“Inspire” Ai Use Cases Focus Day: Friday, December 6, 2019
8:30 am - 9:15 am
REGISTRATION FOR THE “INSPIRE” AI USE CASES FOCUS DAY & NETWORKING BREAKFAST
9:15 am - 9:30 am
CHAIRMAN'S OPENING REMARKS
Henry Lyles -
Director, Global Business Services, Automation COE Lead,
McDonald’s Corporation
9:30 am - 10:20 am
USE CASE #1: HOW AI+ML IS BEING USED AT DHL FOR POC AND BEYOND
Stefan Rank -
Director Global innovation,
DHL
Stefan is leading DHL's innovation team, who are scouting ahead to find the business impact of cutting edge automation technologies. This session won't just be an introduction to the often confusing and complex terminology to Artificial Intelligence and Machine Learning. It will also provide practical insight into how DHL is currently prioritize which tech to deploy; both some of their successes and failures.
Topics of discussion/examination will include, yet will not be limited to:
- Data Clean-up and management of the infrastructure
- What to do with a PoC that doesn't make it to the next stage
10:20 am - 10:40 am
THINK TANK #1: THE BIGGEST BARRIERS AT YOUR ORGANIZATION TO SUCCESS
10:40 am - 11:10 am
MORNING NETWORKING BREAK
11:10 am - 12:00 pm
USE CASE #2: THE JOURNEY TO MAKING EQUIFAX'S BOTS SMARTER WITH MACHINE LEARNING INSPIRE
Jairo Quiros -
Vice President Global Shared Services & Site Leader - Costa Rica SSC,
Equifax
Since it began its journey three years ago, Equifax has demonstrated over 150 uses cases for RPA, deployed over 300 bots and saved half-a-million man hours. Now it’s making them smarter with machine learning capabilities like speech to text and voice recognition. Jairo attributes Equifax’s maturity with an underlying culture of automation and their global CoE framework.
- Selling the culture of advanced automation to the C-Suite: embedding a united vision to engage your organization
- Using the global CoE to deliver Machine Learning across the organization through deployment teams, automation libraries and centrally governed handbook
- Use cases and the problems that can’t be solved with RPA alone, requiring the sophistication of Machine Learning
