TL;DR
Business process intelligence reads the event logs your systems already keep to show how work really runs. A BI dashboard reports the average, while process intelligence traces every case through every step. Process mining is the method inside it, and task mining adds desktop activity. Start with one high-volume process, such as accounts payable, with a clear owner. A focused pilot can find, fix and re-measure one bottleneck within 90 days. Pick a platform that fits your estate, and automate only stable, rule-based steps.
Key Takeaways
Business process intelligence rebuilds each process from event logs, so you see the real path of every invoice, order or ticket.
An event log becomes usable once it holds three fields: a case ID, an activity name and a timestamp.
Averages hide loops, because the slow variants disappear into one number; those variants are where the savings sit.
Process data shows which steps to fix first, which to automate and which still need a person.
A first pilot should prove one measured fix within 90 days before the program expands.
Event data also holds employee and customer details, so access, consent and residency rules apply from day one.
Watch on YouTube
Invoice Processing Automation Case Study: 80% Less Manual Work
Kanerika walks through a real invoice automation project, from the manual bottlenecks in accounts payable to the workflow that cut manual work by 80%.
Why Does an Invoice That Should Take Three Days Take Eleven?
The controller opens the Monday operations review with a green slide. Accounts payable is averaging 5.5 days per invoice, inside its six-day target, so the meeting moves on.
Two weeks later, a key supplier puts the company on credit hold.
By then, that supplier’s invoices were taking eleven days to pay. Nobody noticed, because the dashboard showed one number for all invoices. When an analyst finally pulls the event log from the ERP, the picture changes. About one invoice in ten goes to an approver, comes back with a question and then goes out for approval again.
Each loop adds days, while the average absorbs them quietly. The data was there all along, in timestamps the finance system writes whenever someone touches a record. What was missing was a way to read those timestamps as a process instead of as rows.
That gap is what process intelligence closes. The rest of this guide shows how it works, where it pays off and how to start.
What Is Business Process Intelligence?
Business process intelligence is the practice of using process data, analytics and models to show how work actually runs across your systems. It rebuilds each process from event logs, then compares it with the intended design and points to the changes worth making.
Think of it as an X-ray for operations. A purchase order, a customer ticket or a new-hire record leaves a trail of timestamped events as it moves through SAP, Salesforce, ServiceNow or Workday. When you stitch those events together by case, you get the real flow of work, including the detours nobody drew on the process map.
The output is a short list of answers to practical questions. Where does work wait, and for how long? Which path is slowest overall? Which steps break the rules? And which change would save the most time or money if you made it first?
Process Intelligence vs Process Mining vs Business Intelligence
These three terms often get used as if they mean the same thing, yet each one answers a different question. Business intelligence tells you what a KPI is. Process mining is the technique that rebuilds flows from event logs. Process intelligence wraps mining with business context and a route to action, so each finding ends in a change.
Aspect Business intelligence Process mining Process intelligence
Question it answers What happened, in totals and averages How the work actually flowed, step by step Why it flowed that way, and so what to change Main input Aggregated tables such as KPIs Event logs with case IDs and timestamps Event logs as well as master data, costs and targets Typical output Dashboards and reports Process maps, variants and conformance checks Ranked improvement and automation opportunities Who uses it Analysts and managers Process analysts Process owners, especially in operations and transformation
In practice, BI and process intelligence work best together. A team that already has a solid business intelligence strategy has the KPI layer in place, so process intelligence can explain why those KPIs move.
Why It Matters Now
The category has grown up, and its name has also changed with it. For years the field was simply called process mining, the name the IEEE Task Force used in its Process Mining Manifesto . Gartner’s 2026 report, published May 5, 2026, uses the name Magic Quadrant for Process Intelligence Platforms . That name signals a shift from drawing maps to driving change.
Two business pressures also make the shift urgent. ERP migrations need a baseline of how work really runs before anyone redesigns it. AI agents need the same baseline, because an agent placed inside a broken process repeats the breakage faster. Many of the AI trends shaping 2026 assume clean, well-understood workflows, and process data is how you check that assumption.
How Business Process Intelligence Works: From Event Log to Action
Every process intelligence project follows the same loop, whatever tool runs it. You collect the events, rebuild the flow and compare it with the design. Then you find the causes, change something and watch the numbers.
Collect events. First, extract timestamped records from the systems that run the process, such as ERP tables for invoices.Reconstruct flows. Group events by case and order them by time, so the software can draw every path cases actually took.Compare to the design. Check the real flow against the intended model, often drawn in BPMN 2.0.2 , the Object Management Group notation. This conformance check flags skipped approvals as well as steps done out of order.Find root causes. Slice slow or non-conforming cases by vendor, region, user or product to see what they share.Change and monitor. Finally, fix the cause, automate the stable steps and keep measuring so you can prove the change worked.
The loop matters more than any single map. A team that stops at step two has a picture, but a team that reaches step five has a result. The next turn of the loop then starts from a better baseline.
The Data You Need: Case ID, Activity, Timestamp
An event log is simpler than it sounds. Each row records one thing that happened to one case, and three fields make it usable. The case ID ties events together, while the activity names the step and the timestamp puts the steps in order.
Case ID Activity Timestamp Resource (optional)
INV-10482 Invoice received 2026-03-02 09:14 AP inbox INV-10482 Matched to PO 2026-03-02 11:40 Matching rule INV-10482 Sent for approval 2026-03-02 11:41 Workflow INV-10482 Approved 2026-03-04 16:05 Cost center manager
Optional fields such as user, amount or vendor make the analysis richer, since they help explain why some cases run slow. Pulling them from several systems is an enterprise data integration job, so plan the joins early.
Two open standards also help logs move between tools. The XES format is defined in IEEE 1849-2023 , which replaced the 2016 edition on September 8, 2023. OCEL 2.0 , for its part, is a format for object-centric event logs. That matters when orders, invoices and deliveries share events, because one shipment can close three orders and a single case ID can’t show that.
In most ERPs the log has to be assembled from several tables, because each step writes to a different one. The SQL below is a minimal PostgreSQL sketch for the AP example.
-- PostgreSQL sketch; table and column names are illustrative
CREATE VIEW ap_event_log AS
SELECT invoice_id AS case_id,
'Invoice received' AS activity,
received_at AS event_time,
'AP inbox' AS resource
FROM ap_invoices
UNION ALL
SELECT invoice_id, 'Matched to PO', matched_at, 'Matching rule'
FROM ap_po_matches
UNION ALL
SELECT invoice_id, 'Sent for approval', requested_at, 'Workflow'
FROM ap_approval_steps
UNION ALL
SELECT invoice_id, 'Approved', decided_at, approver_id::text
FROM ap_approval_steps
WHERE decision = 'APPROVED'
UNION ALL
SELECT invoice_id, 'Paid', paid_at, 'Payment run'
FROM ap_payments;
-- Variants: each distinct ordered path counts once
SELECT path, COUNT(*) AS invoices
FROM (
SELECT case_id,
STRING_AGG(activity, ' > ' ORDER BY event_time) AS path
FROM ap_event_log
GROUP BY case_id
) AS traces
GROUP BY path
ORDER BY invoices DESC;
-- Rework rate: share of invoices sent for approval more than once
SELECT ROUND(100.0 * COUNT(*) FILTER (WHERE sends > 1) / COUNT(*), 1) AS rework_pct
FROM (
SELECT case_id,
COUNT(*) FILTER (WHERE activity = 'Sent for approval') AS sends
FROM ap_event_log
GROUP BY case_id
) AS per_case;The second query is the variant count behind the worked example, and the third returns the rework rate. Record data lineage for each field, so an auditor can trace every event back to its source table.
Where Task Mining Fits
System logs only see what happens once work enters a system. Picture an AP clerk who copies a figure from a PDF into a spreadsheet and then emails a buyer. The ERP records nothing until the invoice itself changes. Task mining fills that gap by capturing desktop activity, such as application switches and clicks, on the machines of people who agree to take part.
It is useful for the manual stretches between system steps. Microsoft’s task mining overview for Power Automate, for example, describes recording user actions to find tasks worth automating. That capture carries more privacy weight than process mining, so settle consent before it begins, as the privacy section below explains.
Worked Example: Tracing an Accounts Payable Process
A worked example makes the method concrete, so here is the invoice story from the opening with numbers attached. The figures are illustrative, built for a company processing 10,000 invoices a quarter. They show the kind of pattern an event log reveals, not results from any one client.
The happy path is short: receive the invoice, match it to a purchase order, approve, post and pay. It takes three days, and 55% of invoices follow it. The remaining 45% split into three variants, and each one adds days for a different reason.
Missing purchase orders are the most common detour, especially where buyers order informally. Because a buyer raised the order after the goods arrived, the invoice waits until someone creates the PO. Price mismatches come next, when the invoice price differs from the order and the vendor has to send a credit note. The rarest and slowest variant is the re-approval loop, where an approver questions a line and the invoice starts again.
Variant (illustrative) Share of invoices Invoices a quarter Cycle time Days over happy path
Happy path 55% 5,500 3 days 0 Missing PO 20% 2,000 7 days 4 Price mismatch 15% 1,500 9 days 6 Re-approval loop 10% 1,000 11 days 8
What the Average Hid
Do the arithmetic the dashboard did. Weight each cycle time by its share, and the average comes to 5.5 days, which sits comfortably inside a six-day target. Yet 4,500 invoices a quarter take seven days or more, and 1,000 of them take eleven.
The fixes also differ by variant. First, a purchase-order policy with a “no PO, no pay” rule attacks the largest group. Price tolerances in the matching rule then clear small mismatches without a vendor round trip. Finally, an approval limit by amount stops low-value invoices from looping.
Each fix targets a different cause, so the average alone could never have pointed to them.
Business Process Intelligence Use Cases by Function
Any process is a candidate when it runs through software and repeats at volume. Finance usually goes first, because its systems keep clean logs and its delays carry a direct cash cost. The same approach then carries across to supply chain, service, HR and IT.
Function Process Signal to look for Typical fix
Finance Procure-to-pay Invoices without a PO, repeat approvals and late payments PO policy, matching tolerances and approval limits Finance Order-to-cash Credit holds, disputes and slow collection Credit rules, dispute routing and a dunning sequence Finance Financial close Entries posted after cutoff and manual reconciliations A close calendar and automated reconciliation rules Supply chain Order fulfillment Split shipments and order changes after release Order lock rules, so allocation stays stable Customer service Case handling Tickets bounced between teams, then reopened Routing rules and first-contact resolution scripts HR Employee onboarding Access granted days after the start date Provisioning triggered when the HR record is created IT Incident and request management Reassignments and long waits for approval Assignment rules and pre-approved standard changes Compliance Any controlled process Skipped approvals and segregation-of-duties breaks Control redesign and continuous conformance alerts
Finance teams can go deeper with RPA in finance and these AI in finance use cases . For logistics, these supply chain automation use cases cover the order and warehouse side.
Look closely at the signal column, since every entry is something an event log records and a monthly report blurs.
Checklist
Robotic Process Automation Checklist
Score candidate processes, check automation readiness and reduce implementation risk before you commit budget to your first automation.
Get the Checklist → Benefits You Can Measure
Claims like “better efficiency” are hard to defend, especially in a budget meeting. Process intelligence instead gives you numbers you can track per case, before and after each change.
Cycle time: elapsed time from the first event to the last, per case and per variant.Rework rate: the share of cases that repeat a step at least once.Touchless rate: the share of cases that finish without a manual step.Cost per case: labor and system cost divided by cases completed.Conformance rate: the share of cases that follow the designed path.
In the AP example, the re-approval loop shows up as rework rate, and the missing-PO variant drags the touchless rate down. Conformance sits at 55% because only the happy path follows the design. Each fix you make should move at least one of these numbers.
Which Process to Start With
The first process decides whether the program earns a second one. So pick a process that meets four tests.
High volume. Thousands of cases a month, so small gains add up and patterns stand out clearly.A digital trail exists. The steps run in systems that write timestamps, rather than in email threads.An owner who will act. A named leader who can change policy, rules or staffing when the data says so.Measurable pain. A cost, delay or compliance issue the business already feels and can put a number on.
Accounts payable passes all four in most companies, which is why it is the usual first pick. Our guide to accounts payable automation covers what to do once the bottleneck is known.
How Process Intelligence Decides What to Automate
Automation goes wrong when teams pick tasks by instinct. Process data, however, replaces that guess with evidence, and it gives any process automation program a ranked list instead of a wish list. It shows which steps happen often, which run the same way every time and which follow clear rules.
A simple scoring rule works well here. Multiply volume by how stable the step is, then by how rule-based it is. For example, a step that runs 20,000 times a month on one path with no judgment scores high. A rare step that branches often and needs a person’s call scores low, so it should stay with a person, perhaps with an assistant that drafts the call for review.
The score also tells you when not to automate yet. If the data shows a step is slow because of a broken variant, such as the re-approval loop above, fix the policy first. Otherwise, automating a broken variant only makes the mistake run faster.
Matching Each Step to the Right Tool
Once a step qualifies, its pattern points to the right tool. The table below matches common patterns to the usual fit, from bots to agentic AI with a person in the loop.
Task pattern Best fit Why
Stable screen work when a system has no API RPA Bots repeat the same clicks reliably at volume Handoffs and approvals across apps Workflow automation, such as Power Automate It routes work and also keeps an audit trail Reading invoices, forms or emails Document AI It extracts fields from unstructured inputs Judgment calls with many variants AI agent with human review It handles variation, while a person approves edge cases A slow step caused by the wrong policy Process fix first Automation would copy the defect, so fix it before any bot
After any change, re-measure the same KPIs on the same event log. That before-and-after view is what turns an automation project into a proven result.
For deeper reading, see how RPA for enterprise programs score candidates. The guide to intelligent automation shows how bots combine with AI, and these Power Automate examples cover the workflow route. For invoices and forms, intelligent document processing covers the extraction route.
Process findings also feed higher-level choices. Decision intelligence uses this kind of evidence to model which option to take, while process intelligence supplies the facts about how work actually runs. When a step needs an agent instead of a bot, Kanerika’s agentic AI services cover the design and the human-review controls.
Kanerika Service
RPA Consulting and Implementation Services
Kanerika turns process findings into working automation on UiPath and Power Automate, from candidate scoring to monitored production bots.
Explore RPA Services Process Intelligence Software: How to Choose a Platform in 2026
The right platform usually depends on the systems you already run. Most vendors can build process maps, so the real differences show up in connectors, data models and how findings turn into action. Use this vendor-neutral shortlist as a starting point, and then test each option on your own data.
Platform Natural fit What to test
Celonis Process Intelligence Teams that want a standalone process platform, not tied to one ERP or automation suite Connector depth for your ERP, as well as the effort to build the data model SAP Signavio Process Intelligence SAP-centered estates, since it is the process analysis and mining part of SAP’s Process Transformation Suite Coverage of your non-SAP systems Microsoft Power Automate process mining Microsoft 365 and Power Platform estates that want findings to flow into automation Data volume limits, and how insights hand off to flows UiPath Process Mining Teams that already run UiPath automation Event-log design, data transformations and its automation-potential fields ARIS Teams that also model and simulate processes How mining results connect to models and simulation
If SAP runs your core finance, test SAP Signavio first, while Power Platform or UiPath shops should start with that vendor’s mining tool. Running both? Our UiPath vs Power Automate comparison sets them side by side. A mixed estate with no dominant vendor points toward a standalone platform such as Celonis.
Still, no table can replace a proof of concept. Ask every shortlisted vendor to load the same extract of your event log, then put the five questions below to each of them.
Process Mining, Task Mining or Object-Centric: Which Approach Fits?
The approach follows your data and the shape of your process. Start with what your systems record, then add depth only where the first view leaves gaps.
Approach Use it when Watch out for
Process mining (case-centric) One clear case type, such as an invoice or ticket, runs through systems that log events Manual steps outside the system stay invisible Task mining Key work happens on desktops, for example in spreadsheets, email or legacy screens Consent, works council review and data minimization Object-centric mining Orders, deliveries and invoices share events, so one-to-many links matter More data modeling work up front
Start case-centric when one object type carries the process. Add task mining only where the case view shows long gaps between system steps. Then move to object-centric once a single case ID starts double-counting shared events.
Questions to Ask Before You Buy
Which prebuilt connectors exist for our ERP and CRM, and who maintains them after upgrades?
Will you run a proof of concept on our own event log instead of a demo dataset?
Where is our event data stored and processed, and can we choose the region?
What is the license metric: users, cases, data volume or connectors?
Who builds and maintains the data model, and what skills does that take on our side?
Watch on YouTube
UiPath vs Power Automate: Which One Actually Fits Your Business?
Kanerika compares the two automation platforms by the systems you already run, so the tool you pick matches the routes your process data points to.
What Does Business Process Intelligence Cost, and How Do You Prove ROI?
Pricing is quoted per customer, so the useful question is what drives the cost up or down. Vendors price on different metrics, and the work around the license often costs as much as the license itself. Plan for all five drivers below before you compare quotes.
Cost driver What moves it
License model Pricing by users, cases, data volume or modules Data volume The number of cases and events, as well as how much history you load Connectors Prebuilt extraction, or custom work when no connector exists Data engineering Building the event log and data model, then maintaining both Change management Training process owners so they act on findings
Proving return is more straightforward than pricing it, as long as you set the baseline before you change anything. Agree on each KPI, its current value, a target and an owner. The targets below continue the illustrative AP example.
KPI Baseline (illustrative) Target (illustrative) Owner
Average invoice cycle time 5.5 days 4 days AP manager Re-approval loop share 10% 3% Controller Invoices without a PO 20% 10% Procurement lead Conformance rate 55% 70% Process owner
Then convert the days saved into cash where you can. Fewer late payments protect supplier terms, and fewer manual touches free AP staff for exception work.
Talk to Kanerika
Find the Bottleneck in Your Process
Talk to Kanerika about extracting an event log, setting baselines and sizing the first fix for one high-volume process.
Schedule a Demo → Risks, Privacy and Governance
Event logs are records of people at work, so they need the same care as any personal data. Each row can name the employee who acted and the customer or supplier involved. Governance therefore starts on the first day of extraction, not after the first finding.
Task mining, in particular, needs the most care. In the EU, GDPR Article 88 lets member states or collective agreements set more specific rules for employee data. Works councils in some countries also expect to review monitoring tools, and the UK’s ICO publishes guidance on monitoring workers . So get consent, explain what is captured and mask what you don’t need, in line with the data minimization principle in GDPR Article 5.
Access should also follow roles set by your data governance program. A process analyst may need case-level detail, while most viewers only need aggregated maps. For finance processes, keep the analysis separate from the controls it tests, so SOX Section 404 evidence stays clean.
When AI agents act on the findings, add a risk framework and an AI governance owner. The NIST AI Risk Management Framework 1.0, released January 26, 2023, gives a common structure for mapping and managing those risks. The event log can then double as the audit trail of what each agent changed, which supports explainable AI reviews.
Location matters too, since event data often crosses borders. The case for sovereign AI applies to process data as much as to models.
Three mistakes sink more programs than any technical problem:
“We already have dashboards.” Dashboards report the average, so they can’t show the loop that drives it.Mining without an owner. When nobody acts on the findings, they become slides and then shelfware.Automating a broken variant. Bots on a bad path just reach the wrong result sooner.
A 90-Day Rollout Plan
Your first project should prove value within one quarter. Ninety days is enough to pick a process, find the main bottleneck and test one fix, as long as each phase has a clear exit test.
Above all, keep the scope narrow. One process, one region and one owner produce a result you can defend, and that result then funds the next process.
Phase Work Owner Exit test
Days 0-30: Pick and extract Choose the process, agree the KPIs, then extract and validate the event log Process owner and data engineer The log rebuilds known cases correctly Days 31-60: Discover and baseline Map variants, check conformance and size each bottleneck Process analyst A ranked list of causes, as well as a baseline for each KPI Days 61-90: Pilot and measure Apply one fix or automation to one variant, then re-measure Process owner The target KPI moves against its baseline
At day 90, decide with the numbers: scale the fix, try the next cause or move to a second process.
How Kanerika Implements Business Process Intelligence
Kanerika runs process work in five stages, and each one ends with something the business can use. We assess the process and its KPIs, then integrate the source systems and model the event log. Next we find and size the opportunities. After that we deploy the fixes and the automation, and we keep monitoring against the baseline.
The automation step often runs through our RPA services and FLIP , Kanerika’s workflow automation platform. A global corporate travel management company working across three regions shows how that plays out. Its accounts payable team handled invoices that arrived as PDFs and Excel bundles, so nobody had a single view of where each one stood.
Kanerika combined OCR with LLM-based extraction, built central ingestion with lifecycle tracking and set up vendor-specific pipelines. The result was 75% less manual effort, a 55% faster cycle time and about 90% extraction accuracy. Each FTE could then handle two to three times as many invoices.
Lifecycle tracking also gave the team 100% traceability, the same per-case view this guide argues for. Real-time dashboards cover 40+ operational and financial metrics, and exception resolution time fell by 30-40%. The full story is in the invoice processing case study .
Case Study
75% Less Manual Effort in Invoice Processing with FLIP
A global corporate travel management company across three regions cut manual AP effort by 75% and invoice cycle time by 55% with OCR, LLM extraction and lifecycle tracking.
Read the Case Study → Bottom Line
Process intelligence is worth it when a high-volume process misses its targets and the average can’t tell you why. It takes time to extract a clean event log, but it pays back once an owner acts on what the log shows. If a process runs mostly in email or handles only a few hundred cases a month, a manual walkthrough costs less.
Even so, the first step is small. Pick one process, pull three fields from its system of record and look at the variants. That is business process intelligence in one line: it reads your event logs as a process, finds the costliest paths and fixes them, one measured change at a time.
Frequently Asked Questions
What is business process intelligence?
Business process intelligence is the practice of using process data, analytics and models to show how work actually runs. It rebuilds each process from the event logs your ERP, CRM and service systems already keep. It then compares the real flow with the intended design and ranks the changes that would save the most time or money.
How does business process intelligence work?
It follows a five-step loop. Teams collect timestamped events from source systems and rebuild each case’s path. They compare that path with the designed model, then slice slow cases to find root causes. Finally they change a rule, policy or step, automate what is stable and keep measuring the same KPIs to prove the result.
How much does business process intelligence software cost?
Vendors quote prices per customer, so there is no standard list price. Cost depends on the license metric, the number of cases and events, the connectors you need and the data engineering to build the event log. Change management and training also add cost. Ask each vendor to price the same scope so quotes compare fairly.
How long does it take to implement process intelligence?
A focused first project can run in about 90 days. Spend the first 30 days choosing a process and extracting a validated event log. Use days 31 to 60 to map variants and set baselines. In the final 30 days, pilot one fix or automation and measure whether the target KPI moves against its baseline.
How does process intelligence support automation?
Process intelligence shows which steps are frequent, stable and rule-based, so teams automate the right ones first. It also flags broken variants that need a policy fix before any bot touches them. After automation goes live, the same event log measures cycle time, touchless rate and cost per case to prove the change delivered value.
What is IPA, and how is it different from RPA?
RPA stands for robotic process automation, which uses bots to repeat rule-based screen work. IPA stands for intelligent process automation. It combines RPA with AI capabilities such as document extraction, language models and machine learning, so it can handle unstructured inputs and some judgment. Process intelligence helps decide which tasks suit RPA and which need IPA.
Will AI replace RPA?
AI is more likely to extend RPA, and the two increasingly work as a team. Bots remain a reliable way to operate systems that have no API. AI agents now often decide what should happen and then call bots, workflows or APIs to carry out the steps. Process data helps teams decide which pattern fits each task in a given process.
What is BPM, and how does it relate to process intelligence?
BPM stands for business process management, the discipline of designing, running, measuring and improving processes. Its cycle usually covers design, modeling, execution, monitoring and optimization. Process intelligence strengthens the monitoring and optimization stages, because it measures how processes really run from event data and shows where the designed model and real work differ.
Is process intelligence compliant with privacy rules, including task mining?
It can be, if it is governed from the start. Event logs often name employees, customers and suppliers, so apply role-based access, masking and data minimization. In the EU, GDPR Article 88 allows more specific employee-data rules. Task mining needs informed consent, a clear capture policy and, in some countries, works council review before rollout.
How does process intelligence relate to decision intelligence?
Process intelligence supplies evidence about how work runs today, including bottlenecks, variants and the cost of each path. Decision intelligence uses evidence like this to model the options and choose what to do next. Together they connect what is happening in operations with which change, investment or automation a leader should approve first.
What is the difference between process intelligence and process mining?
Process mining is the technique that rebuilds process flows from event logs and shows variants and conformance. Process intelligence includes mining and adds business context such as costs, targets and master data. It also adds a route to action, so findings become ranked improvements, automation candidates and monitored changes that a process owner can act on.
How is process intelligence different from business intelligence?
Business intelligence reports what happened through totals, averages and KPI dashboards. Process intelligence shows how each case moved through each step and why some cases ran slow. The two work together. BI flags that a KPI slipped, and process intelligence traces the variant, bottleneck or skipped control behind that slip so a team can fix it.
What is the difference between task mining and process mining?
Process mining reads event logs that business systems write, so it sees steps inside ERP, CRM or ticketing tools. Task mining captures desktop activity such as application switches, copying and clicks on the computers of consenting users. Teams combine them to see the manual work between system steps, which logs alone cannot record.
What are the main types of process mining?
The three classic types are discovery, conformance and enhancement. Discovery builds a process model from the event log. Conformance checking compares real cases with a designed model to flag skipped or out-of-order steps. Enhancement adds data such as time, cost or resources to the model, so teams can see where delays and costs concentrate.
What is object-centric process mining?
Object-centric process mining analyzes events that touch several business objects at once, such as orders, deliveries and invoices. A single shipment can close three orders, which one case ID cannot show. The OCEL 2.0 standard defines a format for these logs, so teams can model one-to-many links across processes without duplicating or distorting events.
What data is needed for process intelligence?
At minimum, you need an event log with three fields for every row. These are a case ID, an activity name and a timestamp. Optional fields such as user, vendor, amount or region help explain why some cases run slow. The XES format in IEEE 1849-2023 is a common standard for exchanging these logs between tools.
What are examples of business process intelligence use cases?
Common use cases include procure-to-pay, order-to-cash and the financial close in finance. Teams also apply it to order fulfillment in supply chain, case handling in customer service, onboarding in HR and incident management in IT. Compliance teams use conformance checks to catch skipped approvals and segregation-of-duties breaks across any controlled process.
Which tools are used for business process intelligence?
Widely used platforms include Celonis Process Intelligence, SAP Signavio Process Intelligence, Microsoft Power Automate process mining, UiPath Process Mining and ARIS. The best fit usually depends on your existing estate, such as SAP, Microsoft or UiPath. Test each shortlisted tool on your own event log before you buy, using the same questions for every vendor.
