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Adopting AI into your
Organization
How to Deliver a Successful AI Project
Meet Olivier !
Head of Decision Science @ Moov AI
Chief Product Officer @ Snitch AI
Editor of « quality evaluation guidelines of AI
systems » ISO Standards
Helps companies make better decisions more
efficiently with AI.
Loves motorcycle, guitar, good beer and travel
85% of AI projects will deliver
erroneous outcomes due to
bias in data, algorithms or the
teams responsible.
Gartner predicts that through 2022,
AI journey map
Productivity
Maturity in data science
Data warehouse
reports
& OLAP.
Visualizations
Self-serve BI
Predictive analytics
Machine learning
Prescriptive analytics
Simulation
Autonomous
systems
1
2
3
4
5
What happened?
Why did it happen?
What will happen?
What’s the best solution?
Chasm!
Mostofus
arehere
Wewant
tobehere
Impact vs. Complexity
Complexity in an AI project
Data Task Adoption Integration
Impact
High
Low
High
Low
Complexity
Quick wins Big bets
TBD
Financial pit
Prioritization
How to build an AI model
Test of different
Algorithms/ configurations
Selection of
best model
Validation of
model quality
Deployment of
chosen solution
Framework
Final
optimization
Project
Liftoff
Beta
version
MVM
Minimum Viable Model
MVP
Minimum Viable Product
From a model to a solution
An AI solution is much more
than just AI
Data
collection
Features
extraction
Process
management
tool
Data
verification
AI
Code
Resources
management
Infrastructure
Analytics tool
Configuration
Monitoring
Quality assurance
Validation through the AI project
But Wait...There's More!
Time
Iteration #1 Iteration #3
Performance
Degradation
Iteration #2 …
Thank you !
Questions?

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