Failure patterns

Common AI project failure patterns

AI-assisted delivery and vibe coding often accelerate output until production reality intervenes. Each pattern below is a failure mode we recover for UK teams — open a full page for professional depth, or use the interactive panel on the home page for a quick read.

AI coding pitfall

SMEs

Growing businesses use AI to ship features faster than they can hire. The result is a product that demos well for the board but cannot be operated, extended, or…

Vibe coding pitfall

Small sites

Marketing sites and product MVPs spun up with AI look finished in a weekend, until SEO, accessibility, forms, analytics, and content workflows collide. Prompt-d…

AI coding pitfall

Enterprise

Enterprise AI experiments skip the hard parts of platform engineering: tenancy, identity, audit, data residency, change control. Prototypes escape the lab into …

Vibe coding pitfall

Fintech

AI-generated trading, payments, and ledger code often works in demos and collapses under real load, reconciliation, and regulatory scrutiny. Idempotency, double…

Vibe coding pitfall

Clean code

Vibe coding optimises for "it runs" over readability, tests, and design. You get sprawling functions, duplicated abstractions, and names that describe the promp…

AI coding pitfall

Security

Models confidently produce auth, crypto, and input-handling code that looks professional and is subtly wrong. Secrets land in prompts and repos; access control …

AI coding pitfall

Architectural drift

Each AI session optimises the local problem. Over weeks, the system becomes a collage of styles, data stores, and integration patterns with no coherent boundary…

AI coding pitfall

Cost concerns

AI spend shows up twice: token and tool bills during build, then cloud and rework cost in production. Inefficient generated code, unbounded retries, and over-pr…

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