Generative AI is rapidly becoming a default choice for enterprise AI initiatives. However, misusing it can undermine both value creation and trust. While generative AI excels in areas such as content generation, conversational interfaces, and knowledge discovery, it is not suitable as a primary technique for many core business problems.
Organizations often fall into the trap of applying generative AI broadly due to hype, rather than evaluating whether it is the best fit for the task. As a result, they introduce unnecessary complexity, higher risk, and missed opportunities to use more reliable AI approaches. Generative AI is only one component of a broader AI landscape. Additionally, most enterprise use cases require a combination of techniques rather than a single model type.
Generative AI performs poorly as a primary solution for prediction and forecasting, planning and optimization, and decision intelligence. It is also a weak choice when exact calculations, highly reliable outputs, or full autonomy without human oversight are required. In these scenarios, risks related to output unreliability, data privacy, intellectual property, cybersecurity, liability, and regulatory compliance may outweigh potential benefits.
For many use cases, established alternatives such as nongenerative machine learning, optimization, simulation, rule-based systems, and knowledge graphs provide better accuracy, transparency, and control. Emerging approaches, including causal AI, neuro-symbolic AI, and first-principles AI, are also gaining relevance for complex enterprise decision-making. These techniques are often less expensive, easier to validate, and better understood operationally.
Rather than viewing AI techniques as mutually exclusive, leading organizations combine them to improve performance and reduce risk. Common patterns include pairing non-generative ML with generative models for classification, synthetic data generation, and enterprise search. Knowledge graphs are increasingly combining with generative AI to enable retrieval-augmented generation
The most effective enterprise AI strategies treat generative AI as one component of a broader, well-governed AI system. It is applying where it delivers clear value and complementing by other techniques where it does not.
ソース：
https://www.gartner.com/en/articles/when-not-to-use-generative-ai
