We have worked on over 50 enterprise AI engagements in the last four years. More than half never reached production. After enough post-mortems, the same five failure patterns keep appearing.
1. Starting With the Technology, Not the Problem
The most common cause of failure: a team decides they want to "do AI" or "build an LLM chatbot" without a clear business problem to solve. Without a measurable outcome to optimise for, there is no way to know when you've succeeded — or failed.
Fix: Define the business KPI the AI will move before writing a single line of code.
2. Underestimating Data Readiness
AI models are only as good as the data they run on. Most enterprises significantly overestimate the quality and accessibility of their data. We regularly see projects stall for months in data wrangling that was never budgeted.
Fix: Run a data readiness assessment before committing to an AI roadmap.
3. No Path to Production
Proof-of-concept models built in Jupyter notebooks by data scientists often have no clear path to production deployment. Who owns the infrastructure? How is the model monitored? What happens when it degrades?
Fix: Agree on the production architecture before you start building the model.
4. Ignoring Change Management
AI that changes how people work requires the people to change. This is a people and process problem, not a technology problem. Projects that skip change management and training rarely see adoption.
Fix: Budget 20-30% of the project for change management, training, and communications.
5. No Model Governance
Once a model is in production, who is responsible for it? How is drift detected? How are bias and fairness monitored? The absence of governance is increasingly a regulatory and reputational risk.
Fix: Implement a lightweight model card and monitoring dashboard from day one.
If any of these patterns sound familiar in your organisation, we'd be glad to talk through how to address them.