AI coding pitfall
SMEs failure pattern
Growing businesses use AI to ship features faster than they can hire. The result is often a product that demos well for the board but cannot be operated, extended, or handed to a new developer without a forensic dig through chat history.
How it shows up
- Critical business rules live only in prompts and chat logs, not in the codebase.
- One "full-stack" AI spike becomes the system of record with zero documentation.
- Handover fails: contractors leave, and the team inherits opaque generated modules.
Who this pattern hits
- Founders and product leads who shipped an AI-accelerated MVP that now blocks hiring, support, or sales demos.
- CTOs inheriting a contractor-built system with no architecture notes and business rules living only in chat logs.
- Mid-market teams that need production ownership without funding a multi-year rewrite.
When it matters: When the product demos well for stakeholders but cannot be operated, extended, or handed over safely to a new developer.
How we recover
Our solution
Recover ownership of the product
We reverse-engineer the living system, document the real business rules, and put a small senior team on a recovery plan that your staff can own. The goal is a product you can operate without the original chat history.
- Extract rules and workflows from code and prompts into explicit, reviewable design.
- Establish a thin but solid delivery process: repo, reviews, environments, release.
- Uplift your team so future AI use sits inside clear boundaries, not instead of them.
We recover ownership: reverse-engineer the living system, document real business rules, put senior engineers on a recovery plan, and leave a maintainable baseline your staff can run.
More →Talk through this pattern
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Book free recovery discussionWhat this failure mode is
SME AI project failure is what happens when a mid-market team ships an AI-accelerated product faster than they can staff ownership, operations, and architectural coherence — then discovers production cannot be run without the original chat history or contractor.
Symptoms you can observe
- Only one or two people understand how the system actually works, and they are already overloaded.
- Environments differ: what works on a laptop fails in staging or production for unclear reasons.
- Tickets pile up as “AI bugs” that are really missing boundaries, tests, or runbooks.
- Leadership expected a finished product; engineering has a fragile demo path.
Why AI-accelerated delivery makes it worse
Vibe coding and multi-session AI tools optimise for local green builds, not for a team that must operate the system six months later. Each session invents a slightly different pattern, so the codebase becomes a museum of almost-compatible approaches.
How Mayordomo recovers it
- Stabilise production paths first: observability, deploy, and the few flows that make revenue or compliance risk.
- Document real business rules from code and operators — not from the original prompt logs.
- Put delivery basics back: ownership, tests around risk seams, and a short architecture that the team can defend.
Related services: AI Project Recovery & Realignment · Technical leadership & uplift. See also the failure patterns hub.