Key Takeaways
An 8-week AI operations automation programme for a 60-person logistics company eliminated 280 of 340 monthly manual internal operations hours through four agentic systems: an automated reporting engine replacing 12 weekly manual reports; a document processing AI handling 200+ invoices and proof-of-deliveries daily; an internal communications router classifying and routing 80+ daily internal emails; and a CRM hygiene agent running nightly data enrichment and deduplication. Total build cost: £38,000. Monthly ongoing: £1,400. Labour cost replaced: £12,600/month (4 FTE operations staff redeployed to customer-facing roles). Payback: 4.1 months.
The Operations Audit: Mapping 340 Hours of Waste
Before building anything, we spent 2 weeks mapping exactly where the 60-person company's operations staff spent their time. Method: time tracking software for 2 weeks, interview each operations team member, log every task type and frequency. Result: 340 hours/month identified across 4 task categories. Category 1: reporting (12 manual weekly reports, 3 people, ~90 hours). Category 2: document processing (invoices, PODs, contracts — 200+/day, 2 people, ~110 hours). Category 3: internal communications routing (80+ emails/day requiring manual classification and forwarding, 1 person, ~60 hours). Category 4: CRM data maintenance (deduplication, enrichment, stage updates, ~80 hours). All 4 categories were high-volume, rule-inferrable, and document-heavy — ideal for automation.
System 1: The Automated Reporting Engine
Twelve manual weekly reports were being built by three people pulling from 6 different data sources (TMS, CRM, finance system, driver app, customer portal, and a custom Google Sheet). We built an n8n workflow that: pulls data from all 6 sources via API every Monday at 6am, applies the transformation logic used in each report, generates a formatted PDF or Google Sheets output, and emails to the appropriate distribution list. Total build time: 3 weeks. Three people now spend 2 hours/week reviewing automated reports for anomalies versus 90 hours building them. Those 88 hours were reallocated to customer service.
System 2: Document Processing AI
200+ inbound documents daily — invoices from 40+ carriers, proof-of-delivery PDFs, customs documents, and partner contracts. Previously: 2 staff manually opened each document, extracted key fields (invoice number, amount, date, reference, carrier), entered into TMS, filed in Google Drive. We deployed a document AI (Claude vision + structured extraction prompt) via n8n automation: documents arrive via email or upload portal, AI extracts fields with 96.4% accuracy, extracted data writes to TMS automatically, document files to Drive with structured naming. Human review queue for documents with confidence below 90%: ~8% of volume. 2 FTE reallocated to exception handling and carrier relationship management.
System 3: Internal Communications Router
One operations manager spent 60 hours/month reading internal emails and forwarding them to the right person or queue. Categories: driver queries (route changes, breakdown reports), customer escalations, partner invoicing queries, HR routing, and general enquiries. We built an AI classification and routing system: email arrives to a central operations inbox, Claude classifies by intent and urgency, routes automatically to the correct person or Slack channel, and logs routing decisions for weekly audit. Accuracy at 30 days: 91%. False positive rate (sent to wrong person): 4%. Human review queue for unclassified emails: 5%. The operations manager's time freed: 52 of 60 hours/month.
The Financial Summary
Build cost: £38,000 across 8 weeks. Monthly ongoing (infrastructure + monitoring): £1,400. Labour cost replaced: 280 hours/month at £45 fully-loaded hourly cost = £12,600/month. Payback: 38,000 / (12,600 - 1,400) = 3.4 months. Year 1 net benefit: £12,600 × 12 - £38,000 - £16,800 (12 months ongoing) = £96,400. The 4 staff members were not made redundant — they were redeployed to customer-facing roles where their institutional knowledge and relationship skills created direct commercial value. Two of them produced more than their annual salary in new revenue within 6 months of redeployment.
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