From The Singapore FUTURETECH Conference. What does a Chief AI Officer do?

1. AI DigitalTransformationDrivers
AndThe Role Of The
Chief AI Officer
www.ChiefAIOfficer.org
Prof. Giuseppe Mascarella, C.AI.O
giuseppe@valueamplify.com
FUTURE TECH: BEYOND 2020
SINGAPORE

2. 2020 AI Drivers Survey Results
Value Amplify Confidential
QUESTION:

3. AI Evolution Driver: Data Sources Evolution
Source: www.microsoft.com

4. Data is out there and is free (Open data).
It provides no competitive advantages.
Finding patterns in data is the holy grail.
• Oil in a barrel
• Diesel in an engine

5. From Data Mining To AI Digital Transformation
Value Amplify Confidential

6. By 2022,
75% of the working
assets shipped globally,
will have event driven
decision support system
Value Amplify Confidential

8.  What does it take to be the winner inteyh next wave
of value?
 How do I prepare my Data Science teams to be the
winners?
 How do I build a value driven AI Playbook?
AI and Digital Transformation is becoming a
fierce battlefield for the next wave of value
Which Corporate Role Needs To Find
The Best Answers To These Questions?

9. C.I.O
(I=Innovation)
C.I.O
(I=Information)
Increase ROI/TCO of Technology
Lowest TCO Per Prediction wit Best Speed and Accuracy
Governance of use of IT/Cloud from LOB (Line Of Business)
Architectures and New Business Models For the New
World of Cognitive Opportunities
Quality and Documentation of Company Data
Algorithm Driven Data Strategy (Create/Access Open Data)
Data Protection and Compliance to Industry Regulations
C.AI.O
Scientist Training Path to Legally Explore New Policy Changes
for Maximum AI Use
Value
Competition
Data
Legal
Drive
Change
(Artificial Intelligence)

10. Chief AI Officer Problem

11. Chief AI Officer Playbook | Part 2
Predictive
Maintenance
Stage 1:
Reactive
Stage 2:
Informative
Stage 3:
Predictive
Stage 4:
Transformative
Stage 5:
Game Changer
OUTCOMES
Vision
Schedule and manage using
past operational and
routine performance data
Analyze conditions and
make informed decisions
Discover new insight, and
predict likelihood and
timeframe of failures
Transform the experience
with rea-time insight,
actions and continuous
feedback
Shape new business models
with digital ecosystem
Strategic
Intent
• Define operational rhythm
• Meet SLAs, compliance
and warranty conditions
• Orchestrate and leverage
readily available reports
and operational
observations
• Become purpose-driven
with connected, complete,
correct and connected
data
• Model asset-specific plans
based on the asset
condition
• Easy access to insights on
the whys and the trends
• Manage the Voice of the
Asset
• Instrument the assets to
provide real-time data on
factors affecting asset
condition
• Predict and schedule
maintenance for desired
operations
• Operate Asset as a Service
by altering the asset
behavior in real-time
• Take corrective actions
before a potential failure
• Predict and perform
maintenance based on the
business impact
• Launch digital services,
leveraging design, data
and delivery insight
• Create new customer
experiences and solutions,
integrating partner assets
• Monetize learning
KPIs
• Unplanned downtime
• Regulatory compliance
• Maintenance schedule,
time and costs
• Time between failures
• Spare parts inventory
• Annual budget
• Asset utilization
• Unexpected breakdowns
• Capital and resource
investment
• Global reach
• Revenue or throughput
per asset
• Customer loyalty
• Outcome-based pricing
• New markets
• Cross-selling
• Eco-system maturity
CAPABILITIES: Data, Intelligence and Actions
TECHNOLOGY APPROACH: Architecture Directions Value Amplify Confidential

12. Chief AI Officer Playbook | Part 2
Predictive
Maintenance
Stage 1:
Reactive
Stage 2:
Informative
Stage 3:
Predictive
Stage 4: Transformative Stage 5:
Game Changer
CAPABILITIES
Data
(Sources, time,
quality, access)
• Manufacturers reports
• Asset features
• Failures/repairs reports
• Historical data from
operational systems
• Intermittent updates
• Asset condition data
• Correlated quality, ERP, and
operational data
• Scheduled data queries and
data polling
• Real-time, streaming data
about asset conditions,
environmental factors, and
operating conditions
• Multisite data aggregation
• Data readiness for data science
• Cognitive and feedback data
• Business process / workflow
• Organization data e.g.
operator’s skills
• Events, Smart sensing
• Ecosystem data and services
• External context (customer,
consumer)
• Real-time capability and data
discovery
Intelligence
(Interpretations,
analytics,
insights,
learnings)
• Web-based reports,
dashboards
• Data visualization of historical
and operational data
• Self-service analytics
• Asset condition monitoring
and assessment
• Statistical modeling
• Trend analysis and forecasting
• Predictions using data mining,
modeling and algorithms
across all data
• Stream analytics
• Rolling aggregates, analysis
and recommendations
• Insight at sensor and interface
levels
• Deep learning e.g. vibrations
• Real-time predictions using
current business context and
operating conditions
• Analyze current state behavior
across ecosystem and identify
opportunities
• Evaluate health of data and
algorithms and predict
adjustments
Actions
(New or change
in activities)
• Inventory assets
• Develop plans and schedule
maintenance for assets based
on past performance
• Plan and schedule resources
• Forecast and optimize
schedule and inventory
• Manage critical assets and
business operations
• Manage planned downtime
• Manage resource productivity
• Create knowledgebase
• Check health while in use
• Identify potential causes and
time window, and take
proactive actions
• Generate alerts and propose
best actions
• Support remotely
• Reliability engineering
• Self-identify alternate paths for
continuous operations
• Heal the asset while in use
• Create outcome-based
business processes and
customer experience
• Make every interaction a
source of revenue
• Productize data, intelligence,
algorithms, and business
processes
• Integrate partner services
• Create BOTs
APPROACH
Architecture
Directions
• Systems of Records
• Client/server or distributed
architecture
• Data marts
• Reporting and analytics
• Systems of Engagement
• Service-oriented architecture
• Integration
• Data warehouses
• Analytical modeling
• Systems of Intelligence
• Lambda architecture
• NoSQL
• Data lakes
• Cloud
• Systems of Learning
• Neural network and FOG
architecture
• Cognitive services
• In memory, edge analytics
• Systems of Digital Markets
• Microservices architecture
• APIs

13. AIDigitalTransformationDrivers
AndTheRoleOf The
Chief AI Officer
www.ChiefAIOfficer.org
Prof. Giuseppe Mascarella, C.AI.O
giuseppe@valueamplify.com