Workload automation and robotic process automation (RPA) are two of the most valuable tools in a modern digital workplace‘s automation stack. Though they share the common goal of reducing manual work through automation, they actually serve distinct purposes.
In this comprehensive guide, we will demystify the key differences between workload automation and RPA and show how they can work synergistically to create optimized automated workflows.
What is Workload Automation?
Workload automation (WLA) refers to the process of centralizing, scheduling and managing workflows across an organization‘s entire IT infrastructure. This includes both on-premise and cloud systems.
A workload automation tool acts as a "meta-scheduler" that can initiate and monitor lower-level job schedulers, scripts, batch processes, and more across distributed environments. It provides a "single pane of glass" to schedule and manage interdependent jobs while handling dependencies and exceptions.
Key capabilities include:
- Centralized management of workflows across systems
- Scheduling and orchestrating tasks and batch processes
- Coordination of complex, long-running processes across teams/departments
- Visual mapping of end-to-end transaction flows
- Workload balancing and SLA monitoring
- Comprehensive audit trails and reporting
Workload automation is commonly used across industries like financial services, healthcare, manufacturing, and more. IT teams love it because it helps tame "job sprawl" by consolidating workflow management into a single solution.
Workload Automation Adoption Trends
According to 2022 research from MarketsandMarkets, the global workload automation market is projected to grow from $2.02 billion in 2022 to $3.43 billion by 2027, a CAGR of 11.2%. Key drivers include rising need for optimized resource utilization, increasing IT complexities, and enterprise digitization efforts.
| Year | Market Size | Growth % |
|---|---|---|
| 2022 | $2.02 billion | – |
| 2027 | $3.43 billion | +11.2% CAGR |
Gartner research shows 75% of organizations adopted workload automation in some form as of 2020. And Gartner predicts investment will continue increasing 15-20% annually through 2023 as more enterprises pursue process optimization and digital ops management capabilities.
What is RPA?
Robotic process automation (RPA) refers to using software robots ("bots") to automate repetitive, rules-based digital tasks traditionally performed by humans. The bots interact directly with an application‘s user interface to carry out rote activities like data entry, form filling, report generation and more.
Key capabilities include:
- Automating manual processes without altering back-end systems
- Bots log into applications to copy/paste data, fill forms, complete tasks
- Integrates across multiple apps through screen scraping, APIs if available
- Can extract data from documents and emails
RPA is popular among business teams in IT, customer operations, HR, accounting/finance and more. It allows them to automate legacy business apps that lack APIs and would be time-consuming to integrate via traditional coding.
RPA Adoption Stats
RPA software revenue grew over 60% between 2020 and 2021 as per Gartner, demonstrating the incredible momentum around adoption.
They forecast RPA market size will triple from $1.4 billion to $4.5 billion from 2021 to 2024. Key drivers are substantial hard dollar ROI, faster deployments, and a expanding set of enterprise use cases.
| Year | Market Size | Growth % |
|---|---|---|
| 2021 | $1.4 billion | – |
| 2024 | $4.5 billion | 221% |
Another survey saw 56% of organizations use RPA in some capacity. And for RPA users, process automation rates exceeded expectations with typical net savings between 25-50%.
5 Key Differences Between Workload Automation and RPA
1. Scope of Work
The scope of work for both tools is fundamentally different:
- RPA focuses on automating repetitive human tasks involving front-end systems and graphical user interfaces.
- Workload automation deals with managing automated processes running in the back-end across servers, databases, enterprise apps and more.
2. End Users
While there can be some crossover, the tools appeal to different users:
- RPA is typically used by business analysts who want to improve operations without deep coding skills.
- Workload automation tends to be implemented by IT teams/DevOps engineers who manage infrastructure.
3. Ease of Integration
They also differ significantly regarding integration:
- RPA is light-weight and can integrate via surface-level screen scraping without needing APIs. Minimal coding is required.
- Workload automation requires much more heavy-duty and sophisticated integration with back-end processes and event triggers across systems.
4. Process Complexity
This leads to differences in process complexity each tool can handle:
- RPA works best for simple, repetitive digital tasks with clear rules and guidelines to follow.
- Workload automation is required for large, complex mission-critical processes with sophisticated conditional logic and inter-dependencies.
According to 2022 research from Pega systems analyzing hundreds of client automation scenarios, RPA bots were 3X more likely to be applied for simple, rules-based processes while workload automation was preferred by a wide margin for highly complex workflows:
| Automation Type | Simple Processes | Complex Processes |
|---|---|---|
| RPA | 62% | 21% |
| Workload Automation | 12% | 67% |
5. Operational Efficiency Focus
- RPA improves human productivity by taking rote digital tasks off people‘s plates so they can focus on value-added work.
- Workload automation optimizes infrastructure utilization and system ops by coordinating automated processes seamlessly across technology.
Let‘s analyze this through some empirical metrics:
| Automation Type | Productivity Gain | Cost Reduction | Error Rate Lowering |
|---|---|---|---|
| RPA | 2-3X | 20-25% | 30-50% |
| Workload Automation | Hard to quantify precisely but significantly enhances operational throughput and system utilization | Lowers need for manual sysadmin intervention 15-30% | Mitigates risk of failed batch jobs/transactions by >80% through orchestration, monitoring and redo capabilities |
Integrating With Modern Analytics
Beyond core benefits around productivity and efficiency, an area where the tools diverge is the ability to integrate with cutting-edge analytics and AI capabilities:
- Leading workload automation solutions like Control-M from BMC and Automic from Broadcom integrate with data science platforms, leveraging techniques like predictive analytics, machine learning algorithms and optimization solvers to enable smarter forecasting, dynamic scheduling optimization and simulation during workflow design.
- Meanwhile, top RPA vendors like UiPath, Automation Anywhere and Microsoft are enhancing bots with document understanding, natural language processing, chatbots and other skills considered part of the cognitive RPA and intelligent process automation realm.
These embedded smart capabilities vastly expand the complexity of processes automation tools can manage successfully with minimal human input.
Architecture Overviews
Under the hood, workload automation and RPA solutions have significant architectural differences:
Workload Automation Platform Architecture
A typical workload automation tool consists of a central server and database plus a web app UI, along with a hybrid job scheduling engine that leverages capabilities like:
- High-performance in-memory computing for millisecond response times
- horizontally scalable distributed architectures
- Failover handling and redo-based recovery
- Connectors spanning across 1000+ OS, application, cloud platforms
Figure: High level workload automation platform architecture (source: BMC)
RPA Architecture
A typical RPA tool consists of desktop-based bot runners that connect to centralized server app + database with components like:
- Recorder to track UI actions
- Editor to configure bot logic
- Orchestrator for deployment, monitoring
- AI analytics and ML models
Figure: High level RPA architecture (source: Edureka!)
Comparing runtime environments, workload automation platforms provide robust, enterprise-grade instrumentation around scalability, throughput, and redundancy. RPA tools prioritize faster time to production for individual bots.
Admin interfaces also differ – while RPA management consoles focus on ease of use for business users, workload automation UIs provide IT teams deeper technical controls better suited for complex environments.
Using RPA and Workload Automation Together
Hopefully now it is clear that workload automation and RPA serve very distinct needs. But rather than being an "either/or" decision, they can work hand-in-hand to enable end-to-end process automation.
Here are just a couple ways they can be integrated:
- Triggering RPA bots: Workload automation can initiate unattended RPA bots to handle certain tasks within a larger automated workflow.
- RPA exception handling: RPA bots can alert workload automation tools of any process failures or exceptions, enabling automatic remediation or help desk ticketing.
Ecommerce Order Processing Example
For example, consider an ecommerce site‘s customer order processing workflow:
- Incoming orders data is ingested via ETL into a central database
- Workload automation software monitors and validates new orders, initiating back-end order processing across multiple enterprise applications
- Any order issues trigger unattended RPA bots that perform corrective actions like updating customer records, creating return/exchange records, flagging inventory discrepancies etc.
- Remediated orders re-join the automated processing queue
This hybrid model allows workload automation to handle the overall process flow while leveraging RPA bots on an as-needed basis for specialized tasks.
Figure: Hybrid workload automation + RPA reference architecture
Enterprise-Grade Automation Framework
We can expand this concept of a hybrid automation environment even further to include additional technologies like:
- Integration platform for connecting disparate applications behind the scenes
- Business rules engine for centralizing policies and requirements
- Process mining to auto-discover processes ripe for automation based on system logs
- OCR and data capture tools for ingesting unstructured data
- Chatbots for natural language interactions and inquiries
Truly intelligent automation requires best-of-breed components stitched together in a flexible architecture optimized for enterprise scale, as shown in the sample framework below:
Figure: Sample enterprise process automation framework
The backbone would be a resilient workload automation foundation capable of orchestrating thousands of jobs per day and intelligently handling edge cases. RPA bots and other modular components integrate seamlessly on demand wherever human intervention can be minimized.
Investing in this kind of future-proof, agile automation platform sets companies up for continuous improvement and adaptation as needs evolve.
The Future is Automated
Workload automation and RPA are becoming essential elements of digital transformation strategies across industries. As process complexity rises and competitive pressure builds, successfully leveraging these automation technologies will determine operational efficiency and business performance.
Hopefully this guide has helped explain the unique value propositions of workload automation vs RPA and how they can work synergistically as part of a holistic Intelligent Process Automation (IPA) approach.
To learn more, see the additional resources below: