Large language models (LLMs) just keep getting better. In just about two years since OpenAI jolted the news cycle with the introduction of ChatGPT, we’ve already seen the launch and subsequent upgrades of dozens of competing models. From Llama3.1 to Gemini to Claude3.5 to GPT-o1, the list keeps growing, along with a legion of new tools and platforms used for developing and customizing these models for specific use cases.
In addition, we’ve seen the introduction of a wide variety of small language models (SLMs), industry-specific LLMs, and, most recently, agentic AI models. The sheer number of options and configurations, not to mention the costs associated with these underlying technologies, is multiplying so quickly that it’s creating some very real challenges for businesses that have been investing heavily to incorporate AI-powered capabilities into their workflows.
In fact, business spending on AI rose to companies that deployed AI spent between $300,000 and $2.9 million on inference, grounding, and data integration for just proof-of-concept AI projects. Those numbers are only growing as AI implementations get larger and more complex.
The rise of vertical AI
To address that issue, many enterprise AI applications have started to incorporate vertical AI models. These domain-specific LLMs, which are more focused, and tailor-made for specific industries and use cases, are helping to improve the level of precision and detail needed for certain specialized business functions. Spending on vertical AI has .
About the author:
Rohit Kapoor is chairman and CEO of EXL, a leading data analytics and digital operations and solutions company.
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