sift8
Use Cases

Every department has hidden process leaks. Here is what AI finds.

Process mining is not only for operations. Sales cycles stall, marketing funnels loop, finance closes late, HR onboarding drags. Every process leaves an event log — and every event log contains bottlenecks invisible to dashboards.

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15–30%
Operational capacity recovered on average
48 hrs
To first quantified finding, any department
7
Departments covered by standard process audit
€180k+
Average annual recovery on the first fix
0
Bytes of your data ever leaving your environment
Department Analysis

What process mining finds, by department

Select a department to see the typical bottlenecks discovered, the financial impact, and a real-world scenario.

Operations
Sales
Marketing
Finance
HR
IT
Procurement
⚙️
Operations

Operations generates the densest event logs in any organisation — production orders, warehouse movements, service tickets, delivery chains. The gap between the documented process and what actually happens is where time and cost disappear.

Typical findings
Critical Approval queue accumulation — manual sign-off steps that batch overnight cause 2–4 day latency in production scheduling. 60–80% of order delays trace here.
High Rework loops — 18–25% of work orders re-enter a completed stage due to missing information captured too late in the flow.
High Resource chokepoints — one team or individual handles 40%+ of exceptions, creating a single point of failure invisible to capacity planning.
Medium Parallel branch failures — tasks meant to run concurrently are serialised in practice, adding 3–6 days per case unnecessarily.
Outcome — Manufacturing client, 800 employees
Process analysedProduction order fulfilment
Cases reviewed12,400 work orders
Bottlenecks found4 critical, 7 high
Root causeOvernight approval batching
Time to finding24 hours
Annual recovery (est.)€290,000
Payback period5 weeks
What happened: The production scheduler assumed orders stalled at goods receipt. Process mining showed 73% of delays occurred 18 hours earlier — at a digital signature step where one approver was the sole authority for orders over €5,000. Removing the threshold and delegating to team leads eliminated the bottleneck in three days.
📈
Sales

CRM systems contain one of the richest process event logs in any company — every stage transition, every activity, every touch. Most sales teams analyse outcomes. Process mining analyses the flow that produces them.

Typical findings
Critical Proposal-to-response dead zones — median time between proposal sent and next rep activity is 6.4 days. Deals that stall here convert at 11% vs 38% for those followed up within 24 hours.
High Stage regression loops — 30–40% of opportunities move backwards at least once. Each regression adds 12–18 days to cycle time and correlates with a 22% drop in close probability.
High Missing qualification steps — deals entering negotiation without a completed discovery stage close at half the rate. The gap is systematic, not random.
Medium Discounting before procurement involvement — pricing concessions offered before the customer's procurement team is engaged increase discount depth by 14% with no improvement in close rate.
Outcome — SaaS company, €12M ARR
Process analysedB2B sales cycle (CRM export)
Cases reviewed2,140 opportunities
Bottlenecks found2 critical, 4 high
Root causeProposal follow-up gap + stage regression
Time to finding48 hours
Pipeline impact (est.)+18% close rate
Cycle time reduction−23 days median
What happened: The VP of Sales assumed reps were following up promptly. Process mining showed the median gap between "Proposal Sent" and the next logged activity was 6.4 days — not 1. An automated follow-up trigger at 24 hours, applied only to deals above €20k, increased that segment's close rate from 19% to 31% within one quarter.
📣
Marketing

Marketing attribution shows what converted. Process mining shows why everything else did not — the funnel paths, re-entry loops, and campaign touchpoint sequences that lead to dead ends.

Typical findings
Critical MQL-to-SQL handoff delays — the average gap between a lead hitting MQL threshold and a sales rep first contact is 4.2 days. Each additional day reduces conversion probability by ~8%.
High Re-nurture loops with no exit — 35% of leads cycle through nurture sequences more than twice without ever hitting a qualification trigger. They consume budget indefinitely.
High Content-to-conversion path fragmentation — leads who consume 3+ content pieces convert at 4× the rate, but no workflow enforces or tracks the path. It happens by accident.
Medium Event-triggered campaigns firing in wrong sequence — automation rules overlap, causing some leads to receive competing messages within 48 hours, suppressing response rates.
Outcome — B2B tech firm, 120 employees
Process analysedInbound demand generation funnel
Cases reviewed8,900 lead journeys
Bottlenecks found1 critical, 3 high
Root causeMQL handoff latency + infinite nurture loop
Time to finding24 hours
Lead conversion lift+31% MQL→SQL
Campaign cost reduction−€42k/year
What happened: Marketing assumed handoffs were automated and immediate. Process mining on the CRM + MAP event log showed the actual median handoff was 4.2 days — because "immediate" routing only fired during business hours and queued overnight. A 15-minute fix to the routing rule recovered the entire gap.
💶
Finance

Finance processes — P2P, O2C, record-to-report — are among the most process-mined in enterprise. Yet most organisations run these analyses annually at best. Continuous mining surfaces drift as it happens.

Typical findings
Critical Three-way match failures — 12–18% of invoices require manual intervention due to PO/GR/invoice mismatches. Each exception costs €85–€140 in staff time and delays payment cycles by 8–14 days.
High Duplicate payment pathways — variant analysis typically finds 1–3% of invoices processed through more than one workflow path, creating audit risk and occasional double-payment.
High Month-end close compression — 60%+ of journal entries are posted in the final 2 days of close, not because of workload but because approval routing is sequenced incorrectly.
Medium Approval threshold gaming — a measurable cluster of purchase orders sit just below the next approval tier. Decision-mining surfaces this pattern automatically.
Outcome — Finance team, industrial group
Process analysedPurchase-to-Pay (P2P)
Cases reviewed5,660 invoices
Bottlenecks found3 critical, 5 high
Root causeThree-way match + approval routing
Time to finding48 hours
Annual recovery (est.)€220,000
Close cycle reduction−3.5 days
What happened: Conformance checking showed 0% of invoices followed the documented P2P reference model — not because the process was broken, but because the reference model had never been updated to reflect a system migration 18 months earlier. The real flow was efficient; the documented one was a compliance liability.
👥
Human Resources

Hiring, onboarding, offboarding, and performance cycles are heavily process-dependent — but rarely process-mined. ATS and HRIS exports contain exactly the event data needed to surface where time-to-hire and time-to-productivity bleed.

Typical findings
Critical Interview scheduling latency — the gap between application received and first interview scheduled averages 8–12 days. Top candidates accept competing offers in 5–7. The bottleneck is almost always a single calendar coordination step.
High Onboarding step skipping — 40–60% of new hire onboarding flows have at least one documented step that is never executed. Access provisioning and equipment setup are the most common orphaned tasks.
High Offer approval rework — compensation approval loops back to HR in 25–35% of cases due to missing market benchmarking data that could be attached automatically.
Medium Offboarding compliance gaps — system access revocation and asset return happen in sequence instead of parallel, extending offboarding from 2 to 8+ days and creating security exposure.
Outcome — HR team, 1,200-person org
Process analysedRecruit-to-onboard
Cases reviewed640 hire events (18 months)
Bottlenecks found2 critical, 3 high
Root causeScheduling latency + access provisioning gap
Time to finding24 hours
Time-to-hire reduction−9 days (median)
Offer acceptance lift+14%
What happened: The CHRO believed time-to-hire was a volume problem. Process mining on the ATS export showed 68% of the delay was in a single transition: "interview completed → hiring manager decision." The median gap was 6.4 days because no SLA or reminder existed for that step. Adding a 48-hour automated nudge cut the gap to 1.2 days.
🖥️
IT & Service Management

ITSM platforms (ServiceNow, Jira, Freshservice) are process event logs by design. Most teams use them for ticket counts and SLA compliance. Process mining uses the full event sequence to find why SLAs are missed in the first place.

Typical findings
Critical Escalation loop anti-pattern — P2 incidents that escalate to a senior tier are re-assigned back to L1 in 28% of cases without resolution, adding an average 4.2 hours per bounce and triggering SLA breach.
High Change management approval bottleneck — CAB approval adds 5–9 days to standard change requests that could be pre-approved by category. 60% of change requests are category-eligible but not pre-classified.
High Incident-to-problem record gap — repeated incidents on the same CI rarely generate a problem record. The pattern is detectable from the event log; the intervention prevents future P1 events.
Medium Stale assignment queues — 15–20% of tickets sit in an assignment queue for over 24 hours before first activity, not because no one is available, but because queue visibility tooling is fragmented.
Outcome — IT department, financial services firm
Process analysedITSM incident lifecycle
Cases reviewed18,500 tickets (12 months)
Bottlenecks found2 critical, 4 high
Root causeEscalation loop + stale assignment queue
Time to finding48 hours
SLA breach reduction−61%
MTTR improvement−2.8 hrs (median)
What happened: SLA breach analysis pointed to "insufficient staffing." Process mining pointed to a 28% escalation bounce rate on P2 tickets — incidents being escalated to senior engineers, then reassigned back to L1 without action. A triage rule requiring a resolution note before downgrade eliminated the bounce and cut breach rates by 61% in six weeks.
📦
Procurement

Procurement is the original home of process mining. Requisition-to-order, sourcing, contract renewal — each step leaves an audit-ready event trail. The patterns found here consistently deliver the highest immediate ROI of any department.

Typical findings
Critical Maverick buying — 20–35% of spend bypasses the approved procurement workflow entirely, creating compliance risk and eliminating negotiated savings. The pattern is visible in the event log within hours.
High PO creation after goods receipt — retroactive POs appear in 8–15% of cases. Each one is an audit finding and represents a control failure that process mining surfaces from the timestamp sequence.
High Contract renewal latency — supplier contracts are renewed late in 40% of cases, forcing emergency extensions at above-market rates. The lead time gap is visible in the sourcing event log.
Medium Single-source over-reliance — variant analysis identifies categories where 90%+ of spend goes to one supplier despite a registered alternative, increasing supply chain risk.
Outcome — Procurement, €400M spend organisation
Process analysedPurchase Requisition to Order (PR→PO)
Cases reviewed22,000 purchase events
Bottlenecks found3 critical, 6 high
Root causeMaverick buying + retroactive POs
Time to finding24 hours
Compliance improvement+67% PO compliance rate
Annual savings (est.)€1.2M
What happened: The CFO estimated maverick buying at ~5% of spend. Process mining on ERP event logs identified 31% — including a systematic bypass used for IT purchases that had become standard practice after a previous system migration. Closing the bypass route and automating PO creation for repeat vendors recovered the full compliance gap in two months.
Sample Report

What a Sift8 process audit looks like

The report below is based on a real P2P (Purchase-to-Pay) event log analysis — company details and values removed. Download the full version to share with your team.

Process Intelligence Report — Purchase-to-Pay
Finance · Procurement · Warehouse  |  5,660 cases  |  12-month period  |  Generated by Sift8 AI
Download sample report
5,660
Total cases analysed
5
Process variants discovered
5
Unique activities
7
Resources involved
3
Departments with deviations
0%
Conformance rate
Discovered Process Flow
Create Purchase Requisition
→
Create Purchase Order
→
Receive Goods
→
Verify Invoice
→
Process Payment
Reference model — 0% of cases followed this path exactly. 22,640 step deviations detected across 5,660 cases.
Process Variant Distribution
Verify Invoice only
59.1%
Process Payment only
35.2%
Create Purchase Order
5.0%
Receive Goods
0.6%
Create Purchase Requisition
0.1%

94.3% of cases skip the full 5-step reference process. Most execute only a single activity, indicating severe process fragmentation or system-level capture gaps.

Conformance Analysis
Conformance rate: 0%
0/5,660 cases follow the reference model exactly.
Deviations found: 22,640 total
  Missing steps:          22,640
  Unexpected activities:  0
  Wrong order:            0
Reference model: Create Purchase Requisition → Create Purchase Order
                 → Receive Goods → Verify Invoice → Process Payment
Conformance rate 0%
22,640
Total deviations detected
22,640
Missing steps
0
Unexpected activities

Reference model: Create Purchase Requisition → Create Purchase Order → Receive Goods → Verify Invoice → Process Payment. Zero cases completed all five steps in sequence.

Resource Breakdown
Resource Primary Activity Cases Avg Duration
[Resource 1]Verify Invoice~8500m
[Resource 2]Process Payment~6200m
[Resource 3]Create Purchase Order~2850m
[Resource 4]Receive Goods~350m
[Resource 5]Verify Invoice~8100m
[Resource 6]Create Purchase Requisition~40m
[Resource 7]Process Payment~6200m
Conformance Deviation Detail (Sample)
Case ID Type Position Actual Activity Expected Activity
GR-[XX] missing 0 Receive Goods Create Purchase Requisition
GR-[XX] missing 1 Receive Goods Create Purchase Order
INV-[XXXX] missing 0 Verify Invoice Create Purchase Requisition
INV-[XXXX] missing 1 Verify Invoice Create Purchase Order
PAY-[XXXX] missing 0 Process Payment Create Purchase Requisition

22,640 total deviations across 5,660 cases. Your full report includes every case and position.

Identified Bottlenecks & Findings
# Finding Department Severity Impact
1 Full P2P flow never executed end-to-end Finance · Procurement · Warehouse Critical Compliance, audit risk, data integrity
2 59% of cases are invoice-only — no PO or GR Finance Critical Maverick spend, three-way match failure
3 35% of cases are payment-only — no upstream steps Finance Critical Financial control gap, duplicate payment risk
4 Reference model not reflected in actual system flow Procurement High NIS2/DORA compliance documentation gap
5 Create Purchase Requisition executes in only 0.07% of cases Procurement High Approval bypass, spend policy non-compliance
Recommended Actions
01
Reconcile reference model with actual system configuration

The documented P2P process does not match what the system executes. Update the reference model or re-configure approval routing to enforce the intended flow before any other remediation.

Priority 1
02
Enforce three-way match at invoice creation

Block invoice processing for cases with no linked PO or GR record. Automate the match check and route exceptions to a dedicated review queue rather than allowing bypass.

Est. €85k/yr
03
Deploy automated PR creation for repeat suppliers

35% of payment-only cases are repeat suppliers with predictable order patterns. Auto-generate PRs from historical PO data to close the upstream compliance gap without manual effort.

Est. €120k/yr
04
Generate process documentation as NIS2/DORA compliance artefact

The process mining output — variant map, conformance report, deviation log — satisfies the operational resilience documentation requirements of NIS2 Article 21 and DORA Article 11.

Compliance

Analysis performed by Sift8 AI agents  ·  Data processed on-premise  ·  No data transmitted externally

Download full sample report

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