AI Process Mining: How to Discover What to Automate Before You Automate It

Here's a pattern we see constantly: a business decides to "do AI," picks a process to automate — usually the most visible one or the CEO's pet peeve — spends months building it, and gets a 10% improvement. Meanwhile, a process nobody was looking at was burning £200K a year in hidden inefficiency.

The question isn't "can we automate this?" It's "what should we automate first?"

AI process mining answers that question with data instead of gut feel.

The Automation Prioritisation Problem

Every business has dozens of processes that could be automated. The challenge is sequencing:

• Accounts payable takes 4 hours a week but involves 2 people
• Customer onboarding takes 6 hours but creates downstream delays worth days
• Report generation takes 8 hours monthly but nobody's complaining
• Email triage takes 30 minutes daily but fragments deep work for 15 people

Which do you tackle first? The obvious answer (whatever takes the most time) is usually wrong. Impact isn't just about hours saved — it's about downstream effects, error rates, employee frustration, and customer experience.

Process mining makes this visible.

What Is AI Process Mining?

Traditional process mining (pioneered by tools like Celonis and Minit) analyses event logs from IT systems to reconstruct how processes actually flow. It shows you the real path work takes — not the idealised flowchart on the wall, but the actual sequence of steps, handoffs, reworks, and delays.

AI-enhanced process mining goes further:

1. It Works Without Clean Event Logs

Traditional process mining needs structured data from ERP, CRM, or workflow systems. Most SMEs don't have neat event logs. AI process mining can work with:

• Email patterns — analysing communication flows to map handoffs
• Calendar data — revealing meeting-heavy processes that signal coordination overhead
• Document metadata — tracking how files move between people and systems
• Chat logs — mapping informal processes that happen in Slack or Teams
• Screen recordings — using computer vision to understand manual desktop workflows

2. It Identifies Patterns Humans Miss

An operations manager might know that invoicing is slow. AI process mining reveals why:

• 23% of invoices get stuck waiting for a specific approver who's in meetings every Tuesday
• Invoices over £5,000 take 3x longer not because of policy, but because the finance team manually re-checks them
• The biggest delay isn't processing — it's the 48-hour gap between "invoice created" and "invoice sent" because nobody owns that handoff

3. It Quantifies Automation Potential

Instead of guessing ROI, AI process mining calculates:

• Time spent on each step across hundreds of process instances
• Variation — how much the process differs each time (high variation = harder to automate)
• Error and rework rates — processes with lots of rework have huge automation potential
• Automation readiness score — factoring in data availability, complexity, and integration requirements

How It Works in Practice

Step 1: Connect Your Data Sources

Modern AI process mining tools connect to wherever your work happens:

• Email (Exchange, Gmail) — communication patterns and handoffs
• Cloud storage (Google Drive, SharePoint, Dropbox) — document workflows
• CRM (HubSpot, Salesforce) — customer-facing process flows
• Accounting (Xero, QuickBooks) — financial process patterns
• Project tools (Asana, Monday, Jira) — task and project workflows
• Communication (Slack, Teams) — informal process mapping

The AI doesn't read content — it analyses metadata: who, when, what type, how long between steps.

Step 2: AI Discovers Your Actual Processes

Within days (not months), the AI maps out:

Process Variants: "Your quote-to-cash process has 47 different variants. The happy path takes 3 days. The median path takes 11 days. The worst 10% take over 30 days."

Bottlenecks: "The biggest single delay is between quote approval and order entry. Average wait: 2.8 days. This is because order entry requires data from two systems that aren't integrated, so the ops team batches them every few days."

Rework Loops: "18% of customer orders go through a correction cycle — order entered, error found, corrected, re-entered. The root cause is a free-text field in the quoting system that doesn't validate against the product catalogue."

Shadow Processes: "Your official returns process has 5 steps. In practice, 60% of returns go through an informal WhatsApp group where the warehouse team sorts them before logging them in the system."

Step 3: Prioritised Automation Roadmap

The AI ranks every process and sub-process by automation potential:

Process	Time Cost/Month	Automation Readiness	Est. ROI	Priority
Invoice data entry	32 hrs	High (structured data)	£38K/yr	1
Quote follow-up	20 hrs	High (template-based)	£52K/yr	2
Customer onboarding	45 hrs	Medium (some variation)	£85K/yr	3
Order correction cycle	12 hrs	Very High (root cause fix)	£28K/yr	4
Report compilation	16 hrs	High (data aggregation)	£19K/yr	5

The ROI isn't just time saved — it factors in error reduction, faster cycle times, employee satisfaction, and customer impact.

Real-World Impact

Case: UK Distribution Company

A 200-person distribution business thought their biggest automation opportunity was warehouse picking. It was visible, physical, and the operations director talked about it constantly.

Process mining revealed something different:

• Warehouse picking was already fairly efficient — 6% improvement potential
• Order processing had 34 process variants (should have had 3) — fixing this alone would save £120K/yr
• Customer credit checks were the hidden monster — manual checks were delaying 40% of orders by 2+ days, with £2.1M in revenue delayed at any given time

They automated credit checks first. Three weeks to implement. Immediate impact on cash flow.

Case: Professional Services Firm

A 50-person consulting firm assumed their biggest time sink was proposal writing. Process mining showed:

• Proposals took 8 hours average — but only happened 15 times a month
• Time tracking and billing consumed 25 minutes per person per day — across 50 people, that's 200+ hours monthly
• The billing process had a 72-hour average delay between work completion and invoice generation, directly impacting cash flow

Automating time capture and billing had 4x the impact of automating proposals.

Tools for AI Process Mining

The market has matured significantly in 2026:

Enterprise

• Celonis — Market leader with AI-powered recommendations
• Microsoft Process Mining (within Power Platform) — Integrates with Microsoft 365 data
• SAP Signavio — Strong for SAP-heavy environments

Mid-Market & SME

• Minit (Microsoft) — Accessible entry point
• Apromore — Open-source option with AI capabilities
• Fluxon — Purpose-built for SMEs, connects to common SaaS tools

DIY Approach

For smaller businesses, you can get 80% of the value with:

1. Time tracking data from Toggl, Harvest, or Clockify
2. Email metadata analysis (volume, response times, thread lengths by process)
3. An AI assistant to analyse the data and identify patterns

We've helped clients map their top 10 processes using nothing more than structured interviews, email data, and an AI analysis layer. Not as thorough as dedicated tools, but enough to prioritise correctly.

Getting Started: The 2-Week Discovery Sprint

You don't need a 6-month consulting engagement. Here's a practical approach:

Week 1: Data Collection

• Export email metadata (sender, recipient, subject patterns, timestamps)
• Pull task/project data from your management tools
• Run a simple time-tracking exercise (even manual logging for one week helps)
• Document the top 10 processes you think matter most

Week 2: Analysis & Prioritisation

• Feed data into a process mining tool or AI analysis pipeline
• Map actual vs. expected process flows
• Identify top 3 bottlenecks and their downstream impact
• Build a prioritised automation roadmap with estimated ROI

Total investment: 2 weeks of part-time effort + tooling costs (often free tier for SMEs).

Typical outcome: Discover that your assumed priority list is at least 50% wrong — and find 2-3 high-impact opportunities you weren't aware of.

Common Mistakes

1. Automating the Loudest Complaint

The process people complain about most isn't always the most impactful to fix. Sometimes complaints indicate a process that's visible and annoying but low-impact, while a silent process bleeds money in the background.

2. Starting with Complex Processes

Process mining might reveal that your most complex process has the highest total cost. But complex processes are hard to automate well. Start with high-readiness, medium-impact processes to build capability, then tackle complexity.

3. Ignoring the Human Layer

Process mining shows what happens in systems. It doesn't always capture why. Before automating, validate findings with the people doing the work. That "inefficient" rework loop might exist because the upstream process produces unreliable data — automate the rework without fixing the root cause and you just automate waste.

4. One-Time Analysis

Process mining isn't a project — it's a capability. Your processes change as your business evolves. Continuous monitoring catches new bottlenecks as they emerge and validates that your automations are delivering expected results.

The Bottom Line

The businesses getting the best ROI from AI and automation aren't the ones with the fanciest tools — they're the ones that automated the right things in the right order.

Process mining replaces guesswork with evidence. It shows you where the real inefficiencies are, how much they're actually costing you, and which ones are ready for automation today.

Before you build another automation, ask: are you solving the right problem?

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Caversham Digital helps UK businesses discover and prioritise automation opportunities using AI process mining. Get in touch for a discovery sprint.