Phase 4 of the AI adoption strategy
Deploying an AI model to production isn’t the end of the project — it’s the start of the longest phase. AI models aren’t conventional software that keeps working the same way forever after deployment. They learn from the data available at one point in time, and data changes. The world changes. A model’s performance degrades if nobody monitors and updates it.
Phase 4 of the AI adoption strategy sets up the monitoring, evaluation, and evolution processes that keep your AI investment paying off over time, not just in the first few months after launch.
What we monitor in production
Model performance. The model’s technical metrics in production: precision, recall, F1, MAPE — the right metrics depend on the type of model. We compare production performance with what was measured during development and with the minimum threshold set before deployment. Any drop below that threshold triggers a review.
Business metrics. Technical performance is an indicator, not the goal. Business metrics — inventory turnover, churn rate, OEE, defect rate — are what show whether the AI system is delivering the expected impact. Technical and business metrics don’t always move together, so we actively track the link between them.
Team adoption. How often does the team use the system? Which decisions does it influence? Are there patterns of non-use that point to usability or trust problems? Adoption is the bridge between technical performance and business impact.
Input data drift. If the data the model receives in production drifts significantly from the data it was trained on, performance can degrade before the technical metrics pick it up. Monitoring for data drift adds another early-warning layer.
When to retrain and when to redesign
Periodic model retraining on recent data is the most common way to keep a model performing. But sometimes retraining isn’t enough: when the phenomenon the model predicts has changed structurally (a market shift, a new product, a process change), when new data sources become available that didn’t exist during the original training, or when performance has fallen below the acceptable threshold and retraining doesn’t bring it back.
In those cases, the right call is to redesign the model — and the redesign benefits from everything learned in production.
Quarterly strategy review
Beyond day-to-day monitoring of models in production, Phase 4 includes regular reviews of the company’s AI strategy. In these reviews, we assess progress against the roadmap, fold in lessons from the projects already implemented, reprioritize the remaining initiatives, and identify new use cases that weren’t on the original map.
A company’s AI strategy isn’t a document you write once and execute — it’s an iterative process that evolves with what you learn and with the context.
Maintaining vs. evolving
Maintaining an AI system keeps it working correctly. Evolving it adapts it to the company’s changing needs and to the technological state of the art.
Companies that only maintain their AI systems gradually lose their competitive edge: available technology moves forward, competitors add improvements, and the original system falls behind. A well-designed Phase 4 covers both operational maintenance and continuous evolution.
The full sequence
Monitoring and evolution is Phase 4 — and the permanent state of any AI adoption program. It builds on the projects implemented in Phase 3 and feeds what it learns back into the diagnosis and design of the next initiatives.