The Hidden Costs of AI Projects: What Nobody Tells You
Beyond compute and talent, AI initiatives carry costs that rarely appear in business cases. A realistic look at total cost of ownership.
Turing Labs Team
AI Engineering
When organisations budget for AI projects, they typically account for the obvious costs: cloud compute, data storage, and ML engineering talent. These visible expenses, however, often represent less than half of the true cost of ownership.
The Data Preparation Iceberg
Data scientists spend 60-80% of their time on data preparation, not model development. This reality rarely survives translation into project budgets. The work includes data cleaning, feature engineering, handling missing values, addressing class imbalances, and countless iterations as data issues emerge.
For one financial services client, we estimated 400 hours for model development. Actual data preparation consumed 1,200 hours—and the data was considered 'clean' at project outset. Multiplying your data work estimates by three isn't pessimism; it's realism.
Infrastructure Complexity
Production ML systems require infrastructure beyond typical software deployments: feature stores, model registries, experiment tracking, and serving infrastructure. Each component needs selection, integration, and ongoing maintenance.
We've seen organisations underestimate MLOps infrastructure by 70%. The model is the easy part; the pipelines around it—data ingestion, transformation, validation, deployment, monitoring—represent the bulk of engineering effort.
The Monitoring Burden
Unlike traditional software, ML systems can fail silently. Accuracy degrades gradually as data distributions shift, without obvious errors or crashes. Effective monitoring requires defining meaningful metrics, establishing baselines, building alerting systems, and staffing response capabilities.
One e-commerce client discovered their recommendation model had degraded significantly over six months—they'd been losing revenue to poor recommendations while the system appeared functional. Proper monitoring would have caught this within weeks.
Retraining Economics
Models require periodic retraining as the world changes. This isn't a one-time cost but an ongoing operational expense: compute for training runs, engineering time for pipeline maintenance, and validation efforts for each new model version.
Budget for quarterly retraining at minimum. Many domains require more frequent updates. A dynamic pricing model might need weekly refreshes; a fraud detection system, continuous learning.
Explanation and Compliance
Regulated industries face additional costs for model explainability, bias testing, and compliance documentation. These requirements are expanding—what's voluntary today may be mandatory tomorrow.
Our Guidance
When evaluating AI investments, multiply your initial estimates: 3x for data work, 2x for infrastructure, and add 30% annually for operations. If the business case still works, proceed. If not, you've avoided an expensive disappointment.