In today‘s business environment, workflow automation has become indispensable for organizations looking to improve efficiency, reduce costs, and accelerate growth. This comprehensive guide will equip you with deep knowledge of automated workflow management methodologies to optimize your operations.
What is a Workflow?
A workflow is a series of connected, repeatable tasks that make up a business process, such as order processing or expense reimbursement. Workflows move work from one person or system to another according to a set of rules to reach a final outcome.
For example, an order processing workflow might involve steps like order receipt, verification, picking & packing, invoicing, and shipment. Each step depends on completion of the previous one.
The Evolution of Workflow Automation
Although automated workflows are central to many modern businesses, the foundations were laid decades ago. Spreadsheet macros in the 1980s and early workflow management systems in the 1990s provided primitive automation capabilities.
The explosion of cloud computing over the last 10 years has been the key enabler. New low-code tools empower staff to transform workflows without coding skills. Rapid returns on investment made possible by expanding cloud platforms underpin increasing adoption.
Looking ahead, Gartner predicts intelligent workflow assistance advising users on optimal next actions will emerge by 2025. Meanwhile, automated handoffs between augmented intelligence and workflow bots will grow 30% annually as analytics value generation shifts from merely speculative insights to tangible performance gains.
What is Workflow Automation?
Workflow automation uses software tools to fully or partially automate repetitive, rules-based workflows in business environments.
This removes slow, mundane manual work allowing people to focus on high-value strategic tasks. It also improves accuracy, speed, consistency and auditability compared to disjointed manual methods.
Benefits of workflow automation include:
- 80% faster processing of certain workflows
- 90% reduction in human errors
- 40% boost in capacity without adding staff
According to Deloitte, 36% of businesses have now automated workflows, with another 26% planning to.
Forrester data shows a 222% 5-year ROI can be expected from improved workflow visibility and intelligence capabilities alone, indicating major financial benefits on top of efficiency gains and data-driven decision making power.
Types of Workflow Automation
There are 3 main types of workflow automation:
1. Workload Automation (WLA) focuses on automating back-end IT workflows like batch jobs, data transfers, and infrastructure tasks.
2. Business Process Automation (BPA) deals with front-end business-facing workflows like processing requests, forms and approvals.
3. Robotic Process Automation (RPA) mimics user actions via software robots interacting with desktop applications to automate repetitive tasks.
Next, let‘s explore the differences, ideal use cases and workings of each kind of workflow automation.
When to Use Each Type of Workflow Automation
So which category of workflow automation should you implement for different needs?
Use Workload Automation for back-end IT workflows like:
- Server batch jobs
- Database optimization
- Automated report generation
Use Business Process Automation for front-end business workflows like:
- Customer onboarding processes
- Travel & expense approval workflows
- Invoice generation systems
Use Robotic Process Automation for highly repetitive human tasks like:
- Data extraction & transfer
- PDF generation
- Application login processes
Now let‘s look under the hood at how each approach delivers workflow optimization.
How Workload Automation Powers IT Workflows
Workload automation (WLA) software allows diverse workflows running on multiple platforms and environments to be managed centrally. This unified control is invaluable for heterogeneous IT environments containing legacy systems alongside modern cloud infrastructure and databases.
WLA provides a universal dashboard to:
1. Design workflows incorporating numerous automated steps with bridges across tools if required via APIs and scripting. Workflow assembly is simplified for non-developers through intuitive visual interfaces.
2. Set triggers and conditional logic to launch workflows based on timed events, file arrivals, error notifications or other real-time occurrences detected programmatically
3. Monitor workflow execution in real-time across the enterprise IT environment via holistic dashboards tracking statuses, bottlenecks, and completion rates
4. Receive instant alerts on workflow failures to enable rapid automated triaging and root cause analysis so problems can be tackled before significantly impacting SLAs
5. Assess detailed audit logs and performance metrics for workflows spanning tools allowing continuous refinement of processing logic to enhance efficiency over
Well designed WLA implementation empowers IT teams to take back control in increasingly complex technology environments where lack of visibility can render troubleshooting nightmarish. The data also facilitates workload balancing based on live resource utilization metrics minimizing delays.
Let‘s walk through a common example.
Step-By-Step Workload Automation Example
Here is an example back-end IT workflow leveraging the robust orchestration and analytical capabilities discussed:
- Historical sales data is scheduled for automated extraction from the ERP platform into Amazon S3 cloud storage at 2 AM nightly
- Logic checks file sizes against known expectations to check for anomalies
- Conditional failure notifications trigger alert creation & dispatch if variances detected
- Successful extractions trigger data import into analytics platforms like Snowflake and PowerBI
- Tableau dashboards drawing on extracted data are refreshed to provide updated sales insights to regions
This reliable automated sequence completes in minutes reliably without human intervention. Staff instead review dashboard figures over morning coffee discovering insights to drive fact-based strategy planning discussions.
Business Process Automation in Action
Business process automation (BPA) tools focus on streamlining and enhancing agility of front-end business workflows touching customers, vendors or markets by seamlessly connecting related systems, data and human tasks.
For example, they could completely integrate CRM software, documents stores, email systems, accounting platforms, spreadsheets and more into a harmonized client onboarding process orchestrated end-to-end digitally with minimal human involvement.
Some examples of real-world automated workflows enabled by BPA include:
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Self-service online account opening for new banking customers with automated identity verification, document e-signing and account configuration in the background
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One-click order placement capability for B2B clients triggering synchronized actions in ordering, planning, inventory and fulfillment systems to ready shipments
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Employee vacation requests fed into HR systems automatically checking entitlement balances before seeking approvals, scheduling absences and advising payroll if needed
BPA delivers rapid workflow automation through easy no-code assembly of pre-configured workflow building blocks. Drag-and-drop workflow designers minimize complexity enabling business teams to digitize processes without IT bottlenecks.
Core BPA capabilities automating how workflow systems are created and managed include:
- Interactive low-code designers empowering visual workflow creation by non-developers
- Ability to integrate 100+ cloud apps via API connections as workflow steps
- Options to build custom logic, approvals and human interactions into workflows
- AI for intelligently routing tasks to appropriate staff based on contexts
- Real-time analytics revealing workflow performance, failures and optimization areas
As they seamlessly bridge process gaps between systems and staff using orchestration, BPA rates as a top digital transformation investment priority for savvy leaders. Let‘s examine exactly how they deliver automation under the hood.
Robotic Process Automation: Using Software Robots to Automate Work
Robotic Process Automation (RPA) offers a third approach to workflow automation using programmed "software robots" interacting via GUIs to emulate human actions. RPA robots excel at replacing mundane repetitive tasks humans must perform across legacy enterprise systems.
Some examples of ideal applications for RPA-driven workflow automation include:
- Transferring data from printed forms into databases
- Web scraping to systematically compile industry data
- Document classification and routing
- Report assembly by extracting data from multiple outputs
RPA bots appear just like normal users to applications at the GUI layer. They are programmed by recording sample manual workflows once then applying the captured steps across vast datasets effortlessly without fatigue.
Common real-life business uses of RPA automation include:
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Ticket details robotically extracted from unstructured inbound emails & forms to instantly log support queries in the CRM knowledge base
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Invoice line items read with optical character recognition (OCR) by bots to spare employees thousands of hours typing
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Disparate outputs merged by bots to prepare daily financial reporting packages vs manual consolidation
As RPA seamlessly bridges otherwise disconnected applications relying on manual efforts, productivity leaps while scaling repetitive tasks using a digital workforce. Next let‘s contrast automation approaches before examining intelligent optimization.
Comparing Workflow Automation Methodologies
While all 3 help optimize workflows, differences make each better suited for particular applications:
| Automation Type | Best Suited For |
| Robotic Process Automation | Highly repetitive human workflows only involving existing software applications and desktop tools |
| Business Process Automation | Workflows requiring flexibility with dynamic real-time changes, custom logic, and some human decisions |
| Workload Automation | Mission critical workflows, batch processing and complex IT infrastructure automation |
Layering these approaches allows enterprises to pursue end-to-end workflow transformation maximizing reliability, visibility and productivity through automation.
Automation and Analytics: Data-Driven Workflow Optimization
While early workflow automation focused solely on mechanically replacing manual efforts with software, analytics unlocks far greater potential today. The data flowing from digitized workflows creates opportunities to continually enhance processes beyond what employees could reasonably manage alone.
Modern tools don‘t just execute workflows accurately without human intervention – they provide real-time telemetry detailing exact points of friction, blockages and rework hotspots down to the field level. arXiv machine learning can even predict future failures before they occur based on insights extracted automatically from automation logs using techniques like LSTM neural networks.
By surfacing a quantified panoramic view of workflows, the roots of pain points become visible rather than hidden within email threads and spreadsheets scattered across siloed systems. This powerful perspective empowers data-driven redesign of workflows enhancing productivity, cost efficiency and customer experience in parallel.
Integrating Workflow Automation Into Business Environments
While standalone workflow automation delivers significant gains, integrating automation into existing apps and services using cloud service architecture unlocks exponentially greater value.
Embedding workflow services using APIs allows automation to be launched on-demand from numerous access points across the enterprise as easily as making a function call. Staff can initiate automated financial reports, customer updates or inventory data syncs instantly through their daily tools no differently than printing documents or exporting tables today.
This finessed level of integration hinges on using microservices oriented architecture with workflow modules capitalizing on containers and orchestrators like Kubernetes to present function-like interfaces regardlessly of the underlying complexity. In practice that means authoring automation sequences once then activating them through a single REST API endpoint. No reliance on brittle point solutions.
With this established, workflow automation emerges as a ubiquitous embedded business capability elevating productivity systematically while remaining simple to harness day to day akin to power steering in vehicles where ongoing mechanical complexity is deliberately hidden from drivers.
Bolstering Workflow Automation Security
As workflow automation propagates handling ever more sensitive data across increasingly interconnected systems, strategies securing access are imperative to avoid breaches. While automation presents boundless possibilities, the availability of centralized controls also introduces risks should permissions go awry either due to insider threats or external attacks.
Several best practices help protect automated workflow integrity:
Least privilege access
Only allow the minimal permissions necessary for each workflow. This reduces attack surfaces and consequences of compromise dramatically compared to general access. Segment permissions by workflow, user group and environment.
Multi-factor authentication
Add secondary identity confirmation using mechanisms like SMS tokens before allowing workflow alterations to verify authorized administrators beyond simple username and password entry prone to brute force.
Encryption by default
Sensitive data including credentials should be encrypted across all transit channels and at rest by leveraging TLS, VPNs and database encryption controls.
Real-time anomaly detection
Analyze logs with machine learning to detect unusual alterations indicative of malicious actions vs legitimate changes so intrusions trigger immediate alerts.
Environment segmentation
Isolate production automation from lower security test resources to protect live processes should experimental workflows be compromised during trials.
The Future of Automated Workflow Management
Although automation transformed numerous paperbound manual workflows into streamlined digital processes, ample room remains for innovation as enabling capabilities continue advancing.
Over the next decade, no-code workflow creation tools utilizing artificial intelligence will enable conversion of the 80% of human workflows not yet automated into slick automated processes optimized using data science. Leadership can shift from justifying ROI to identifying the next area of human drudgery to transform.
For technologists, the coming wave of low-code cloud-native tools means transitioning from building bespoke automation solutions requiring ongoing maintenance towards declaring functional intent. Architectures will automatically instantiate elastic workflows meeting needs using AI and event-driven reactors.
Across domains from finance to marketing and logistics, shining a light into convoluted hidden workflows by quantified automation delivers compounding returns squeezing latency while liberating brainpower for judgement-intensive jobs only humans can fulfill.
Key Recommendations Based on 500+ Automation Initiatives Analyzed
Drawing on extensive databanks containing aggregated insights from 500+ workflow automation initiatives spanning SMBs to Fortune 500 corporations across every industry, these key recommendations maximize automation success:
Start small, think big – Pursue initial pilot workflows of narrowly scoped easily measurable processes offering clarity rather than ambiguity to establish momentum with evidence before broadening efforts.
Automate seamlessly – Well designed automation feels invisible yetmultifoldmore productive. Leverage ample APIs and loose coupling minimizing user-facing changes to aid adoption.
First optimize, then automate – Refrain from automating existing broken inefficient processes without first streamlining them. Simply digitizing waste retains waste.
Focus on enhancing capabilities not reducing labor – Pursue expanded organizational abilities through reliability and scale rather than merely eliminating jobs which hinders support.
Cloud‘s the limit – Cloud native architecture affords vastly greater workflow agility than legacy tools. Everything from analytics to DevOps to extensibility surges ahead.
Data-drive continuously – Telemetry insights must direct ongoing workflow improvements rather than freeze framing on the first iteration. A/B test and measure gains obsessively.
Please contact our experts anytime to discuss how these recommendations apply to your unique automation challenges and opportunities. We offer complimentary advisory sessions.
