From Cutting-Edge AI Research to Business Impact - What Everyone Ought to Know About Outcome Based Automation

From Cutting-Edge AI Research to Business Impact - What Everyone Ought To Know About Outcome Based Automation

Bringing to you the benefits of our Fewer Clients More Attention model

With 25,000 employees in 26 countries, we are big enough to scale, yet the right size to care

Being privately-owned allows us to be flexible to your needs andConfidential go Beyond and Proprietary. the© 2019Contract UST Global toInc serve 2 Our global presence to ensure access to talent and seamless delivery

42 London 34 Leeds Operating Ireland Delivery Centers Geneva Centers Madrid Copenhagen Cologne Gdansk Netherlands Algeciras Barcelona

Singapore Aliso Viejo Manila New York, NY Shanghai Norfolk, VA Penang Dayton, OH Tel Aviv Kuala Lumpur Leon Atlanta, GA Cyberjaya Guadalajara Bentonville, AR Taiwan Mexico City Toronto Hong Kong Costa Rica Brazil Trivandrum Kochi Coimbatore Bangalore Chennai Hyderabad Bhopal Pune Mumbai Sydney Major Delivery Centers Delhi Auckland

Confidential and Proprietary. © 2019 UST Global Inc 3 USA India Poland Philippines Mexico Malaysia Spain Singapore China Taiwan Germany Australia Adnan Masood, Ph.D. - Chief Architect - Artificial Intelligence and Machine Learning at UST Global, Visiting Scholar at Stanford University, and Microsoft MVP (Most Valuable Professional) for AI.

• Automation on Steroids • AI Trends in Automation • Message from the Trenches

"A lot of cutting edge AI has filtered into "Every time we figure out a piece general applications, often without of it, it stops being magical; we being called AI because once something say, 'Oh, that’s just a becomes useful enough and common computation”’ enough it's not labelled AI anymore”

Rodney Brooks Nick Bostrom

"AI is whatever hasn't been done “As soon as it works, no one calls it AI yet.” - Tesler's Theorem as quoted any more”

Confidential and Proprietary. © 2019 UST Global Inc 12 Confidential and Proprietary. © 2019 UST Global Inc 13 Enterprise AI Systems are built with recurring business processes which can be captured, and optimized.

Knowledge Intelligent Autonomous Automation Management Monitoring Operations

Natural Language Business Transaction Autonomous Runbook Automation Understanding Monitoring Monitoring

Data Fusion with Self Service Workflows User Behavior Tracking Anomaly Detection Computer Vision

Cognitive RPA (OASIS) Knowledge Graph Support Activity Fault Prediction

Robotic Process Application Preemptive Dark Data Mining Automation Performance Intervention

Self Service/Virtual Cognitive Search Business Performance Self Healing Assistants

Search Service Advanced Cognitive Search with and other paid/subscription content – understand Intent and Context

Users Summarization Service Sales, and Marketing Teams – Cognitive Extractive and Abstractive Content Writers, Marketers, Search and Document Summarization Distributed Sales Team Summarization

Corpora Internal Document subscription data Multi-media content, images, videos, infographics

“[What are] industry best practices and “I have built 10 PoCs and I have no hope that innovations in the scaling of AI and Machine any of them will be put into production” Learning and connecting it to the business, to leadership, and to customers”

“How do we automate some of our business functions more effectively? Right now there are “How do we benchmark ourselves against a lot of manual processes. How can we apply leaders in data science and automation like the autonomous operations concept to business Google, Uber, and Wayfair? processes?

Opportunity costs of AI investment 24%

There is not a unified belief in a clear ROI to AI 18% Don’t know what the We are not sure how to apply it to our business 16% right problems are

We are not sure where to apply it to our business 15%

Lack of skills to implement and operate such systems 22% Don’t know how 20% Change management to replace existing data and… to do it We don't have a well-curated collection of data to… 15%

Introduction of new security threats 23% Privacy concerns 22% Workforce concerns about job security 21% Don’t trust it Concern about unintended, potentially negative, and… 18%

Inability to maintain oversight and governance 17%

ConfidentialSource: and2,594 Proprietary.Data and analytics ©decision 2019-makers UST whose Global firm is Inc interested in using/planning to use/currently usingAI - Forrester 21 Enterprise software companies building seamless AI into applications

Schema generation & data Suggests next steps Natural language generation transformations

Getting Better with Intelligence • Generative Adversarial Networks • Transfer Learning & Pre-built Models • Reinforcement Learning • Augmented Intelligence

MIT Computer Science and Artificial Intelligence Laboratory

Clients share their problem statements and host sessions with researchers and UST to partner on focused research projects

Access to researchers, open source tools, and visiting scholar program to engage, build, and learn with clients

Collaboration with researchers and faculty to launch and manage strategic centers of excellence around specific business & research problems

Big data ingestion, processing and Identify data that is relevant to preparation the business goal

Understand Integrate and enrich the Prepare data into an analytical

data data set Development Measure the effectiveness Monitor of the model in ML the real world Models

Production Run statistical and ML Use the model in Deploy Model algorithms to find the applications model

Test the model to make Model scoring/ sure it will work Evaluate Model training inferencing in using ML applications algorithms

Data Data Ingestion Data Analysis Data Validation Data Splitting Transformation

Trainer Building a Model Model Validation Training At Scale Logging

Roll-out Serving Monitoring Optimization

Explain your AI

Confidential and Proprietary. © 2019 UST Global Inc 34 “Responsible AI” foretells massive enterprise AI adoption Confidential and Proprietary. © 2019 UST Global Inc 35 Ethical Machine Learning and Interpretability

Data scientists aren’t just expensive. They are inefficient.

Data is fuel for the Machine Learning engine

Agent

State Reward Action

St Rt At

Rt+1 Environment St+1

Exceeds human Greater productivity & less Avoids the long cognitive abilities change management tail

Better Value Faster to decisions faster develop

Automation Knowledge Management Intelligent Monitoring Autonomous Operations

Natural Language Business Transaction Runbook Automation Autonomous Monitoring Understanding Monitoring

Data Fusion and Computer Self Service Workflows User Behavior Monitoring Anomaly Detection Vision

Cognitive RPA (OASIS) Knowledge Graph Support Activity Monitoring Fault Prediction

Robotic Process Application Performance Dark Data Mining Preemptive Intervention Automation Monitoring

Self Service Virtual Business Performance Cognitive Search Self Healing Assistants Monitoring … … … … Confidential and Proprietary. © 2019 UST Global Inc 56 Confidential and Proprietary. © 2019 UST Global Inc 57 Copyright and Confidentiality Notice

Copyright © 2019 by UST Global Inc. All rights reserved. This document is protected under the copyright laws of United States, India, and other countries as an unpublished work and contains information that shall not be reproduced, published, used in the preparation of derivative works, and/or distributed, in whole or in part, by the recipient for any purpose other than to evaluate this document. Further, all information contained herein is proprietary and confidential to UST Global Inc and may not be disclosed to any third party. Exceptions to this notice are permitted only with the express, written permission of UST Global Inc.

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