Skip to content

1. Pitfalls Catalogue for AI Projects

1. Purpose

This catalogue consolidates the most common pitfalls in AI projects, grouped by theme. Each pitfall includes a description, the risk and a reference to the Blueprint module that describes the mitigation.

2. Governance & Organisation

# Pitfall Risk Mitigation (Blueprint reference)
G-01 No governance framework — AI projects start without clear roles, gates or responsibilities Uncontrollable outcomes, compliance risk Governance Model
G-02 Rubber stamping — Human reviewer approves AI output blindly Errors pass unnoticed Collaboration Modes — Mode 2
G-03 AI tool sprawl — Teams use unapproved AI services (AI sprawl) Data leaks, vendor lock-in, compliance violations Approved Tools
G-04 Missing escalation paths — No clear procedure when AI fails Delayed incident response Incident Playbooks
G-05 Governance as blocker — Excessive governance for low-risk applications Delayed time-to-value, team frustration Fast Lane

3. Technical & Engineering

# Pitfall Risk Mitigation (Blueprint reference)
T-01 Blind copy-paste — Accepting AI code without understanding it Hidden bugs, security vulnerabilities, technical debt Engineering Patterns
T-02 Prompt perfectionism — More time on the prompt than on the solution Delayed delivery Engineering Patterns — Anti-patterns
T-03 Unvalidated chain — Multiple AI steps without intermediate checks Hallucination escalation Validation Model
T-04 AI-accelerated technical debt — AI generates code faster than the team can review Debt accumulates exponentially SDD Pattern
T-05 Context pollution — Too much or irrelevant context provided to AI Lower quality, higher costs Context Builder
T-06 Infinite agent loop — Agent repeats steps without progress Cost explosion Agentic AI Engineering
T-07 Agent scope creep — Agent interprets mandate more broadly than intended Unauthorised actions Acceptance Criteria Mode 4-5

4. Data & Quality

# Pitfall Risk Mitigation (Blueprint reference)
D-01 Undetected data bias — Training or RAG data contains systematic distortions Discriminatory output Ethical Guidelines
D-02 No baseline — No measurement of current performance before AI deployment Impossible to demonstrate improvement Metrics & Dashboards
D-03 Silent degradation — Model quality gradually declines without alarm Users receive progressively worse output Performance Degradation Detection
D-04 Unmitigated hallucinations — AI generates plausible but incorrect facts Legal risk, reputational damage Red Teaming
D-05 Stale knowledge base — RAG sources are not updated Incorrect answers based on outdated information Management & Optimisation

5. Organisation & People

# Pitfall Risk Mitigation (Blueprint reference)
O-01 Skill atrophy — Team loses domain expertise as AI takes over work Nobody can assess AI output any more Collaboration Modes — Mode 4 risk
O-02 AI theatre — Pilots without measurable business value Wasted budget, stakeholder fatigue Benefits Realisation
O-03 No adoption strategy — AI tools available but not used Licence costs without value Adoption Manager
O-04 Autonomy leap — Jumping directly to Mode 4-5 without learning phases Unmanageable systems Start low, scale up
O-05 Missing owner — No clear owner for AI system in production Drift goes unnoticed, incidents unresolved Roles & Responsibilities

6. Cost & ROI

# Pitfall Risk Mitigation (Blueprint reference)
K-01 Only usage costs calculated — TCO misses governance, monitoring, integration Budget overrun Cost Optimisation
K-02 No cost limit per agent task — Agent runs without limits Bill shock from infinite loops Agentic AI Engineering — Cost Management
K-03 ROI measured too early — Drawing conclusions about value after 4-6 weeks Premature cancellation of promising projects Benefits Realisation
K-04 Rework not measured — Time savings from AI are negated by correction work False productivity picture Engineering Patterns — Rework

7. Using this Catalogue

  • At project start: Walk through the categories relevant to the risk profile.
  • At gate reviews: Verify that identified pitfalls have been mitigated.
  • At retrospectives: Use the catalogue as a checklist for lessons learned.