AI in production: Lessons learned after implementing ML at scale

Artificial intelligence (AI) has moved beyond being a futuristic promise to become a critical engine of operational efficiency. However, there is a significant gap between training a model in a controlled environment and successfully deploying it at scale in production.

At Rootstack, we have supported numerous organizations through this transition. We have seen how promising projects stall after the Minimum Viable Product (MVP) stage, while others manage to transform their industries.

The difference usually does not lie in the sophistication of the algorithm, but rather in the deployment strategy, data management, and organizational culture.

This article distills our experience into practical lessons about what really happens when AI leaves the lab and enters the real world.

The post-deployment reality: what no one tells you

The “Go-Live” is not the end of the project; it is just the beginning. When a Machine Learning (ML) model enters production, it faces unpredictable variables that did not exist during training.

What really happens after an AI solution goes into production?

Once live, the model begins to degrade. Unlike traditional software, which works the same way until the code is changed, ML models depend on data. If user behavior, market trends, or input formats change, the model’s accuracy drops. This requires constant vigilance.

Critical definitions for leaders

To navigate this stage, it is essential to master these concepts:

• Model Drift: The degradation of a model’s predictive performance over time due to changes in real-world data compared to the training data.
• MLOps (Machine Learning Operations): A set of practices that combines ML, DevOps, and data engineering to deploy and maintain AI models in production reliably and efficiently.
• Data Governance: Policies and standards that ensure data is accurate, secure, and compliant with regulations throughout its lifecycle.
• Model Observability: The ability to monitor and understand the internal state of a model in production based on its outputs and performance metrics.

Why AI projects fail after the MVP

Many leaders ask themselves: Why do so many AI projects fail after the MVP?

The answer often lies in underestimating operational complexity. A successful MVP on a laptop does not guarantee scalability. The most common failures occur because the infrastructure cannot support real-world load, inference costs skyrocket, or the team lacks a clear plan to retrain the model.

Common mistakes in scaling implementation

Through our experience, we have identified recurring failure patterns:

• Treating AI as static software: Ignoring the need for continuous retraining.
• Data silos: Data science teams disconnected from engineering and operations teams.
• Lack of monitoring: Not detecting model drift until it impacts business results.
• Hidden technical debt: Building quick MVP solutions that are unsustainable in the long term.

Lessons learned: from theory to practice

Below, we present the key lessons we have extracted from real-world implementations, designed to guide your AI strategy.

Lesson 1: Data quality is a continuous problem, not a one-time task

The challenge: In real-world scenarios, input data may arrive corrupted, incomplete, or in unexpected formats. A robust model must be able to handle these anomalies without collapsing the system.

Recommended action:

• Implement automated data validation pipelines before information reaches the model.
• Set up automatic alerts for statistical deviations in input data.

Lesson 2: MLOps is not optional for scaling

The challenge: Manually managing versions of models, data, and code is unfeasible at scale. Without MLOps, deployment times for new versions become slow and prone to human error.

Recommended action:

• Adopt a CI/CD (Continuous Integration / Continuous Deployment) architecture specifically designed for ML.
• Automate the entire lifecycle: from data ingestion and training to deployment and monitoring.

Lesson 3: Inference cost can kill ROI

The challenge: Extremely complex models may be accurate, but expensive to run in the cloud every time a user makes a request.

Critical decision:

• Evaluate the balance between accuracy and computational cost.
• Consider optimization techniques such as model quantization or using lighter architectures if latency and cost are priorities.

Lesson 4: Human alignment is as important as technological alignment

The challenge: Resistance to change. If operational employees do not trust AI predictions or do not understand how to use them, adoption will be nonexistent.

Key lesson:

• Involve end users from the MVP design stage.
• Prioritize model explainability (Explainable AI) so users understand why AI makes certain decisions.

Action framework for leaders

To ensure long-term success, we recommend following this simplified framework:

• Infrastructure audit: Can your current architecture support the required real-time data processing?
• Governance strategy: Define who owns the data, who approves models for production, and how privacy is ensured.
• Maintenance plan: Allocate budget and resources not only for development, but also for continuous monitoring and retraining (at least 50% of the total effort).
• Business KPI definition: Do not measure only algorithm accuracy (e.g., accuracy); measure business impact (e.g., churn reduction, conversion increase).

Operational, organizational, and cultural challenges

Beyond code, operational, organizational, and cultural challenges arise after deployment:

• Operational: The need for 24/7 support for critical AI-based systems.
• Organizational: Redefining roles. Data engineers and data scientists must work closely with business domain experts.
• Cultural: Fostering a data-driven culture where intuition is complemented by algorithmic evidence.

Conclusion

Bringing AI into production is a complex journey that requires more than data science talent; it demands engineering maturity, strategic vision, and operational excellence. Mistakes are costly, but the lessons learned pave the way toward a real competitive advantage.

At Rootstack, we understand that AI is not magic—it is engineering applied at scale. We help companies navigate this path, ensuring their investments in artificial intelligence translate into robust, governable, and, above all, profitable solutions.

Are you ready to scale your AI strategy with a partner who has been there and done it? Let’s talk about how to take your models from the lab to production.

Want to learn more about Rootstack? We invite you to watch this video.