AI Process Mining: How to Find the Hidden Bottlenecks Costing Your Business Thousands

Every business has a process they think works one way — and actually works another.

The purchase order that "takes 2 days" actually takes 11 because it bounces between three people who each sit on it for 3 days. The customer onboarding that's "fully digital" actually requires someone to manually re-enter data into a legacy system. The returns process that's "streamlined" has 47 variants, only 3 of which match the documented procedure.

AI process mining finds all of this. Automatically. By watching what actually happens in your systems — not what the process document says should happen.

And the gap between "documented process" and "actual process" is where most operational cost hides.

What Is AI Process Mining?

Process mining uses event log data from your existing business systems (ERP, CRM, ticketing, email, finance) to reconstruct what actually happens when work flows through your organisation.

Traditional approach:

• Interview staff → "How does this process work?"
• Get a clean, idealised flowchart
• Miss 80% of the variants, workarounds, and bottlenecks

AI process mining approach:

• Extract event logs from your systems (timestamps, actions, users)
• AI reconstructs the actual process flow — every variant, every loop, every delay
• Automatically identifies bottlenecks, rework, compliance violations, and automation opportunities
• Continuously monitors for drift and degradation

The difference is like asking someone to draw you a map of their commute versus putting a GPS tracker on their car. One gives you what they remember; the other gives you what actually happened.

Why It Matters Now

Three things have converged to make process mining transformative for UK businesses in 2026:

1. AI Makes It Accessible

Early process mining tools required data engineers and process consultants. Modern AI-powered platforms can:

• Automatically discover processes from raw event logs without predefined models
• Use NLP to understand unstructured data (emails, chat logs, ticket notes) alongside structured system logs
• Generate plain-English explanations of what they find — no data science degree required
• Recommend specific actions rather than just presenting dashboards

2. Integration Is Now Trivial

Modern platforms have pre-built connectors for SAP, Salesforce, ServiceNow, Xero, Microsoft 365, and dozens more. What used to take 6 months of integration work now takes days.

3. The ROI Is Proven

Gartner reports that organisations using process mining typically identify 20-30% cost reduction opportunities within their first analysis. That's not aspirational — it's the median result.

What Process Mining Actually Finds

Hidden Rework Loops

The most common finding: processes that loop back on themselves because something wasn't done right the first time.

Example: A UK manufacturer discovered that 34% of their sales orders went through the credit check process twice — because the initial check used outdated customer data, failed, triggered a manual review, and then passed on the second attempt. Fixing the data sync eliminated 12,000 unnecessary credit checks per year.

Bottleneck People and Systems

Process mining reveals where work queues up — and it's rarely where management thinks it is.

Example: A professional services firm believed their project delivery was bottlenecked by technical resources. Process mining showed the actual bottleneck was legal contract review — contracts sat in the legal team's queue for an average of 8 days, while technical work started within 1 day of assignment. Restructuring legal review (adding AI-assisted contract analysis) cut end-to-end project start time by 40%.

Process Variants That Shouldn't Exist

Real-world processes develop variants like weeds. Some are valid adaptations; many are workarounds for broken systems.

Example: A UK council discovered their planning application process had 312 unique variants for what was supposed to be a single, standardised workflow. 40% of variants existed because staff had developed workarounds for a legacy system that couldn't handle certain application types. Modernising the system eliminated 180 variants and reduced processing time by 25%.

Compliance Violations Nobody Knew About

Processes that skip required steps — not maliciously, but because the step is inconvenient or people don't know it's required.

Example: A financial services firm discovered that 18% of their client account changes bypassed the required dual-authorisation step. The system allowed it due to a configuration error nobody had noticed. Process mining flagged the gap; fixing it closed a significant compliance exposure before the regulator noticed.

Automation Sweet Spots

Not every process step should be automated — but process mining tells you exactly which ones should:

• High frequency + low complexity + high consistency = automate immediately
• High frequency + high complexity + many variants = simplify first, then automate
• Low frequency + any complexity = probably not worth automating

How It Works: A Practical Walkthrough

Phase 1: Data Extraction (1-2 weeks)

Process mining needs event logs — records of things happening in your systems. The minimum viable event log has three fields:

Field	Example	Purpose
Case ID	Order #12345	Groups events into process instances
Activity	"Invoice approved"	What happened
Timestamp	2026-02-10 14:30:00	When it happened

Additional useful fields: who performed the activity, which system, duration, cost, outcome.

Most business systems already log this data — ERP audit trails, CRM activity logs, ticketing system histories, email metadata. The challenge isn't generating data; it's connecting it across systems to see the end-to-end process.

Phase 2: Process Discovery (1-2 weeks)

AI analyses the event logs and automatically generates a process map showing:

• The happy path — the most common route from start to finish
• Every variant — all the different ways the process actually executes
• Frequency and duration — how often each path is taken and how long it takes
• Bottlenecks — where time accumulates between activities

Modern tools like Celonis, ABBYY Timeline, Minit, and Microsoft Power Automate Process Advisor do this automatically.

Phase 3: Analysis and Insight (2-4 weeks)

This is where the AI earns its keep:

• Root cause analysis — why do certain cases take 10x longer than others?
• Conformance checking — which cases deviate from the intended process, and why?
• Prediction — based on current patterns, which in-flight cases are likely to breach SLAs?
• Recommendation — what specific changes would have the biggest impact?

Phase 4: Action (Ongoing)

Process mining isn't a one-off diagnostic — it's a continuous monitoring system:

• Set alerts for process degradation (average time increasing, new variants appearing)
• Track the impact of changes you make (did the new approval workflow actually speed things up?)
• Feed findings into automation roadmaps (process mining tells you what to automate; RPA/AI agents do the automation)

Tools for UK Businesses

Enterprise (£50K+ annual)

• Celonis — Market leader, deep ERP integration, AI-powered recommendations
• ABBYY Timeline — Strong on document-heavy processes, good NLP capabilities
• SAP Signavio — Best for SAP-heavy environments (integrates natively)
• UiPath Process Mining — Excellent bridge from process mining to RPA automation

Mid-Market (£10K-£50K annual)

• Microsoft Power Automate Process Advisor — Included with some Microsoft 365 plans, good starting point
• Minit (part of Microsoft) — User-friendly, good for non-technical teams
• QPR ProcessAnalyzer — Strong analytics, good for mid-market budgets

SME / Getting Started (Under £10K)

• Apromore — Open-source core, academic-grade algorithms, affordable cloud option
• ProM — Free academic tool, requires technical skills but very powerful
• DIY with Python — Libraries like pm4py enable process mining from event logs

The Build vs Buy Decision for Process Mining

Most businesses should buy (use a platform) rather than build. The algorithms are well-established, and the value comes from insights, not custom tech. Build custom only if:

• You have unusual data sources that no platform supports
• Process mining is a core part of your product/service offering
• You need real-time, embedded process intelligence (not just dashboards)

Real UK Case Studies

Manufacturing: Finding £800K in Hidden Waste

A UK aerospace parts manufacturer used process mining on their order-to-delivery workflow. They discovered:

• 23% of orders were being expedited manually — at significant cost — because standard lead times were being quoted incorrectly at the sales stage
• Quality inspection was happening twice for 40% of parts (once at manufacturing, once at assembly) due to unclear handoff procedures
• Material procurement was triggering 3 separate approval chains depending on which system the purchaser used

Impact: £800K annual savings from eliminating duplicate inspections and fixing the quoting process. The expediting reduction alone saved £200K in overtime and premium freight costs.

Financial Services: Cutting Onboarding from 21 Days to 6

A UK wealth management firm analysed their client onboarding process. Process mining revealed:

• Average onboarding: 21 days (they thought it was 10)
• The "identity verification" step had 8 variants, 5 of which involved manual workarounds for the verification platform's limitations
• 30% of cases went backwards at least once (additional documents requested after initial submission)

Impact: Redesigned the onboarding flow based on process mining data. Implemented smart document checklist (AI predicts which documents will be needed based on client profile). New average: 6 days. Client satisfaction up 45%.

NHS Trust: Reducing Patient Waiting Times

A UK NHS Trust used process mining on their outpatient referral-to-treatment pathway:

• Discovered that 40% of delays occurred not in clinical stages but in administrative handoffs between departments
• Identified a specific bottleneck: consultant availability data was updated weekly, not real-time, causing scheduling conflicts that required manual resolution
• Found that 15% of referrals were being processed as "new" when they were actually follow-ups — triggering unnecessary triage steps

Impact: Administrative wait times reduced by 35%. Overall referral-to-treatment time improved by 18 days for affected pathways.

Getting Started: The 30-Day Sprint

Week 1: Identify Your Target Process

• Pick a process that's high-volume, cross-functional, and has measurable outcomes
• Good candidates: order-to-cash, procure-to-pay, hire-to-retire, incident-to-resolution
• Gather event log data from your core systems (even Excel exports work for a proof of concept)

Week 2: Run Initial Discovery

• Load data into your chosen tool (Power Automate Process Advisor is free if you have Microsoft 365)
• Generate the process map
• Compare it to your documented process — the gaps will be immediately visible

Week 3: Analyse and Quantify

• Identify the top 3 bottlenecks by time impact
• Calculate the cost of each (hours wasted × hourly rate × frequency)
• Identify the top 3 automation candidates (high frequency, low complexity, consistent pattern)

Week 4: Present and Plan

• Build a business case for the top opportunities
• Propose quick wins (process changes) and longer-term improvements (automation)
• Set up continuous monitoring for the process

Most businesses find enough savings in the first analysis to pay for the entire process mining initiative multiple times over.

The Connection to AI Automation

Process mining and AI automation are natural partners:

1. Process mining tells you WHAT to automate — it identifies the repetitive, consistent, high-volume steps
2. AI agents do the automation — they handle the actual work
3. Process mining monitors the results — it confirms the automation is working and flags when it isn't

Without process mining, automation projects are essentially guesses. You automate what you think is the bottleneck, not what the data shows is the bottleneck. Most failed automation projects fail because they automated the wrong thing — not because the automation technology didn't work.

The Bottom Line

Every business has processes that are silently burning money. Not because anyone is doing anything wrong — but because complex workflows develop inefficiencies that are invisible from the inside.

AI process mining makes the invisible visible. It shows you what's actually happening, where the waste is, and exactly what to fix first.

The technology is mature, the tools are affordable, and the ROI is proven. The only question is how long you want to keep paying for inefficiencies you can't see.

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At Caversham Digital, we help UK businesses use AI process mining to find hidden inefficiencies and build automation roadmaps grounded in data. Start with a process health check →