1.
Underwritten by:Presented by: Nathaniel Palmer
#AIIMYour Digital Transformation Begins with
Intelligent Information Management
Striving for Better Business Outcomes? Who Isn’t!
Learn How a New Generation of Workflow Tools Enables
Cooperation between Business and IT
Presented June 12, 2019
Striving for Better Business Outcomes? Who Isn’t!
Learn How a New Generation of Workflow Tools Enables
Cooperation between Business and IT

2.
Underwritten by:Presented by: Nathaniel Palmer
#AIIMYour Digital Transformation Begins with
Intelligent Information ManagementYour Digital Transformation begins with
Intelligent Information Management

3.
Underwritten by:Presented by: Nathaniel Palmer
Today’s Speakers
Nathaniel Palmer
Transformation Leader &
Influential Thought Leader in BPM
Michael Nishiki
North American DBA Solutions Leader
IBM
Host: Theresa Resek, CIP
Director
AIIM

4.
Underwritten by:Presented by: Nathaniel Palmer
Nathaniel Palmer
Transformation Leader &
Influential Thought Leader in BPM
Introducing our Speaker

5.
Underwritten by:Presented by: Nathaniel Palmer
What Does Workflow Automation Look Like?

6.
Underwritten by:Presented by: Nathaniel Palmer

7.
Underwritten by:Presented by: Nathaniel Palmer
What Does Intelligent Automation Look Like?

8.
Underwritten by:Presented by: Nathaniel Palmer
Three R’s Define Intelligent Automation
Robots and things that learn
Rules and decisioning
Relationships
(not relational per se but relationship data)

9.
Underwritten by:Presented by: Nathaniel Palmer
Robots
Require
Rules

10.
Underwritten by:Presented by: Nathaniel Palmer
Following Rules vs. Learning

11.
Underwritten by:Presented by: Nathaniel Palmer
Digital Transformation of Operations
Requires Intelligent Automation
Data-Driven = understands the context of work (knows what’s
inside the payload and where it is in the process)
Goal-Oriented = decision logic drives optimal outcomes
Adaptive = changes course in response to business events
Actionable Intelligence = understanding of the process,
goals, business events and decision rules to expand the
envelope of what can be automated

12.
Underwritten by:Presented by: Nathaniel Palmer
Predefined
Main
Path
Exception
Path
Pathways are Predefined and Ordered
The Structure of Work is Defined by Pathways and UI
Traditional Workflow Automation

13.
Underwritten by:Presented by: Nathaniel Palmer
Adaptable Process:
Knowledge Work is Driven by Goals and Milestones
Eligible Enabled
Outcome
Path
Paths are not Predefined
but Prescribe by Rules
Events Affect the Specific
Path and Sequence of Steps
What We Do and How We
Do it is Determined by
Context: Dynamically
Responding to Event Data,
Rules, and Learned Patterns

14.
Event Cloud of 50 Billion Things
APIs & Microservices
Event Gateway (most likely in the cloud, not ESB)
Things & Sensors Voice & Actions
AI & Machine Learning
Pattern Detection
& Correlation
Optimization Algorithms
Robotic Automation
Robotic Process
Automation (RPA)
Intelligent Agents
Decision Management
Business Rules,
Alerts and Actions
Process Management
Work Management,
Control Flow
Intelligent Automation Platform
Transactions & Reports Bots & Cogs

15.
Event Cloud of 50 Billion Things
APIs & Microservices
Event Gateway (most likely in the cloud, not ESB)
Things & Sensors Voice & Actions
AI & Machine Learning
Pattern Detection
& Correlation
Optimization Algorithms
Robotic Automation
Robotic Process
Automation (RPA)
Intelligent Agents
Decision Management
Business Rules,
Alerts and Actions
Process Management
Work Management,
Control Flow
Transactions & Reports Bots & Cogs
Where is the User Interface?

16.
Underwritten by:Presented by: Nathaniel Palmer
The Interface is “Things” not a “Thing”
Digital Transformation Represents a Cambrian
Explosion of New Interface Types, Forever Changing
Customer Expectations for Access and Interaction

17.
Underwritten by:Presented by: Nathaniel Palmer
Intelligent Automation requires
re-envisioning the structure of the task –
to be not a single, discrete unit of work,
but to remove the distinction between
what supports a task and the task itself.

18.
Underwritten by:Presented by: Nathaniel Palmer
Case Study: Assisted Knowledge Work
• Processing of end-to-end workflow via RPA to increase accuracy and resolution.
Uses RPA as a “virtual worker” within Case Management system ensuring the
same rules and controls are applied, reporting is maintained, and QA is followed.
• Worked approximately 40,000 to completion and 60,000 tasks overall over a 15 day
period (out of more than 160,000 tasks), avoiding need for regular users to work them
• Averaged about 12 tasks/minute and took between 4-8 minutes/task at up to 40 Robot
sessions (2-4 times faster than Regular Users)
• Was key to meeting applicable SLA for task turnaround supporting the efforts of workers
by offloading “low complexity” tasks and allowing them to focus on higher value work

19.
Underwritten by:Presented by: Nathaniel Palmer1
9
Perform Task
Dynamic Work
Assignment
Existing Data
Services
Robot Service
Get Next Task
New Data
Services
New RPA
Eligibility Service
Components: Middleware Decision Services Case Management
New Services
High-Level Architecture

20.
Underwritten by:Presented by: Nathaniel Palmer
User Eligibility Decision Service
RPAEligibility (rule service to determine if task can be performed by RPA)
U
ComplexityRating TaskTypeGroup RPAUserSetting RPAEligibility Description
Input Input Input Outcome Informational Note
1 "Low" "ValidForRPAUser"
"RPAUserOnly",
"RPAAndESWUser"
true
RPA online with qualifying
task
2 "High" false non-qualifying case
3 "ValidForESWUser" false non-qualifying task type
4 “ESWUserOnly" false RPA offline

21.
Underwritten by:Presented by: Nathaniel Palmer
TIPS Log-on Error Handling
Log-on To System
Log-on Error Handling
Log-on
To System Main
Screen
Handle
System Log-on
Error
System Log-on
Session Ended With
Error Reported
RPA User
Work Day
Started System Log-on
Error Occurred
Log-on
Session
Attempted
RPA User
Work Day
Ended Per
Schedule
Exceeds
Count Limit?
Check
Log-on
Retry Count
No
Log-on
Retry Wait Time
Observed
Yes
RPA User
Work Day
Ended
Per Work Done
Launch
System User
Work
Log-off
From System Main
Screen
Log-on
Session
Concluded
System User Work
launched multiple times
until no work remains
OR the work day ends
Multiple
Parallel RPA
User Log-offs
Multiple
Parallel RPA
User Log-ons
Work-in-process completed but no new work is started, ending the
multiple parallel RPA User sessions still active and moving to log-offs
Something prevents log-on from completing, ending
the attempt and leading to an error handling screen
Logon Error Handling:
• Robot attempts to logon.
• If logon is successful, then
Get Next Task is launched;
otherwise (logon is not
successful), Robot handles
the error by closing the
session and retrying if retry
count is less than the pre-
set limit.
• If the retry count is at the
limit, then “sleep” for a time
before retrying again with
the retry count reset to 0.
Robot Service Design:
Logging On To The Case Management System

22.
Underwritten by:Presented by: Nathaniel Palmer
RPAEligibility
Decision
Service
User Work Screen
Error Handling
Launch
System User Work
Started
Invoke
RPA Eligibility
Service
Launch
System User
Work Screens
Launch
System User Work
Ended Successfully
RPA Eligibility Service
Error Detected
Service
Error Occurred
Handle
Service
Error
Launch
System User Work
Ended Unsuccessfully
RPA Eligibility
Service
Request
RPA Eligibility
Service
Response
Service error(s) prevent the user work
screens from launching ending the attempt
and leading to an error handling screen
UI Form Error Handling:
If launch of the main case management process is successful, then any
remaining error handling moments are presented on the returned
form UI, which are then handled as appropriate by the Robot.
Robot Service Design
Launching the Process

23.
Underwritten by:Presented by: Nathaniel Palmer
Verification Form Data Entry Handling
Close-out
Assisted Work
Screen Processing
Inspect
Data Presented By
Initial Work Screen
Launch
System User
Work Screens
Started
RPA User
Eligible?
Work Screen
Data Entry Completed
Unsuccessfully
With Routing To
“Regular User Only”
Yes
User Type?
No
RPA User
Continue
Normal Work Screen
Processing
Regular User
Data Entry
Branching Point
One Applicable?
Complete
Assisted Work
Screen Processing 1
Yes
Data Entry
Branching Point
Two Applicable?
No
Complete
Assisted Work
Screen Processing 2
Yes
Complete
Assisted Work
Screen Processing 3
No
Work Screen
Data Entry Completed
Successfully
Continue
Assisted Work
Screen Processing
Path of Inbound
Tasks Marked
“Regular User Only”
Path RPA User Takes
when it is not eligible
to work the Task
Streamlined portion of Work Screens that the RPA User will complete
(that a Regular User would also have to complete)
UI Form Data Entry Handling:
Branching moments in the data entry determine
the actions of the Robot.
Robot Service Design
Entering Data Into The Case Management System

24.
Underwritten by:Presented by: Nathaniel Palmer
Robots will do only what it is “taught” to do, which will be to follow the same set of actions/reactions to what
is presented on screen that an Regular User would follow
• RPA-eligible work is “low complexity” work that the Robot is able to work to completion after it
interprets the evaluation of this possibility by the Eligibility Service
ANY user will have to know what to do for the following and what to do if the following doesn’t work
• Logging on and handling any errors that are thrown
• Launching specific processes/sets of screens and handling any errors that are thrown
• Navigating specific processes/sets of screens along pre-determined lines of data entry or UI actions
Robots will be “taught” what to do through the application of process models created to capture these
distinct situations and the cataloguing of the relevant screen shots to facilitate designing the Robot Service
• Robot design tool will use these artifacts to create a flowchart-like path that the Robot must take and
simply cannot deviate from in its execution
Training Robots as Knowledge Workers

25.
Underwritten by:Presented by: Nathaniel Palmer
Automation technology is often difficult to explain to non-IT stakeholders
• Conditioned perception of what a robot does complicates its true understanding –
e.g., errors the robot might make are seen as different though other users can make the same ones
• Translating contextual decision-making informed through experience and training into algorithmic
pathways can be a rough transition for those feeding the design
Automation design skills have more in common with those of functional testers and/or
usability leads, because they better understand the step-by-step UI action/reaction
1st Generation automation can enable low-complexity work over a UI-based system to
become an essentially straight-through-processing (STP)-like sequence, extending its utility
by leveraging what it already does
Simple algorithmic pathways are the first to go, but automation platforms will expand their
reach as designer tools become more robust and the use of AI enables the processing of less
deterministic and structured situations
Design Considerations & Learnings

26.
Underwritten by:Presented by: Nathaniel Palmer
Michael Nishiki
North American DBA Solutions Leader
IBM
Introducing our Speaker

27.
Underwritten by:Presented by: Nathaniel Palmer
The Intelligent Automation Journey

28.
Underwritten by:Presented by: Nathaniel Palmer
© 2019 IBM CorporationIBM Cloud | Digital Business Automation
The IBM Digital Business Automation Platform
Financial
Applications
Insurance
Applications
Public Sector
Applications
Mobile
Applications
Manufacturing
Applications
Shipping
Applications
Healthcare
Applications

29.
Underwritten by:Presented by: Nathaniel Palmer
Intelligent Automation Can Redirect Knowledge Work to
Focus on Missed Opportunities
Many information gathering tasks included in
managing a claim can be automated, allowing
manpower to focus on investigations, determination
and settlement tasks.
Basic automation of processes can eliminate errors,
reduce biases and perform transactional work in a
fraction of the time it takes humans.
Intelligent automation systems can analyze data up to
25 times faster than the human brain, function around
the clock every day of the week, and interact with
employees and customers in natural language, all with
incredible accuracy.

30.
Underwritten by:Presented by: Nathaniel Palmer
AI Expertise Requires Trusted Information that
Conforms to Governance and Regulation Standards
Dynamic
Remain current as information evolves
Transparent
Provenance and lineage
Relevant
Verified content from trusted sources
Defensible
Auditable to confirm responses
Is this the right information?
Is this the best information?
Is this information correct?
Is this information approved?
A guided process to engage domain experts for
curation, classification and review and approval
tasks
Increase accuracy with governance and
transparency
Ensure compliance to regulatory requirements

31.
Underwritten by:Presented by: Nathaniel Palmer
Collect
Scalable cloud
capability to
capture end to end
business data from
different instances
of DBA platform
components into a
data lake.
Visualize
Provides real-time visibility to
Business Managers via pre-defined
or user configured dashboards.
Measure
Correlate and measure the
data based on collected
business and operational
metrics.
Learn & Guide
Enable Data Scientists to apply MLs on the
operational data lake to make
recommendations to business managers and
knowledge workers
Gain visibility and deep understanding of business operations
running on the automation platform*
© 2019 IBM CorporationIBM Cloud | Digital Business Automation
IBM Business Automation Insights

32.
Underwritten by:Presented by: Nathaniel Palmer
Knowledge Worker Enabled by
Business Automation Insights

Knowledge worker
Next BestTask
Two of your team members
have worked on a similar task.
Result1
Result2
Geez. I don’t even
know where to start.
What would Bob do?
Recommendation when in
doubt
Automatic task prioritization

33.
Underwritten by:Presented by: Nathaniel Palmer
How IBM Intelligent Automation Works
Do not leave behind. Duplication is strictly prohibited. © 2019 IBM Corporation

34.
Underwritten by:Presented by: Nathaniel Palmer
Knowledge Intensive Digital Workplace Experience
In-context Automation Example
Recommendatio
n
Client Profile
(from CRM)
For a given business process…
In-Context
Microcontent
In-Context
Recommendation
In-Context
Persistent Case
Record
In-Context
Experts
In-Context
Microcontent
In-Context
Priority Tasks
In-Context Related
Cases
A simple, powerful, in-context content-
centric-process experience
Do not leave behind. Duplication is strictly prohibited. © 2019 IBM Corporation
In-Context
Documents
In-Context
Documents

35.
Underwritten by:Presented by: Nathaniel Palmer
Learn More
Find out about IBM Digital Business Automation Platform
https://www.ibm.com/automation/ibm-automation-platform-digital-business
See a short demo of IBM Business Automation Workflow
https://youtu.be/C_rOtLh64so
What is IBM Business Automation Insights?
https://www.ibm.com/support/knowledgecenter/SSYHZ8_18.0.x/com.ibm.dba.offerings/topics/con_bai.html