automation in the age of cognitive computing - …...automation in the age of cognitive computing...

Automation in the age ofcognitive computingInstitute for Robotic Process Automation (IRPA)7 December 2016

Loren WilliamsChief Data Scientist and Artificial Intelligence Leader,Ernst & Young LLP, US

Institute for Robotic Process Automation

Automation in the age of cognitive computing

AI has been around for awhile, but this time it’s different

The Ancients► Formal reasoning► Mathematical logic

1

The Golden Age► Turing test► Dartmouth conference 2

Expert systems► XCON Configurator► Deep Blue chess match

3

Deep learning► Watson wins on Jeopardy► AlphaGo wins 3 out of 5 4

1950

1980

2010


What does Artificial Intelligence mean to us?

Not this …

Dolores, Westworld► AI in a humanoid robot► Communicates verbally and

non-verbally► Effectively human?

Not this …

HAL 9000► AI embodied in machine► Communicates verbally► General purpose, in context of

mission

But this …

Automation system► Interacts naturally, e.g., reads

and writes documents likehuman, conversational

► Learns, i.e., becomes moreeffective through use andexperience

► Fit to purposeImage source: Skotcher Image source: HBO Image source: Techipedia


If AI is an automation system, how does it differ fromRobotic Process Automation (RPA)?

► Unstructured Input▬ Image, speech, text▬ Search to Word2Vec

► Inferential▬ Machine “learning” is statistical

“estimation”▬ Probabilistic outcomes

► Rapidly evolving Image source: Horses for Forces, July 2015


RPA is extremely disruptive…

Every reason to expect that the valueproposition is enduringMarket in RPA professional services

… because of a combination of lean deployment withclear benefits

Low-risknon-invasivetechnologyRPA can be overlaid on existingsystems, allowing creation of aplatform compatible with ongoingdevelopments in sophisticatedalgorithms andmachine-learning tools.

Right shoringGeographical independencewithout businesscase impact.

ReliabilityNo sick days, servicesare provided 365 days a year.

Audit trailFully maintained logsessential for compliance.

ConsistencyIdentical processes and

tasks, eliminatingoutput variations.

AccuracyThe right result, decision

or calculation the first time.

ScalabilityInstant ramp up and downto match demand peaksand troughs.

RetentionShifts toward more

stimulating tasks.

ProductivityFreed-up humanresources for highervalue-added tasks.

Cross-industryRPA can be used acrossindustries since it followsprocedures in use.

DurationRPA projects run 9 to12months with a return of

investment below 1 year.

Savingpotentials

30%–50%Over offshore costs


Cognitive Automation is equally disruptive and equallycompelling

Market growth expected to follow a similartrajectory

► The tasks that can be automated are high valuework, done by highly paid humans► “Artificial intelligence is now telling doctors how

to treat you,” - Wired, June 2014► “Artificial intelligence disrupting the business of

law,” - FT, October 2016► “The robots are coming to Wall Street,” - NYT,

February 2016

► The information that can be ingested to drivethe automation is vastly richer

► It can, and should, also be non-invasive tousers

Market professional services related to cognitive computing


Ensemble solution

Cognitive RPA EngineDat

ain

gest

ion

Azure

appservice

Data sourcesStructured data

RPA portalweb app

Azure SQL Azure DataLake

Cognitiveservices

Machinelearning Data factory Bot

frameworkStorageBusiness rules

EY

Database

FlexRule Engine

Information

consumption

Structured andunstructuredoutput

Cortana

Power Business Intelligence (BI) reporting

Data sourcesUnstructured data Decisions and

automation

RPA tool


Machine learning

Cognitive Automation: AI integrated in workflow automation

Populate emailnotifications

Optical characterrecognition (OCR)

noticesTraining Testing Write to output

Sourcing Digitizing Manual tagging Model testing

2,000 Lotus NotesMail Client

S3 Storage

OCR tuning

► Validate results ofOCR process

► Tune OCRparameters toimprove quality

Tagging 2,000 Notices to extract dataattributes for training set

Write to output

Exception handling

► Route the non-extractedemail notices to humans

► Write the dataextracted from thenotices to a file

Test 50–100 June notices toValidate success criteria fortime saving provided byMachine Extraction

► Configure MachineModel using the dataand train it

► Fine-tune MachineModel Features

S3 Storage

CloudApplication

LotusNotes


Is it RPA, or is it CA?

► The process uses standard technology, with a very well understood interface

► There are multiple paths to successful resolution of the process; but the rulesfor choosing a path are clear

► It is a pervasive and time-consuming manual process,


Convergence

► Traditional RPA will bedeployed with AI technologies

► General purpose processautomation tools will grow

► Special purpose AI systemswill automate tasks andprocesses

Image source: Brown Bird Design; Photo: Kenji Aokihttps://www.wired.com/2013/04/convergence/


The human element: A critical success factor

Technical capability often exceeds behavioralalignment

Low High

Technical capability(Analytics production)► Data science► Data quality► Infrastructure and tools

Low

Hig

hBehavioral alignment(Analytics consumption)

► Culture and mental models► Organization and process

design► Learning and development► Incentives and rewards

Technicalcapability gap

Dangerzone

Behavioralalignment gap

Valuecreation

Lesson for cognitive automation

► Humans will train cognitive bots► Bulk training► Reinforcement training

► Many cognitive bots will augmentwork of humans

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