How to implement an AI Recommendation Model

The adoption of Artificial Intelligence (AI)-based recommendation models has become one of the most effective ways to personalise experiences, increase conversions and improve the efficiency of digital processes. However, implementing an AI Recommendation Model effectively requires much more than integrating an off-the-shelf algorithm or activating a feature in a platform.

The real impact occurs when there is a structured process, prepared data and a clear vision of how the model will generate business value.

What is essential to start implementation

Before moving on to the technical component, it is crucial to ensure that minimum conditions exist to support a functional and scalable recommendation model:

Minimum data maturity

• You do not need a perfect data ecosystem, but it is critical to have accessible, structured and consistent data. The model will only be as good as the quality and variety of the information it receives.

Essential data sources

Depending on the use case, a Recommendation Model typically uses three groups of data:

• Behavioural (navigation, interactions, clicks)

• Transactional (purchases, usage history)

• Catalogue or inventory (product, service or content attributes)

The richer and more coherent these data are, the more robust the recommendations will be.

Infrastructure that enables testing, integration and scaling

• This does not imply a complex architecture, but it is necessary to ensure that the model can be trained, versioned, tested and integrated into existing systems (such as websites, apps or CRM tools).

Multidisciplinary team

• Implementation combines technical and business skills. Typically, it involves profiles from data science, data engineering, product and commercial or operational areas.

The types of data that support intelligent recommendations

For the model to understand users, contexts and preferences, it is important to feed the system with representative and up-to-date data.

1. User and behavioural data - These show interest, intent and patterns. They are essential for relevant recommendations.

2. Catalogue data - They enable the model to understand characteristics, attributes and relationships between items.

3. Contextual data - Location, time of day, device, ongoing campaigns or seasonality.

4. Performance data - Click-through rates, conversion and engagement are fundamental to adjusting the model over time.

The most important point: it is not necessary to start with all of them. Many companies begin with transactional and behavioural data and evolve gradually.

Implementation methodology: a practical framework

To ensure consistent results, we recommend a phased approach that reduces risk and accelerates time-to-value.

Phase 1: Definition of priority use cases

• Choose a concrete, measurable challenge with direct business impact: e-commerce personalisation, content recommendation, cross-sell, lead prioritisation, among others.This avoids dispersion and enables quick results.

Phase 2: Data mapping and preparation

• Identify available sources, assess quality and prepare data for model training.This is often the most critical phase — and where a large part of future success is achieved.

Phase 3: Model design

• Here, different approaches are evaluated, such as filtering, hybrid models, embeddings or deep learning-based models.Without going into technical detail, the objective is to choose the approach that best fits the volume, diversity and dynamics of the data.

Phase 4: Pilot and validation

• Implement in a controlled environment to assess metrics, test hypotheses and adjust parameters. At this stage, it is common to test multiple models and compare results.

Phase 5: Integration with systems and channels

• Recommendations must be presented to the user at the right moment and in the right place. Web, app, notifications, marketing campaigns, digital assistants — everything must be orchestrated.

Phase 6: Monitoring and continuous improvement

• Recommendation models are not static. It is essential to monitor business metrics, update data and adjust the model to avoid performance degradation.
  

  
  

Best practices

• Start small and scale quickly - A well-defined use case is preferable to an overly ambitious project.

• Combine technical metrics with business metrics - An excellent model can fail if it does not generate real impact on revenue, engagement or efficiency.

• Integrate business rules - Not all recommendations can be left to the algorithm (e.g., stock, commercial priorities, segmentation).

• Test continuously - A/B tests are essential to validate impact and identify drift.

• Maintain ethical personalisation practices - Transparency and respect for the user strengthen trust and increase engagement.

Common mistakes companies should avoid

• Choosing use cases that are too vague or broad

• Using only historical data and ignoring context

• Implementing without considering the user experience

• Lack of alignment between technical and business teams

• Moving to production without a pilot or continuous monitoring

How to measure success in a recommendation model

Effective evaluation combines three dimensions:

1. Business metrics - Revenue generated, increased conversions, retention, reduced churn.

2. Model metrics - Accuracy, coverage, diversity, temporal relevance.

3. Experience metrics - Click rates, interaction, user satisfaction, abandonment.

Well-designed measurement enables continuous improvement of the model.

What we learned in real projects

Our experience shows that companies that extract value most quickly follow three principles:

• They define short, clear and measurable use cases;

• They work with data from the beginning, even when it is not “perfect”;

• They iterate continuously, adjusting recommendations and business rules based on real results.

Each sector benefits from different patterns, but the logic is universal: better data + continuous iteration = better recommendations.

Implementing an AI Recommendation Model is about structuring a process that combines data, strategy and user experience. Organisations that follow a phased approach, measure impact and evolve the model continuously are the ones that achieve consistent and sustainable results.

If your business is looking to increase efficiency, improve personalisation or maximise revenue through intelligent recommendations, this is the right time to move forward.

Ready to turn the potential of your data into intelligent recommendations?

Talk to us and find out how we can help implement recommendation models aligned with your objectives, processes and technological context.