Artificial Intelligence program

AI Paris June 12th 2018

LES CHIFFRES DU GROUPE AIR FRANCE KLM

milliards d’euros de chiffre d’affaires

552 avions en exploitation

314 destinations dans plus de 116 pays

80 595 collaborateurs

25,8

98,7 millions de passagers transportés en 2017

200 compagnies aériennes clientes

2 000 avions traités

3

AI program : an IT initiativeLead by CIO Office and OR/DS dept

Ambition

Reinforce AFKL value proposition by offering cognitive services to customers and employees

Impact AFKL profitability substantially by optimizing processes and transform organizations

Objectives

Create awareness on AI through use cases

Coordinated different organization around similar initiatives

Reinforce internal capabilities

4

Program organization : 3 waves to deliver AI value

2017 2018

October March September

Wave 1

• Achieve quick wins with highly feasible initiatives leveraging off-the-shelf solutions around conversational• Launch internal initiative building on existing momentum with business teams: cargo rebooking automation

Build credibility

Wave 2

• Finalize the completion of all quick win initiatives• Launch new initiatives with significant company value : Augmented Reality, Smart

Automation (fraud detection)

Deliver significant value

Wave 3

• Implementation of skills & sourcing strategy in AI• Work on organization• Propose a full catalogue of AI services

Sustainability

2019

5 months

Communicate on achievements & raise awareness on AI capabilities

December

5 months 4 months

• Long-term success : Set organization for AI initiatives within the Group

Post program

TODAY

5

Cognitive Councils

3 sessions July 7th November 29th

Participants VP – SVP representative from

all businesses

VP - SVP representatives

BlueLink / Cygnific

Location Paris CDG HQ AMS Digital Studio

Content • AI in the industry

• Watson capabilities

• Conversational tools

• HR re-imagined by AI

Workshops • Digital Assistant

• Customer facing staff

• E&M / Cargo / Commercial

/ Ground Ops

• HR journey

May 17th

Representative from all

businesses, BL CY

Paris Airport HQ

• Smart automation

• RPA using AI

AI Game :

Ethical impact of AI

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Introduction to Repair

Remaining capacity after passengers is allocated to cargo

Sometimes, shipments cannot go in their associated flight: Repaired bookings

Multiple causes:

• Late shipment

• Cancelled flight, strike, …

• Wrong overbooking

• Priority bookings or previous repairs

Repairs must then be reallocated to new flights : Time consuming task, no previously existing process

How to reallocate the repairs?

CargoPax

7

Introduction to Repair

Today :

• Analysts are doing it manually

• Time consuming (10-15% of their time)

• Not efficient (multiple application to dig into)

• Solution not optimal

Opportunities :

• Let analysts focus on added value tasks

• Time saving

• Good quality of solution

• Better quality of service

CargoPax

8

Reinforcement Learning

Reinforcement Learning allows machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance

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Cargo Smart Repair

First idea was to look at historical data to apply Machine Learning algorithms; but it was not usable regarding the disparity in the process• We needed to explore a new domain : simulations

Automatize cargo repairprocess

Historical data

• Create fictive flights • Create fictive bookings/events• Environment representation :

• State: Booking configurations and available capacities of flights• Actions: Remove booking of the category volume and put it in a backlog• Rewards: Penalty corresponding to the removed booking category

Simulations

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Timeline & Results

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32

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48

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Iteration

DQN Dueling DDQN Greedy

• More training, tuning of the model, modelisation

• Run a pilot this summer on selected flights

• Implement the solution to give an advise to analyst before the end of the year : real time data + integration

Nexts steps

• First discussions in oct 2017 to define the use case

• Historical data exploration in nov-dec 2017

• Modelisation and simulations 3 months jan to march 2018

• Proposal in april 2018

Timeline

11

Organizational support from the ProgramFocus on conversational tools

AI program support for chatbot initiatives

• Participation in the definition of bot architecture

• Referencing all conversational tools initiatives within the group (13)

• Technical support and provide guidance for NLP / training

• Ideation and finding new uses cases

• Investigating vocal tools Google Home & Amazon Alexa

We created the Chatbot Board with businesses

• To make sure that we are becoming more and more efficient & performant on bot’s

deployment

• Ensure a good coordination between different bots’ initiatives.

One monthly meeting

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2

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Text

Ramp-up of AI capabilities

End-to-end AI pipeline

Skills

Ecosystem of AI partners

Increase learning from data & simulations

Text, Speech & Image processing

Technology

13

Concrete achievement :

• Scientific role in the industry : Part of France IA• Set up a modern and stimulating work environment : cooperation with Digital

Studio/Digital Factory• We provided internal trainings : Text Mining, Reinforcement Learning, Computer

Vision• We offer exposure to AI conferences

Point to address and be share with IT :

• Work on data organization with IT• Build critical mass in the AI community by collocating teams if necessary• Adapt people policies to the competitiveness of the AI market

Text

Where do we stand regarding key enablers to attract and develop AI talents

14

Return on analysis on AI partnership ecosystem

• Raise company AI awareness via presentations• Leverage best in-class APIs to save development

time and improve performance

• Conduct workshop by business with specialized vendors to identify solution fit with use case

• Implement the off-the-shelf solutions for a given use case to achieve quick results

Internal capabilities

AI as a service Rapid external implementation

Robust internal implementation

• Support the implementation of strategic initiatives and help ramp-up internal capabilities

• Identify new opportunities and foster change in the organization

Exploration

• Explore cutting-edge technologies• Lower cost and longer term partnerships

Smallvendors

Largevendors

AI specialists

AI labs

03/10/2017

Artificial Intelligence program