Uses Case (data science view)

Artificial Intelligence creates value when it is applied to real business problems, integrated into processes, and interpreted within the correct operational context.
Alice Data Science designs AI solutions that address concrete use cases across multiple industries, combining analytical rigor with domain knowledge.

Below are representative examples of how AI and advanced analytics are applied in enterprise environments.

Warehouse & Inventory Management

AI enables warehouses to move from reactive operations to predictive and optimized logistics.

Typical applications include:

  • Demand forecasting and stock level optimization
  • Identification of slow-moving or obsolete inventory
  • Detection of anomalous inventory movements
  • Optimization of picking, replenishment, and storage strategies

Methods involved:
Supervised Learning, Time Series Models, Deep Learning, Anomaly Detection

Business value:
Reduced stockouts, lower inventory costs, improved service levels.

Hotels & Hospitality

In hospitality, AI supports both operational efficiency and customer experience.

Typical applications include:

  • Booking cancellation prediction
  • Dynamic pricing and demand forecasting
  • Customer segmentation based on behavior and preferences
  • Recommendation of personalized offers and services

Methods involved:
Supervised Learning, Unsupervised Learning (clustering), Recommendation Systems

Business value:
Higher occupancy rates, reduced cancellations, improved guest satisfaction.

Sales, Marketing & Market Segmentation

AI allows organizations to understand markets and customers beyond surface-level metrics.

Typical applications include:

  • Customer and prospect segmentation
  • Identification of high-value and at-risk customers
  • Sales forecasting and pipeline prioritization
  • Next-best-action and personalized campaign design

Methods involved:
Clustering, Classification Models, Recommendation Systems, LLM-assisted analysis

Business value:
Improved conversion rates, more effective targeting, better allocation of sales resources.

User & Behavioral Segmentation

Understanding user behavior is critical in digital platforms and services.

Typical applications include:

  • Behavioral clustering based on usage patterns
  • Identification of user journeys and friction points
  • Detection of emerging or atypical user behaviors
  • Personalization of content, interfaces, or workflows

Methods involved:
Unsupervised Learning, Sequence Models, Deep Learning

Business value:
Higher engagement, reduced churn, better user experience design.

Forensics, Compliance & Investigative Analysis

In forensic and compliance contexts, AI supports evidence discovery and pattern analysis at scale.

Typical applications include:

  • Identification of hidden correlations in large datasets
  • Detection of anomalous access patterns or activities
  • Comparison and similarity analysis across documents or files
  • Support for investigative hypothesis testing

Methods involved:
Unsupervised Learning, Anomaly Detection, Similarity Models, LLM-assisted document analysis

Business value:
Faster investigations, improved accuracy, defensible and traceable analytical results.

Discovery of Hidden Regularities in Data

One of the most powerful applications of AI is the discovery of non-obvious structures in complex datasets.

Typical applications include:

  • Identification of latent factors driving performance or risk
  • Detection of recurring but hidden patterns across time or entities
  • Reduction of data complexity through meaningful representations
  • Support for hypothesis generation and strategic insight

Methods involved:
Clustering, Dimensionality Reduction, Autoencoders, Deep Learning

Business value:
New insights, better strategic decisions, competitive differentiation.

From Use Case to Enterprise Capability

These examples illustrate a common principle:
AI delivers value when it is used to augment human understanding, not to replace it.

Alice Data Science designs solutions that:

  • Are aligned with specific business questions
  • Are interpretable and controllable
  • Integrate Human-in-the-Loop mechanisms
  • Evolve into protected and sustainable enterprise assets

Through this approach, AI becomes a reliable partner for decision-making, across industries and organizational functions.