Why Your AI Project Failed: It Wasn't the AI, It Was the Requirements

The 80% Stat Unpacked

When RAND Corporation found that 80% of AI projects fail, the industry assumed it was a technology problem. It isn't. The vast majority of failures are non-technical: vague requirements, missing acceptance criteria, undefined success metrics, organisational misalignment, and poor data quality.

The Top 5 AI Project Failure Modes

1. Vague Requirements

"Build us an AI that improves customer service" is not a requirement. Without specifics - which customer interactions, what improvement looks like, what data is available - vendors build what they think you need, which is rarely what you actually need.

2. No Acceptance Criteria

If you can't define what "working AI" looks like in business terms before the project starts, you'll never agree on whether it's been delivered. This is the single most common source of vendor disputes.

3. Undefined Success Metrics

"Make it better" isn't measurable. Every AI project needs quantified success criteria: response times, accuracy thresholds, false positive rates, cost savings targets, user adoption rates.

4. Organisational Misalignment

The technology team builds one thing, the business team expects another, and leadership measures something else entirely. Without stakeholder alignment from day one, AI projects fragment.

5. Poor Data Quality

AI models are only as good as their training data. If your data is incomplete, inconsistent, or inaccessible, no amount of clever engineering will compensate.

How Business Analysis Prevents These Failures

The fix for all five failure modes is the same discipline: structured business analysis applied to AI projects. Requirements engineering, stakeholder workshops, acceptance criteria definition, process mapping, and data readiness assessment - the tools that have prevented project failure for decades, adapted for the specific challenges of AI.

This is exactly what EfficiencyAI does. We're the business analyst who speaks both AI and human.