26 - 27 February, 2019 | PARKROYAL Darling Harbour, Sydney, NSW
Jothi Periasamy, Chief Data Scientist at MIT Sloan School of Management (US)

Jothi Periasamy


Chief Data Scientist
MIT Sloan School of Management (US)

Check out the incredible speaker line-up to see who will be joining Jothi.

Download The Latest Agenda

Workshops


7:00 AM How to Develop use Cases Effectively to Smoothly Transition Towards Intelligent Automation?

The value of automation in the business is well understood, however it can be challenging to completely buy-in to the introduction of automation solutions without practical examples and results. In this workshop, you will be taken through how to build proper business cases that showcase the benefits of automating selected processes and how to work from the ground-up to ensure the right approach is being taken from the get-go.

Learning Outcomes:
  • Identifying which process is beneficial to automate
  • Working through use cases to advocate automation initiatives and benefits
  • How to set the right RPA strategy and structure efficient operating models to assist the transition from simple RPA to IA
  • Championing change makers to promote and share knowledge

Objectives:
  • Set realistic objectives to better manage funding
  • Educate your staff through development of solutions
  • Develop a scope for long-term growth and intelligent automation

Conference Day One: Tuesday, 26th February 2019

Tuesday, February 26th, 2019


Conference Day Two: Wednesday, 27th February 2019

Wednesday, February 27th, 2019


2:10 PM CASE STUDY: How An AI Implementation Framework Improved An Oil & Gas Business’ Discovery and Spend

In this session participants will learn how artificial intelligence reinvents mature fields of upstream oil and gas business. Also, you will gain real-world problem solving skills and knowledge by learning enterprise AI implementation framework to address some of the key business challenges which wasn’t resolved using traditional approach, including:

  • Identification of sick wells proactively
  • Predicting underperforming wells in the early stage
  • Incorporating new data in current sick well analysis
  • Overcoming data integration issues and inefficiencies caused by repetitive work