Watch the PPT to learn how intelligent automation enables companies to build remote working capabilities, increase productivity, and optimize the workforce.

1. Webinar
Improve business decisions with
Intelligent Automation

2. Ram Kanumuri
Head – Emerging
Technologies
Kellton Tech Solutions
Murali Polakam
Leader – Emerging
Technologies
Kellton Tech Solutions
Speakers

3. Kellton Tech, A Pioneer of Digital Transformation
Foundation
2009
Footprint
North America, Europe,
and Asia
Clients
Startups to Fortune 500
Core Strength
People and Technology
ISO 9001:2015 &
CMMi Level 5
Ownership
Public Limited
BSE, NSE: KELLTOTEC
Team
1500+ Employees

4. Go Digital with Kellton Tech
Digital Transformation Strategy and Advisory Services
Business - IT Strategy &
Planning
Enterprise Reference
Architecture
IT Portfolio
Management
Governance
Digital Tech Trends
And Design Thinking
• Digital Commerce & Marketing
• Enterprise Mobility
• Digital Governance
• IoT, Big Data & Analytics
• Software R&D Labs
• Outsourced Product Development
• Enterprise Resource Planning. • Product Lifecycle Management
• Supply Chain Management. •Customer Relationship Management
Enterprise Solutions
Systems of Record and Core IT
Successful S/4 HANA migration
implementation for enterprise clients
• API Strategy & Management • Enterprise Integration
• Enterprise Analytics. • IoT System Integration
• OT-IT Integration
Standalone Applications
& Interconnections
Integrated Enterprise
Environment
Digital Transformation
Systems of Engagement
Digital Integration (DI)
Systems of Insight and Integration

5. Agenda
Introduction to Automation
Intelligent Automation, RPA and
beyond
Achieve high levels of precision
with Intelligent Automation
Core elements of Modern
Automation
Best Practices for implementing
Automation CoE

6. Introduction to
Automation

7. What is Robotic Process Automation (RPA)?
Emulates a person by executing
manual, repetitive tasks in existing
applications
Makes decisions based on
set rules
Integrates and updates
seamlessly with other
applications
Robotic Process Automation is a digital work force that sits on top of existing systems and performs
manual, repetitive, and rule-based activities traditionally performed by individuals.

8. Automation technologies are evolving across increasing levels of sophistication
Automation Continuum
RPA/ROBOTICS COGNITIVE
AUTOMATION
ARTIFICIAL
INTELLIGENCE
Mimics Human
Actions
Mimics Human
Judgement
Mimics Human
Intelligence
• Acts based on set rules
• Robotic Process
Automation (RPA)
• Refines initial rules
• Machine Learning;
Natural Language
Processing
• Sophisticated algorithms
• Deep Learning;
Autonomous Vehicles

9. Types of Automation
Modern RPA platforms includes key components such as attended,
unattended and Intelligent Automation
Attended Automation
Configure Bots to work together
with Human
Front Office Automation
Unattended Automation
Fully automate the robot by teaching
how to do it
Back Office Automation
Intelligent Automation
Make sense of all data formats
Unstructured Data

10. What Processes are suitable for RPA?
Digital trigger-initiated process supported by digital data
Functioning and stable process
High-Volume process
Rule-based processes

11. Intelligent Automation
RPA and beyond

12. The Pandemic has Accelerated Digital Transformation

13. Intelligent Automation –
Achieving high levels of precision

14. Humans and Bots – Strengths and Weaknesses
Strength: Understand decision
Logic in the absence of large
Amounts of data
Strength: can analyze huge
Amount of data to find signals
And patterns
Strength: adapt to sea changes Strength: models can relearn
Quickly from new data
Weakness: unable to analyze
Large data sets
Weakness: poor adaptability
To sudden change
Weakness: cognitive biases can
Lead to poor decisions
Weakness: subject to mistake
By data scientists
Human Computer

15. Integration of humans with advanced AI components
Primary difference Primary technology
Machine acts autonomously in closed loop
Human and machines share authority
Human control automation
Deterministic human/machine
Interaction
Deep learning and computer vision
Machine learning
Conversational intelligence and RPA
Digital process automation and RPA
Autonomous
Semiautonomous
Employee-driven
Software-controlled

16. Automation Digital Workforce Value Cycle
Data
Understanding
Process
Discovery
Optimize
Capture
Discover
Digital Workforce
Platform
RPA
Automate
Analytics
Fast ROI and business value
Business resiliency​
Labor optimization​
Faster cycle times​
Customer experience
Employee productivity​
Compliance and security​
Artificial Intelligence

17. RPA and AI Convergence
RPA
Scrape data
Read
Sentiment
Make
Predictions
Detect
Languages
Moving File/Folders
Activites
Read/write to
databases
Capture
Tasks
Log in to
apps
Copy/Paste
Email/Event
Fill Forms
Extract
Structured
Data
Log in to
apps
Detect
anomalies
Summarize
text
Classify
text
Detect
Fraud
Understand
Documents
Unstructured
Data
Semi-structured
Data
Forms in
VDI
Speech to
text
Chatbots
Translate
Detect Objects in
Images
AI
Assist humans and focus on “”Thinking””
Increasingly cognitive and complex tasks
Pattern recognition
Semi-Structured and Unstructured data
Probabilistic and high-variability

18. Core elements of Modern
Automation

19. Digital Integration Models
Modern Automation is backed by Enterprise Integration
Enterprise Integration API Economy Cloud Systems
Hybrid Integration Strategy API Management SaaS/ PaaS/ IaaS Applications

20. BPM and RPA
Optimize
Monitor
Execute
BPM
Maintain logs for
every step
Your 24X7 virtual
employee
No physical
robot
Time- to-market within a
few week
Mimics human tasks on
existing application
No changes in your existing
infrastructure needed
RPA
Vs
Design
Model
Implement

21. Automation CoE – Best Practices

22. Patterns of CoE
Hackathons, gamification, skills workshop
Expansion of automation offerings;
E.g., workflow, AI, analytics
Center of excllence/incubators
distributed COE networks
new teams and functions
RPA vendor selection
Creation of pilots
Proof of concepts
Vendor evaluation
5. Feedback
Taking innovation
Ideas from
hackathons/
Workshops back into
the funnel
For pilots/proofs of
Concepts, etc.
4. Innovating
3. Scaling
2. Building
1. Embarking

23. Process and People Frist Approach for RPA Practice
CoE
control,
Standardize
CoE
Empower,
Educate
Enable
PROCESS-FIRST
1. Program Definition and
Governance
2. Service
Definition and
Refinement
3. Process Mastery
PEOPLE-FIRST
4. End-User Engagement
and Ideation
5. Mentoring
and Review
6. Deployment and Scaling
7. Outcome
Analysis and
Solution
Harvesting

24. RPA vs Intelligent Automation CoE
Organizations need to make the strategic decision on whether to enable AI through separate teams or under a single
Intelligent Automation CoE..
Individual CoEs Singular CoEs
RPA CoE
Solution
Architect
RPA
Developer
Business
Analyst
+
Data
scientist
Data
Engineer
Data and Analyst CoE
Intelligent Automation CoE
CoE Lead
Solution
Architect
RPA
Dev
Citizen
Dev
Business
Analyst
Data
Scientist
Data
Engineer
Technical Architecture
Operational Management
Design Authority
Process support
Governance
Service/Release
management

25. The Development Life Cycle
Identify Design Develop Test Deploy Maintain
Will leveraging RPA+AI center solve the
problem addressed?
If using AI center, what should the ML model solving?
Should RPA be designed any differently with the
addition of ML?
What data preparation is necessary for ML
modeling?
What orchestration between AI/ML teams and RPA
CoE is necessary?
Does the RPA CoE handle development of the
model to AI center?
Does the AI/ML team maintain model after
deployment?
Does the RPA CoE handle development of the
model to AI center?
How do teams mitigate and manage impact of
external changes?
Does the AI/ML team maintain model after
deployment?
Identify
Design
Develop
Test
Deploy
Maintain

26. Intelligent Automation Best Practices
Asking the correct questions with the correct participants are pivotal when assessing processes to
leverage RPA + AI
Recommended Workshop Participants
Process
SME
RPA solution
Architect
Data
scientist
Process
Owner
RPA
BA
Note : Not every opportunity will follow these considerations. Take extra consideration when assessing high-impact opportunities.
Data Sample size:
• Is there a large enough sample size to train an ML model?
Data noise:
• Does the data set contain irrelevant information or randomness?
Data completeness:
• Does the data sample contain all information necessary for modelling?
Data availability:
• Do the stakeholders have access to produce data for modelling?
• Can production data be retrieved in a timely manner?
Time to Invest:
• Do stakeholders have enough time for model creation?
• Does adding ML skills justify the time investment?
Considetations

27. Cloud Deployment Models
Fully hosted and managed in
the Automation Anywhere
Enterprise Cloud
The value:
Full SaaS experience, with
instant access to the complete
platform – secure, always up to
date and zero infrastructure to
maintain.
Data on your infrastructure and
management functions in the
Automation Anywhere
Enterprise Cloud
The value:
Keep your data on-premises
while enjoying a SaaS
experience and lower total cost
of ownership.
PURE CLOUD CLOUD ENABLED
ON-PREMISES WITH
UPDATES VIA CLOUD
Fully hosted on your infrastructure
with optional updates from the
Automation Anywhere
Enterprise Cloud
The value:
Deploy fully on-premises with
control over receiving updates and
new AI capabilities from the cloud.

28. Technology Partner Ecosystem
BPM/Workflow
Cloud Platforms
Conversational AI
Data Science & AI
Security
Enterprise Software
Process Mining
Service Orchestration

30. GET IN TOUCH
+1.844.469.8900
ask@kelltontech.com
www.kelltontech.com