AI-driven automation: How born-analog companies can compete with digital natives

1. AI-driven automation:
How born-analog companies
can compete with digital natives
Theo Piletski, Capgemini
Kyle Hoback, WorkFusion
September 18, 2018

2. People are living longer,
but companies are dying younger
Human lifespanCompany lifespan

3. Average tenure of S&P companies is
18 years and falling
33 years
lifespan of a
S&P500
company in
1965
Projected to go
down to
▼12 years

4. We know why companies die

5. And we know why they dominate

6. Company survival is hard, but simple
“A company cannot endure
without reinventing itself.”

7. Most companies are fighting to survive
with brute force
$130b Spent by on data entry
just in US
$42b
Spent on manually
intensive data work by
financial services industry
alone
$250b
Global business
process
outsourcing
industry
$3t Data processing and data
collection work worldwide
CONFIDENTIAL

8. High Upfront Cost
Do IT projects Hire more people
High Operating Cost
?
But old models aren’t working

9. The space between enterprise systems and front-end
services is where work happens
Apps
Productivity
Legacy
Business
Cloud
Services
Front-office
Back-
office
Supply
Chain

10. Apps
Productivity
Legacy
Business
Cloud
Services
Front-office
Back-
office
Supply
Chain
RPA
The first generation of RPA promised to automate it

11. Apps
Productivity
Legacy
Business
Cloud
Services
Front-office
Back-
office
Supply
Chain
RPA OCR
But it wasn’t a complete solution

12. Apps
Productivity
Legacy
Business
Cloud
Services
Front-office
Back-
office
Supply
Chain
RPA OCR BPM
And it needed more…

13. Apps
Productivity
Legacy
Business
Cloud
Services
Front-office
Back-
office
Supply
Chain
RPA OCR BPM BI
and more...

14. Apps
Productivity
Legacy
Business
Cloud
Services
Front-office
Back-
office
Supply
Chain
RPA OCR BPM BI ML
and more…

15. Apps
Productivity
Legacy
Business
Cloud
Services
Front-office
Back-
office
Supply
Chain
RPA OCR BPM BI ML ITSM
and more…

16. Apps
Productivity
Legacy
Business
Cloud
Services
Front-office
Back-
office
Supply
Chain
RPA OCR BPM BI ML ITSM APM
No single system to automate COMPLETE human role
and more integration...

17. Until it created an even bigger mess than before
Salesforce
SAP
Oracle
Concur
Flexcube
LoanIQ
Mainframe
Mantis
Bloomberg
TIBCO
Mulesoft
SOA
Pega CRM
ERB EBS
ABBBY
Kofax
BPO #1
BPO #2
BizTalk
Excel
Microstrategy
Cognos
Custom BI
TensorFlow
Custom
Rage
Watson
Python
SAS
R
Custom #2 US
Account Opening
Treasury
Services
Anti-Money
Laundering
Mortgage
Services
Pega App #2
Pega App #3
SFDC
BPO #1
BPO #2
India
Costa Rica
Ireland

18. The first wave of RPA is business-as-usual
dressed as transformation
Transformation program timeline
Scale
POC
1 month
“A few
bots”
6 months
“We
hit a
wall”
12 months
“Unstructured data”
“Exceptions handling”
“Bot governance”
“Analytics”

19. RPA 1.0 is still old-fashioned rules-based software

20. Software 1.0: Lots of rules-based code and
a little data to automate tasks
code
data
simple task

21. Digital natives are using “Software 2.0” – code that
learns and improves from data

22. Software 2.0 uses data to automate more, faster
code
codedata
data
simple task
entire role

23. AI changes how enterprises digitize operations
Train software,
don’t code
Use screen,
don’t integrate
Scale real-time,
don’t deploy/hire
Reduce touchpoints,
don’t re-engineer
Learn,
don’t standardize
Before After
Engineers coding Business training
Licensed software Subscription software
Multi-year IT deployments 4 week Agile sprints
Six-Sigma operational excellence Digital workforce orchestration
Offshoring to BPO In-sourcing with bots

24. Apps
Productivity
Legacy
Business
Cloud
Services
Front-office
Back-
office
Supply
Chain
WorkFusion SPA is built on Software 2.0 and unifies all
automation capabilities into one platform
Robotics
Governance
Bot-Human
Workflow
Intelligent
Robots
Intelligent
Digitization
Intelligent
Analytics
Security

25. Apps Services
WorkFusion Intelligent Automation AI Platform
Robotics
Governance
Bot-Human Workflow
Intelligent
Robots
Intelligent
Digitization
Intelligence
Analytics
Security
Differential
Privacy
AutoQC
OCR
AutoML
Preventative
Controls
Ops
Analytics

26. 2 flagship products to start and scale AI-driven automation

27. “As RPA matures, analytics will dictate which providers lead the pack. Vendors that provide text analytics and artificial
intelligence (AI)...will position themselves to deliver successfully”
WorkFusion leading AI-led wave of automation
“As RPA matures, analytics
will dictate which providers
lead the pack. Vendors that
provide artificial intelligence
(AI)...will position themselves
to deliver successfully”
RPA 1.0
Incumbents
AI RPA 2.0

28. Leader in Intelligent Automation
CONFIDENTIAL 28
“5 out of 5
in Analytics
and AI”
Forrester
2018 RPA Magic Wave
“Best in
capabilities and
vision”
Everest
2018 FIT Matrix
“Choice for
organizations
dealing with data”
Gartner
2017 Market Guide

29. But What If…?
What if I already have BPM?
What if I already have RPA?
What if I’ve already done a POC on ML?

30. What if I already have BPM?
CONFIDENTIAL 30
2. SPA can be called by BPM2
3. SPA can be called by BPM mid-process3 4. SPA can function as BPM4
Only RPA software with complete
automation capabilities
1. SPA can call BPM1

31. What if I already have RPA?
Transformation program timeline
Scale
POC
1 month
“A few
bots”
6 months
“We
hit a
wall”
12 months
“Unstructured data”
“Exceptions handling”
“Bot governance”
“Analytics”
Options:
1. Ecosystem of Automation Software
2. Single Automation Platform

32. What if I’ve already done a POC on Machine Learning?
Typical POC gets here
1. Initial
Model
2. Execute
-able
“In Production” gets here
9. Next
Model(s)…
4. Handle
Exceptions
5. Monitor
Quality
6. Audit Data 7. New Data
8. Re-
training
3.
Connected
Real Production is here
And, on to next projects
Options:
1. Ecosystem: different software for each step
2. WorkFusion SPA: packaged solution for each step

33. Three Common Scenarios
1. Robots Exist But Cannot Scale – robots without much value
2. Disparate Businesses or Processes – a lot of different things
3. Existing BPO Customers – lean but still manual approaches

34. Adding Cognitive Bots into basic-RPA landscape for
Existing British multinational BFS company
Existing Bot was
extracting information
from an Excel file and
entering it into SAP
Non-excel information
must be extracted and
mapped manually by
employees
The information then
converted into specific
web formats for
processing
RPA
RPA could handle
structured inputs
only and couldn’t
handle exceptions
Cognitive RPA
WF Models
trained to map the
data correctly and
verify the
information
Bots input and
map verified
data for
processing
The results
Accuracy
Rate for
RPA
Accuracy
Rate for ML 90%
vs
100%
Automated 90% of all
completely manual process
Reduced transaction
processing time from 18
minutes to
3 minutes
WorkSpace
Exceptions that
cannot be mapped
correctly are sent
to employees

35. Automating a wide array of LOBs and Internal Processes
for Insurance Customer
Sales
Claims*
Core work focused on
intake and adjudication
of claims that require
human decisioning.
Core work focuses on
sales operations such
as set up of new
business, adding
additional policy holder to
groups, and executing
benefit changes.
Operations
Core work focuses on
policy holder
support such as:
• Deleting / adding
dependents
• Verification of
Death, and
• Appointments
• Fulfillment delivery
Federated LOBs focusing on
reactive and transactional
work through manual labor
force under increasing volume
demand. Siloed LOB specific
implementations of Automation
resulting in limited reusability
and limited opportunities of to
implement operational
changes to respond to new
growth and parallel operational
efficiency requests due to
customer experience
constraints. This is not an exhaustive
list of what these
businesses do.
Internal
Processes*
Internal processes such
as travel approvals,
expense reporting,
internal and external
Invoicing, Accounts
Payable/Receivable,
Compliance
Workforce
Orchestration
Workforce Orchestration
and Productivity
measurement.
Departmental and
Individual SLA and KPI
tracking
Other
* Utilizing “Niche” LOB-specific Automation

36. Claims Processing for Existing BPO Customer
Client was processing 2M+ claims
and is estimating 1M+ claims per
year to be added
Client prefers an onsite (higher
cost) delivery solution for the BPO
scope
Processing takes place across
multiple teams and at least 8
systems
Problems to AddressClaim Requests are
submitted for
processing
Capgemini optimizes
the existing claims
process
Review, adjudication and
payment must be also
entered into multiple
systems manually
Automate the extraction of
data from claims payment
form
Automate the data entry
multiple systems
Lean but Still Manual
approach that can be
automated
Capgemini optimizes the process
using ESOAR methodology
Data from multiple client systems is
moved to Capgemini BPO Solution
for Insurance
There are still some COTS systems
team has to navigate across uses
that can not be moved
WF Platform BPM to Drive further
process Optimization
Over 35% Efficiency gains by
leveraging WF RPA and Cognitive
automation (vs 19% for RPA Only
platform)
Leverage WorkFusion reporting and
visibility capabilities for
SLAs/Metrics
Achieve a high degree of
financial payment accuracy and
reduce Claims over-payments

37. Demo
Mortgage Processing

38. Apps Services
WorkFusion Intelligent Automation AI Platform
Robotics
Governance
Bot-Human Workflow
Intelligent
Robots
Intelligent
Digitization
Intelligence
Analytics
Security
Differential
Privacy
AutoQC
OCR
AutoML
Preventative
Controls
Ops
Analytics

39. Mortgage Processing requires collecting data from
documents, but it’s not a template-driven OCR problem

40. Solving Mortgage Processing requires the combination of
capabilities

41. Building the process is simplified and more cost effective
within one environment

42. Interacting with the UI is an important aspect

43. But, the true impact comes from working with the
mortgage documents themselves

44. And, having the auditability and governance of this
process (and others)

45. With the ability to understand the health and impact of all
processes being automated

46. Thank You