Expanding process automation opportunities using agentic AI
Beyond RPA and BPA, agentic AI transforms process automation. This guide explores how to choose the right tools for specific use cases to maximize business value, agility, and operational innovation. Dökümanı indirinProcess automation | SAP Build
The agentic AI opportunity
in process automation
A guide to traditional and agentic use cases
The agentic AI opportunity
in process automation
A guide to traditional and agentic use cases
2 | 36
Table of
contents
4 Understanding the promise of process
automation
5 Reviewing the traditional process
automation playbook
9 Understanding the agentic automation
opportunity
12 Comparing traditional process automation
with agentic process automation
15 Considering common use case categories in
the new automation playbook
16 Common use case categories for business process
automation
19 Common use case categories for robotic process
automation
22 Common use case categories for agentic process
automation
25 The future of process automation is hybrid
26 Reviewing solutions from SAP
28 Appendix: Automation opportunities across
key processes
29 Invoice processing
30 Account reconciliation
31 Order management
32 Returns and claims handling
33 Preboarding
34 Learning and development
35 Intake management
36 Dispute and claim resolution
Table of
contents
4 Understanding the promise of process
automation
5 Reviewing the traditional process
automation playbook
9 Understanding the agentic automation
opportunity
12 Comparing traditional process automation
with agentic process automation
15 Considering common use case categories in
the new automation playbook
16 Common use case categories for business process
automation
19 Common use case categories for robotic process
automation
22 Common use case categories for agentic process
automation
25 The future of process automation is hybrid
26 Reviewing solutions from SAP
28 Appendix: Automation opportunities across
key processes
29 Invoice processing
30 Account reconciliation
31 Order management
32 Returns and claims handling
33 Preboarding
34 Learning and development
35 Intake management
36 Dispute and claim resolution
3 | 36The agentic AI opportunity in process automation
Technologies such as business process
automation (BPA) and robotic process
automation (RPA) deliver clear value,
helping companies optimize operations
and enhance agility and innovation.
But a new technology, agentic AI,
promises to expand the traditional
process automation playbook. Which tools
are best suited for each use case type,
and how can choosing the right technology
maximize business value?
Technologies such as business process
automation (BPA) and robotic process
automation (RPA) deliver clear value,
helping companies optimize operations
and enhance agility and innovation.
But a new technology, agentic AI,
promises to expand the traditional
process automation playbook. Which tools
are best suited for each use case type,
and how can choosing the right technology
maximize business value?
4 | 36The agentic AI opportunity in process automation
Understanding the promise of process automation
Make the leap from efficiency to transformation
Established process automation technologies
offer proven value (see Figure 1). For many years,
companies have used these tools to automate
repetitive tasks, workflows, and business
operations, enabling benefits such as:
• Optimized operations by increasing
efficiency, reducing cycle times, decreasing
errors, and improving compliance
• A foundation for agility and innovation that
enables a faster response to change and the
adoption of new technologies
• Trustworthy, consistent outcomes through
orchestration, governance, and built-in security
features
But as the automation landscape changes, new AI
technology is extending the traditional process
automation playbook.
Agentic AI uses autonomous systems or “agents”
to make decisions or perform tasks, achieving
complex goals with little to no supervision or human
intervention. Agentic AI solutions can plan, reason,
adapt, and act autonomously. Unlike traditional
approaches, agentic AI enables the automation of
nondeterministic, adaptive processes that must
respond to changing conditions, evolving data,
and uncertain environments.
This paper examines the fundamental differences
between traditional BPA and RPA technologies
and process automation enabled by agentic AI.
It offers strategic recommendations for choosing
the right technology and explores use case
categories.
Figure 1: Critical value of process automation
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Traditional process automation
Agentic process automation
AI agents are systems that have the agency to act
with intent aligned to your organizational goals.
Understanding the promise of process automation
Make the leap from efficiency to transformation
Established process automation technologies
offer proven value (see Figure 1). For many years,
companies have used these tools to automate
repetitive tasks, workflows, and business
operations, enabling benefits such as:
• Optimized operations by increasing
efficiency, reducing cycle times, decreasing
errors, and improving compliance
• A foundation for agility and innovation that
enables a faster response to change and the
adoption of new technologies
• Trustworthy, consistent outcomes through
orchestration, governance, and built-in security
features
But as the automation landscape changes, new AI
technology is extending the traditional process
automation playbook.
Agentic AI uses autonomous systems or “agents”
to make decisions or perform tasks, achieving
complex goals with little to no supervision or human
intervention. Agentic AI solutions can plan, reason,
adapt, and act autonomously. Unlike traditional
approaches, agentic AI enables the automation of
nondeterministic, adaptive processes that must
respond to changing conditions, evolving data,
and uncertain environments.
This paper examines the fundamental differences
between traditional BPA and RPA technologies
and process automation enabled by agentic AI.
It offers strategic recommendations for choosing
the right technology and explores use case
categories.
Figure 1: Critical value of process automation
E
n
h
a
n
cedagilit
y
a
n
d
i
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I m p r o v
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Traditional process automation
Agentic process automation
AI agents are systems that have the agency to act
with intent aligned to your organizational goals.
5 | 36The agentic AI opportunity in process automation
Reviewing the traditional process automation
playbook
Traditional process automation systems and
tools automate repetitive tasks, workflows, and
business operations. These solutions excel at
deterministic process flows where inputs, steps,
and outcomes are clearly defined.
Key technologies in traditional process
automation
The technologies typically used with traditional
process automation include the following:
• Packaged business applications, such as ERP
systems, that automate and integrate business
functions such as accounting, HR, supply chain
management, and customer relationship
management.
• Workflow management tools, which help
you create and adapt a defined series of steps,
tasks, documents, and information that results
in a process flow designed to achieve a specific
outcome.
• RPA bots that are integrated into business
systems, helping you automate and simplify
tasks and interactions. These bots emulate and
copy human interactions to complete repetitive
business tasks. They can also navigate systems,
read and enter data, and perform a wide range
of rules-based tasks.
• Business rules engines that allow
organizations to define, manage, and execute
business rules, using predefined criteria to
automate decisions. These rules engines are
often used to support regulatory compliance,
pricing models, and workflow automation.
The use of AI in traditional process
automation
AI already plays a role in traditional process
automation by automating or enhancing specific
process steps that don’t lend themselves to other
technologies. AI-enhanced process automation,
often called “intelligent process automation,”
can be used to support the following three
key uses.
Defining AI
AI refers to the capability of machines
to perform tasks that typically
require human intelligence—such as
understanding language, recognizing
patterns, learning from data, and making
decisions. It encompasses a range of
technologies that enable systems to
perceive, reason, and act autonomously
to achieve defined goals.
Reviewing the traditional process automation
playbook
Traditional process automation systems and
tools automate repetitive tasks, workflows, and
business operations. These solutions excel at
deterministic process flows where inputs, steps,
and outcomes are clearly defined.
Key technologies in traditional process
automation
The technologies typically used with traditional
process automation include the following:
• Packaged business applications, such as ERP
systems, that automate and integrate business
functions such as accounting, HR, supply chain
management, and customer relationship
management.
• Workflow management tools, which help
you create and adapt a defined series of steps,
tasks, documents, and information that results
in a process flow designed to achieve a specific
outcome.
• RPA bots that are integrated into business
systems, helping you automate and simplify
tasks and interactions. These bots emulate and
copy human interactions to complete repetitive
business tasks. They can also navigate systems,
read and enter data, and perform a wide range
of rules-based tasks.
• Business rules engines that allow
organizations to define, manage, and execute
business rules, using predefined criteria to
automate decisions. These rules engines are
often used to support regulatory compliance,
pricing models, and workflow automation.
The use of AI in traditional process
automation
AI already plays a role in traditional process
automation by automating or enhancing specific
process steps that don’t lend themselves to other
technologies. AI-enhanced process automation,
often called “intelligent process automation,”
can be used to support the following three
key uses.
Defining AI
AI refers to the capability of machines
to perform tasks that typically
require human intelligence—such as
understanding language, recognizing
patterns, learning from data, and making
decisions. It encompasses a range of
technologies that enable systems to
perceive, reason, and act autonomously
to achieve defined goals.