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What are the popular generative AI products?
When should I not use generative AI?
Focusing exclusively on GenAI is a guarantee to fail. To unlock the full potential of AI, adopt a holistic approach that integrates GenAI with alternative AI techniques, ensuring that investments are aligned with real business needs and not limited by the hype cycle of any single technology.
In particular, avoid using GenAI for:
Prediction and forecasting: GenAI models are not well-suited for tasks that require numerical predictive modeling. They lack the explicit optimization and planning capabilities necessary for valuable forecasting tasks. Alternative AI techniques, such as traditional machine learning models, are more reliable for these use cases.
Critical decision intelligence: The models may produce inaccurate or misleading outputs that inform poor decisions. Use alternative AI techniques that provide more robust and interpretable results for decision-making processes.
Handling sensitive data: When dealing with proprietary, sensitive or confidential information, using GenAI — especially public instances — can introduce risks related to data privacy and security.
Complex problem domains: GenAI may struggle where there is limited training data or where the relationships between data points are not well understood. In such cases, simpler AI techniques or rule-based systems may yield better results.