH ASSETS7 86 9
AI STRATEGIC CANVAS May 2019Date:
Sensors (1min)
BOM (1 hour)
Fault Prediction (24hr)
Sentiments (+ve, -ve)
Categorise
Positive and
Negative
Sentiments
85% Accuracy
for 48hrs
Network Fault
Prediction MVP
Rank: 1 Size: M
Social Sentiment
Analysis MVP
Rank: 2 Size: S
Chatbot (Later)
Rank: 4 Size: L
TACTICS
AI Driven CXTopic: Head of AIContributors: CDOSponsor:
Iteratively Scaling AI Enterprise Wide
1
2
3
4 5
Value
Time
Horizon 1
AI Foundations
Lighthouse Initiatives
Plan
Prove Scale
Improve
Align
Plan
Prove Scale
Improve
Align
2
3
4
Horizon 2
AI Community of Practice
Assets and Patterns
Wider Initiatives
Iteratively Scaling AI Enterprise Wide
1
2
3
4 5
Value
Time
Horizon 1
AI Foundations
Lighthouse Initiatives
Iteratively Scaling AI Enterprise Wide
Value
Time
2
Horizon 3
AI Perpetual Innovation
Defensive Assets
Horizon 1
AI Foundations
Lighthouse Initiatives
1
2
3
4 5
Value
Time
Plan
Prove Scale
Improve
Align
Horizon 2
AI Community of Practice
Assets and Patterns
Wider Initiatives
2
3
4
Key Takeaways
01 | Enterprise AI in 2019, Trends & Challenges
02 | Practical steps to drive Enterprise-Wide AI Strategy
03 | Reference Framework for Execution and Scale
ALIGN IT PLAN IT PROVE IT SCALE IT IMPROVE IT
But there’s something no AI can
ever match,
And It’s Awesome Contino Stickers and Swag!
Stand R3
Come find us at our stand!
• Cool Swag
• Learn more about what we do
• Find your next career opportunity
Contact us
• Sydney and Melbourne Offices
• hello@contino.io
• www.contino.io

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A Data Driven Roadmap to Enterprise AI Strategy (Sponsored by Contino) - AWS Summit Sydney

  • 1. S U M M I T SYDNEY
  • 2. A Data-Driven Roadmap to Enterprise AI Strategy Yun Zhi Lin, Head of Engineering at Contino
  • 3. Cloud Platform Build & Migration Enterprise DevOps Transformation DevSecOps and Cloud Security Cloud Native Software Development Data Platforms & Analytics I’m Yun, Head of Engineering.
  • 4. Key Takeaways 01 | Enterprise AI in 2019, Trends & Challenges 02 | Practical steps to drive Enterprise-Wide AI Strategy 03 | Reference Framework for Execution and Scale
  • 5. Enterprise AI in 2019, Trends and Challenges Source: still from Blade Runner 1982
  • 6. Our Definition of Artificial Intelligence Using a computer to interpret data and apply knowledge, logic and understanding. Hmm learn all the things! KNOWLEDGE DATA
  • 7. We are living the Age of Implementation where AI is an Equalizer allowing all companies to create unique Virtuous Cycle and Defensive Data Moats Better Product More Quality Data More Happy Users V AWS
  • 8. but Generate Value and form Defensive Businesses New Product or CX More Intelligent Product or CX Hyper Automation & Optimisation 1 2 3 Most AI Use Cases are Grounded & “Non-Exotic”
  • 9. 9 2019 - Enterprise AI Roadblocks
  • 10. Trust my “gut feel”! 2019 - Enterprise AI Roadblocks Have more Strategy! Execute without Strategy! Analysis paralysis or duplicate initiatives
  • 12. Patterns? Automation? 2019 - Enterprise AI Roadblocks Business vs IT Compliance as an Afterthought? Ethics? Consumer Data Right?
  • 13. Enterprise AI challenges are not really about algorithms or technology. Coherent Strategy Operating Model Execution The key ingredients of AI adoption are simple: IT Business 1 2 3
  • 14. Practical steps to drive Enterprise-Wide AI Strategy
  • 15. Remember these are our Challenges
  • 16. Need: Align Al Products to Corporate Strategy Vision New Product or Service More Intelligence Experience Defensive Data Moat Corporate Strategy IT Strategy Data Strategy Digital Strategy AI Strategy Smarter Business Processes, Maintenance, WFM and Automation AI Product 3AI Product 2AI Product 1
  • 17. AI Strategy Objectives Goals Tactic 1 Measure 1 Objectives Goals Tactics Measures Objectives Goals Tactics Measures Tactic 2 Measure 2 Corporate Strategy Objectives Goals (O KPI) Tactics Measures (T KPI) Vision AI Product 1 Translate & Align Objectives, Goals, Tactics and Measures AI Product 2
  • 18. Need: Operating Model for Enterprise-Wide AI Innovation, Collaboration and Consumption Business IT AI Product 3AI Product 2 Process Ethics & Legal People 3Cs Customers Culture Comms AI Product 1 Data Assets Technology Assets AI Product 4
  • 19. TACTICS Products we can build to reach the goals. MEASURES Tactics KPIs OBJECTIVES Overarching pursuits over the long term GOALS Objectives KPIs PEOPLE ETHICS & LEGAL Topic: Contributors: Sponsor: Date: 1 2 3 4 5 PROCESSES TECH ASSETS7 86 9 AI STRATEGIC CANVAS DATA ASSETS
  • 20. ALIGN IT Objectives & Goals PLAN IT PROVE IT SCALE IT IMPROVE IT Stakeholder Education AI Maturity Assessment Tactics and Measures AI Strategy Development MVP Products (Lighthouses) AI Community Of Practice Improve or Fail Fast Continuous Training Measures Validation AI Innovation & Consumption Model Consolidate Artefacts and Platform Defensive AI Asset Product Driven AI Transformation Road Map CYCLE OF SCALING INNOVATION AI Business Case Revise AI Strategy New Product Features New MVPs
  • 21. Vision Alignment Photo from Contino Exec Data Roundtable 2019 ALIGN IT
  • 22. Telco X Executive Summary Use of AI to measure Social Sentiment and Fault Prediction Concierge for increased customer interaction and provide low cost solution for CSR Rapidly innovate on future Enhancements and Products ideas such as auto fault remediation, workforce management 1 2 3 Current Corporate Strategy Document: 52 pages excluding 400 page appendices Summary: Telco X is to looking to build AI-driven products to enhance CX and Automation. Long term goal is to prepare for greater competition due to Consumer Data Right. Short term is to increase NPS from 70% to 80% for FY20. In order to do this, Telco X thinks the following initiatives are important:
  • 23. DATA ASSETS TACTICSOBJECTIVES AI Driven CX and Automation GOALS FY20 NPS Increase 70% → 80% PEOPLE Exec Sponsor: CDO ETHICS & LEGAL 1 2 3 4 5 PROCESSES TECH ASSETS7 86 9 AI STRATEGIC CANVAS AI Driven CXTopic: Head of AIContributors: CDOSponsor: MEASURES May 2019Date:
  • 25. Picking the Right Lighthouse MVP LargeLow Low Small Long High Short Business Sponsorship Duration Importance Project Size Pick this project Data Quality
  • 26. Multi Disciplinary AI Squad Engineers Data Scientists UX Business SMEs Customers Site Reliability Engineer“Brent”
  • 27. ETHICS & LEGAL MEASURES TACTICS DATA ASSETS OBJECTIVES AI Driven CX and Automation GOALS FY20 NPS Increase 70% → 80% PEOPLE Exec Sponsor: CDO 1 2 3 4 5 PROCESSES TECH ASSETS7 86 9 AI STRATEGIC CANVAS AI Driven CXTopic: Head of AIContributors: CDOSponsor: Categorise Positive and Negative Sentiments 85% Accuracy over 48 hrs Network Fault Prediction MVP Rank: 1 Size: M Social Sentiment Analysis MVP Rank: 2 Size: S Chatbot (Later) Rank: 4 Size: L May 2019Date:
  • 28. DATA ASSETS ETHICS & LEGAL • Privacy and PII • Opt In Opt out • 7 Laws of AI (EU) • Diversity & Bias MEASURES TACTICSOBJECTIVES Predict Faults in advance using Weather and Network Usage data GOALS 85% accuracy for predictions over a 48 hours time frame PEOPLE • Exec Sponsor: CDO • Owner: Head of Operations • Team: DaVinci AI Squad • Customers: Castle Hill Beta Users 1 2 3 4 5 PROCESSES TECH ASSETS7 86 9 AI PRODUCT CANVAS Network Fault PredictionTopic: IoT Sensor Data Ingestion Weather Data Ingestion Fault Model Sensor (IoT) BOM Data (HTTP) Fault Forecast (Deep AR) Legal & Compliance Review Training Client Engagement Communications Strategy Sales & Marketing Team CHANGE MANAGEMENT Ingest Weather data per hour Ingest Sensors data per min 85% Accuracy for 48 hours May 2019Date:
  • 29. Reference Framework for Execution and Scale PROVE IT SCALE IT IMPROVE IT
  • 30. Treat SageMaker like Cattle! Not Pet!
  • 31. Machine Learning Life Cycle Product OGTM Split Data Train ModelTest ModelDeploy Model Monitor and Validate AI Problem Definition Validation Data Training DataTest DataLive Data Pretrained Model / Service Collect and Prepare Data
  • 32. Self Service and Automation Patterns SRE / DevOpsSelf Service and Governance ML Engineers / Data ScientistsApplication Engineers Application Products SageMaker Products Model Notebook Training JobAPI Gateway Inference Lambda Client De-Identified Data Lake Schedule Cleanup Lambda Endpoint Cognito Auth Encryption Key Service Catalog Trained Model Inference Image Deploy Endpoint Training Image
  • 33. MEASURES DATA ASSETS ETHICS & LEGAL • Privacy and PII • Opt In Opt out • 7 Laws of AI (EU) OBJECTIVES AI Driven CX and Automation GOALS FY20 NPS Increase 70% → 80% PEOPLE • Exec Sponsor: CDO • AI COP: DaVinci + Tesla • Customers: Castle Hill 1 2 3 4 5 PROCESSES • Internal Open Source Policy • Asset Custodians • Change Management TECH ASSETS7 86 9 AI STRATEGIC CANVAS May 2019Date: Sensors (1min) BOM (1 hour) Fault Prediction (24hr) Sentiments (+ve, -ve) Categorise Positive and Negative Sentiments 85% Accuracy for 48hrs Network Fault Prediction MVP Rank: 1 Size: M Social Sentiment Analysis MVP Rank: 2 Size: S Chatbot (Later) Rank: 4 Size: L TACTICS AI Driven CXTopic: Head of AIContributors: CDOSponsor:
  • 34. Iteratively Scaling AI Enterprise Wide 1 2 3 4 5 Value Time Horizon 1 AI Foundations Lighthouse Initiatives Plan Prove Scale Improve Align
  • 35. Plan Prove Scale Improve Align 2 3 4 Horizon 2 AI Community of Practice Assets and Patterns Wider Initiatives Iteratively Scaling AI Enterprise Wide 1 2 3 4 5 Value Time Horizon 1 AI Foundations Lighthouse Initiatives
  • 36. Iteratively Scaling AI Enterprise Wide Value Time 2 Horizon 3 AI Perpetual Innovation Defensive Assets Horizon 1 AI Foundations Lighthouse Initiatives 1 2 3 4 5 Value Time Plan Prove Scale Improve Align Horizon 2 AI Community of Practice Assets and Patterns Wider Initiatives 2 3 4
  • 37. Key Takeaways 01 | Enterprise AI in 2019, Trends & Challenges 02 | Practical steps to drive Enterprise-Wide AI Strategy 03 | Reference Framework for Execution and Scale ALIGN IT PLAN IT PROVE IT SCALE IT IMPROVE IT
  • 38. But there’s something no AI can ever match,
  • 39. And It’s Awesome Contino Stickers and Swag! Stand R3 Come find us at our stand! • Cool Swag • Learn more about what we do • Find your next career opportunity Contact us • Sydney and Melbourne Offices • hello@contino.io • www.contino.io