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Underwritten by:Presented by: Nathaniel Palmer
#AIIMYour Digital Transformation Begins with
Intelligent Information Manage...
Underwritten by:Presented by: Nathaniel Palmer
#AIIMYour Digital Transformation Begins with
Intelligent Information Manage...
Underwritten by:Presented by: Nathaniel Palmer
Today’s Speakers
Nathaniel Palmer
Transformation Leader &
Influential Thoug...
Underwritten by:Presented by: Nathaniel Palmer
Nathaniel Palmer
Transformation Leader &
Influential Thought Leader in BPM
...
Underwritten by:Presented by: Nathaniel Palmer
What Does Workflow Automation Look Like?
Underwritten by:Presented by: Nathaniel Palmer
Underwritten by:Presented by: Nathaniel Palmer
What Does Intelligent Automation Look Like?
Underwritten by:Presented by: Nathaniel Palmer
Three R’s Define Intelligent Automation
Robots and things that learn
Rules ...
Underwritten by:Presented by: Nathaniel Palmer
Robots
Require
Rules
Underwritten by:Presented by: Nathaniel Palmer
Following Rules vs. Learning
Underwritten by:Presented by: Nathaniel Palmer
Digital Transformation of Operations
Requires Intelligent Automation
Data-D...
Underwritten by:Presented by: Nathaniel Palmer
Predefined
Main
Path
Exception
Path
Pathways are Predefined and Ordered
The...
Underwritten by:Presented by: Nathaniel Palmer
Adaptable Process:
Knowledge Work is Driven by Goals and Milestones
Eligibl...
Event Cloud of 50 Billion Things
APIs & Microservices
Event Gateway (most likely in the cloud, not ESB)
Things & Sensors V...
Event Cloud of 50 Billion Things
APIs & Microservices
Event Gateway (most likely in the cloud, not ESB)
Things & Sensors V...
Underwritten by:Presented by: Nathaniel Palmer
The Interface is “Things” not a “Thing”
Digital Transformation Represents a...
Underwritten by:Presented by: Nathaniel Palmer
Intelligent Automation requires
re-envisioning the structure of the task –
...
Underwritten by:Presented by: Nathaniel Palmer
Case Study: Assisted Knowledge Work
• Processing of end-to-end workflow via...
Underwritten by:Presented by: Nathaniel Palmer1
9
Perform Task
Dynamic Work
Assignment
Existing Data
Services
Robot Servic...
Underwritten by:Presented by: Nathaniel Palmer
User Eligibility Decision Service
RPAEligibility (rule service to determine...
Underwritten by:Presented by: Nathaniel Palmer
TIPS Log-on Error Handling
Log-on To System
Log-on Error Handling
Log-on
To...
Underwritten by:Presented by: Nathaniel Palmer
RPAEligibility
Decision
Service
User Work Screen
Error Handling
Launch
Syst...
Underwritten by:Presented by: Nathaniel Palmer
Verification Form Data Entry Handling
Close-out
Assisted Work
Screen Proces...
Underwritten by:Presented by: Nathaniel Palmer
Robots will do only what it is “taught” to do, which will be to follow the ...
Underwritten by:Presented by: Nathaniel Palmer
Automation technology is often difficult to explain to non-IT stakeholders
...
Underwritten by:Presented by: Nathaniel Palmer
Michael Nishiki
North American DBA Solutions Leader
IBM
Introducing our Spe...
Underwritten by:Presented by: Nathaniel Palmer
The Intelligent Automation Journey
Underwritten by:Presented by: Nathaniel Palmer
© 2019 IBM CorporationIBM Cloud | Digital Business Automation
The IBM Digit...
Underwritten by:Presented by: Nathaniel Palmer
Intelligent Automation Can Redirect Knowledge Work to
Focus on Missed Oppor...
Underwritten by:Presented by: Nathaniel Palmer
AI Expertise Requires Trusted Information that
Conforms to Governance and R...
Underwritten by:Presented by: Nathaniel Palmer
Collect
Scalable cloud
capability to
capture end to end
business data from
...
Underwritten by:Presented by: Nathaniel Palmer
Knowledge Worker Enabled by
Business Automation Insights

Knowledge worker...
Underwritten by:Presented by: Nathaniel Palmer
How IBM Intelligent Automation Works
Do not leave behind. Duplication is st...
Underwritten by:Presented by: Nathaniel Palmer
Knowledge Intensive Digital Workplace Experience
In-context Automation Exam...
Underwritten by:Presented by: Nathaniel Palmer
Learn More
Find out about IBM Digital Business Automation Platform
https://...
[Webinar Slides] Striving for Better Business Outcomes? Who Isn’t! Learn How a New Generation of Workflow Tools Enables Co...
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[Webinar Slides] Striving for Better Business Outcomes? Who Isn’t! Learn How a New Generation of Workflow Tools Enables Cooperation between Business and IT

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In this webinar, we’ll take a close look at how a new generation of workflow tools enables cooperation between business and IT. And, with IBM’s help, demonstrate how adaptable and flexible intelligent automation tools make that a reality.

Want to follow along with the webinar replay? Download it here for FREE: https://info.aiim.org/striving-for-better-business-outcomes-who-isnt

Publicada em: Tecnologia
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[Webinar Slides] Striving for Better Business Outcomes? Who Isn’t! Learn How a New Generation of Workflow Tools Enables Cooperation between Business and IT

  1. 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. 2. Underwritten by:Presented by: Nathaniel Palmer #AIIMYour Digital Transformation Begins with Intelligent Information ManagementYour Digital Transformation begins with Intelligent Information Management
  3. 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. 4. Underwritten by:Presented by: Nathaniel Palmer Nathaniel Palmer Transformation Leader & Influential Thought Leader in BPM Introducing our Speaker
  5. 5. Underwritten by:Presented by: Nathaniel Palmer What Does Workflow Automation Look Like?
  6. 6. Underwritten by:Presented by: Nathaniel Palmer
  7. 7. Underwritten by:Presented by: Nathaniel Palmer What Does Intelligent Automation Look Like?
  8. 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. 9. Underwritten by:Presented by: Nathaniel Palmer Robots Require Rules
  10. 10. Underwritten by:Presented by: Nathaniel Palmer Following Rules vs. Learning
  11. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 26. Underwritten by:Presented by: Nathaniel Palmer Michael Nishiki North American DBA Solutions Leader IBM Introducing our Speaker
  27. 27. Underwritten by:Presented by: Nathaniel Palmer The Intelligent Automation Journey
  28. 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. 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. 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. 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. 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. 33. Underwritten by:Presented by: Nathaniel Palmer How IBM Intelligent Automation Works Do not leave behind. Duplication is strictly prohibited. © 2019 IBM Corporation
  34. 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. 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

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