AI AUTOMATION
Enabling the AI-Driven Enterprise
Rajiv Shah
DataRobot

AI Importance
Artisan AI
Automated AI
Demo

AI-Driven Business

AI industry will grow to $70 billion by 2020
from just $8.2 billion in 2013, according
to IDC research.

“If your competitor is rushing to build AI and
you don't, it will crush you.”
-Elon Musk

Enabling the AI-Driven Enterprise
Where AI is applied in every business process to predict outcomes.
The AI-Driven Enterprise adapts to new conditions at incredible
speeds and continually self-optimizes based on predicting the future.

THE OPPORTUNITY FOR MACHINE LEARNING IN ANY BUSINESS
Banking
Insurance
Healthcare
Media
Pharma
Telco
Retail
Government
Energy
Transportation
Marketing Sales Risk
Human Resources Logistics
Predicting customer Lifetime Value (LTV)
Churn
Customer segmentation
Product mix (best product mix to reduce churn)
Cross selling/recommendation algorithms
Up selling
Channel optimization
Discount targeting
Responses rates
Reactivation likelihood
Adwords optimization and ad buying
In store traffic patterns
Aircraft scheduling
Lead prioritization
Demand forecasting
Pricing
Market Basket
Inventory management / Dynamic Pricing
Promos/upgrades/offers
Resume screening
Employee churn
Training recommendation
Talent management
Credit Risk
Fraud detection
Accounts Payable Recovery
Anti-money laundering
Insurance Claims prediction
Readmission Risk
Warranty Analytics
Claim Prediction
Procurement
Warehousing
Cost Analysis
Product life cycle
Demand Forecast
Assembly
Turnover

Everyone is taking advantage of
AI, the question is how fast can
you go

Artisans: Data Scientists

http://www.eyeonsouthflorida.com/wp-content/uploads/2016/08/artisan3.jpg

THE AI BOTTLENECK: DATA SCIENTISTS
Data
Scientist
Math &
Stats
Domain
Expertise
Data
Scientist
Programming
Skills
Knowledge of the business
Knowledge of the data
Ability to write code to gather data
Ability to write code to explore/inspect data
Ability to write code to manipulate data
Ability to write code to extract actionable intel
Ability to write code to build models
Ability to write code to implement models
Foundational statistics
Internals of algorithms
Practical knowledge and experience
Knowing how to interpret and explain models
PREREQUISITES

Accelerate the process of researching, testing, and deploying predictive algorithms. Enable more
people to help research, test, and deploy predictive algorithms.
Unmet demand for
Data Science
THE PROBLEM
KEY
Demand for predictive models
Supply of data scientists

There are not enough data
scientists (and it's not changing
anytime soon)

80 Billion -- 1200 Billion

AUTOMATED MACHINE LEARNING: THE NEW PREREQUISITES
Data
Scientist
Math &
Stats
Programming
Skills
Knowledge of the business
Knowledge of the data
PREREQUISITESDomain
Expertise

Automated AI will fill
the gap of data scientists

Issues around Automated AI

CONTROL

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Create data science superheroes
with Automated AI

ACCURACY

Automated AI is about lots of
relevant options - - - not the
dogma of AI elitists

OPEN SOURCE

An AI solution should utilize
existing open source AI
algorithms

INTERPRETABILITY

How?
partial dependence
feature importance
Why?
Reason codes

No Black Boxes!
Understand a model at the
prediction level

DEPLOYING AI

“don't do it right, do it twice”

Build & deploy should be as
similar as possible

Demo of Automated AI

AI AUTOMATION
Enabling the AI-Driven Enterprise
Rajiv Shah
rajiv.shah@datarobot.com
rajcs4