AI Beyond the Hype

Artificial intelligence and machine learning have genuine, proven business applications — but they are also surrounded by hype. Not every problem needs ML, and not every ML solution delivers ROI. At Nexis Limited, we apply AI and ML where they provide clear, measurable value: demand forecasting for Bondorix logistics, intelligent recommendations in Digital Menu, and predictive analytics in Ultimate HRM.

Where ML Genuinely Adds Value

Demand Forecasting

Predicting future demand based on historical patterns, seasonality, and external factors. Applications include inventory management, staffing optimization, and capacity planning. Time series models (Prophet, ARIMA) and gradient boosting (XGBoost, LightGBM) are common approaches. The ROI is direct: better forecasting reduces waste, stockouts, and over-provisioning.

Recommendation Systems

Suggesting products, content, or actions based on user behavior and preferences. Our Digital Menu platform suggests food items based on order history, time of day, and popular combinations. Collaborative filtering, content-based filtering, and hybrid approaches each have strengths depending on data availability and use case.

Anomaly Detection

Identifying unusual patterns in data that may indicate fraud, system failures, or emerging issues. Applications include payment fraud detection, infrastructure monitoring, and quality control. Isolation forests, autoencoders, and statistical methods detect anomalies without requiring labeled examples of every possible anomaly type.

Natural Language Processing (NLP)

Processing and understanding human language for chatbots, sentiment analysis, document classification, and text extraction. Large language models (LLMs) have transformed NLP capabilities, enabling sophisticated conversational AI and content generation. Our support systems use NLP for ticket classification and suggested responses.

When NOT to Use ML

  • When rules work: If a simple if-then rule solves the problem, use the rule. ML adds complexity that is not always justified.
  • When data is insufficient: ML models need training data. If you have fewer than a few hundred relevant examples, traditional approaches may work better.
  • When explainability is critical: Some ML models (deep neural networks) are difficult to explain. Regulated industries may require interpretable models.
  • When the cost exceeds the benefit: ML infrastructure (data pipelines, model training, deployment, monitoring) has significant upfront and ongoing costs.

ML in Production: Beyond Model Training

Training a model is 20% of the effort. Production ML requires:

  • Data pipelines: Reliable data collection, cleaning, and feature engineering.
  • Model serving: Low-latency inference via REST APIs or batch processing.
  • Monitoring: Track model performance, data drift, and prediction quality over time.
  • Retraining: Periodically retrain models with fresh data to prevent performance degradation.
  • A/B testing: Compare model versions against baselines and each other.

Large Language Models (LLMs) in Business

LLMs have created new business applications — customer support chatbots, content generation, code assistance, and document analysis. Key considerations:

  • Use LLMs for tasks where approximate answers are acceptable (summarization, drafting).
  • Implement retrieval-augmented generation (RAG) to ground responses in your specific data.
  • Be transparent with users about AI-generated content.
  • Monitor for hallucinations and implement guardrails.

Conclusion

AI and ML are powerful tools when applied to the right problems with sufficient data and clear business value. Start with proven use cases (forecasting, recommendations, anomaly detection), validate with prototypes, and invest in production infrastructure only after demonstrating value.

Exploring AI for your business? Our team can help identify high-value ML opportunities.