IBM Process Intelligence
IBM Process Mining reads your system event logs and reconstructs how work actually moves, surfacing every variant, bottleneck, and deviation from the designed path. At Salient, we implement it as the discovery foundation that determines where automation belongs before a single sprint starts.
4 Weeks
to Findings
15+
Years Gold Partner
600+
Projects delivered
Platform Capabilities
Process Mining reads the actual evidence. It ingests event logs from your ERP, BPM, CRM, and case management systems and reconstructs how work moves, every variant included. The output is a process map built from data, not interviews.
Process Mining reads your system event logs and reconstructs how work actually moves, surfacing every variant, bottleneck, and deviation from the designed path.
Event Log Analysis: Ingest event data from any enterprise system and reconstruct exactly how work moves from start to finish, every variant included.
Variant Identification: Every unique path a case takes through the process surfaces as a distinct variant, ranked by volume and cost.
Conformance Detection: Mining compares actual process behavior against the designed path and quantifies the gap by frequency, cost, and root cause.
Every bottleneck quantified before you spend a dollar. Mining identifies not just where work stalls, but why — down to specific activities, teams, and routing logic.
Activity-Level Detection: Where time, cost, and rework concentrate surfaces as activity-level data. Each bottleneck is measured: cases, days added, cost per year.
Root Cause Analysis: Mining identifies not just where work stalls, but why. Specific activities, teams, time periods, and routing logic all appear in the data.
Exception Quantification: Exception volumes, rework loops, and SLA breach patterns surface with case-level detail before designing a fix.
From findings to a build-ready roadmap. What-if simulation runs against a digital twin. ROI is quantified per opportunity before any sprint starts.
What-If Simulation: Proposed process changes run against a digital twin. ROI is quantified per automation opportunity before any sprint starts.
Prioritized Roadmap: A ranked list of automation candidates with ROI simulation attached. Prioritization based on measured bottleneck cost and automation readiness.
Blueworks Live Integration: Mining findings feed directly into IBM Blueworks Live for future-state process design and governance of the automation roadmap.
What Mining Surfaces
Salient surfaces findings like these before any automation is scoped. Each one changes where and how the investment gets made.
Large Energy & Utilities Organization · Salient Process
33%
Rework no one could see
46 days
Cycle time they thought was normal
Procurement Approval Loop, IBM Maximo: Leadership called it a capacity problem. The approval queue was backing up and the assumption was that the team needed more bandwidth. Mining of IBM Maximo event logs told a different story. One-third of all cases were looping back at the same step, a structural flaw in the approval design that had never surfaced in any meeting or process review. The bottleneck wasn’t headcount. It was a loop no one could see until the data showed it.
Large Government & Technology Services Org · Salient Process
4 stages
Discovery that drove the build
Live BAW
Production, not a pilot
Discovery to Production, IBM BAW:Â Â The organization had documented its processes. What it didn’t have was evidence of how those processes ran. Without that data, every automation decision was built on assumption. Salient ran the full IBM mining lifecycle (Map, Discover, Contextualize, What-if) and used the findings to drive every design decision that followed. The program shipped to a live IBM BAW deployment with instrumented KPIs and a framework the client could extend independently.infrastructure.
Major Federal Systems Integrator · IBM + Salient Process
$80M
Found in two quarters
17%→37%
Eligibility gap, closed
Invoice Submission Discovery, Financial Ops:Â The automation program had hit a wall. Low-hanging fruit was gone and complex initiatives couldn’t move because nobody had deep visibility into how the underlying processes worked. Mining was applied to financial operations and surfaced exactly where cash flow was leaking: specific steps, specific volumes, specific costs. That clarity restarted the program. In under two quarters it delivered $80M in total impact, and the findings expanded the program to three new domains.
Process Patterns by Industry
These are the highest-value process mining analyses we run by industry. Each one surfaces a specific type of bottleneck, deviation, or waste pattern that interview-based discovery routinely misses.
Mining surfaces which exception types are being resolved quickly and which are aging. SLA breach timing, approval path length, and dual-control routing gaps all appear as data. The team can see exactly which exceptions are the capacity problem before any automation is designed.
Typical finding: 15% of exception types driving 60% of SLA breaches
Mining maps every path a loan application takes from submission to decision. Rework loops, re-request cycles, and routing detours surface as quantified bottlenecks. Teams see not just that cycle time is long, but exactly which activity is adding the most days and why.
Typical finding: 30-40% of applications looping at document intake, adding 10+ days per case
Mining reconstructs the onboarding journey from application to account activation. Wait times between screening, verification, and system provisioning become visible. Teams learn which steps they assumed were fast are actually where most accounts stall.
Typical finding: 70% of onboarding delay concentrated in 2 system handoff steps
Mining compares actual report assembly steps against the designed process to find where manual workarounds have become the de facto standard. Deviations that create audit exposure show up in the data, not in a workshop conversation where no one volunteers that they've been doing it differently.
Typical finding: 20-30% of reporting cases deviating from the compliant path
AI reads First Notice of Loss submissions from any channel, extracts incident type, severity indicators, fraud signals, and coverage match, then routes the claim to the right adjuster tier or automated track before any human opens the file.
15–30% cycle-time reduction
Structured and unstructured submissions are read by AI that extracts risk-relevant data points, flags missing information, and cross-references external data sources. Underwriters receive a structured risk summary instead of raw documents.
Outcome: Submission handling time reduced by 60%, 40–60% faster submission intake.
Technologies: watsonx.ai
Models score every policy at renewal time for flight risk based on payment history, engagement signals, competitive pricing exposure, and life event indicators. High-risk policies route to proactive retention campaigns.
Outcome: 20-30% improvement in retention for at-risk policyholders when proactively engaged
Technologies: watsonx.ai
AI processes satellite imagery, inspection photos, weather history, and building permit records to generate property risk profiles for underwriting and claims validation. Human review focuses on edge cases.
Outcome: Underwriting accuracy improves. Inspection costs reduced by 30% through AI pre-screening
Technologies: watsonx.ai, watsonx.governance
AI reads clinical notes and payer coverage guidelines simultaneously, extracting clinical evidence and mapping it against payer-specific medical necessity criteria. Submissions arrive complete the first time rather than being returned for missing documentation.
Outcome: 60% reduction in auth cycle time
Technologies: watsonx.ai, watsonx Orchestrate
AI classifies denied claims by root cause: coding error, missing auth, eligibility mismatch, timely filing. For each denial category, correction logic is applied and supporting documentation is generated automatically. Resubmission rates improve because the fix targets the actual cause.
Outcome: 30% improvement in first-pass resolution on resubmission. Revenue recovery accelerates.
Technologies: watsonx.ai, watsonx Orchestrate
AI matches referred patients to in-network specialists using availability, distance, clinical specialty match, and insurance eligibility simultaneously. The model recommends the optimal match in seconds rather than requiring coordinators to work through provider directories manually.
Outcome: 50% reduction in referral cycle time
Technologies: watsonx.ai, watsonx Orchestrate
Natural language processing reads completed clinical notes after each encounter, extracts diagnosis and procedure information, maps to the correct coding and billing fields, and routes documentation to the right downstream system. Providers document in their natural workflow; AI handles the classification.
Outcome: 40% reduction in documentation time per provider
Technologies: watsonx.ai, watsonx Orchestrate
Sensors on production assets feed a predictive model that detects degradation patterns before failure. When a threshold is crossed, AI automatically creates a prioritized work order in the CMMS, pulls the relevant maintenance procedure, checks parts availability, and notifies the technician.
Outcome: Unplanned downtime reduced by up to 20%
Technologies: watsonx Orchestrate, watsonx.ai
Computer vision on the production line flags non-conforming parts. AI classifies the defect type, traces the batch to its origin, generates a non-conformance report, and initiates a supplier corrective action request with evidence attached. No manual inspection routing.
Outcome: Defect escape rate drops
Technologies: watsonx Orchestrate, watsonx.ai
AI monitors purchase orders, shipment ETAs, and inventory levels in real time. When a disruption is detected (late delivery, stock-out risk, supplier issue), an orchestrated workflow identifies alternative sourcing options, calculates impact to production, and routes a decision package to the supply chain manager.
Outcome: Response time to supply exceptions drops from days to hours
Technologies: watsonx Orchestrate, watsonx.ai
Engineering submits design specs. AI extracts key parameters, generates draft SOPs, work instructions, quality control plans, and regulatory documentation aligned to the product category. Technical writers review and finalize instead of authoring from scratch.
Outcome: NPI documentation cycle compressed by 50%. Consistency improves across product lines
Technologies: watsonx Orchestrate, watsonx.ai
AI models combine POS data, weather, local events, promotional calendars, and supplier lead times to generate store-level and SKU-level demand forecasts daily. Replenishment orders are generated from the forecast rather than from reorder points set months ago.
Outcome: 30% fewer stockouts. 15% reduction in inventory carrying costs
Technologies: watsonx.ai, watsonx Orchestrate
AI extracts structured data from orders arriving in any format (EDI, email, portal, fax), validates pricing against active contracts and promotional agreements, flags discrepancies before fulfillment, and scores each order for credit risk. On the back end, cash application models match incoming payments to open invoices automatically, even when remittance information is incomplete or inconsistent. Deductions and short payments are classified by reason code and routed with context.
Outcome: 80% of orders and payments processed without manual intervention
Technologies: watsonx.ai, watsonx Orchestrate
AI verifies purchase eligibility, classifies the return reason, and determines the most economical resolution path: full refund, exchange, store credit, or no-return refund where shipping costs exceed item value. Models trained on return history learn which resolution paths drive repurchase and loyalty versus which create friction. Fraud scoring runs in parallel to flag serial returners or policy abuse before the resolution is issued.
Outcome: Return resolution time drops from days to minutes
Technologies: watsonx.ai, Watson Assistant, watsonx Orchestrate
AI forecasts customer traffic by location and time of day, translates it into staffing requirements by role, and generates compliant schedules from employee availability data. Managers receive a schedule recommendation rather than building one from scratch against a spreadsheet.
Outcome: 60% reduction in scheduling time
Technologies: watsonx.ai, watsonx Orchestrate
AI indexes the organization's internal knowledge base: policies, SOPs, product documentation, contracts, past case notes. Employees ask questions in natural language and receive answers with cited sources, replacing manual document searches.
Outcome: Employee time searching for information reduced by 50%
Technologies: watsonx.ai, Watson Assistant
AI reads incoming documents from any channel, classifies type, extracts key fields, validates completeness, and routes to the appropriate system or workflow. Replaces manual data entry across AP, claims, HR, compliance, and operations.
Outcome: Document processing cost reduced 60-80%
Technologies: watsonx.ai, watsonx Orchestrate
AI reads lengthy case histories, email chains, meeting transcripts, and reports and generates structured summaries in seconds.
Outcome: Knowledge worker productivity increases 30-40%
Technologies: watsonx.ai
An orchestrator AI receives an intent from an employee or customer, decomposes it into tasks, selects the right downstream agents and tools, and coordinates execution to completion. Multi-system tasks that previously required multiple teams complete in a single conversational interaction.
Outcome: Task completion time drops 50-70%
Technologies: watsonx Orchestrate, watsonx.ai
Salient's Process Mining engagements connect directly to your automation roadmap. You get a prioritized list of automation candidates with quantified ROI before a single workflow is modeled.
Why Salient Process
For clients with accessible event log data, Salient deploys IBM Process Mining as the discovery foundation before automation is scoped. We use it to validate the process model built in IBM Blueworks Live and to rank automation candidates by measured impact, not perceived impact.
600+
Projects
4 Weeks
to Findings
15+ Years IBM Gold Partner
Unplanned downtime reduction
Process mapping repository, ROI simulation, and scenario modeling platform. As-is and to-be simulations with conservative, base, and upside projections. Business case generation with documented assumptions traceable to process data.
Salient's proprietary process discovery AI solution. Captures actual execution across systems, email, spreadsheets, and manual steps. Produces a complete as-is map including all off-system work that standard tooling cannot replicate or document on its own.
Cloud-based process mapping repository. BPMN-native, collaborative, accessible from any location. 200+ process templates. Direct integration with Blueworks Insights for analytics and reporting.
Event log analysis that reconstructs how processes actually executed from ERP, CRM, and other system data. Surfaces deviation patterns, throughput bottlenecks, and compliance deviations invisible to workshop-based discovery.
Your Process Mining Questions, Answered
The real questions from COOs, CFOs, and CIOs evaluating Process Mining for the first time.
Process Mining works from event logs: timestamped records of activities associated with a case ID, pulled from your ERP, BPM, CRM, or case management system. Most enterprise systems produce this data automatically as a byproduct of normal operations. Salient's first step in every mining engagement is a data readiness assessment: we confirm what logs are available, at what granularity, and whether the event data is sufficient to reconstruct the process variants you care about. If you run SAP, Oracle, Salesforce, IBM BAW, or ServiceNow, you almost certainly have the data.
Workshop-based process mapping produces a documented version of how your team believes the process runs, or how it was designed to run. Process Mining produces a data-driven reconstruction of how it actually runs, based on system evidence. The two approaches find different things. Workshops capture institutional knowledge, edge cases, and policy intent. Mining captures actual volumes, timing, variants, and conformance gaps. Salient uses both: workshops inform the process model, mining validates and sharpens it. When they disagree, the data wins.
For clients with clean, accessible event logs, Salient can deliver an initial process reconstruction and bottleneck analysis within two to three weeks of data access. A full analysis with variant deep-dive, conformance check, simulation, and prioritized automation roadmap typically completes in four to six weeks. The timeline depends on data quality, the number of processes in scope, and the complexity of the system landscape we're pulling from.
No. Mining and workshops serve different purposes. Mining tells you what is actually happening. Workshops tell you what was intended and what the team knows about exceptions, edge cases, and policy rationale. Salient uses mining to validate and challenge the workshop output. When a process map drawn in a workshop diverges from what the data shows, mining wins. The two tools are most powerful together. For clients with the event log data to support it, Salient combines IBM Process Mining with IBM Blueworks Live to validate the process map before automation is built.
That connection is the point. Mining surfaces where waste is concentrated and what's causing it. The findings feed into a what-if simulation that models the ROI of automating each identified bottleneck or deviation before any sprint starts. The output is a ranked list of automation candidates with projected cycle time reduction, cost savings, and FTE capacity gain attached to each. Salient then connects those findings directly to IBM Blueworks Live for future-state design and to the automation platform — whether that's IBM BAW, watsonx Orchestrate, or a combination.
Yes, and it often finds something the RPA program missed. Mining applied to a process with existing automation surfaces whether the bots are performing as designed, where exceptions are falling out of the automated path, and what volume of cases the automation isn't reaching. It also helps identify which adjacent processes are strong candidates for the next automation investment, ranked by measured impact. RPA programs that hit a plateau often find mining is what restarts their roadmap.
In 60 minutes, Salient's Process Mining specialists review your existing event log landscape and scope a discovery analysis.
