EDITOR’ S QUESTION
Of course, some applications will still benefit from the scale and flexibility of LLMs. Use cases like multilingual chatbots, creative content generation or multidocument analysis across unstructured data sets may require larger models to achieve the desired outcome. But these should be the exception, not the default.
When LLMs are used, they should be used surgically, not indiscriminately. That means training only on what’ s necessary, optimizing for performance and choosing deployment strategies that align with business goals. Whether public or private, these models need to be continuously evaluated for costeffectiveness and impact.
In 2025 and beyond, the most successful CIOs will take a balanced approach. They’ ll treat LLMs and SLMs as parts of a broader toolkit – not as competing options, but as complementary components.
SLMs help teams move fast and stay focused. LLMs expand the frontier once the value is proven. Together, they form a flexible foundation for enterprise-grade AI that scales responsibly and delivers measurable results.
Instead of chasing scale for its own sake, CIOs should prioritize precision, adaptability and cost control.
Start small. Build for what matters. Scale only when you’ re sure the foundation is strong. In this time of rapid change, that kind of clarity isn’ t optional – it’ s the edge that sets leaders apart. p
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