Key Takeaways
An Aroluxa AI automation engagement failed to deliver within the promised timeline, resulting in contract termination and £200k revenue loss. Root causes: underestimated data cleaning requirements, undocumented client workflows, and overconfident timeline scoping. The failure produced three systemic changes: a mandatory data audit before engagement, a workflow documentation sprint as project phase 0, and conservative timeline commitments with milestone gates. This post-mortem is shared publicly to establish credibility through transparency and to help other AI agencies avoid identical scoping failures.
The Engagement That Went Wrong
We signed a 12-month AI automation contract with a mid-market logistics company. Scope: automate their dispatch coordination, invoice processing, and driver communication workflows. Our projection: 70% time savings within 90 days. We were wrong — not about the outcome, but about how long it would take to get there. We hit 70% savings at month 7, not month 3. They cancelled at month 5.
Root Cause #1: Data We Didn't Know About
The client had three separate CRM systems. We knew about two. The third — a legacy system used by one regional team — contained 40% of the customer data we needed. Cleaning and migrating that data took 11 weeks. Our original estimate: 2 weeks. We should have run a data audit as the first deliverable, not assumed the client's data description was complete.
Root Cause #2: Undocumented Tribal Knowledge
The dispatch coordinator role relied on institutional knowledge accumulated over 9 years. When we mapped the workflow in our pre-engagement calls, we documented the 'official' process. The actual process — the one that handled 60% of edge cases — existed only in one person's head. We discovered this when our AI agent started making decisions that the coordinator called 'obviously wrong.'
Root Cause #3: We Oversold the Timeline
We wanted the contract. We gave them a timeline we were 65% confident in, not the 90% confidence timeline we use internally. That 25% confidence gap cost us £200k and, more importantly, cost them time and trust. Every agency faces this pressure. We now have a rule: never commit to a timeline below 85% internal confidence, regardless of what the client wants to hear.
The Three Changes We Made
1. Data Audit as Phase 0 — before signing any SOW, we run a 2-week paid data audit. Non-negotiable. 2. Workflow Documentation Sprint — the client's team documents their actual workflow, not the official one, before we touch any code. 3. Conservative Milestones — we now build in 40% buffer on all AI implementation timelines. We deliver faster than promised, not slower.
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