Not necessarily. Many IoT deployments deliver value with threshold-based alerts and dashboards alone. AI adds value when you have complex patterns that simple rules can't capture, when you need predictions rather than just monitoring, or when the volume of data exceeds human analysis capacity.

It depends on the use case. Some models require months of historical data to capture seasonal patterns. Others can be trained on weeks of data. We assess your data availability early and recommend approaches that work with what you have—including transfer learning and pre-trained models when appropriate.

Often yes. Modern edge devices and gateways can run optimized ML models for inference. We use techniques like model quantization and pruning to reduce compute requirements. For more complex models, dedicated AI accelerators or edge servers may be needed.

Accuracy depends on data quality, failure history, and equipment complexity. Well-implemented predictive maintenance models typically achieve 70-90% accuracy in predicting failures within a defined window. We set realistic expectations and validate models rigorously before deployment.

Models can degrade when operating conditions shift—a concept called data drift. We implement monitoring to detect drift and retrain models as needed. Continuous learning pipelines can automate this process for models that need frequent updates.