Automation is powerful — but automating the wrong process?
That’s how you take a messy workflow… and make it faster at being messy.
Most businesses jump straight to buying tools, building bots, or deploying AI agents without actually understanding how their processes work in real life — not how management thinks they work, not how SOPs say they work, but how people actually navigate tools and data in day-to-day operations.
This is exactly where process mining comes in.
And if you use it right, it becomes one of the smartest investments you can make before starting any automation initiative.
Let’s discuss the idea in the simplest language possible -What is process mining, and why it is important and how you can combine the two to create workflows that are more efficient, as well as cleaner and transformative.
What Is Process Mining (in Plain English)?
Process mining is basically X-ray vision for your business workflows.
Instead of sitting in meetings asking people how they work — or looking at outdated flowcharts — process mining uses actual system logs (ERP logs, CRM logs, helpdesk activity, timestamped transactions, etc.) to reconstruct how work REALLY flows through your organization.
It answers questions like:
- Where does work get stuck?
- Who spends the longest time on which step?
- Which steps are unnecessary repeats?
- Where do errors occur most often?
- Where are approvals slow?
- How often do people skip steps?
- What shortcuts are people inventing?
In short: it shows the real process vs. the imagined process.
This is priceless when planning automation.
Why Automating Without Process Mining Is a Bad Idea
Let’s be honest: many organizations automate based on intuition, assumptions, or outdated SOPs.
That leads to mistakes like:
❌ Automating a broken workflow
You make the inefficiency happen faster.
❌ Automating edge cases instead of core paths
Wasting time building automation for scenarios that happen twice a month.
❌ Missing the real bottlenecks
You automate Step 3… when the real issue is Step 6.
❌ Building automations no one actually uses
Because employees work differently than the official documentation says.
❌ Creating brittle automations
Because you automate a process that changes constantly.
Process mining fixes all of these by showing you what needs automating, what needs improving, and what needs deleting completely.
How Process Mining Feeds Automation (A Realistic Framework)
Think of automation as the engine.
Process mining is the map.
You don’t build the engine first and then figure out where to drive.
You map the terrain, find your path, and then build the engine that fits.
Here’s how they fit together:
Step 1: Discover the Real Workflow
Process mining reconstructs your processes from event logs, showing:
- variations
- delays
- loops
- repeated tasks
- best performers
- worst performers
- handoff inefficiencies
This gives you a baseline truth to work from.
Step 2: Identify the High-ROI Automation Targets
Once you see the real process, it becomes obvious:
- Which steps are repetitive
- Which steps are error-prone
- Which handoffs slow everything down
- Where compliance issues occur
- Where queues form
- What employees waste the most time on
This is your automation goldmine.
Step 3: Redesign the Workflow (Don’t Just Automate It)
Before automation, clean up the process:
- remove unnecessary steps
- consolidate approvals
- eliminate loops
- standardize formats
- document best practices
- enforce data discipline
- fix bottlenecks
This leads to automation that is stable, efficient, and future-proof.
Step 4: Automate the Improved Workflow
Now you apply automation tools — RPA, no-code automation, AI agents, workflow engines — whatever fits your environment.
Because you already “cleaned” the process, the automation becomes:
- faster to build
- cheaper to maintain
- less fragile
- more scalable
- more accurate
Step 5: Monitor, Measure & Improve
Since process mining continuously analyzes logs, you can:
- track automation performance
- find new bottlenecks
- discover exceptions
- catch compliance slips
- measure time saved
- refine processes
This turns automation into a continuous improvement loop, not a one-off project.
Real Use Cases: Process Mining + Automation in Action
To make this practical, here are scenarios where process mining makes automation dramatically more effective.
1. Order-to-Cash (O2C)
Process mining reveals:
- stuck invoices
- delayed approvals
- repeated data entry
- manual reconciliation
Then automation handles:
- data extraction
- invoice validation
- payment reminders
- ledger updates
Result: faster cash flow + fewer errors.
2. Procurement
Process mining uncovers:
- long vendor onboarding steps
- bottlenecks in PO approvals
- duplicate checks
Automation then speeds up:
- vendor validation
- PO creation
- price comparison
- GRN-to-invoice reconciliation
3. Customer Support
Mining reveals:
- repeat queries
- long resolution paths
- routing inefficiencies
Automation handles:
- ticket classification
- data lookup
- canned responses
- escalation triggers
4. HR Onboarding
Mining often shows:
- inconsistent document submission
- slow cross-department handoffs
- too many approval layers
Automation then streamlines:
- form filling
- doc verification
- access provisioning
Tools That Support Process Mining + Automation Together
A few platforms specialize in combining the two:
Process Mining Platforms
- Celonis (the global leader)
- UiPath Process Mining
- Software AG (ARIS)
- Apromore
- IBM Process Mining
Automation Platforms That Integrate with Mining
- UiPath
- Automation Anywhere
- Power Automate
- SAP Signavio + SAP workflow automation
- Workato / Make.com (for light mining + automation)
AI Agents + Mining
Pairing mining with AI agents (like Manus-style UI automators) is the next frontier:
- mining finds repetitive UI-based tasks
- AI agents execute them autonomously
This combo is especially powerful for legacy systems and browser-only workflows.
Practical Toolkit: How Businesses Should Actually Use Process Mining
Here’s a simple, actionable blueprint:
1. Start With Logs You Already Have
ERP
CRM
Service desk
Finance systems
Billing systems
Workflow tools
Even CSV logs or timestamped spreadsheets work.
2. Pick One High-Value Process First
Examples:
- order processing
- invoice handling
- ticketing
- onboarding
- procurement
3. Gather Cross-Functional Stakeholders
Because process mining reveals uncomfortable truths — you need buy-in before the surprises come out.
4. Map the “Happy Path” and “Chaos Paths”
Most processes have:
- 1 ideal path
- 10–50 real paths people follow
- 300–500 rare variations
Mining clarifies what’s worth automating.
5. Fix Before You Automate
Remove steps.
Simplify flows.
Define owners.
Clean the data.
Standardize inputs.
Then automate.
6. Monitor Continuously
Mining is not a one-time diagnostic — it’s ongoing visibility.
Why This Approach Works Better Than Traditional Automation
Most failed automation projects collapse because they automate complexity instead of simplifying it.
Process mining flips that script.
You understand → optimize → THEN automate.
Not the other way around.
This leads to:
- higher adoption
- lower maintenance
- fewer errors
- scalable automation
- measurable ROI
It’s automation with intelligence.
Conclusion: Don’t Automate Blindly — Discover First
Before you let a bot, a workflow engine, or an AI agent loose on your processes, you need to know what your processes actually are. Process mining gives you the truth — the whole messy, real, human truth of how work flows through your organization.
When you combine that clarity with automation, you’re not just speeding up work…
You’re transforming it.
This is how modern companies build workflows that are:
- faster
- smarter
- more stable
- more compliant
- and radically more efficient
Discovery first → automation second.
That’s the future of operational excellence.
People-First FAQs
1. What size company benefits from process mining?
Even teams of 20–50 people can use light process mining. Full-scale enterprise mining helps companies with complex, multi-step processes.
2. Can I use process mining without automation?
Absolutely. Many companies start with mining to understand processes before deciding whether automation is worth the investment.
3. Do I need perfect data to do process mining?
No — mining tools are designed to work with messy, real-world logs. You’ll refine data quality over time.
4. Is process mining expensive?
Advanced tools like Celonis can be costly, but there are lightweight or open-source options for smaller organizations.
5. Can process mining work with legacy systems?
Yes. As long as the system generates logs (even CSVs), mining tools can reconstruct the process.