
AI Business Analysis & Requirements
80% of AI failures aren't technology failures - they're analysis failures. We apply rigorous BA methodology specifically to AI projects, ensuring requirements are right before a single model is trained.
The problem
Every AI consultancy can build a model. Almost none can write the requirements that ensure the model solves the right problem.
The AI consulting market is full of firms that jump straight to implementation. The result? 80% of AI projects fail - not because the technology doesn't work, but because the requirements were wrong, the objectives were unclear, or the organisation wasn't ready.
EfficiencyAI leads with analysis. We bring decades of business analysis rigour - requirements engineering, process mapping, stakeholder alignment, acceptance criteria - and apply it specifically to AI projects. The result: AI that's built to solve the right problem, scoped so vendors can deliver accurate pricing, and governed so it stays on track.
AI Requirements Engineering
AI Use Case Discovery & Prioritisation
Agentic Workflow Analysis
AI Vendor Analysis & Translation
AI Business Cases & Financial Analysis
AI Testing & Quality Assurance
Who this is for
Why choose us
Transparent pricing guidance
All engagements start with a free consultation. Pricing depends on scope, complexity, and number of stakeholders involved.
Frequently asked questions
What's the difference between an AI consultant and an AI business analyst?
Most AI consultants focus on the technology - selecting models, building pipelines, deploying solutions. An AI business analyst focuses on the business problem first: defining what success looks like, writing the requirements, ensuring stakeholder alignment, and governing the project through to delivery. We bridge both worlds.
Do I need requirements engineering before starting an AI project?
Absolutely. The 80% failure stat exists precisely because most companies skip this step. Without clear requirements, vendors build what they think you need - which is rarely what you actually need. Proper requirements save money, reduce risk, and dramatically increase the odds of success.
How do you write acceptance criteria for AI systems?
We define success in business terms, not just technical metrics. That means specifying acceptable accuracy thresholds, false positive/negative tolerances, response times, edge case handling, human-in-the-loop triggers, and bias limits - all tailored to your specific use case and industry.
Can you help with a stalled AI project that's already underway?
Yes - this is one of our most common engagements. We conduct a requirements gap analysis, identify where things went wrong, rewrite the specification, and work with your vendor (or find a new one) to get the project back on track.
What does a 90-day AI roadmap include?
A prioritised list of AI opportunities ranked by business impact and feasibility, clear next steps for each opportunity, resource and budget estimates, data readiness requirements, and a recommended implementation sequence. It gives you a concrete plan, not a vague strategy deck.