When AI Makes Sense: A Framework for Enterprise Decision-Making
Not every problem needs AI. We share our evaluation framework for determining when machine learning adds genuine value versus when simpler solutions suffice.
Turing Labs Team
AI Engineering
The enthusiasm around artificial intelligence has led many organisations to pursue ML solutions for problems that don't require them. After years of consulting across defence, healthcare, and enterprise sectors, we've developed a framework for making this determination objectively.
The AI Necessity Matrix
Before committing resources to an AI project, we evaluate opportunities against four critical dimensions: complexity of the decision logic, volume and velocity of data, tolerance for approximation, and availability of training data.
Complexity of Decision Logic: If the rules governing a decision can be explicitly articulated and coded, traditional software often outperforms ML. AI excels when patterns are too nuanced or numerous for human specification—recognising anomalies in medical imaging, for instance, or predicting equipment failure from sensor arrays.
Volume and Velocity: Machine learning shines with high-volume, high-velocity data streams where human review is impractical. A fraud detection system processing millions of transactions daily benefits from ML. A monthly financial reconciliation with hundreds of entries likely doesn't.
When Simple Solutions Win
We've seen organisations spend months building ML models for problems solvable with SQL queries and business rules. The cost isn't just development time—it's the ongoing burden of model monitoring, retraining, and explaining decisions that could have been deterministic.
Consider a client who approached us to build an 'AI-powered' inventory management system. After analysis, we discovered their stock-out issues stemmed from inconsistent reorder points across warehouses. A straightforward rules engine, implemented in two weeks, solved 80% of their problem. The remaining 20%—demand forecasting for seasonal products—genuinely warranted ML.
The Hidden Costs of Unnecessary AI
Every ML system introduces operational complexity: infrastructure for training and inference, pipelines for data preparation, processes for monitoring drift and degradation, and explanations for stakeholders who need to understand decisions. When AI isn't necessary, these costs are pure overhead.
Our Recommendation Framework
We advise clients to pursue AI only when three conditions are met: the problem genuinely requires learning from data rather than encoding rules; sufficient quality training data exists or can be economically acquired; and the organisation has—or can build—the capability to operate ML systems responsibly.
The most valuable AI consultants are often those who tell you when you don't need AI. That's the foundation of our approach at Turing Labs.