Beyond Large Language Models: Why the future of enterprise AI will depend on specialised, purpose-built models
By Devyani Gupta, Co-founder and CEO, Arrowhead AI
There is a tendency in the AI industry to measure progress by the size of a model, the number of parameters it has or how well it performs on a benchmark. But businesses do not run on benchmarks. They run on customer conversations, transactions, decisions and workflows that need to work reliably every day.
That is where the conversation around enterprise AI needs to change. Large language models (LLMs) have made AI far more accessible, but the next phase of adoption will depend on how well we adapt these capabilities to specific business problems.
Consider a customer speaking to a bank’s voice assistant. The customer says, “No, not that one.” The system needs to recognise the words, understand what they refer to and respond without an awkward delay. A model that can write an excellent report but cannot reliably interpret this exchange...
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